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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms

arXiv

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arXiv 2025年

作者： Li, Hepeng Liu, Yuhong Yan, Jun Department of Electrical & Computer Engineering University of Maine Orono United States Santa Clara University Santa Clara United States Concordia Institute for Information Systems Engineering Concordia University Montreal Canada

Artificially intelligent (AI) agents that are capable of autonomous learning and independent decision-making hold great promise for addressing complex challenges across domains like transportation, energy systems, and manufacturing. However, the surge in AI systems’ design and deployment driven by various stakeholders with distinct and unaligned objectives introduces a crucial challenge: how can uncoordinated AI systems coexist and evolve harmoniously in shared environments without creating chaos? To address this, we advocate for a fundamental rethinking of existing multi-agent frameworks, such as multi-agent systems and game theory, which are largely limited to predefined rules and static objective structures. We posit that AI agents should be empowered to dynamically adjust their objectives, make compromises, form coalitions, and safely compete or cooperate through evolving relationships and social feedback. Through this paper, we call for a shift toward the emergent, self-organizing, and context-aware nature of these systems. © 2025, CC BY-NC-ND.

关键词： Decision making

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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GPT’s Devastated and LLaMA’s Content: Emotion Representation Alignment in LLMs for Keyword-based Generation

arXiv

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arXiv 2025年

作者： Choudhury, Shadab Kumar, Asha Martin, Lara J. Computer Science and Electrical Engineering Department University of Maryland Baltimore County United States Information Systems Department University of Maryland Baltimore County United States

In controlled text generation using large language models (LLMs), gaps arise between the language model’s interpretation and human expectations. We look at the problem of controlling emotions in keyword-based sentence generation for both GPT-4 and LLaMA-3. We selected four emotion representations: Words, Valence-Arousal-Dominance (VAD) dimensions expressed in both Lexical and Numeric forms, and Emojis. Our human evaluation looked at the Human-LLM alignment for each representation, as well as the accuracy and realism of the generated sentences. While representations like VAD break emotions into easy-to-compute components, our findings show that people agree more with how LLMs generate when conditioned on English words (e.g., "angry") rather than VAD scales. This difference is especially visible when comparing Numeric VAD to words. However, we found that converting the originally-numeric VAD scales to Lexical scales (e.g., +4.0 becomes "High") dramatically improved agreement. Furthermore, the perception of how much a generated sentence conveys an emotion is highly dependent on the LLM, representation type, and which emotion it is. © 2025, CC BY.

关键词： Human form models

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare systems

An Intelligent Data Analytics Approach for Crisis Management...

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International Conference on computer and Applications (ICCA)

作者： Fatma A. Madian Gamal A. Ebrahim Mostafa M. Salah Marwa A. Abdelaal Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Electrical Engineering Department Future University in Egypt Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%.

关键词： Crisis management computer viruses Simulation Decision making Time series analysis Organizations Predictive models Strategic planning Root mean square Long short term memory

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Sensitivity of DC Network Representation for GIC Analysis

Sensitivity of DC Network Representation for GIC Analysis

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IEEE Texas Power and Energy Conference (TPEC)

作者： Aniruddh Mishra Arthur K. Barnes Jose E. Tabarez Adam Mate Department of Electrical and Computer Engineering University of Texas at Austin Austin TX Information Systems and Modeling Group Los Alamos National Laboratory Los Alamos NM

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, including the method of representing the blocking devices, result in significant variations to the power loss calculations. To analyze these effects, we conclude the paper by comparing the losses on a sample network with different modeling implementations.

关键词： Analytical models Sensitivity Substations Power transmission lines Windings Circuit faults Security Reliability Electric fields Saturation magnetization

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Development of a Smart Electric Vehicle Charging Station Management System 24

Development of a Smart Electric Vehicle Charging Station Man...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics

作者： Konstantinos Papapostolou Vassilis Kapsalis Postgraduate Student in Master's Course in Information Systems Hellenic Open University Patras Greece Department of Electrical and Computer Engineering University of the Peloponnese Patras Greece

ISBN: (纸本)9798400713170

This paper presents the development of a smart charging station management system (CSMS) for electric vehicle charging stations (CSs), consisting of Electric Vehicle Supply Equipment (EVSE) units, that optimizes the charging of Electric Vehicles (EVs), by calculating charging schedules based on day-ahead (DA) electricity prices aiming at the minimization of energy costs. The basic CSMS operations that have been implemented are user authorization, transaction management, energy tracking, charger monitoring as well as charging optimization. The communication is based on the Open Charge Point Protocol (OCPP) version 2.0.1.

关键词： Charging Station (CS)

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Exploring Fusion of the Face and Voice Modalities Using CNN Features for a Better Performance

Exploring Fusion of the Face and Voice Modalities Using CNN ...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Comfort O. Lawal Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Covenant University Ota Ogun Nigeria Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa

Face recognition and speaker recognition have gained attention in recent times in the large-scale space. Large-scale face databases such as VGG-Face and VGGFace2 have been created using a semi-automated pipeline and used to develop methods for face recognition achieving state-of-the-art performance. This is also true for speaker recognition with large-scale databases such as VoxCeleb and VoxCeleb2. Howbeit, these two modalities have been treated individually. Although some works have explored the fusion of both modalities, they have played in small-scale space. This work aims at creating a large-scale face and corresponding voice database from YouTube under unconstrained conditions with a size comparable to the earlier mentioned and explores the fusion of both face and voice modalities for recognition in the large-scale space. To this end, a face and corresponding voice database of Nigerians available on YouTube was created for 2,656 Nigerians containing 2,055,169 face images and 195 hours of voice recording using a semi-automated curation pipeline. Convolutional Neural Networks (CNNs) were used to perform face recognition and speaker recognition individually. This was followed by the use of CNN for a combination of both modalities achieving an Equal Error Rate (EER) more than 5 times lower than the best result in the individual cases.

关键词： Video on demand Databases Face recognition Pipelines Speech recognition Feature extraction Speaker recognition

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An Efficient Medium Access Control Mechanism for Flying Ad-hoc Networks

引用

computer systems Science & engineering 2021年第7期38卷 47-63页

作者： Muhammad Asghar Khan Fazal Noor Insaf Ullah Sajjad ur Rehman Shibli Nisar Mohaira Ahmad Department of Electronic Engineering ISRA UniversityIslamabad44000Pakistan Department of Computer Science and Information Systems Islamic University of MadinahMadinah400411Saudi Arabia Hamdard Institute of Engineering&Technology Islamabad44000Pakistan Department of Electrical Engineering Namal InstituteMianwali42200Pakistan Department of Electrical Engineering National University of Sciences and Technology(NUST)Islamabad44000Pakistan

The Flying Ad-hoc Networks(FANETs)is characterized by the transition from a single large Unmanned Aerial Vehicle(UAV)to multiple small UAVs connected in an ad-hoc *** high mobility is the core feature of such networks,they are prone to route breaks within the *** issue of connectivity loss can be coped with,to some extent,by making use of omnidirectional *** modification,however,curtails Quality-of-Service(QoS)requirements of networks in terms of bandwidth,media access delay,coverage and ***,directional antennas have advantages over omnidirectional antennas such as improved transmission range,spatial reuse and high ***,its introduction raises location-dependent issues to the Medium Access Control(MAC)*** calls for an efficient MAC protocol that can cater to new directional antenna models and,at the same time,can counter the constraints associated with the dynamic ***,in this article,we consider a UAV interconnection mechanism that lets the UAVs execute the communication tasks using the directional MAC *** technique is advantageous as compared to the approach of utilizing the MAC protocol using omnidirectional *** scheme is being implemented as a case study for Industry 4.0 inventory and traceability applications in the *** modeling and simulation purposes,we use the Optimized Network engineering Tool(OPNET)and aim to seek an evaluation with respect to throughput,media access delay,retransmission attempts and data *** results obtained demonstrate the effectiveness of the proposed scheme.

关键词： FANETs QoS Industry 4.0 IEEE 802.11 MAC OPNET mobility models directional antenna

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Latency Optimization in LEO Satellite Communications with Hybrid Beam Pattern and Interference Control

arXiv

引用

arXiv 2024年

作者： Zhang, Qianqian Hu, Ye Jung, Minchae Department of Electrical and Computer Engineering Rowan University NJ United States Department of Industrial and Systems Engineering University of Miami FL United States Department of Electronics and Information Engineering Sejong University Seoul Korea Republic of

The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management, complicating coexistence with other communication systems. To address these limitations, this paper proposes a novel framework for optimizing the beam scheduling and resource allocation in multi-beam LEO systems. To satisfy the uneven terrestrial traffic demand, a hybrid beam pattern is employed to enhance the downlink quality of service and minimize the transmission latency from LEO satellites to ground user terminals. Additionally, a dynamic co-channel interference (CCI) control mechanism is developed to mitigate inter-beam interference within the LEO constellation and limit cross-system interference affecting protected users from other networks. The problem of user-beam-frequency allocation with power optimization is formulated as a mixed-integer dynamic programming model and solved using a low-complexity neural network-based graph generation algorithm. Simulation results show that the proposed approach outperforms the baseline methods of full frequency reuse and single-channel transmission, and highlights the potential for further performance improvement with multi-user transmissions. © 2024, CC BY.

关键词： Frequency allocation

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