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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Reyes, Ma Julianna Re-An D.G. Dicreto, Eirnan Santos, Emmanuel Gabriel D. Limbag, Daniella Franxene P. Sampedro, Gabriel Avelino College of Computer Studies De La Salle University Manila Philippines Networks and Distributed Systems Laboratory University of the Philippines Diliman Quezon City Philippines

ISBN: (纸本)9798331510756

Chessis a complex game characterized by diverse strategies and time constraints, making quick decision-making essential for success. While Elo ratings are widely recognized as indicators of player skill, the predictability of match outcomes based solely on these ratings remains a challenge. The study aims to develop a model for accurately predicting the outcome of chess games using logistic regression, focusing on Elo rating differences and the number of moves in each game. The dataset includes over 20,000 games from ***, and only games with decisive outcomes (excluding draws) were used. The research categorizes Elo ratings into various classes and evaluates model performance across these ranges. The model achieves a predictive accuracy of 68.18%, demonstrating the significance of Elo ratings and move counts in determining game results. Performance metrics, including precision, recall, and Fl-score, further validate the model's effectiveness. The study concludes that while Elo ratings and move count are strong predictors of chess outcomes, further refinement is needed to improve performance at high-ranking skill levels. The model performs well for experts but loses accuracy in master-level games, reflecting higher skill-level complexities. The insights gained from this research contribute to a deeper understanding of predictive modeling in chess, sug-gesting potential avenues for further investigation into additional Influencing factors. © 2025 IEEE.

关键词： Logistic regression

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Electromagnetic metamaterial agent

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Light(Science & Applications) 2025年第1期14卷 118-130页

作者： Shengguo Hu Mingyi Li Jiawen Xu Hongrui Zhang Shanghang Zhang Tie Jun Cui Philipp del Hougne Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing100871China National Key Laboratory for Multimedia Information Processing School of Computer SciencePeking UniversityBeijing100871China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Pazhou Laboratory(Huangpu) GuangzhouGuangdong510555China Univ Rennes CNRSIETR-UMR 6164F-35000RennesFrance

Metamaterials have revolutionized wave control;in the last two decades,they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified *** deep-learning techniques play an increasingly important role in metamaterial inverse design,measurement post-processing and end-to-end optimization,their role is ultimately still limited to approximating specific mathematical relations;the metamaterial is still limited to serving as proxy of a human operator,realizing a predefined ***,we propose and experimentally prototype a paradigm shift toward a metamaterial agent(coined metaAgent)endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks,including electromagnetic(EM)field manipulations and interactions with robots and *** recently released foundation models,metaAgent reasons in high-level natural language,acting upon diverse prompts from an evolving complex ***,metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism,where agents are domain experts in sensing,planning,grounding,and *** response to live environmental feedback within a real-world setting emulating an ambient-assisted living context(including human requests in natural language),our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a *** masters foundational EM manipulation skills related to wireless communications and sensing,and it memorizes and learns from past experience based on human feedback.

关键词： agent prompt reasoning

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A Reliable Federated Learning Server Rotation Algorithm in IoV

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IEEE Internet of Things Journal 2025年第11期12卷 15501-15513页

作者： Cai, Xuelian Yang, Junyi Chang, Tianyu Fu, Yuchuan Yu, F. Richard Cheng, Nan Li, Changle Hui, Yilong The State Key Laboratory of Integrated Services Networks Xidian University Shaanxi 710071 China Research Institute of Smart Transportation Xidian University Shaanxi Xi’an710071 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL. © 2014 IEEE.

关键词： Federated learning

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A Comparison of Quantization Techniques for Mobile Olive Disease Detection

A Comparison of Quantization Techniques for Mobile Olive Dis...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Said Chouchf El Akhal Hicham Wafae Zarrik Abdelbaki El Belrhiti El Alaoui Computer Networks and Systems Laboratory Faculty of Sciences Moulay Ismail University of Meknes Meknes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This study aims to examines the integration of artificial intelligence (AI) in edge devices, addressing challenges such as memory, energy, and computational constraints through model quantization techniques. It focuses on Post-Training Quantization (PTQ), which simplifies the process by applying quantization after training, and Quantization-Aware Training (QAT), which incorporates quantization during training to enhance accuracy. A comparative analysis highlights that while both methods reduce model size and improve latency, QAT generally preserves higher accuracy. The practical application of these optimized models is demonstrated in a mobile application for identification of olive diseases using embedded intelligent as a study case, showcasing the benefits and challenges of deploying AI on edge devices.

关键词： Training Performance evaluation Quantization (signal) Accuracy Computational modeling Reliability Object recognition Artificial intelligence Standards Diseases

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Transcoding-Enabled Edge Caching Strategy Optimization: A Dual-Timescale Meta-Learning Based Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Dan Zhu, Keke Song, Bin Yu, F. Richard Xidian University State Key Laboratory of Integrated Services Networks 710071 China Carleton University Department of Systems and Computer Engineering Ottawa Canada

The explosive growth in video services has significantly strained current mobile network infrastructure, leading to spectrum scarcity, backhaul congestion, and degraded quality of experience. While edge caching has emerged as a promising solution to address these challenges and deliver seamless video playback experience, multi-version edge caching for heterogeneous clients remains challenging due to varying client requirements and network conditions. This paper proposes a transcoding enabled edge caching framework for mobile edge-cloud computing networks. Specifically, we combine video transcoding with edge caching to support both "direct cache hits" and "soft cache hits" for reducing transmission latency. We model this joint caching and resource allocation problem as a Stackelberg game to minimize video transmission latency. To solve this problem, we develop a novel dual timescale Model Agnostic Meta-Learning (MAML) based Stackelberg game (DTMSG) optimization approach that determines the delay-optimal Stackelberg equilibrium (SE) and accelerates convergence. Simulation results demonstrate that our DTMSG optimization algorithm efficiently converges to the SE point, maximizing the utility function of the MVNO and BSs while reducing the average video transmission delay. © 2014 IEEE.

关键词： Mobile edge computing

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Edge-Driven Industrial Computing Power networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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EloMetrics: Advanced Outcome Prediction for Chess Matches with Elo Ratings and Logistic Regression

EloMetrics: Advanced Outcome Prediction for Chess Matches wi...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Ma. Julianna Re-an DG. Reyes Eirnan Dicreto Emmanuel Gabriel D. Santos Daniella Franxene P. Limbag Gabriel Avelino Sampedro College of Computer Studies De La Salle University Manila Philippines Networks and Distributed Systems Laboratory University of the Philippines Diliman Quezon City Philippines

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

关键词： Training Measurement Analytical models Logistic regression Accuracy Focusing Psychology Games Predictive models Time factors

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Intelligent Cooperative Sensing for Connected and Autonomous Vehicles: An Improved Decision Transformer Approach

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IEEE Internet of Things Journal 2025年第11期12卷 15424-15437页

作者： Pincan Zhao Changle Li Xinrui Zhang F. Richard Yu Yuchuan Fu State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Information Technology Carleton University Ottawa ON Canada Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

Effective sensing capabilities are crucial for the safe and reliable operation of Connected and autonomous vehicles (CAVs). While traditional approaches focus on enhancing onboard sensors, the integration of road sensor networks (RSNs) into the CAV ecosystem presents a promising solution to improve sensing performance, but also introduces significant challenges, including heterogeneous sensing requirements, inconsistencies in multisource sensor data, and efficient resource utilization. To address these challenges, this article proposes a novel cooperative sensing framework that leverages multisource and multilevel sensing information from RSNs to optimize CAV sensing performance in resource-constrained scenarios. We develop an improved decision transformer (DT)-based approach that dynamically adapts to diverse driving conditions and efficiently fuses sensor data at various abstraction levels. To tackle the issue of long-delayed rewards, we introduce a reshaped reward function and a bi-level optimization framework that enables effective propagation of rewards along decision sequences. An advanced gradient approximation technique is employed to efficiently solve the optimization problem. Extensive simulations demonstrate the superior performance of our improved DT approach compared to state-of-the-art reinforcement learning (RL) methods in terms of sensing accuracy, coverage, and data efficiency under various traffic conditions.

关键词： Connected vehicles Fuses Thermal sensors Sensor fusion Transformers Data models Sensors Resource management Optimization Autonomous vehicles

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A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular networks

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ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Benarous, Leila Zeadally, Sherali Boudjit, Saadi Mellouk, Abdelhamid Department of Computer science Amar Telidji University of Laghouat Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Créteil France College of Communication and Information University of Kentucky Lexington United States Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of Litis Lab Intelligent Transportation Systems Group University of Rouen Normandy Mont-Saint-Aignan France Lissi Laboratory Department of Networks and Telecom University Paris-Est Créteil Val de Marne Créteil France

Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users' location privacy may be perilous beyond simple curiosity as it may scale to cyberstalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies, which have been considered as the most appropriate solution that maintains the tradeoff between ease of application, good protection level, and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Advancing low-resource dialect identification: A hybrid cross-lingual model leveraging CAMeLBERT and FastText for Algerian Arabic

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Expert systems with Applications 2025年 284卷

作者： Chabane, Mafaza Harrag, Fouzi Shaalan, Khaled Departement of Computer Science University of Ferhat Abbas Setif Algeria Laboratory of Networks and Distributed Systems University of Ferhat Abbas Setif Algeria Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates

This study addresses the issue of dialect identification in low-resource contexts, specifically Algerian Arabic. To mitigate the lack of labeled data for these dialects, we propose an innovative hybrid model that utilizes cross-lingual transfer learning from Modern Standard Arabic (MSA), a high-resource language. Our hybrid model integrates pre-trained CAMeLBERT and FastText embeddings, both trained on MSA, to handle dialectal input via two parallel routes. The CAMeLBERT pathway generates profound contextual embeddings, whereas the FastText pathway utilizes a convolutional layer, a Bidirectional GRU, and an attention mechanism. The combined results are input into a multi-layer perceptron (MLP) for precise dialect classification. The novelty of our work lies in the strategic leveraging of MSA as a bridge language, enabling the transfer of its rich linguistic knowledge to low-resource dialects. The efficacy of our approach is confirmed through meticulous experimentation plus a comprehensive ablation study. The model's versatility is further evidenced by its testing across various datasets that reflect distinct dialects. Significantly, our model surpassed multiple current methodologies in these assessments. These findings highlight the capacity of cross-lingual transfer learning to improve Natural Language Processing (NLP) tasks for under-resourced languages by utilizing the established knowledge of their high-resource equivalents. © 2025 Elsevier Ltd

关键词： Sentiment analysis

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