1{"work_id": 7160863705, "title": "Multi-Objective Scheduling for Large Language Model Inference with Prompt-Level Cost Prediction and SLO Awareness", "abstract": "Large language model (LLM) inference in multi-tenant clouds is becoming an increasingly important contributor to data-center carbon emissions, yet existing carbon-aware scheduling techniques target long-running training jobs and are ill-suited for the short, bursty, SLO-sensitive nature of online serving. We propose CAPS (Carbon\u2013Aware Prompt Scheduling), an online bi-objective scheduler that jointly optimizes goodput and per-request carbon cost for multi-tenant LLM inference. CAPS first employs a lightweight prompt complexity predictor to estimate token generation cost and latency risk for each incoming request. It then combines real-time grid carbon intensity, GPU energy profiles, and per-tenant SLO tiers to route each request to one of three execution pools: a low-latency pool, a low-carbon pool, or a delay-tolerant batch pool. A composite reward function balances goodput, carbon emissions, and SLO violation rate. In trace-driven simulations using public conversation traces and regional carbon intensity data, CAPS reduces average carbon emissions per 1K generated tokens by 26.8% compared to round-robin scheduling while achieving an SLO attainment rate that matches or exceeds a dedicated SLO-aware baseline."}2{"work_id": 4408363154, "title": "From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI", "abstract": "The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. The findings contribute to advancing pervasive environmental intelligence, offering a roadmap for building smarter, more sustainable infrastructure."}3{"work_id": 4402762189, "title": "Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology\u2014A Systematic Analysis", "abstract": "This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students\u2019 performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes."}4{"work_id": 7160954670, "title": "Multi-Cloud Program Management For Large Scale Deployment", "abstract": "Multi-cloud strategies are one of the enablers that have gained urgent importance in large-scale enterprise implementations to provide better resilience, independence, and optimization of their performance. There is, however, a great challenge associated with the governance, cost control, security compliance, interoperability, and service-level assurance of managing large-scale programs across heterogeneous cloud platforms. The paper introduces a multi-cloud program management scheme that is comprehensive and capable of assisting the scaling, reliable, and policy-oriented deployment of cloud environments, which are globally distributed. The framework proposed would combine orchestration as a centralized system, standardized policies of governance, automated monitoring of compliance and performance-aware placement of workloads to ensure that technical performance is aligned to strategic business goals. The major dimensions in program management such as alignment of portfolios, risk management, governing costs, vendor integration, and control over operations are taken care of in a structured manner using the layered architecture. The framework also includes analytics-based decision support to facilitate resource optimization as well as continuous improvement in dynamic multi-cloud ecosystems. Through experimental assessment and case study-inspired industry scenarios, it has been shown that the proposed solution leads to much better deployment agility, cost-effectiveness, service reliability and cross-cloud interoperability than the traditional single-cloud and ad-hoc multi-cloud management practice. The findings have demonstrated the relevance of program management in the practicality of the multi-cloud architectures to large-scale and mission-critical deployments."}5{"work_id": 4394946189, "title": "Automatic Root Cause Analysis via Large Language Models for Cloud Incidents", "abstract": "Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years."}6{"work_id": 3094605801, "title": "Self-supervised Graph Learning for Recommendation", "abstract": "Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage and LightGCN. Despite effectiveness, we argue that they suffer from two limitations: (1) high-degree nodes exert larger impact on the representation learning, deteriorating the recommendations of low-degree (long-tail) items; and (2) representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme further enlarges the impact of observed edges."}7{"work_id": 4293770403, "title": "Energy efficiency in cloud computing data centers: a survey on software technologies", "abstract": "Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage."}8{"work_id": 153405511, "title": "An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP", "abstract": "Voice over IP (VoIP) is a real time application that allows transmitting voice through the Internet network. Recently there has been amazing progress in this field, mainly due to the development of voice codecs that react appropriately under conditions of packet loss, and the improvement of intelligent jitter buffers that perform better under conditions of variable inter packet delay. In addition, there are other factors that indirectly benefited VoIP. Today, computer networks are faster due to the advances in hardware and breakthrough algorithms. As a result, the quality of VoIP calls has improved considerably. However, the quality of VoIP calls under extreme conditions of packet loss still remains a major problem that needs to be addressed for the next generation of VoIP services. This thesis concentrates in making an analysis of the effects that network impairments, such as: delay, jitter, and packet loss have in the quality of VoIP calls and approaches to solve this problem. Finally, we analyze the impact of introducing forward error correction (FEC) Piggybacking and Reed Solomon codes for VoIP. To measure the mean opinion score of VoIP calls we develop an application based on the E-Model, and utilize perceptual evaluation of speech quality (PESQ)."}9{"work_id": 4362498422, "title": "Security and privacy in the internet of things", "abstract": "\u03a3\u03c4\u03b7 \u03c8\u03b7\u03c6\u03b9\u03b1\u03ba\u03ae \u03b5\u03c0\u03bf\u03c7\u03ae \u03c4\u03c9\u03bd \u03ad\u03be\u03c5\u03c0\u03bd\u03c9\u03bd \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ce\u03bd, \u03c4\u03bf \u0394\u03b9\u03b1\u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c4\u03c9\u03bd \u03a0\u03c1\u03b1\u03b3\u03bc\u03ac\u03c4\u03c9\u03bd (\u0394\u03c4\u03a0) (Internet of Things \u2013 IoT) \u03b4\u03b9\u03b1\u03b4\u03c1\u03b1\u03bc\u03b1\u03c4\u03af\u03b6\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03c1\u03cc\u03bb\u03bf \u03c3\u03c4\u03bf\u03bd \u03ba\u03cd\u03ba\u03bb\u03bf \u03b6\u03c9\u03ae\u03c2 \u03c4\u03c9\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03c9\u03bd \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ce\u03bd, \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b4\u03b9\u03ac\u03c6\u03bf\u03c1\u03b1 \u03c0\u03bb\u03b5\u03bf\u03bd\u03b5\u03ba\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03cc\u03c0\u03c9\u03c2 \u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c9\u03bc\u03ad\u03bd\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b5\u03c2 \u03b1\u03c5\u03c4\u03bf\u03b8\u03b5\u03c1\u03b1\u03c0\u03b5\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c3\u03c4\u03b5\u03c1\u03bf\u03c5 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7\u03c2. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03b7 \u03c1\u03b1\u03b3\u03b4\u03b1\u03af\u03b1 \u03b5\u03be\u03ad\u03bb\u03b9\u03be\u03b7 \u03c4\u03c9\u03bd \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03b9\u03b1\u03ba\u03ce\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b5\u03b3\u03b5\u03af\u03c1\u03b5\u03b9 \u03c3\u03bf\u03b2\u03b1\u03c1\u03ac \u03b6\u03b7\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2, \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03c3\u03b5 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03bf\u03b9\u03ba\u03bf\u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1. \u0395\u03b9\u03b4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b1, \u03c4\u03bf \u0394\u03c4\u03a0 \u03b5\u03b9\u03c3\u03ac\u03b3\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b5\u03c4\u03b5\u03c1\u03bf\u03b3\u03b5\u03bd\u03ce\u03bd \u03c4\u03b5\u03c7\u03bd\u03bf\u03bb\u03bf\u03b3\u03b9\u03ce\u03bd, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c4\u03bf \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03bf\u03c5 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03c4\u03cd\u03bf\u03c5 (Internet), \u03c4\u03bf \u03cc\u03c0\u03bf\u03b9\u03bf \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03b5\u03c5\u03c0\u03b1\u03b8\u03b5\u03b9\u03ce\u03bd. \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03c4\u03b1 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03bf\u03b9\u03ba\u03bf\u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c1\u03c1\u03b5\u03c0\u03ae \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03c5\u03c1\u03cd \u03c6\u03ac\u03c3\u03bc\u03b1 \u03b1\u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ce\u03bd \u03ba\u03b1\u03b9 \u03b5\u03c5\u03c0\u03b1\u03b8\u03b5\u03b9\u03ce\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2. \u0393\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c4\u03b1 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03b1 \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1\u03c2 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c0\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03be\u03bf\u03c5\u03c3\u03b9\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03af \u03b5\u03c0\u03b9\u03c4\u03b9\u03b8\u03ad\u03bc\u03b5\u03bd\u03bf\u03b9 \u03ad\u03c7\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03ac\u03c1\u03bd\u03b7\u03c3\u03b7\u03c2 \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03b9\u03ce\u03bd (Denial of Service \u2013 DoS) \u03ba\u03b1\u03b9 \u00ab\u0391\u03bd\u03b8\u03c1\u03ce\u03c0\u03bf\u03c5 \u03a3\u03c4\u03b7 \u039c\u03ad\u03c3\u03b7\u00bb (Man In The Middle - MITM). \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03bf\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b5\u03c2 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ad\u03bd\u03b1\u03bd \u03b5\u03bb\u03ba\u03c5\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c4\u03cc\u03c7\u03bf \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd \u03b1\u03c5\u03be\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03b5\u03c0\u03b9\u03c4\u03b9\u03b8\u03ad\u03bc\u03b5\u03bd\u03c9\u03bd, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03bf\u03b9 \u03b4\u03cd\u03bd\u03b1\u03bd\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03c0\u03c1\u03bf\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b5\u03c0\u03af\u03bc\u03bf\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b5\u03b9\u03bb\u03ad\u03c2 (Advanced Persistent Threats - APT). \u03a7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 APT \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03bf\u03b9 \u03b5\u03ba\u03c3\u03c4\u03c1\u03b1\u03c4\u03b5\u03af\u03b5\u03c2: Industroyer, Stuxnet \u03ba\u03b1\u03b9 TRITON. \u03a4\u03ad\u03bb\u03bf\u03c2, \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03c0\u03c9\u03c2 \u03c4\u03bf \u0394\u03c4\u03a0 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c5\u03c2, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03bf\u03b9 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2.\u039c\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b9\u03c2 \u03c0\u03c1\u03bf\u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03b8\u03b5\u03af\u03c3\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c6\u03b1\u03bd\u03ae\u03c2 \u03b7 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b7 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03c9\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf \u0394\u03c4\u03a0. \u03a3\u03c5\u03bd\u03b5\u03c0\u03ce\u03c2, \u03bf \u03ba\u03cd\u03c1\u03b9\u03bf\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1\u03c2 \u03b4\u03b9\u03b4\u03b1\u03ba\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae\u03c2 \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03b5\u03c1\u03b5\u03cd\u03bd\u03b7\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b6\u03b7\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b9\u03b4\u03b9\u03c9\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c4\u03bf \u0394\u03c4\u03a0, \u03c0\u03b1\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 1, \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03c4\u03b7\u03bd \u03b5\u03b9\u03c3\u03b1\u03b3\u03c9\u03b3\u03ae \u03c3\u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1 \u03b4\u03b9\u03b4\u03b1\u03ba\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae, \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5\u03c2, \u03c4\u03b7 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1, \u03c4\u03b7 \u03c3\u03c5\u03bd\u03b5\u03b9\u03c3\u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, \u03bf\u03b9 \u03b1\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2, \u03c0\u03c1\u03bf\u03ba\u03bb\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b1\u03c0\u03b5\u03b9\u03bb\u03ad\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03c3\u03c4\u03bf \u03bf\u03b9\u03ba\u03bf\u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 2, \u03b4\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03c3\u03c4\u03b1 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c4\u03c9\u03bd \u03bf\u03bd\u03c4\u03bf\u03c4\u03ae\u03c4\u03c9\u03bd \u03c3\u03c4\u03bf \u03bf\u03b9\u03ba\u03bf\u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0. \u03a3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1, \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03bd\u03ad\u03b1 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b1\u03c0\u03b5\u03b9\u03bb\u03ce\u03bd \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0, \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 (Common Attack Pattern Enumeration and Classification - CAPEC), \u03b5\u03bd\u03ce \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b7 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd \u03c5\u03c6\u03b9\u03c3\u03c4\u03ac\u03bc\u03b5\u03bd\u03c9\u03bd \u03b1\u03bd\u03c4\u03b9\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd. \u0391\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03cc\u03c4\u03b9 \u03c4\u03cc\u03c3\u03bf \u03b7 \u03b1\u03ba\u03b1\u03b4\u03b7\u03bc\u03b1\u03ca\u03ba\u03ae \u03ba\u03bf\u03b9\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03b7 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03af\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03bd\u03b1\u03c0\u03c4\u03cd\u03be\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2, \u03cc\u03c0\u03c9\u03c2 \u03bf\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03c1\u03c5\u03c0\u03c4\u03bf\u03b3\u03c1\u03ac\u03c6\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03be\u03bf\u03c5\u03c3\u03b9\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd \u03c0\u03c1\u03c9\u03c4\u03bf\u03ba\u03cc\u03bb\u03bb\u03c9\u03bd \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1\u03c2 \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03bf\u03b9 \u03c5\u03c6\u03b9\u03c3\u03c4\u03ac\u03bc\u03b5\u03bd\u03bf\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 \u03b4\u03b5\u03bd \u03ad\u03c7\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03c5\u03b9\u03bf\u03b8\u03b5\u03c4\u03b7\u03b8\u03bf\u03cd\u03bd \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03c3\u03b5 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u03bb\u03cc\u03b3\u03c9 \u03c4\u03bf\u03c5 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03bf\u03c5 \u03c1\u03cc\u03bb\u03bf\u03c5 \u03c4\u03c9\u03bd \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0392\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03bf\u03cd \u0395\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 (Industrial Control Systems - ICS) \u03ba\u03b1\u03b9 \u03c4\u03c9\u03bd \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0395\u03c0\u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03cd \u0395\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03a3\u03c5\u03bb\u03bb\u03bf\u03b3\u03ae\u03c2 \u0394\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd (Supervision Control And Data Acquisition - SCADA). \u039a\u03b1\u03c4\u03ac \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b1, \u03b7 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03a0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2 \u0395\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd (\u03a3\u0391\u03a0\u0395) (Intrusion Detection and Prevention Systems - IDPS) \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b7.\u039c\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b1\u03bc\u03c5\u03bd\u03c4\u03b9\u03ba\u03ce\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03c3\u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03ba\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf, \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 3 \u03b5\u03c0\u03b9\u03ba\u03b5\u03bd\u03c4\u03c1\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd. \u0391\u03ba\u03c1\u03b9\u03b2\u03ad\u03c3\u03c4\u03b5\u03c1\u03b1, \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03ac\u03c4\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c0\u03c1\u03bf\u03b4\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ad\u03c2 \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03a3\u0391\u03a0\u0395, \u03b1\u03bd\u03b1\u03bb\u03cd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c4\u03b9\u03c2 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b9\u03c2 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2 \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03c4\u03bf\u03c0\u03bf\u03b8\u03ad\u03c4\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5\u03c2. \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03ad\u03bc\u03c6\u03b1\u03c3\u03b7 \u03b4\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 (Artificial Intelligence \u2013 AI), \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03b4\u03cd\u03bd\u03b1\u03bd\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03a3\u0391\u03a0\u0395 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03b3\u03bd\u03ce\u03c1\u03b9\u03c3\u03b7 \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03b5\u03c0\u03b9\u03b8\u03ad\u03c3\u03b5\u03c9\u03bd \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c9\u03bc\u03b1\u03bb\u03b9\u03ce\u03bd. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03b1\u03bd\u03b1\u03bb\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd, \u03cc\u03c0\u03c9\u03c2 \u03bf\u03b9 \u03c0\u03b1\u03b3\u03af\u03b4\u03b5\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd (honeypots) \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf\u03b9 \u03b1\u03c0\u03cc \u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc (Software-Defined Networking - SDN). \u03a4\u03ad\u03bb\u03bf\u03c2, \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03ba\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03c4\u03b1\u03b9 \u03bf \u03c1\u03cc\u03bb\u03bf\u03c2 \u03c4\u03c9\u03bd \u03a3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0394\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7\u03c2 \u03a0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03ce\u03bd \u03ba\u03b1\u03b9 \u03a3\u03c5\u03bc\u03b2\u03ac\u03bd\u03c4\u03c9\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2 (\u03a3\u0394\u03a0\u03a3\u0391) (Security Information and Event Management -SIEM) \u03c3\u03c4\u03b1 \u03bf\u03b9\u03ba\u03bf\u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u0394\u03c4\u03a0, \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c3\u03c4\u03b9\u03c2 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd \u03b4\u03b9\u03b1\u03c6\u03cc\u03c1\u03c9\u03bd \u03c3\u03c5\u03bc\u03b2\u03ac\u03bd\u03c4\u03c9\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1\u03c2. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03c3\u03c4\u03bf \u039a\u03b5\u03c6\u03ac\u03bb\u03b1\u03b9\u03bf 4, \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bc\u03b9\u03b1 \u03b4\u03b9\u03b5\u03be\u03bf\u03b4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03c9\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03ce\u03bd \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u0388\u03be\u03c5\u03c0\u03bd\u03c9\u03bd \u0394\u03b9\u03ba\u03c4\u03cd\u03c9\u03bd \u0397\u03bb\u03b5\u03ba\u03c4\u03c1\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7\u03c2 (\u0395\u0394\u0397) (Smart Grid - SG). \u03a3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1, \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u0395\u0394\u0397 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03c4\u03b7 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c4\u03bf\u03c5 \u0394\u03c4\u03a0, \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ac \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03ac, \u03cc\u03c0\u03c9\u03c2 \u03b7 \u03a0\u03c1\u03bf\u03b7\u03b3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03a5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ad\u03c2 \u039c\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7\u03c2 (Advanced Metering Infrastructure - AMI), \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b2\u03b9\u03bf\u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03bf\u03cd \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5, \u03bf\u03b9 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b1\u03b8\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c3\u03c5\u03b3\u03c7\u03c1\u03bf\u03bd\u03b9\u03c3\u03c4\u03ad\u03c2. \u03a3\u03c5\u03bd\u03b5\u03c0\u03ce\u03c2, \u03b3\u03b9\u03b1 \u03ba\u03b1\u03b8\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03b1 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u0395\u0394\u0397 \u03bc\u03b5\u03bb\u03b5\u03c4\u03ce\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd (\u03cc\u03c0\u03c9\u03c2 \u03a3\u0391\u03a0\u0395 \u03ba\u03b1\u03b9 \u03c0\u03b1\u03b3\u03af\u03b4\u03b5\u03c2 \u03b5\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd), \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c3\u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c4\u03bf\u03c5\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7, \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03ce\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b1\u03b9\u03c4\u03ad\u03c1\u03c9 \u03c4\u03b1 \u03c0\u03bb\u03b5\u03bf\u03bd\u03b5\u03ba\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c4\u03c9\u03bd \u03c5\u03c6\u03b9\u03c3\u03c4\u03ac\u03bc\u03b5\u03bd\u03c9\u03bd \u03bb\u03cd\u03c3\u03b5\u03c9\u03bd, 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\u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03c4\u03b5\u03c5\u03b8\u03cd\u03bd\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b5\u03c2: (\u03b1) \u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u0395\u03b9\u03c3\u03b2\u03bf\u03bb\u03ce\u03bd \u03ba\u03b1\u03b9 \u0391\u03bd\u03c9\u03bc\u03b1\u03bb\u03b9\u03ce\u03bd \u03bc\u03b5 \u03a7\u03c1\u03ae\u03c3\u03b7 \u039f\u03bc\u03bf\u03c3\u03c0\u03bf\u03bd\u03b4\u03b9\u03b1\u03ba\u03ae\u03c2 \u039c\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, (\u03b2) \u039c\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u03a3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7\u03c2 \u03bc\u03b5 \u03a7\u03c1\u03ae\u03c3\u03b7 \u039c\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2 \u03a3\u03c5\u03c3\u03c7\u03b5\u03c4\u03af\u03c3\u03b5\u03c9\u03bd, (\u03b3) \u03a3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 \u039c\u03b5\u03c4\u03c1\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd \u03bc\u03b5 \u03a4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u0395\u03bd\u03b9\u03c3\u03c7\u03c5\u03c4\u03b9\u03ba\u03ae\u03c2 \u039c\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2, (\u03b4) \u039c\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03af \u0391\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7\u03c2 \u03bc\u03b5 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03a4\u03b5\u03c7\u03bd\u03bf\u03bb\u03bf\u03b3\u03af\u03b1\u03c2 \u039a\u03b1\u03b8\u03bf\u03c1\u03b9\u03b6\u03cc\u03bc\u03b5\u03bd\u03b7\u03c2 \u03b1\u03c0\u03cc \u039b\u03bf\u03b3\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc \u03bc\u03b5 \u03a7\u03c1\u03ae\u03c3\u03b7 \u039d\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03ce\u03bd \u0394\u03b9\u03ba\u03c4\u03cd\u03c9\u03bd \u0393\u03c1\u03ac\u03c6\u03c9\u03bd \u03ba\u03b1\u03b9 (\u03b5) \u03a4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u0395\u03be\u03ae\u03b3\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u0391\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7\u03c2 \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 \u03b3\u03b9\u03b1 \u039c\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u0391\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u039c\u03b5\u03c4\u03c1\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2."}10{"work_id": 7161556515, "title": "DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference", "abstract": "Deploying large language model (LLM) inference services in serverless, multi-tenant environments present compounding challenges: cold start latency, GPU memory fragmentation, inter-tenant resource contention, and unpredictable tail latency. Existing systems optimize individual aspects but fail to jointly address service-level objective (SLO) compliance and cost efficiency under dynamic, heterogeneous workloads. We present DeepServeCB, an elastic scheduling framework that formulates the joint SLO--cost optimization as a contextual bandit problem. The system introduces a Request Profiler that extracts online features---prompt length, historical KV-cache hit ratio, and predicted generation length---and feeds them into a contextual bandit agent that adaptively selects batch sizes, concurrency levels, KV-cache eviction policies, and warm-standby strategies. We evaluate DeepServeCB on ShareGPT and BurstGPT traces using LLaMA-2-13B and Mixtral-8x7B models on NVIDIA A100 GPUs. Results show that DeepServeCB reduces P99 latency by 38--62% compared to state-of-the-art baselines while improving GPU utilization by 14--23% and reducing per-request cost by up to 27%, with only a modest increase in scheduling overhead."}11{"work_id": 4395047770, "title": "Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions", "abstract": "Abstract With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling."}12{"work_id": 4386585104, "title": "ACCESS: Advancing Innovation", "abstract": "As the National Science Foundation evolves its investments in cyberinfrastructure, it has made a significant investment in the ACCESS (Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support) program instantiating a novel set of services along with a novel governance and management model. Research cyberinfrastructure (CI) is a key catalyst for discovery and innovation and plays a critical role in ensuring U.S. leadership in science and engineering, economic competitiveness, and national security, consistent with NSF\u2019s mission. Funding of a set of awards through the ACCESS program has established a suite of CI coordination services targeted at supporting a broad and diverse set of requirements, researchers, and usage modalities spanning all areas of science and engineering research and education complemented by support for the collective and coordinated operation of the overall ACCESS program."}13{"work_id": 4361200200, "title": "Spatial Data Science", "abstract": "Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes \u2013 array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/."}14{"work_id": 4229658977, "title": "Google Colaboratory", "abstract": "Google Colaboratory more commonly referred to as \u201cGoogle Colab\u201d or just simply \u201cColab\u201d is a research project for prototyping machine learning models on powerful hardware options such as GPUs and TPUs. It provides a serverless Jupyter notebook environment for interactive development. Google Colab is free to use like other G Suite products."}15{"work_id": 4407389811, "title": "Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data", "abstract": "The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT\u2019s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience."}16{"work_id": 4406207083, "title": "Mobile Edge Intelligence for Large Language Models: A Contemporary Survey", "abstract": "On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest owing to their superior privacy, reduced latency, and bandwidth saving. Nonetheless, the capabilities of on-device LLMs are intrinsically constrained by the limited capacity of edge devices compared to the much more powerful cloud centers. To bridge the gap between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a viable solution to this problem by provisioning AI capabilities within the edge of mobile networks with improved privacy and latency relative to cloud computing. MEI sits between on-device AI and cloud-based AI, featuring wireless communications and more powerful computing resources than end devices. This article provides a contemporary survey on harnessing MEI for LLMs. We first cover the preliminaries of LLMs, starting with LLMs and MEI, followed by resource-efficient LLM techniques. We then illustrate several killer applications to demonstrate the need for deploying LLMs at the network edge and present an architectural overview of MEI for LLMs (MEI4LLM). Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We aim to inspire researchers in the field to leverage mobile edge computing to facilitate LLM deployment in close proximity to users, thereby unleashing the potential of LLMs across various privacy-and delay-sensitive applications."}17{"work_id": 2602956630, "title": "Internet of Things Strategic Research Roadmap", "abstract": "Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual \u201cthings\u201d have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network."}18{"work_id": 2950865323, "title": "Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing", "abstract": "With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI."}19{"work_id": 4300092207, "title": "OrbitalBrain: A Distributed Framework for Training ML Models in Space", "abstract": "Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52\u00d7-12.4\u00d7 speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications."}20{"work_id": 2593864460, "title": "Billion-Scale Similarity Search with GPUs", "abstract": "Similarity search finds application in database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data parallel tasks such as distance computation, prior approaches in this domain are bottlenecked by algorithms that expose less parallelism, such ask-min selection, or make poor use of the memory hierarchy. We propose a novel design fork-selection. We apply it in different similarity search scenarios, by optimizing brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation operates at up to 55 percent of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5 \u00d7 faster than prior GPU state of the art. It enables the construction of a high accuracyk-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility."}21{"work_id": 4401806686, "title": "AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications", "abstract": "Artificial Intelligence (AI) technology's rapid advancement has significantly changed various industries' operations. This comprehensive review paper aims to provide readers with a deep understanding of AI's applications & implementations, workings, and potential impacts across different sectors. It also discusses its future, threats, and integration into new policy. Through extensive research on more than 200 research and many other sources, the authors have made every effort to present an accurate overview of the numerous applications of AI nowadays in industries such as agriculture, education, autonomous systems, healthcare, finance, entertainment, transportation, military, manufacturing, and more. The paper explores various AI technologies, including machine learning, deep learning, robotics, big data, the Internet of Things, natural language processing, image processing, object detection, virtual reality, augmented reality, speech recognition, and computer vision. It provides real-world examples of their applications and implementations. Moreover, it highlights and evaluates the future potential, challenges, and limitations associated with the widespread use of AI. Our study incorporates the latest research to offer a comprehensive and nuanced understanding of AI's potential benefits and challenges. This data-driven review case study highlights the immense potential of AI technology and addresses the ethical, societal, and economic considerations related to its implementation."}22{"work_id": 7118010419, "title": "Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices", "abstract": "The increasing popularity of digitization in retail has emphasized application performance as a critical component of customer satisfaction, business continuity, and operational efficiency. Retail applications today operate in hyper-dynamic environments, where performance can be introduced by concerns such as workload peaks, scaling cloud infrastructure performance, spikes in transactions, and real-time data processing. Poor performance erodes user experience, depletes objectives, and lowers competitive benefit. Although many surveys of cloud or performance monitoring exist, they do not incorporate retail contexts that emphasize customer facing KPIs, seasonal demand variability, and other omnichannel complexities. The current paper seeks to address this gap in research by providing a systematic review of 45 peer-reviewed studies (2015\u20132025) specific to monitoring tools, optimization frameworks, and best practices in retail performance monitoring. The findings in the review synthesize performance monitoring platforms (i.e. Splunk, Datadog, New Relic), log management tools, key performance indicators, performance continuous monitoring, performance visualization, and performance integration into retail systems. The paper also offers comparison evaluations of Amazon and Google Cloud monitoring offerings, as well as limitations for optimization planning, and the effect of intelligent analytics to improve scalability and resiliency. The paper contributes by connecting scholarly viewpoints with practical suggestions, providing a distinctive road map for practitioners and academics to develop and enhance high-performance retail applications in cloud-based corporate settings."}23{"work_id": 2784822088, "title": "A microservice architecture for the processing of large geospatial data in the Cloud", "abstract": "Mit der wachsenden Zahl an Ger\u00e4ten, die spatio-temporale Informationen aufnehmen k\u00f6nnen sowie immer besser werdenden Sensoren, steigt auch die Menge an Geodaten. Vor der Benutzung m\u00fcssen die rohen gesammelten Informationen verarbeitet werden. Zurzeit greifen Experten auf desktop-basierte Geographische Informationssysteme zur\u00fcck, um Prozessierungsworkflows durchzuf\u00fchren. Allerdings \u00fcbersteigt das Datenvolumen sowie die Komplexit\u00e4t der Verarbeitungsalgorithmen l\u00e4ngst die Kapazit\u00e4t ihrer Workstations. Zurzeit findet ein Paradigmenwechsel von Desktop-L\u00f6sungen zur Cloud statt, aber die Entwickler von Prozessierungsalgorithmen sind oft keine Informatiker und haben deshalb wenig Erfahrung im Bereich Cloud-Computing. Unsere Forschungshypothese ist, dass eine Microservice-Architektur und dom\u00e4nenspezifische Sprachen genutzt werden k\u00f6nnen, um existierende Algorithmen zu orchestrieren und Workflows f\u00fcr die Prozessierung von Geodaten in der Cloud auszuf\u00fchren, und damit eine effiziente Anwendungsentwicklung erm\u00f6glichen und die Erfahrung von Stakeholdern verbessern. Wir pr\u00e4sentieren eine Softwarearchitektur, die Erweiterungspunkte f\u00fcr Prozessierungsalgorithmen (oder Microservices) enth\u00e4lt, eine Workflow-Management-Komponente f\u00fcr die verteilte Service-Orchestrierung, und einen Workflow-Editor basierend auf einer dom\u00e4nenspezifischen Sprache. Ziel ist es, Benutzern und Entwicklern ohne tiefergehendes Wissen in verteilten Systemen den Zugang zur Cloud zu erm\u00f6glichen. Unsere Forschungsmethode basiert auf der Design Science Research Methodology. Wir f\u00fchren eine Analyse der Problemdom\u00e4ne durch und sammeln Anforderungen und Qualit\u00e4tsattribute f\u00fcr unsere Architektur. Um unsere Forschungsziele zu erreichen, entwickeln wir die Architektur sowie Ans\u00e4tze f\u00fcr Workflow-Management und -Modellierung. Wir stellen den Nutzen unserer L\u00f6sung dar, indem wir sie auf zwei praktische Anwendungsf\u00e4lle anwenden. Au\u00dferdem evaluieren wir ihre Qualit\u00e4t anhand von definierten Szenarien. Abschlie\u00dfend f\u00fchren wir eine kritische Bewertung unserer Ergebnisse durch. Unsere wissenschaftlichen Beitr\u00e4ge k\u00f6nnen in drei Bereiche gegliedert werden. Wir pr\u00e4sentieren eine skalierbare und erweiterbare Microservice-Architektur f\u00fcr die Geodatenprozessierung, die eine verteilte Entwicklung erm\u00f6glicht sowie eine hohe Verf\u00fcgbarkeit bietet. Au\u00dferdem pr\u00e4sentieren wir neue Ans\u00e4tze f\u00fcr die Service- Integration und -Orchestrierung in der Cloud sowie regelbasiertes und dynamisches Workflow-Management ohne a priori Wissen im Entwurf. F\u00fcr die Workflow- Modellierung entwickeln wir eine dom\u00e4nenspezifische Sprache sowie eine neue Methode f\u00fcrs Sprachdesign. Die Ergebnisse unserer Arbeit st\u00fctzen unsere Forschungshypothese. Die Microservice- Architektur erm\u00f6glicht eine effiziente Entwicklung eines verteilten Systems. Die dom\u00e4nenspezifische Sprache sowie unser Ansatz zur Service-Integration verbessern die Erfahrung der Stakeholder. Unsere Arbeit stellt einen gro\u00dfen Schritt im Paradigmenwechsel zur Cloud dar und bietet M\u00f6glichkeiten f\u00fcr weitere Forschung."}24{"work_id": 4394994587, "title": "Recommender Systems in the Era of Large Language Models (LLMs)", "abstract": "With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an indispensable and important component in our daily lives, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have achieved significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating their textual side information, these DNN-based methods still exhibit some limitations, such as difficulties in effectively understanding users' interests and capturing textual side information, inabilities in generalizing to various seen/unseen recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the development of Large Language Models (LLMs), such as ChatGPT and GPT-4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, so as to provide researchers and practitioners in relevant fields with an in-depth understanding. Therefore, in this survey, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including pre-training, fine-tuning, and prompting paradigms. More specifically, we first introduce the representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we systematically review the emerging advanced techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss the promising future directions in this emerging field."}25{"work_id": 4408156900, "title": "Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities", "abstract": "The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and artificial intelligence (AI) to improve cloud computing security while preserving privacy, delivering real-time threat detection, and meeting regulatory requirements. The current research follows a systematic literature review (SLR) approach, which examined 30 studies published between 2020 and 2024 and followed the PRISMA 2020 checklist. The analysis shows that FL provides significant privacy risk reduction by 25%, especially in healthcare and similar domains, and it improves threat detection by 40% in critical infrastructure areas. A total of 80% of reviewed implementations showed improved privacy, but challenges like communication overhead and resource limitations persist, with 50% of studies reporting latency issues. To overcome these obstacles, this study also explores some emerging solutions, which include model compression, hybrid federated architectures, and cryptographic enhancements. Additionally, this paper demonstrates the unexploited capability of FL for real-time decision-making in dynamic edge environments and highlights its potential across autonomous systems, Industrial Internet of Things (IIoT), and cybersecurity frameworks. The paper\u2019s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era."}26{"work_id": 3012404397, "title": "Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment", "abstract": "\ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9(BFT: Byzantine Fault Tolerance)\uc740 \ube14\ub85d\uccb4\uc778 \ud658\uacbd\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\ub294 \ube14\ub85d\uccb4\uc778 \ud569\uc758 \uc54c\uace0\ub9ac\uc998 \uc911\uc758 \ud558\ub098\uc774\ub2e4. BFT\ub294 \ud569\uc758\ub97c \ud558\uae30 \uc704\ud574 \ubaa8\ub4e0 \ucc38\uc5ec\uc790\uac00 \ud569\uc758\uc5d0 \ucc38\uc5ec\ud558\ub294 \uad6c\uc870\ub85c \ub418\uc5b4\uc788\uc5b4 \ucc38\uc5ec \ub178\ub4dc\uac00 \ub9ce\uc544\uc9c0\uba74 \ucc98\ub9ac\uc18d\ub3c4\uac00 \ud604\uaca9\ud788 \uac10\uc18c\ud558\ub294 \ud655\uc7a5\uc131\uc758 \ubb38\uc81c\uac00 \uc788\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c \ub54c\ubb38\uc5d0 \ub9ce\uc740 \ucc38\uc5ec \ub178\ub4dc\uac00 \uc788\ub294 \ube14\ub85d\uccb4\uc778\uc5d0\uc11c \ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud655\uc7a5\uc131 \uac1c\uc120\uc774 \ud544\uc218\uc801\uc774\ub2e4. \ubcf8 \uc5f0\uad6c\uc5d0\uc11c\ub294 \ud504\ub77c\uc774\ube57 \ube14\ub85d\uccb4\uc778\uc5d0\uc11c \ud569\uc758 \uc5c6\uc774 \ub370\uc774\ud130\ub97c \uae30\ub85d\ud558\ub2e4\uac00 \uc5d0\uc774\uc804\ud2b8\uac00 \ud569\uc758\uac00 \ud544\uc694\ud55c \uc21c\uac04\uc744 \uc54c\ub824\uc8fc\ub294 \uc804\ucc98\ub9ac \uae30\ubc95\uc744 \uc81c\uc548\ud55c\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ube14\ub85d\uccb4\uc778\uc758 \ub178\ub4dc\ub97c \ubc29\ubb38\ud558\uba74\uc11c \ub450 \ub2e8\uacc4\uc5d0 \uac78\uccd0 \ud569\uc758\uac00 \ud544\uc694\ud55c \uc21c\uac04\uc744 \uc54c\ub824\uc900\ub2e4. \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc9c0\uae08\uae4c\uc9c0 \uae30\ub85d\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub4e0 \ub178\ub4dc\uc5d0\uc11c \uc77c\uce58\ud558\ub294\uc9c0 \ud655\uc778\ud558\uba70, \ub450 \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc5b4\ub5a0\ud55c \ub370\uc774\ud130\uac00 \ubd88\uc77c\uce58\ud558\uc5ec \ud569\uc758\uac00 \ud544\uc694\ud55c\uc9c0 \uc54c\ub824\uc900\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ube14\ub85d\uccb4\uc778 \ub124\ud2b8\uc6cc\ud06c\uc5d0\uc11c \ub3c5\ub9bd\uc801\uc778 \uac1d\uccb4\ub85c\uc11c \ube14\ub85d\uccb4\uc778\uc5d0 \ucc38\uc5ec\ud558\ub294 \uc784\uc758\uc758 \ub178\ub4dc\uc5d0 \uc758\ud574 \uc791\ub3d9\uc774 \ub41c\ub2e4. \ubcf8 \ub17c\ubb38\uc5d0\uc11c\ub294 \uc81c\uc548 \uae30\ubc95\uc744 \ud504\ub77c\uc774\ube57 \ube14\ub85d\uccb4\uc778 \ud658\uacbd\uc5d0\uc11c \uc2e4\uc6a9\uc801 \ube44\uc794\ud2f4 \uc7a5\uc560 \ud5c8\uc6a9(PBFT: Practical Byzantine Fault Tolerance) \ud569\uc758\uc5d0 \uc801\uc6a9\ud55c \uacb0\uacfc \ub178\ub4dc\uc758 \uc218\uac00 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \ubc18\uc751 \uc2dc\uac04\uc774 \ud3c9\uade0 39% \uac10\uc18c\ud558\uc600\ub2e4\ub294 \uac83\uc744 \uc99d\uba85\ud558\uc600\ub2e4."}27{"work_id": 2120510885, "title": "The Byzantine generals problem", "abstract": "Abstract The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an \"incorrect \" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than one-third of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures."}28{"work_id": 2795038878, "title": "Datasheets for datasets", "abstract": "Documentation to facilitate communication between dataset creators and consumers."}29{"work_id": 4387321091, "title": "Efficient Memory Management for Large Language Model Serving with PagedAttention", "abstract": "High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2--4\u00d7 with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm."}30{"work_id": 4415124086, "title": "The Faiss Library", "abstract": "Vector databases typically manage large collections of embedding vectors. As AI applications are growing rapidly, the number of embeddings that need to be stored and indexed is increasing. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper describes the trade-offs in vector search and the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected use cases to highlight its broad applicability."}31{"work_id": 2963507231, "title": "Untangling Blockchain: A Data Processing View of Blockchain Systems", "abstract": "Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol, and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases."}32{"work_id": 4288083766, "title": "Deep Learning Based Recommender System", "abstract": "With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field."}33{"work_id": 1572499759, "title": "Microservices Architecture", "abstract": "This paper provides a comprehensive overview of microservices architecture, focusing on its implementation.We discuss the core principles, key components, deployment considerations, challenges, and best practices for building robust and scalable microservices-based applications with C#.The paper emphasizes the benefits of adopting microservices, such as improved scalability, agility, and maintainability, while also addressing common challenges like distributed system complexity and data management.Specific examples and code snippets illustrate the practical aspects of implementing microservices with ASP.NET Core and related technologies."}34{"work_id": 3097300053, "title": "Graph Neural Networks in Recommender Systems: A Survey", "abstract": "With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS ."}35{"work_id": 4401857375, "title": "A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models", "abstract": "As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the quality of the generated content of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: Furthermore, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at: https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/"}36{"work_id": 3014294420, "title": "Introduction to Data Mining", "abstract": "Chapter Objectives \u2713 To learn about the concepts of data mining. \u2713 To understand the need for, and the applications of data mining \u2713 To differentiate between data mining and machine learning \u2713 To understand the process of data mining. \u2713 To understand the difference between data mining and machine learning. Introduction to Data Mining In the age of information, an enormous amount of data is available in different industries and organizations. The availability of this massive data is of no use unless it is transformed into valuable information. Otherwise, we are sinking in data, but starving for knowledge. The solution to this problem is data mining which is the extraction of useful information from the huge amount of data that is available. Data mining is defined as follows: \u2018Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so they may be used in an enterprise's decision making.\u2019 From this definition, the important take aways are: \u2022 Data mining is a process of automated discovery of previously unknown patterns in large volumes of data. \u2022 This large volume of data is usually the historical data of an organization known as the data warehouse. \u2022 Data mining deals with large volumes of data, in Gigabytes or Terabytes of data and sometimes as much as Zetabytes of data (in case of big data). \u2022 Patterns must be valid, novel, useful and understandable. \u2022 Data mining allows businesses to determine historical patterns to predict future behaviour. \u2022 Although data mining is possible with smaller amounts of data, the bigger the data the better the accuracy in prediction. \u2022 There is considerable hype about data mining at present, and the Gartner Group has listed data mining as one of the top ten technologies to watch. Need of Data Mining Data mining is a recent buzz word in the field of Computer Science. It is a computing process that uses intelligent mathematical algorithms to extract the relevant data and computes the probability of future actions. It is also known as Knowledge Discovery in Data (KDD)."}37{"work_id": 4395064458, "title": "Wireless Communications", "abstract": "Now reissued by Cambridge University Press, the updated second edition of this definitive textbook provides an unrivaled introduction to the theoretical and practical fundamentals of wireless communications. Key technical concepts are developed from first principles, and demonstrated to students using over 50 carefully curated worked examples. Over 200 end-of-chapter problems, based on real-world industry scenarios, help cement student understanding. The book provides a thorough coverage of foundational wireless technologies, including wireless local area networks (WLAN), 3G systems, and Bluetooth along with refreshed summaries of recent cellular standards leading to 4G and 5G, insights into the new areas of mobile satellite communications and fixed wireless access, and extra homework problems. Supported online by a solutions manual and lecture slides for instructors, this is the ideal foundation for senior undergraduate and graduate courses in wireless communications."}38{"work_id": 2947690458, "title": "Blockchain for Internet of Things: A Survey", "abstract": "Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area."}39{"work_id": 7123361055, "title": "Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions", "abstract": "The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multi-modal environments, reason contextually, and adapt proactively through continuous perception\u2013reasoning\u2013action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI\u2019s capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments."}40{"work_id": 3037795158, "title": "Applications of digital technology in COVID-19 pandemic planning and response", "abstract": "With high transmissibility and no effective vaccine or therapy, COVID-19 is now a global pandemic. Government-coordinated efforts across the globe have focused on containment and mitigation, with varying degrees of success. Countries that have maintained low COVID-19 per-capita mortality rates appear to share strategies that include early surveillance, testing, contact tracing, and strict quarantine. The scale of coordination and data management required for effective implementation of these strategies has-in most successful countries-relied on adopting digital technology and integrating it into policy and health care. This Viewpoint provides a framework for the application of digital technologies in pandemic management and response, highlighting ways in which successful countries have adopted these technologies for pandemic planning, surveillance, testing, contact tracing, quarantine, and health care."}41{"work_id": 4323539384, "title": "Scientific dataset recommendation with semantic techniques", "abstract": "Scientific datasets are important in scientific research. Researchers always want to find a way to improve their research. Data discovery and reuse is a recent and popular way of finding more scientific datasets to help researchers. There is much current research work for data discovery and reuse. Nevertheless, most of these works are limited because researchers only trust and reuse data through social networks (such as colleagues or collaborators). Also, the scientific data from social networks bring limitations. For instance, they will only recommend data that they consider helpful. However, one scientist's useful data sometimes might be another's noisy data. A dataset search engine is a solution to help researchers find data outside their social networks by exploring scientific datasets in open data repositories or hubs. Such a dataset search engine typically returns related or relevant datasets based on a keyword query from researchers. Several existing dataset search engines already provide millions of open datasets from open-access sources. However, the performance of a dataset search engine is highly related to the quality of the keyword queries submitted by researchers. For instance, two different queries with the same meaning would get different returns from the same dataset search engine. The main question of this thesis is how to find more relevant scientific datasets from a query on a dataset search engine. A new idea is provided as the solution with dataset recommendation, which is adding one step, \"if you like this dataset, do you also like these other datasets,\" after researchers or users submitted a query to a dataset search engine. Users submit keywords for query to a dataset search engine and then get returned list of datasets. Users can then tell the dataset search engine which returned dataset they like, and the dataset search engine will say, \"If you like this dataset, you would also like those other datasets,\" to recommend more datasets. This thesis aims to help dataset search engines with such scientific dataset recommendations using Semantic Web technologies. As mentioned, the main idea of this thesis is \"if you like this dataset, you would also like that other dataset\". This thesis states the challenges and possibilities of using Semantic Web technologies on scientific dataset recommendation, as well as explores 1) how ontologies can be used for dataset recommendation, and 2) how knowledge graphs can be used for dataset recommendation? These two questions are addressed in six chapters. A scientific item (dataset or paper) recommendation benchmark was provided during the exploring work of this thesis to provide a standard for evaluating scientific item recommendation methods. This benchmark contains a large-scale corpus with millions of datasets and papers as well as the trusted links between them. Also, a collection of entity embeddings was created in the work of this thesis. These embeddings are scientific item embeddings by using knowledge graph embedding on the citation network from Microsoft Academic knowledge graph."}42{"work_id": 3148181069, "title": "Classification Based on Decision Tree Algorithm for Machine Learning", "abstract": "Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed."}43{"work_id": 3104550706, "title": "Gossip Algorithms for Distributed Signal Processing", "abstract": "Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression."}44{"work_id": 4415028496, "title": "Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey", "abstract": "Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing across distributed communication networks. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these networked systems, yet introduce significant challenges in model deployment, network resource management, and cross-layer optimization. In this survey, we comprehensively examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, communication protocols, and network-aware computing frameworks. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and communication overhead across heterogeneous networks. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examine practical deployments through diverse networked applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex distributed systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements in communications with practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments over next-generation communication networks, fostering innovation in intelligent networked systems."}45{"work_id": 4395483946, "title": "Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives", "abstract": "Abstract In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and research processes. Despite widespread research on GenAI in various fields, there is a lack of multicultural perspectives on its impact and concerns in HE. This study addresses this gap by examining the usage, benefits, and concerns of GenAI in higher education from a multicultural standpoint. We employed an online survey that collected responses from 1217 participants across 76 countries, encompassing a broad range of gender categories, academic disciplines, geographical locations, and cultural orientations. Our findings revealed a high level of awareness and familiarity with GenAI tools among respondents. A significant portion had prior experience and expressed the intention to continue using these tools, primarily for information retrieval and text paraphrasing. The study emphasizes the importance of GenAI integration in higher education, highlighting both its potential benefits and concerns. Notably, there is a strong correlation between cultural dimensions and respondents\u2019 views on the benefits and concerns related to GenAI, including its potential as academic dishonesty and the need for ethical guidelines. We, therefore, argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations. We discussed the findings and offered recommendations for researchers, educators, and policymakers, aiming to promote the ethical and effective integration of GenAI tools in higher education."}46{"work_id": 4362632858, "title": "Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices", "abstract": "Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing."}47{"work_id": 3098508067, "title": "Personal volunteer computing", "abstract": "In this dissertation, we articulate the new Personal Volunteer Computing paradigm, that refines Volunteer Computing by focusing on solutions that are personal to the user on multiple dimensions: they target personal projects, they leverage the participation of volunteers from their personal social network, and they are built into personal tools that can be deployed on personal devices and can be easily maintained by a single developer.We then present Pando, a new and first tool for personal volunteer computing written in and for JavaScript, that distributes the application of a function on a stream of inputs into the browsers of participating devices. Pando dynamically scales to new devices, gracefully tolerates sudden disconnections, and is easy to program because it is based on a declarative concurrent programming paradigm, in which the non-determinism of parallel executions is not observable by users. We follow with a more detailed presentation of the implementation of Pando, based on the new Limiter, StreamLender, and DistributedMap abstractions. Our presentation uses a high-level notation, independent of JavaScript and that simplifies reasoning about concurrent aspects, to introduce all the algorithms that implement the Limiter, StreamLender, and DistributedMap abstractions. Because the concurrent aspects make StreamLender challenging to implement, even with a clear description, we present a run-time verification approach to ensure it is correct. The approach is easy to parallelize, with Pando for example, and quickly generates a large number of random executions to ensure a high-probability of correctness. The combination of clear descriptions and testing strategy should make Pando easy to reimplement in other programming environments.We then present a large scope of applications that we implemented for Pando, based on existing libraries and examples. These applications represent various dataflow patterns and show Pando can be used not only for compute-intensive tasks but also for crowd-processing. We then measure the throughput performance of these applications in three networking scenarios: (1) over a local-area Wi-Fi network, with personal laptop and smartphones, (2) over a virtual private network distributed throughout France, with the Grid5000 nodes, and (3) with a wide-area network throughout Europe on the Internet, with the PlanetLab EU nodes. We show personal devices are competitive in all scenarios, sometimes with older devices competing with newer models, and other times with combinations of personal devices outcompeting remote server nodes. The flexible and easy support of all these scenarios is, to the best of our knowledge, a first in the volunteer computing literature. We then present Genet, a new fat-tree overlay for WebRTC that enables Pando to overcome the limits of WebRTC in the number of connections, and showed the resulting combination of Pando and Genet to be able to scale to a thousand browsers in 30-55 seconds on local networks. Those results are possible because the design of Genet only uses local information to deterministically route the WebRTC connection messages, while ensuring the resulting tree is probabilistically balanced.We conclude by outlining new exciting research directions that take into account the limits to growth our society is currently facing. Compared to the current trends in research, these new directions make smaller whole-system designs done by small teams viable again, but bring a stronger focus on leveraging abundant personal computing devices and taking into account the growing importance of efficiently using renewable electricity sources"}48{"work_id": 3092103025, "title": "Bias and Debias in Recommender System: A Survey and Future Directions", "abstract": "While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, and so on. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology \u201cbias\u201d is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing ."}49{"work_id": 7125650829, "title": "AI Flow: perspectives, scenarios, and approaches", "abstract": "Abstract Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and communication technologies (IT/CT) has created an unbroken wave of connectivity and computation. This synergy has sparked a technological revolution, now reaching its peak with large artificial intelligence (AI) models that are reshaping industries and redefining human-machine collaboration. However, the realization of ubiquitous intelligence faces considerable challenges due to substantial resource consumption in large models and high communication bandwidth demands. To address these challenges, AI Flow has been introduced as a multidisciplinary framework that integrates cutting-edge IT and CT advancements, with a particular emphasis on the following three key points. First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters to optimize scalability and efficiency for low-latency model inference. Second, we introduce the concept of familial models , which refers to a series of different-sized models with aligned hidden features, enabling effective collaboration and the flexibility to adapt to varying resource constraints and dynamic scenarios. Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow. By leveraging communication networks to enhance connectivity, the collaboration among AI models across heterogeneous nodes achieves emergent intelligence that surpasses the capability of any single model. The innovations of AI Flow provide enhanced intelligence, timely responsiveness, and ubiquitous accessibility to AI services, paving the way for the tighter fusion of AI techniques and communication systems. These advancements are crucial to numerous application scenarios, including but not limited to embodied AI, wearable devices, and smart cities."}50{"work_id": 3045200674, "title": "LightGCN", "abstract": "Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs -- feature transformation and nonlinear activation -- contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance."}51{"work_id": 4406085118, "title": "Data-centric Artificial Intelligence: A Survey", "abstract": "Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI ."}52{"work_id": 4386827074, "title": "Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste", "abstract": "The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges."}53{"work_id": 4378832451, "title": "Overview of Amazon Web Services", "abstract": "With today\u2019s technological boom, the demand for cloud computing services is increasing day by day. Individuals, companies, and multinational businesses are shifting from self-owned web services to cloud services. Among cloud services providers, the most prominent are Amazon Web Services (AWS), Google Cloud Platform GCP, IBM, and Oracle Cloud."}54{"work_id": 4393370257, "title": "Distributed methods for resource allocation - a passivity-based approach", "abstract": "Since the complexity and scale of systems have been growing in the last years, distributed approaches for control and decision making are becoming more prevalent. This dissertation focuses on an important problem involving distributed control and decision making, the dynamic resource allocation in a network. To address this problem, we explore a consensus--based algorithm that does not require any centralized computation, and that is capable to deal with applications modeled either by dynamical systems or by memoryless functions. The main contribution of our research is to prove, by means of graph theoretical tools and passivity analysis, that the proposed controller asymptotically reaches an optimal solution without the need of full information. In order to illustrate the relevance of our main result, we address several engineering applications including: distributed control for energy saving in smart buildings, management of the customers of an aggregating entity in a smart grid environment, and development of an exact distributed optimization method that deals with resource allocation problems subject to lower--bound constraints. Finally, we explore resource allocation techniques based on classic population dynamics models. In order to make them distributed, we introduce the concept of non--well--mixed population dynamics. We show that these dynamics are capable to deal with constrained information structures that are characterized by non--complete graphs. Although the proposed non--well--mixed population dynamics use partial information, they preserve similar properties of their classic counterpart, which uses full information. Specifically, we prove mass conservation and convergence to Nash equilibrium."}55{"work_id": 2974429275, "title": "Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT", "abstract": "The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security."}56{"work_id": 2897450989, "title": "Blockchain challenges and opportunities: a survey", "abstract": "Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology."}57{"work_id": 4252792641, "title": "PRIORITIZED TASK SCHEDULING IN FOG COMPUTING", "abstract": "Cloud computing is an environment where virtual resources are shared among the many users over network. A user of Cloud services is billed according to pay-per-use model associated with this environment. To keep this bill to a minimum, efficient resource allocation is of great importance. To handle the many requests sent to Cloud by the clients, the tasks need to be processed according to the SLAs defined by the client. The increase in the usage of Cloud services on a daily basis has introduced delays in the transmission of requests. These delays can cause clients to wait for the response of the tasks beyond the deadline assigned. To overcome these concerns, Fog Computing is helpful as it is physically placed closer to the clients. This layer is placed between the client and the Cloud layer, and it reduces the delay in the transmission of the requests, processing and the response sent back to the client greatly. This paper discusses an algorithm which schedules tasks by calculating the priority of a task in the Fog layer. The tasks with higher priority are processed first so that the deadline is met, which makes the algorithm practical and efficient."}58{"work_id": 4322576964, "title": "On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds", "abstract": "Fifth generation (5G) mobile communication systems have entered the stage of commercial deployment, providing users with new services, improved user experiences as well as a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified to stimulate the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed."}59{"work_id": 7160823269, "title": "TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location", "abstract": "In the context of GPU scarcity, multi-model and multi-tenant inference services for autoregressive large language models require finer-grained resource sharing to improve utilization and throughput. Existing approaches rely on coarse-grained spatial or temporal multiplexing, which often leads to suboptimal GPU utilization and increased tail latencies. We present TokenPool-Scheduler, a token-level GPU pooling and resource slicing framework that breaks LLM inference execution into fine-grained segments. By employing a token-level cost model and adaptive scheduling policies, TokenPool-Scheduler dynamically determines optimal slice sizes, preemption points, and resource quotas for heterogeneous models. Experimental results demonstrate that TokenPool-Scheduler achieves up to 33% higher GPU utilization and 63% improvement in goodput compared to static allocation schemes, while reducing P99 time-to-first-token (TTFT) latency by 43% under high load (250 req/s per GPU). The system reduces model switching overhead by 69% compared to existing spatial-temporal multiplexing approaches."}60{"work_id": 7130586201, "title": "Dynamic Event-Triggered Control for Human\u2013Machine Cooperative Systems Based on Dynamic Authority Allocation", "abstract": "This article addresses the challenging problem of constrained optimal control for human\u2013machine systems subject to external disturbances and the bounded rationality of the human operator. To this end, a novel game-theoretic framework is proposed. Unlike monolithic game formulations, the framework uniquely disaggregates the control problem by transforming it into a multifaceted game via logarithmic barrier functions (BFs): it models human\u2013machine cooperation as a positive-sum game oriented toward shared objectives, and disturbance rejection as a zero-sum game tailored for robustness enhancement. To capture the nonideal human decision-making, we integrate the level-$k$reasoning framework to model the operator\u2019s bounded cognitive dynamics. The corresponding coupled Hamilton\u2013Jacobi\u2013Isaacs (HJI) equations for this human\u2013machine game are derived, and critically, a rigorous proof of global asymptotic stability (GAS) for the transformed system is provided, establishing a solid theoretical foundation. For online implementation without requiring prior knowledge of the system dynamics, we develop a resource-efficient learning architecture based on the adaptive dynamic programming (ADP) and a novel dynamic event-triggered mechanism (DETM). A key feature of this architecture is a fuzzy logic-based module for dynamic authority allocation, which adaptively adjusts control sharing in real time. Rigorous analysis demonstrates that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is precluded. Simulation results are presented to validate the effectiveness and superiority of the proposed control strategy."}61{"work_id": 2122465391, "title": "MapReduce: Simplified Data Processing on Large Cluster", "abstract": "MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.\r\n\r\nPrograms written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.\r\n\r\nOur implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day."}62{"work_id": 2962850638, "title": "Logiikka ja vaativuus hajautetussa laskennassa", "abstract": "This dissertation studies the theory of distributed computing. In the distributed setting, computation is carried out by multiple independent computational units. They communicate with neighbouring units and collectively solve a problem. Systems of this kind are widespread in the information society as well as in the natural world: prime examples include the Internet, multi-processor computer systems, the cells of a biological organism and human social networks. Therefore it is important to gain knowledge on the fundamental capabilities and limitations of distributed systems. Our work takes place in the context of deterministic and synchronous message-passing models of distributed computing. Such models abstract away some possible features of distributed systems, such as asynchrony, congestion and faults, while putting emphasis on the amount of communication needed between the units. The computational units are expected to solve a problem related to the structure of the underlying communication network. More specifically, we study the standard LOCAL model, the standard port-numbering model and several weaker variants of the latter. The contributions of this dissertation are two-fold. First, we give new methods for studying distributed computing by establishing a strong connection between several weak variants of the port-numbering model and corresponding variants of modal logic. This makes it possible to apply existing logical tools, such as bisimulation, to understand computation in the distributed setting. We also give a full characterisation of the relationships between the models of computing, which has implications on the side of mathematical logic. Second, we prove the existence of various new non-empty complexity classes. One of our results studies the relationship between time and space complexity, which is a relatively new topic in distributed computing research. We demonstrate the existence of a problem that can be solved in a constant amount of space in a very weak model but nonetheless requires a non-constant amount of time even in a much stronger model of computing. Another result continues the recent success on developing complexity theory for the LOCAL model. We introduce a new technique which shows the existence of an infinite hierarchy of problems with novel time complexities."}63{"work_id": 4320712473, "title": "The Internet of Things (IoT) in healthcare: Taking stock and moving forward", "abstract": "Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and a bibliometric analysis were performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the results of the keyword co-occurrence network, IoT healthcare applications, blockchain applications, Artificial Intelligence (AI) techniques, 5G telecommunications, as well as data analytics and computing technologies emerged as important topics. The co-citation network analysis reveals other important themes, including authentication schemes, fog computing, cloud-IoT integration, and cognitive smart healthcare. Overall, the review offers scholars an improved understanding of the current status of IoT research in healthcare and identifies knowledge gaps for future research. This review also informs healthcare professionals about the latest developments and applications of IoT in the healthcare sector."}64{"work_id": 1973501242, "title": "Time, clocks, and the ordering of events in a distributed system", "abstract": "The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become."}65{"work_id": 2762829140, "title": "Deep Learning based Intrusion Detection System for Internet of Things", "abstract": "With the increase in number of Internet connected devices, security and privacy concerns are the major obstacles impeding the widespread adoption of Internet of Things (IoT). Securing IoT has become a huge area of concern for all, including the consumers, organizations as well as the government. While attacks on any system cannot be fully prevented forever, real-time detection of the attacks are critical to defend the systems in an effective manner. Limited research exists on efficient intrusion detection systems suitable for IoT environment. In this thesis, we propose a novel intrusion detection system that uses machine learning algorithms to detect security anomalies in IoT networks. This detection platform provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We provide a framework of the proposed system and discuss the intrusion detection process in detail. The proposed intrusion detection system is evaluated using both, real network traces for providing a proof-of-concept, and on simulation for providing evidence of its scalability. Our results confirm that the proposed intrusion detection system is capable of detecting real-world intrusions effectively."}66{"work_id": 2973502831, "title": "Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework", "abstract": "Blockchains are distributed ledgers that use a tamper-sensitive, append-only data structure in conjunction with a consensus protocol to enable mutually distrusting parties to maintain a global set of states. A primary barrier to adoption of blockchain technology by industry is the current performance and scalability limitations of these systems, which lag far behind incumbent database systems. Of particular interest are \u201clottery-style\u201d consensus algorithms, which are relatively scalable to many participants but suffer from low throughput (performance). Proof-of-Elapsed-Time (PoET) is one such algorithm with great promise for use in industry, though the parameters that govern its performance have not been well studied. This thesis explores, through simulation, key performance outcomes in PoET blockchain networks implemented with the Hyperledger Sawtooth framework. A better quantitative understanding of the interactions among these system parameters will be crucial for efficiently optimizing real world blockchain networks and facilitating adoption by industry."}67{"work_id": 4400494356, "title": "Digital health research governance: from FAIR to RE-AIM", "abstract": "This doctoral research aims to develop a governance model for digital health research data to enhance the quality of collected data and translate research outcomes into an integrated framework for planning, implementing, and evaluating public health initiatives. Employing a mixed-methods approach, the study combines qualitative and quantitative methods, qualifying as an exploratory and explanatory study. The specific objectives include proposing the adoption of frameworks and tools to promote a data quality model for scientific research in digital health, developing a tool with data collection, management, and evaluation, and proposing an implementation research model based on digital health principles and guidelines. The work is grounded in FAIR principles and the RE-AIM framework, addressing data quality and governance in health research, focusing on tuberculosis (TB) and rare diseases (RD) in the Brazilian context. The research significantly contributes to the understanding of digital data governance in health, highlighting the importance of standardized practices and collaborative efforts to improve the quality of health research data in Brazil. Challenges addressed include integrating diverse data sources, ensuring security and confidentiality of data, while enhancing public health outcomes through digital innovations. The presented model emphasizes data quality optimization, being fundamental for planning, executing, and evaluating health interventions. The research operates under robust computational research structures and health information systems, ensuring transparent data management. The interdisciplinary nature of the research highlights its significant strength, integrating insights from data science, public health, and health policy, offering a holistic view of the challenges and opportunities in digital health. By creating a unified implementation quality manual, the study provides mechanisms for network collaboration, strengthening robust data management, and promoting a culture of shared practices essential for elevating the overall quality of data in health research."}68{"work_id": 2103863878, "title": "Majority is not enough", "abstract": "The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed. We show that the Bitcoin mining protocol is not incentive-compatible. We present an attack with which colluding miners' revenue is larger than their fair share. The attack can have significant consequences for Bitcoin: Rational miners will prefer to join the attackers, and the colluding group will increase in size until it becomes a majority. At this point, the Bitcoin system ceases to be a decentralized currency. Unless certain assumptions are made, selfish mining may be feasible for any coalition size of colluding miners. We propose a practical modification to the Bitcoin protocol that protects Bitcoin in the general case. It prohibits selfish mining by a coalition that command less than 1/4 of the resources. This threshold is lower than the wrongly assumed 1/2 bound, but better than the current reality where a coalition of any size can compromise the system."}69{"work_id": 2912978522, "title": "Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges", "abstract": "Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored."}70{"work_id": 3036601975, "title": "In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication", "abstract": "Broadcasting and aggregation dominate the communication overhead in distributed systems, from machine learning training to data analytics. Current acceleration approaches require specialized hardware (RDMA) or dedicated resources (DPDK), limiting their deployment in commodity clouds. However, we present a counter-intuitive alternative: rather than bypassing the kernel, we move operations into it using eBPF. While this imposes severe constraints including no floating-point, limited memory, and stateless execution, we show these restrictions paradoxically drive innovative protocol designs that yield unexpected benefits. We introduce AggBox, which implements broadcast and aggregation operations entirely within eBPF\u2019s constrained environment. Our key innovations include stateless group acknowledgments for reliability, edge quantization for floating-point aggregation using only integer arithmetic, and tail-call chains that create virtual memory beyond eBPF\u2019s 512-byte stack limit. These designs emerge from and exploit the constraints rather than fighting them. AggBox achieves remarkable performance on commodity hardware: 84.5% reduction in broadcast latency, 43\u00d7 speedup for MapReduce workloads, and 56.1% faster ML gradient aggregation, all without specialized NICs or dedicated cores. Beyond performance, our work demonstrates that constrained environments can drive fundamental innovation in protocol design, offering insights for future resource-limited and verified systems."}71{"work_id": 110250037, "title": "The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications", "abstract": "The use of Multimedia materials has been widely accepted as an useful and effective tool in the field of second language acquisition (SLA). Many studies and researchers have examined multimedia material\u2019s effectiveness from a number of aspects, including four skills of language learning: listening, speaking, reading (including vocabulary comprehension) and writing. However, the effectiveness of multimedia material from the aspect of L2 grammar comprehension hasn\u2019t been well explored.\\nThis study examines the effectiveness of multimedia material in teaching second language grammar comprehension among beginning and intermediate-level Chinese learners. In particular, it investigates the relative efficacy of three different modes used in teaching an important Chinese grammar, directional complements: text alone, text with a still picture and text with video clips. The study\u2019s focal issue is to determine which mode or modes \u2014 text alone, text with still picture or text with dynamic video clip \u2014 is most effective in aiding grammar acquisition in both short-term and long-term.\\nThe study employed an immediate post-test and a delayed post-test. The participants were 53 college students, beginning level Chinese language learners who had studied Chinese for a half year, and were naturally assigned to one of the three parallel groups. The three groups differed as to the use of the different mode used to present the teaching material: text-only, text-picture, and text-video.\\nAnalysis of the collected data yielded three main findings. First, learners who received text-video material outperformed those who received text-picture; and learners who received text-picture material outperformed those who received text-only. Secondly, learners who received text-video had significantly better long-term comprehension than the other two groups. Finally, the performance advantage of text-video treatment was particularly significant with more complex target forms.\\nThe results demonstrate that multimedia material can help L2 learners\u2019 grammar comprehension. Text-picture and text-video were more effective than text-only, and text-video material was more effective than text-picture."}72{"work_id": 3080729669, "title": "Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database", "abstract": "Salah satu tahap dalam perancangan basis data untuk pengembangan sebuah\r\nperangkat lunak adalah proses perancangan basis data yang dibuat berdasarkan\r\nhasil perancangan model konseptual dan relasional. Proses yang cukup sulit\r\nadalah pada saat pemetaan konsep basis data. Ketika daftar kebutuhan pengguna\r\nmengalami perubahan, akan berdampak pada perancangan Sistem Basis data\r\nyang sudah dibuat sebelumnya. Selain itu, struktur basis data yang kompleks,\r\nketika mengalami perubahan akan memakan banyak waktu untuk dipetakan\r\nkembali secara manual. Berdasarkan permasalahan tersebut, maka dibuatlah\r\nsuatu Sistem untuk memetakan model basis data berbentuk ERD agar langsung\r\ndapat digunakan pada database mySQL. Dengan tujuan mempemudah pemetaan,\r\nmaka sistem ini menggunakan notasi Chen. Notasi ini adalah notasi yang paling\r\nsering digunakan dalam data modeling tool karena bentuknya yang mudah\r\ndipahami dan digambarkan. Namun notasi ini jarang digunakan oleh aplikasi ERD\r\nmapping. Sistem ini menggunakan tujuh langkah pemetaan model relasi entitas\r\nsehingga sesuai dengan kaidah Rekaya Perangkat Lunak. Sistem dibuat dengan\r\nberbasis web, menggunakan pemrograman berorientasi objek, dan menggunakan\r\nframework codeigniter. Sistem menghasilkan 16 buah kebutuhan yang telah diuji\r\ndengan menggunakan pengujian white-box dan black-box mendapatkan hasil valid\r\nuntuk keseluruhannya dan juga 95% untuk pengujian compatibility sistem"}73{"work_id": 4324129606, "title": "Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA", "abstract": "With the proliferation of online data collection in human-subjects research, concerns have been raised over the presence of inattentive survey participants and non-human respondents (bots). We compared the quality of the data collected through five commonly used platforms. Data quality was indicated by the percentage of participants who meaningfully respond to the researcher's question (high quality) versus those who only contribute noise (low quality). We found that compared to MTurk, Qualtrics, or an undergraduate student sample (i.e., SONA), participants on Prolific and CloudResearch were more likely to pass various attention checks, provide meaningful answers, follow instructions, remember previously presented information, have a unique IP address and geolocation, and work slowly enough to be able to read all the items. We divided the samples into high- and low-quality respondents and computed the cost we paid per high-quality respondent. Prolific ($1.90) and CloudResearch ($2.00) were cheaper than MTurk ($4.36) and Qualtrics ($8.17). SONA cost $0.00, yet took the longest to collect the data."}74{"work_id": 2914721378, "title": "Graph Neural Networks for Social Recommendation", "abstract": "In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec."}75{"work_id": 4408925021, "title": "The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review", "abstract": "The integration of artificial intelligence in education has the potential to revolutionize personalized learning by adapting instructional methods, content, and pace to the individual needs of students. This systematic review investigates the integration of artificial intelligence into personalized learning within higher education. An extensive literature search was conducted across multiple databases, yielding 17,899 records from which 45 studies met the inclusion criteria. The risk of bias was assessed using a standardized ranking system. This systematic review follows the PRISMA guidelines to ensure transparency in study selection, data extraction, and synthesis. The findings of the review are synthesized to examine how AI-driven solutions enhance adaptive learning, improve student engagement, and streamline administrative processes. The results indicate that AI technologies can significantly optimize educational outcomes by tailoring content and feedback to individual learner needs. However, several challenges persist, such as ethical concerns, data privacy issues, and the necessity for effective teacher training to support technology integration. This analysis reveals considerable potential for AI to transform educational practices, while also emphasizing the importance of establishing standardized evaluation frameworks and conducting longitudinal studies. The implications of these findings are critical for educators, policymakers, and university administrators aiming to leverage AI for educational innovation and sustainable transformation."}76{"work_id": 4313646859, "title": "Remote patient monitoring using artificial intelligence: Current state, applications, and challenges", "abstract": "Abstract The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in\u2010home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI\u2010enabled RPM. This review explores the benefits and challenges of patient\u2010centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI\u2010enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends. This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Internet of Things"}77{"work_id": 2970155754, "title": "Learning Generalizable Device Placement Algorithms for Distributed Machine Learning", "abstract": "Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019"}78{"work_id": 2967467, "title": "Social networks and collaborative filtering for large-scale requirements elicitation", "abstract": "Within the field of software engineering, requirements elicitation is the activity in which stakeholder needs are understood. In large-scale software projects, requirements elicitation tends to be beset by three problems: information overload, inadequate stakeholder input, and biased prioritisation of requirements. The work described in this thesis addresses these problems using social networks and collaborative filtering. The work has developed StakeNet, a novel method that uses social networks to identify and prioritise stakeholders. Using StakeNet, the requirements engineer asks an initial list of stakeholders to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritises the stakeholders using a variety of social network measures. The work has also developed StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritise requirements. Using StakeRare, the requirements engineer asks the stakeholders identified by StakeNet to rate an initial list of requirements and suggest other requirements, recommends other relevant requirements to the stakeholders using collaborative filtering, and prioritises the requirements using the ratings and the stakeholders\u2019 priority from StakeNet. Finally, to support the methods, this work has developed StakeSource, a novel software tool that automates the manual processes in StakeNet. StakeSource collects recommendations from stakeholders, builds the social network, and prioritises the stakeholders automatically. The methods and tool have been evaluated using real large-scale software projects. The empirical evaluation of both StakeNet and StakeRare using a real large-scale software project demonstrates that the methods identify a highly complete set of stakeholders and their requirements, and prioritise the stakeholders and their requirements accurately. These methods outperform the existing methods used in the project, and require significantly less time from the stakeholders and requirements engineers. StakeSource has been evaluated with real large-scale projects by practitioners. The tool is now used in major software projects, and organisations are adopting it. The methods, tool, and evaluation described in this thesis provide evidence that social networks and collaborative filtering can effectively support requirements elicitation in large-scale software projects."}79{"work_id": 4394947112, "title": "A Survey on Retrieval-Augmented Text Generation for Large Language Models", "abstract": "Retrieval-Augmented Generation (RAG) merges information retrieval (IR) techniques with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but possibly incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this article organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation, offering a detailed perspective from the retrieval viewpoint. It outlines RAG\u2019s mechanics and discusses the field\u2019s progression through the analysis of significant studies. Additionally, the article introduces evaluation methods for RAG, addressing the challenges faced and proposing future research directions. By offering an organized framework and categorization, the study aims at consolidating existing research on RAG, clarify its technological underpinnings, and highlight its potential to broaden the adaptability and applications of LLMs."}80{"work_id": 4408444355, "title": "The effects of data quality on machine learning performance on tabular data", "abstract": "Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency. We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations."}81{"work_id": 7161586974, "title": "Paleobiology Database User Guide Version 2.0", "abstract": "The Paleobiology Database is an online, non-governmental, non-profit public resource for paleontological data. It is organized and operated by a multi-disciplinary, multi-institutional, international group of paleobiological researchers. This is the second edition of the volume designed to be a comprehensive guide for Paleobiology Database users, both General and Contributory. It covers most database uses from data retrieval and mapping to data contribution of all types. It contains numerous examples to illustrate database use as well as definitions of terms and additional links to numerous other sources. We hope that this user guide will help all users access the great volume of data in the Paleobiology Database and lead others to start and continue to add data to the system."}82{"work_id": 2963707260, "title": "Heterogeneous Information Network Embedding for Recommendation", "abstract": "Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, calledHIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance."}83{"work_id": 4380791906, "title": "NoSQL Databases in Kubernetes", "abstract": "With the increasing popularity of deploying applications in containers, Kubernetes (K8s) has become one of the most accepted container orchestration systems. Kubernetes helps maintain containers smoothly and simplifies DevOps with powerful automations. It was originally developed as a tool to manage stateless microservices that run seamlessly in containers. The ephemeral nature of pods, the smallest deployable unit, in Kubernetes was well-aligned with stateless applications since destroying and recreating pods didn\u2019t impact applications. There was a need to provision solutions around stateful workloads like databases so as to take advantage of K8s. This project explores this need, the challenges associated and the available solutions for running databases in Kubernetes. Most of the current research is focused towards SQL-like databases in K8s even though the DNA of NoSQL distributed databases is more aligned with K8s. With no research being done with NoSQL databases, this project outlines the process behind setting up two famous NoSQL databases in K8s: MongoDB and Cassandra. The project also shows a representative viewpoint of the performance comparison between them using the YCSB benchmark. The project lays a foundation around the setup of these databases using K8s Operators and their benchmarking. The goal of the project is to describe the advantages of having databases in K8s, provide developers a clear path for setup and provide insights on basic benchmark performance."}84{"work_id": 4392909747, "title": "Cloud Computing: Principles And Paradigms", "abstract": "Capturing the current state of the art in Cloud Computing technology and applications is the main goal of this book. The book\u2019s secondary objective is to chart a course for future studies and technological developments that will pave the way for the establishment of a worldwide marketplace for cloud computing services catering to commercial, government, academic, and consumer needs. The book is designed to be a reference for a wide range of readers, including systems architects, developers, practitioners, new researchers, and graduate students."}85{"work_id": 2562507292, "title": "Internet of Things Applications - From Research and Innovation to Market Deployment", "abstract": "ISBN : 9788793102941"}86{"work_id": 4387316367, "title": "Ontological Approach to a Knowledge Graph Construction in a Semantic Library", "abstract": "Abstract The paper considers an approach to a knowledge graph construction based on the ontological representation of scientific subject areas. The presentation is based on concepts related to information and data mining, such as knowledge, knowledge extraction, domain ontology, scientific domain, thesaurus, semantic digital library, user information need, ontological design method and, in fact, the knowledge graph. The digital semantic library LibMeta is presented as a repository of various structured data with the possibility of their integration with other data sources. Assumes the possibility of specifying personal content by describing a local subject area within LibMeta. The ontology of the content of the semantic library acts as a means of formalization. This paper addresses the experience of building semantic libraries based on thesauri and ontological design. Building ontologies based on the thesaurus of the subject area LibMeta allows us to say that the presence of internal semantic links ensures the consistency and reliability of search results, which is a necessary condition for extracting scientific knowledge. The digital library ontology defines the data structure of the library content. Each data element loaded into the library can be associated with an ontology vertex (top) that determines the position of the data element in the ontology. Based on the ontology links and the links defined at the design stage, you can build a data graph. On the example of the ontology of the LibMeta semantic library, the technology of forming the knowledge graph of modern applications in mathematics is discussed. The problems of filling a graph, embedding in a graph, extracting links and nodes of a graph are discussed."}87{"work_id": 4398201281, "title": "Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems", "abstract": "The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation."}88{"work_id": 2945623882, "title": "KGAT", "abstract": "To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations --- which connect two items with one or multiple linked attributes --- are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism. We release the codes and datasets at https://github.com/xiangwang1223/knowledge_graph_attention_network."}89{"work_id": 2251066406, "title": "Bypassing Races in Live Applications with Execution Filters", "abstract": "Deployed multithreaded applications contain many races because these applications are difficult to write, test, and debug. Worse, the number of races in deployed applications may drastically increase due to the rise of multicore hardware and the immaturity of current race detectors.LOOM is a live-workaround system designed to quickly and safely bypass application races at runtime. LOOM provides a flexible and safe language for developers to write execution filters that explicitly synchronize code. It then uses an evacuation algorithm to safely install the filters to live applications to avoid races. It reduces its performance overhead using hybrid instrumentation that combines static and dynamic instrumentation.We evaluated LOOM on nine real races from a diverse set of six applications, including MySQL and Apache. Our results show that (1) LOOM can safely fix all evaluated races in a timely manner, thereby increasing application availability; (2) LOOM incurs little performance overhead; (3) LOOM scales well with the number of application threads; and (4) LOOM is easy to use."}90{"work_id": 1500148551, "title": "Time-based Fairness Improves Performance in Multi-rate WLANs", "abstract": "The performance seen by individual clients on a wireless local area network (WLAN) is heavily influenced by the manner in which wireless channel capacity is allocated. The popular MAC protocol DCF (Distributed Coordination Function) used in 802.11 networks provides equal long-term transmission opportunities to competing nodes when all nodes experience similar channel conditions. When similar-sized packets are also used, DCF leads to equal achieved throughputs (throughput-based fairness) among contending nodes. Because of varying indoor channel conditions, the 802.11 standard supports multiple data transmission rates to exploit the trade-off between data rate and bit error rate. This leads to considerable rate diversity, particularly when the network is congested. Under such conditions, throughput-based fairness can lead to drastically reduced aggregate throughput. In this paper, we argue the advantages of time-based fairness, in which each competing node receives an equal share of the wireless channel occupancy time. We demonstrate that this notion of fairness can lead to significant improvements in aggregate performance while still guaranteeing that no node receives worse channel access than it would in a single-rate WLAN. We also describe our algorithm, TBR (Time-based Regulator), which runs on the AP and works with any MAC protocol to provide time-based fairness by regulating packets. Through experiments, we show that our practical and backward compatible implementation of TBR in conjunction with an existing implementation of DCF achieves time-based fairness."}91{"work_id": 2945827670, "title": "Neural Graph Collaborative Filtering", "abstract": "Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect."}92{"work_id": 2962883549, "title": "Deep Learning in Mobile and Wireless Networking: A Survey", "abstract": "The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research."}93{"work_id": 4364375730, "title": "Social Health Protection During the COVID-Pandemic Using IoT", "abstract": "The agenda of this book chapter is to review existing technologies that aid societal health protection and recommend some possible approaches which will assist the mentioned scenario. Automations like big data and artificial intelligence (AI) deployed in healthcare sector can expedite pandemic response in ways that are strenuous to achieve all in all by humans. The sudden epiphany to trace COVID-19 in public has powered the innovation of data dashboards that visually unveil coronavirus epicentres. A cloud-based AI-assisted CT service is being engaged to differentiate pneumonia from the pandemic which dwindles risk factor in the present school of thought of the citizens worldwide. In conclusion, social health protection was an indispensable mechanism in prior to these challenging times and is escalating by prominence for delivering support to individuals during the crisis."}94{"work_id": 2902511784, "title": "Dynamic data race detection for OpenMP programs", "abstract": "Two concurrent accesses to a shared variable that are unordered by synchronization are said to be a data race if at least one access is a write. Data races cause shared memory parallel programs to behave unpredictably and are typically considered a bug. It is hard to manually identify memory access instructions causing data races in large concurrent programs due to non-deterministic behavior affected by thread schedule. Tools for detecting data races are desired to help application developers debug data races in an efficient way. Dynamic data race detection tools detect data races by monitoring programs as they execute. Building accurate and efficient data race detection tools requires domain knowledge of concurrency control primitives provided by the specific parallel programming model upon which a program is built. OpenMP is the de-facto standard API for node level shared memory parallel programming. The complexity of OpenMP, which includes primitives for managing data environments, SPMD parallelism, work sharing, tasking, mutual exclusion, and ordering, presents a formidable challenge for precise dynamic data race detection. This dissertation describes several techniques that enable accurate dynamic data race detection for OpenMP programs. First, I describe a hybrid data race detection algorithm that allows data race detection to be executed in parallel. Second, I describe an OpenMP task labeling technique that assigns each OpenMP task a label to facilitate OpenMP-aware accurate happens-before analysis. This technique is the foundation for callback functions provided by the OpenMP Tools interface known as OMPT to get notifications about OpenMP synchronization events and create task labels based on a set of label creation rules. Third, I propose techniques for optimizing dynamic data race detection performance via a redundant memory access pruning algorithm, a readers-writer concurrency control protocol for coordinating threads as they maintain access histories for memory locations, and an OpenMP-aware access record management protocol. The second and third contributions combine to form a system called ROMP, which is a dynamic data race detector for OpenMP programs. Experiments with standard data race detection benchmarks shows that ROMP achieves high accuracy for detecting data races in OpenMP programs that contain host-side CPU-centric parallel constructs. Experiments with several OpenMP performance benchmarks show the effectiveness of proposed optimization techniques."}95{"work_id": 2408538552, "title": "Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks", "abstract": "Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users\u2019 preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user\u2019s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly."}96{"work_id": 4396219753, "title": "Machine Learning Mastery", "abstract": "This chapter delves into the core principles of machine learning, offering practical insights for effective data processing. From foundational concepts to advanced techniques, the narrative unfolds as a comprehensive guide for harnessing the power of machine learning in real-world scenarios. The chapter explores data preprocessing methods, addressing the importance of cleaning and quality assurance, outlier detection, handling missing data, and employing noise reduction techniques. Through illustrative examples and case studies, readers gain actionable knowledge on building a robust foundation for machine learning applications. Emphasizing the significance of data quality in model performance, the chapter serves as a valuable resource for both beginners and experienced practitioners seeking mastery in the art of data processing for machine learning success."}97{"work_id": 7118085507, "title": "Retrieval-Augmented Generation for AI-Generated Content: A Survey", "abstract": "Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC). Despite its notable successes, AIGC still faces hurdles such as updating knowledge, handling long-tail data, mitigating data leakage, and managing high training and inference costs. Retrieval-augmented generation (RAG) has recently emerged as a paradigm to address such challenges. In particular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness. In this paper, we comprehensively review existing efforts that integrate RAG techniques into AIGC scenarios. We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators. This unified perspective encompasses all RAG scenarios, illuminating advancements and pivotal technologies that help with potential future progress. We also summarize additional enhancement methods for RAG, facilitating effective engineering and implementation of RAG systems. Then from another view, we survey practical applications of RAG across different modalities and tasks, offering valuable references for researchers and practitioners. Furthermore, we introduce the benchmarks for RAG, discuss the limitations of current RAG systems, and suggest potential directions for future research."}98{"work_id": 4315977496, "title": "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions", "abstract": "Recommender system is one of the most important information services on today\u2019s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories: spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems ."}99{"work_id": 4293584584, "title": "A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)", "abstract": "Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%."}100{"work_id": 2940715617, "title": "Offloading Real-time DDoS Attack Detection to Programmable Data Planes", "abstract": "Nos \u00faltimos anos, ataques distribu\u00eddos de nega\u00e7\u00e3o de servi\u00e7o v\u00eam crescendo tanto em frequ\u00eancia quanto em volume de tr\u00e1fego com surtos atingindo taxas da ordem de terabits por segundo e compremetendo a disponibilidade de infraestruturas supostamente resilientes (e.g., DNS e hospedagem Web na nuvem). Na pr\u00e1tica, as solu\u00e7\u00f5es de detec\u00e7\u00e3o existentes valem-se de uma combina\u00e7\u00e3o de mecanismos, como amostragem de pacotes e transmiss\u00e3o dos dados coletados a um software externo, que dificulta a obten\u00e7\u00e3o de uma boa rela\u00e7ao entre acur\u00e1cia (maior \u00e9 melhor), consumo de recursos e lat\u00eancia (menor \u00e9 melhor). Planos de dados program\u00e1veis emergem como uma abordagem promissora para ajudar a cumprir esses requisitos, visto que dispositivos comutadores de pacotes podem ser configurados para executar algoritmos e examinar o tr\u00e1fego em velocidade de linha. Neste trabalho, exploramos primitivas em P4 a fim de projetar um mecanismo de inspe\u00e7\u00e3o de tr\u00e1fego com baixa granularidade, baixo consumo de recursos e baixa lat\u00eancia para a detec\u00e7\u00e3o de ataques distribu\u00eddos de nega\u00e7\u00e3o de servi\u00e7o em tempo real. A nossa proposta \u2013 a primeira a ser completamente implementada em plano de dados \u2013 contribui para lan\u00e7ar luz sobre os desafios da implementa\u00e7\u00e3o de l\u00f3gica de seguran\u00e7a sofisticada nesse contexto, dado que, para operar a altas taxas de transfer\u00eancia, a inspe\u00e7\u00e3o (e o processamento em geral) de pacotes est\u00e1 sujeita a um or\u00e7amento de tempo reduzido (dezenas de nanossegundos) e um espa\u00e7o de mem\u00f3ria limitado (da ordem de dezenas de megabytes). N\u00f3s avaliamos o mecanismo proposto usando capturas de pacotes da CAIDA. Os resultados mostram a detec\u00e7\u00e3o de ataques exclusivamente a partir do plano de dados com alta acur\u00e1cia (98,2%) e baixa lat\u00eancia ( 250 ms) mantendo o consumo de recursos reduzido (dezenas de kilobytes de SRAM por link de 1 Gbps e poucas centenas de entradas TCAM)."}101{"work_id": 3023617420, "title": "A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact", "abstract": "The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak."}102{"work_id": 4255535209, "title": "Blockchain challenges and opportunities: a survey", "abstract": "Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology."}103{"work_id": 3005599391, "title": "Entangled Devices : An ethnographic study of students, mobile phones and capitalism", "abstract": "This ethnographic research looks at the ways in which mobile phones are present in the school life of upper-secondary school students. The research analyses the affects phones have on the spatiality and power relation of school. The research has been undertaken as part of Textm\u00f6ten research project and draws from ethnographic data produced in two Finnish upper-secondary schools during 2015-2016. The research consists of three peer-reviewed articles and a 94-page summary. The three articles of the dissertation examine the connections of school and mobile phones from different perspectives. The first article analyses the historically ambivalent relationship between school and technology, including the fact that technology in school seldom works in anticipated ways. The difference between many earlier technologies and mobile phones is that this time students are the ones bringing them to school. The second article analyses how phones are present during lessons, how different apps are used and what phones mean to young people. The conclusion is that phones have become a familiar presence during lessons in the research schools. Both the amounts of phone use and the ways the phones are used vary among the students but phones were used significantly throughout the research. In interviews, students emphasized the importance of the phones. They signified independence, adulthood and a space of one\u2019s own. On the other hand, students mentioned the occasionally laborious nature of phones which had to do with a constant stream of messages and notifications. The third article focuses on one particular lesson in order to examine the multi-faceted character of phones and the connections they enable. Phones foster new agencies and bring opportunities for re-evaluating school spaces and power relations. However, at the same time they offer commercial actors a way into the classroom and give them a foothold in school. The research engages Gilles Deleuze and F\u00e9lix Guattari\u2019s concept of assemblage to analyse the social and political connections of technology. Mobile phones are approached as part of a wider context. The research takes advantage of the concepts of platform capitalism and digital labour. Contemporary mobile devices are used mainly through platforms owned by global corporations such as Google, Facebook, Microsoft or Apple. Platforms connect individual user activity onto a database controlled by the platform owner. As data is today a pivotal economic factor, enterprises seek to collect as much data as possible. Schools are some of the most interesting sites for data extraction for technology companies. This is why we are seeing so many actively seeking to enter school spaces in which the presence of commercial actors has so far been tightly controlled. The research asks: are large technology companies quietly gaining a foothold in school spaces through mobile phones without a political debate on the issue? ________________________________________ Keywords: school, technology, phones, mobile devices, capitalism, assemblage, platform capitalism, digitalisation"}104{"work_id": 4394867512, "title": "Privacy-enabled scalable recommender systems", "abstract": "Electronic content is ubiquitous in our daily lives. Several factors such as the development of Web 2.0 technologies, the increased access to mobile devices and the deployment of mobile networks has undoubtedly augmented the amount of information easily available to users. Given the limited attention span of the user and the extensiveness of the available streams of information ready to be consumed, automatic systems must be available for the user to prioritize, suggest or screen content suitable for the user interests and situation. One of the most popular initiatives created to solve the information overload problem are Recommender Systems [Adomavicius 2005]. Recommender Systems are information filtering systems that use the historical information about the user (what the user has considered relevant or irrelevant on the past, among other information) to build and accurate representation of the user's interests that is used to predict the relevance of a large collection of available items for a specific user. Recommendation systems are used by several online retailers, online content streaming services and social networking sites to improve the user's experience of their services by automatically filtering their content or offers of items to the ones most likely to interest the user. Generally speaking, recommender systems can be classified into two categories: Content Based and Collaborative Filtering. The former category relies on the definition of explicit features that describe the item domain and assigns them weights to describe the affinity between the feature and the item. For example in the movie domain, items can be described by features such as the genre to which they belong, the director, writer and actors that take part in the movie. On the other hand Collaborative Filtering is content-agnostic and relies on correlations between users and items based on the historical consumption patterns between the users and the items. It has been shown generally that Collaborative Filtering methods present better results than Content-Based [Pil'aszy 2009], however due to inherent shortcomings of single approaches, a better predictive performance is achieved by developing a model that integrates different paradigms (Hybrid approaches). To keep their users satisfied, personalization services that operate recommendation methods should present relevant recommendations even when the number of users, items and user-item interactions in the system increase"}105{"work_id": 2885100636, "title": "Blockchain and IoT Integration: A Systematic Survey", "abstract": "The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its \"security by design,\" Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy."}106{"work_id": 4400531953, "title": "C-Pack: Packed Resources For General Chinese Embeddings", "abstract": "We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation of vast unlabeled corpora and the integration of high-quality labeled corpora. 2) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 3) BGE is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by more than +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for BGE. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models also achieve state-of-the-art performance on the MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. Both Chinese and English datasets are the largest public release of training data for text embeddings. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding."}107{"work_id": 1710433637, "title": "Stream Control Transmission Protocol", "abstract": "This document is an Internet-Draft and is in full conformance with all provisions of Section 10 of RFC 2026. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet- Drafts as reference material or to cite them other than as \u2018\u2018work in progress.\u2019\u2019 The list of current Internet-Drafts can be accessed at"}108{"work_id": 3032576892, "title": "Comparative study on Python web frameworks: Flask and Django", "abstract": "The purpose of the thesis was to study the various features, advantages, and the limitation of two web development frameworks for Python programming language. It aims to compare the usage of Django and Flask frameworks from a novice point of view. The theoretical part of the thesis presents the various types of programming languages and web technologies. In the practical part, however, the study is divided into two parts, each part observing the respective web application framework. \\n \\n \\nIn order to perform the comparison, a social network and eCommerce like application was built for Flask and Django respectively. The comparison was started by developing the social network application first with Flask and finished with the e-commerce application using Django. Python programing language, SQLite database for the backend and HTML, JavaScript, and Ajax were used for the frontend technology. At the end of the project, both applications were deployed to the cloud platform called Heroku. \\n \\nAfter the comparison, it was found that the most significant advantages of Flask were that it provides simplicity, flexibility, fine-grained control and quick and easy to learn. On the other hand, Django was easy to work with because of its extensive features and support for libraries. Another main advantage of Django is its scalability. It is best fit for a large-scale application. Each framework has its limitations and radiates a fair share of disadvantages. For example, Django is a bit cumbersome for smaller sized applications. However, Flask is too simple to not have the necessary features within the framework. \\n \\nAs a result of this study, it seems that both frameworks are capable of serving production-grade web applications while having a fair share of advantages and disadvantages."}109{"work_id": 2784168960, "title": "Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters", "abstract": "Cataloged from PDF version of article."}110{"work_id": 4385584867, "title": "A Study on Smart Energy Management Framework Using Cloud Computing", "abstract": "This study focuses on investigating energy-aware resources and designing an energy-efficient cloud architecture. It aims to optimize the decentralization model and establish migration behavior to improve IT business operations using cloud computing. Several models were evaluated for performance metrics such as physical host use, power utilization, and service level agreement (SLA) misuse. These models include smart cloud optimization for resource configuration handling (SCORCH), V-MAN, decentralized energy-aware collaborative model (DEACM), efficient energy-aware resource management model (EEARMM), and energy and power-aware dynamic migration (EPADM). To handle resource configuration efficiently, the study employed V-MAN, DEACM, EEARMM, and EPADM within the framework of smart cloud optimization. Among these models, EPADM proved to be the most effective for resource utilization through dynamic VM migration. By leveraging EPADM, the cloud system can dynamically migrate virtual machines (VMs) to optimize resource allocation and energy consumption."}111{"work_id": 2793346081, "title": "Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT", "abstract": "The Internet of Things (IoT) is stepping out of its infancy into full maturity and establishing itself as a part of the future Internet. One of the technical challenges of having billions of devices deployed worldwide is the ability to manage them. Although access management technologies exist in IoT, they are based on centralized models which introduce a new variety of technical limitations to manage them globally. In this paper, we propose a new architecture for arbitrating roles and permissions in IoT. The new architecture is a fully distributed access control system for IoT based on blockchain technology. The architecture is backed by a proof of concept implementation and evaluated in realistic IoT scenarios. The results show that the blockchain technology could be used as access management technology in specific scalable IoT scenarios."}112{"work_id": 2908737503, "title": "A Decentralized Privacy-Preserving Healthcare Blockchain for IoT", "abstract": "Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network."}113{"work_id": 1823805418, "title": "TCP Extensions for Multipath Operation with Multiple Addresses", "abstract": "TCP/IP communication is currently restricted to a single path per connection, yet multiple paths often exist between peers. The simultaneous use of these multiple paths for a TCP/IP session would improve resource usage within the network and thus improve user experience through higher throughput and improved resilience to network failure. Multipath TCP provides the ability to simultaneously use multiple paths between peers. This document presents a set of extensions to traditional TCP to support multipath operation. The protocol offers the same type of service to applications as TCP (i.e., a reliable bytestream), and it provides the components necessary to establish and use multiple TCP flows across potentially disjoint paths. This document specifies v1 of Multipath TCP, obsoleting v0 as specified in RFC 6824, through clarifications and modifications primarily driven by deployment experience. This document is a product of the Multipath TCP Working Group of the IETF. This is now a Proposed Standard."}114{"work_id": 2803371618, "title": "On blockchain and its integration with IoT. Challenges and opportunities", "abstract": "In the Internet of Things (IoT) vision, conventional devices become smart and autonomous. This vision is turning into a reality thanks to advances in technology, but there are still challenges to address, particularly in the security domain e.g., data reliability. Taking into account the predicted evolution of the IoT in the coming years, it is necessary to provide confidence in this huge incoming information source. Blockchain has emerged as a key technology that will transform the way in which we share information. Building trust in distributed environments without the need for authorities is a technological advance that has the potential to change many industries, the IoT among them. Disruptive technologies such as big data and cloud computing have been leveraged by IoT to overcome its limitations since its conception, and we think blockchain will be one of the next ones. This paper focuses on this relationship, investigates challenges in blockchain IoT applications, and surveys the most relevant work in order to analyze how blockchain could potentially improve the IoT."}115{"work_id": 4285039189, "title": "Mobile ad-hoc networks (MANET)", "abstract": "\u0388\u03bd\u03b1 \u039a\u03b9\u03bd\u03b7\u03c4\u03cc \u0391\u03b4\u03cc\u03bc\u03b7\u03c4\u03bf \u0394\u03af\u03ba\u03c4\u03c5\u03bf \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03ba\u03cc\u03bc\u03b2\u03bf\u03c5\u03c2 \u03c0\u03bf\u03c5 \u03ba\u03b9\u03bd\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9, \u03b5\u03af\u03c4\u03b5 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ce\u03bd\u03c4\u03b1\u03c2 \u03ba\u03ac\u03c0\u03bf\u03b9\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03ba\u03af\u03bd\u03b7\u03c3\u03b7\u03c2, \u03b5\u03af\u03c4\u03b5 \u03c4\u03c5\u03c7\u03b1\u03af\u03b1, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03ba\u03ac\u03c0\u03bf\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae \u03c0\u03bf\u03c5 \u03bd\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03c4\u03bf\u03c5. \u0395\u03be \u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd, \u03c3\u03c4\u03bf \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03b1\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03bd \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03b9 \u03c1\u03cc\u03bb\u03bf\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 \u03ba\u03cc\u03bc\u03b2\u03bf\u03c5\u03c2 \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bc\u03bc\u03b5\u03c4\u03ad\u03c7\u03bf\u03c5\u03bd, \u03b1\u03bb\u03bb\u03ac \u03c4\u03b1 \u03b4\u03b9\u03ac\u03c6\u03bf\u03c1\u03b1 \u03ba\u03b1\u03b8\u03ae\u03ba\u03bf\u03bd\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03b8\u03ad\u03c3\u03b7\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c4\u03bf \u03c7\u03ce\u03c1\u03bf, \u03c4\u03c9\u03bd \u03b5\u03bd\u03b5\u03c1\u03b3\u03b5\u03b9\u03b1\u03ba\u03ce\u03bd \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ce\u03bd \u03b1\u03c0\u03bf\u03b8\u03b5\u03bc\u03ac\u03c4\u03c9\u03bd \u03c4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03c2 \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03ac\u03c2 \u03c4\u03bf\u03c5\u03c2. \u039a\u03b1\u03c4\u03ac \u03c4\u03b7 \u03b4\u03b9\u03ac\u03c1\u03ba\u03b5\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03bd\u03ae\u03b8\u03bf\u03c5\u03c2 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2 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\u03c0\u03b1\u03b9\u03b3\u03bd\u03af\u03c9\u03bd \u03ba\u03b1\u03b9 \u03ad\u03c4\u03c3\u03b9 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03c4\u03b1\u03b9."}116{"work_id": 7125196921, "title": "Energy, Scalability, Data, and Security in Massive IoT: Current Landscape and Future Directions", "abstract": "The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating green technologies to reduce environmental impact. Through extensive comparative analysis, this study identifies critical innovations and architectural adaptations required to support efficient, resilient, and scalable MIoT deployments. Key insights include the role of network slicing and intelligent resource management for scalability, adaptive protocols for real-time data handling, and lightweight AI models suited to the constraints of MIoT devices. This research ultimately contributes to a deeper understanding of how MIoT systems can evolve to meet the growing demand for seamless, reliable connectivity while prioritizing sustainability, security, and performance across diverse applications. Our findings serve as a roadmap for future advancements, underscoring the potential of MIoT to support a globally interconnected, intelligent infrastructure."}117{"work_id": 2772457693, "title": "Deep Learning for IoT Big Data and Streaming Analytics: A Survey", "abstract": "In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature."}118{"work_id": 7127083419, "title": "From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications", "abstract": "With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components\u2014including agents, world models, planners, knowledge bases, tools, and memory modules\u2014 as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems."}119{"work_id": 4386961654, "title": "Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey", "abstract": "Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector."}120{"work_id": 4416378800, "title": "Pinching-Antenna Systems (PASS): A Tutorial", "abstract": "Pinching-antenna systems (PASS) present a break-through among the flexible-antenna technologies, and distinguish themselves by facilitating large-scale antenna reconfiguration, line-of-sight creation, scalable implementation, and near-field benefits, thus bringing wireless communications from the \u201clast mile\u201d to the \u201clast meter\u201d. To illustrate the benefits of PASS in next-generation wireless networks, a comprehensive tutorial is presented in this paper. First, the fundamentals of PASS are discussed, including PASS signal models, hardware models, power radiation models, and pinching antenna (PA) activation methods. Building upon this, the information-theoretic capacity limits achieved by PASS are characterized, and several typical performance metrics of PASS-based communications are analyzed to demonstrate its superiority over conventional antenna technologies. Next, the pinching beamforming design is investigated. The corresponding power scaling law is first characterized for the single-waveguide single-user case. For the joint transmit and pinching design in the general multiple-waveguide case, 1) a pair of transmission strategies is proposed for PASS-based single-user communications to validate the superiority of PASS, namely sub-connected and fully connected structures in terms of the connections between the baseband radio frequency chains and waveguides; and 2) three practical protocols are proposed for facilitating PASS-based multi-user communications, namely waveguide switching, waveguide division, and waveguide multiplexing. A possible implementation of PASS in wideband communications is further highlighted. Moreover, the channel state information (CSI) acquisition in PASS is elaborated with a pair of promising solutions, based on pilot-based channel estimation and beam training, respectively. To overcome the high complexity and suboptimality inherent in conventional convex-optimization-based approaches, machine-learning-based methods for operating PASS are also explored, focusing on selected deep neural network architectures and training algorithms. Finally, several promising applications of PASS in next-generation wire-less networks are highlighted to motivate future works."}121{"work_id": 3182706339, "title": "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", "abstract": "Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R -squared or R 2 ) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination ( R -squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R -squared as standard metric to evaluate regression analyses in any scientific domain."}122{"work_id": 7123349468, "title": "SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis", "abstract": "Compound fault diagnosis in multi-joint industrial robots is a critical yet underexplored problem in industrial internet of things, where the simultaneous degradation of multiple joints poses a severe challenge for reliable operation. Unlike conventional methods limited to single-fault scenarios, this paper addresses the compositional generalization challenge\u2014requiring models trained only on simple faults to accurately recognize unseen higher-order fault compositions. To this end, we propose StateMix Network (SMNet), a multi-stage architecture that preserves atomic joint-level representations before compositional diagnosis. Specifically, a Single-Joint Feature Extraction (SJFE) backbone extracts clean joint-private features, which are then fused by an Attention-Guided Dilated Fusion (AGDF) neck employing parallel Cascaded Dilated Convolution Blocks (CDCBs) bracketed by a dual-path attention mechanism for scale- and context-aware integration. Finally, a Mamba-based sequence mixer models long-range cross-joint dependencies to capture global fault dynamics. Extensive experiments on in-situ vibration data from a single six-joint industrial robot platform, under a strict train-on-simple/evaluate-on-complex protocol, demonstrate that SMNet consistently outperforms representative baselines in macro-Precision, Recall, and F1-score, particularly on unseen triple- and quadruple-joint compositions. Ablation and sensitivity analyses further validate the effectiveness of each module. This work presents a diagnostic approach that effectively generalizes from simple to complex fault scenarios in industrial robots."}123{"work_id": 7129024920, "title": "TinyLSN: A Lightweight Network for Real-Time Marine Pipeline Leakage Detection in IoT Systems", "abstract": "Intelligent acoustic emission-based pipeline leak detection technology plays a critical role in Internet of Things structural health monitoring for offshore platforms. However, traditional deep networks possess large parameter counts and high computational complexity, making them infeasible for deployment on resource-constrained edge nodes, while lightweight methods universally adopt single-scale feature extraction and cannot simultaneously capture short-duration burst and long-range attenuation characteristics of acoustic emission signals, resulting in insufficient discriminative capability for adjacent valves. To address this, this paper proposes Tiny Leak Sense Net (TinyLSN), a novel lightweight leak localization framework specifically designed for Internet of Things edge nodes. TinyLSN achieves optimal balance between computational efficiency and detection performance through three innovative components we designed including the Inverted Residual Block (IRB), Multi-Scale Dilated Perception Module (MSDPM), and Large Kernel Feed-Forward Network (LK-FFN), which respectively enhance cross-channel interactions, capture multi-scale temporal features, and extract global attenuation patterns. On our self-constructed experimental dataset simulating real offshore platform operational pipeline leakage, TinyLSN achieved detection accuracy of 97.11% to 97.45% and an extremely low false positive rate of 0.27% to 0.32%, significantly outperforming lightweight baseline methods. Validation on publicly available benchmark datasets further confirmed its generalization capability. When deployed on the STM32H7B3I-DK microcontroller, TinyLSN requires only 267.16 KiB Flash memory and achieves 3.417 ms inference latency. Furthermore, TinyLSN maintains over 90% accuracy under strong noise and achieves 94.83% accuracy with only 10% training samples, fully validating its reliability in harsh industrial environments and providing an efficient and feasible solution for offshore platform Internet of Things edge intelligence."}124{"work_id": 3129486236, "title": "Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial", "abstract": "Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work."}125{"work_id": 4292259483, "title": "The IEEE International Conference on Communications 2022 (ICC'22)", "abstract": "The IEEE International Conference on Communications 2022 (ICC 2022) was held in Seoul, Korea, from 16\u201320 May 2022. Headquartered at Seoul COEX, the largest exhibition and convention venue in the Republic of Korea, ICC 2022 was in hybrid format with virtual (on-line) sessions as well as on-site sessions for in-person participants. ICC 2022 put together cutting-edge research results and technical contributions from academics, industry practitioners, experts and leaders in the area of communications and networking from all over the world. The technical program included physical sessions with on-site presentations and virtual (on-line) sessions for remote participants."}126{"work_id": 3194620979, "title": "Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond", "abstract": "As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks."}127{"work_id": 2974110037, "title": "Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges", "abstract": null}128{"work_id": 7123363719, "title": "ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints", "abstract": null}129{"work_id": 3011330593, "title": "A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments", "abstract": null}130{"work_id": 4206510620, "title": "The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications", "abstract": null}131{"work_id": 4386450904, "title": "Linked Data - The Story So Far", "abstract": null}132{"work_id": 3202749896, "title": "Data quality of platforms and panels for online behavioral research", "abstract": null}133{"work_id": 4401834466, "title": "A survey on large language models for recommendation", "abstract": null}134{"work_id": 7118998287, "title": "Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation", "abstract": null}135{"work_id": 2793823461, "title": "Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree", "abstract": null}136{"work_id": 3204616723, "title": "Data Mining Concepts and Techniques Third Edition", "abstract": null}137{"work_id": 4381304672, "title": "Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct", "abstract": null}138{"work_id": 3017623177, "title": "Industry 4.0 technologies and their applications in fighting COVID-19 pandemic", "abstract": null}139{"work_id": 7134136722, "title": "Hybrid Al-Biruni and Puma Optimization (BERPO) for boosting the classification of Quality-of-Service (QoS) in 5G networks", "abstract": null}140{"work_id": 7125115033, "title": "Integrated Sensing and Communications Over the Years: An Evolution Perspective", "abstract": null}141{"work_id": 2789491284, "title": "A hybrid model of Internet of Things and cloud computing to manage big data in health services applications", "abstract": null}142{"work_id": 7164884893, "title": "Research on Informer Long Sequence Dependency Modeling Method for Short-Term Prediction of Request Volume of Cloud-Native Backend Services", "abstract": null}143{"work_id": 2983008824, "title": "A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT", "abstract": null}144{"work_id": 7118165812, "title": "Leveraging hierarchy-aware diffusion model and knowledge-enhanced contrastive learning for recommendation", "abstract": null}145{"work_id": 1502629466, "title": "Principles of Distributed Database Systems", "abstract": null}146{"work_id": 3023398824, "title": "Internet of things (IoT) applications to fight against COVID-19 pandemic", "abstract": null}147{"work_id": 172918197, "title": "Computational Collective Intelligence", "abstract": null}148{"work_id": 4386975643, "title": "Cloud-based non-destructive characterization", "abstract": null}149{"work_id": 2946627595, "title": "A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems", "abstract": null}150{"work_id": 7124594522, "title": "Energy-efficient 5G-enabled sensor networks for smart environmental monitoring in sustainable cities", "abstract": null}151{"work_id": 3006456063, "title": "Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0", "abstract": null}152{"work_id": 2274895364, "title": "The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities", "abstract": null}153