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International Conference on computer and Communications (ICCC)

作者： Salabat Khan Fei Luo Mansoor Khan Insaf Ullah Farhan Amin Shamsher Ullah Mussadiq Abdul Rahim School of Computer and Enformation Engineering Qilu Institute of Technology Jinan China School of Computing and Information Technology Great Bay University Dongguan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Institute for Analytics and Data Science University of Essex Colchester UK School of Computer Science and Engineering Yeungnam University South Korea National Engineering Laboratory for Big Data Shenzhen University Shenzhen China King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Metaverse is the next generation and the successor to the Internet. Various sectors allocated capital to adapt the metaverse due to its inherent importance. Cooperative Intelligent Transportation Systems (C-ITS) is one of the important sectors that can benefit from metaverse adaptation and integration. The C-ITS and metaverse integration give rise to new use cases and cyber attacks. However, traditional Pseudonym Certificate (PCert) authentication based on Vehicular Public Key Infrastructure (VPKI) cannot meet the new use cases and thwart cyber attacks. We present a Differential Privacy (DP)-based PCert and avatar update model to ensure the privacy of end-devices (e.g., vehicles) in the C-ITS metaverse. The proposed DP-based model adds noise to avatar attributes (e.g., color, behavior) to prevent avatar linkage during the PCert update process. Our method ensures that PCerts and avatars cannot be linked to end-devices and the previous PCerts and avatars. We simulate the DP-based method to investigate it in terms of privacy, latency, color distortion, and adversarial success rate, and the results show that DP-based is effective and practical for avatar updates.

关键词： Privacy Metaverse Avatars Computational modeling Authentication Public key Color Colored noise Intelligent transportation systems Cyberattack

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Research on positioning algorithm of binocular camera based on multi-media 2021

Research on positioning algorithm of binocular camera based ...

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2nd International Conference on Computing and Data science, CONF-CDS 2021

作者： Xiong, Qianghua Yang, Lei Chen, Qingqiang Dongguan University of Technology School of Computer Science and Technology Dongguan China Dongguan University of Technology School of Electronic Engineering and Intelligence Dongguan China School of Mechatronics and Control Engineering Shenzhen University Dongguan University of Technology Dongguan China

ISBN: (纸本)9781450389570

With the development of computer technology, image processing technology has become an increasing number of mature. However, the accuracy of target positioning has not reached the expectation in the case of multi-medium. Based on the binocular positioning algorithm, Aiming at the binocular positioning algorithm, this paper proposes a multi-medium binocular positioning algorithm. First, from the calibration of the camera, the internal parameters of the camera is obtained and the distortion caused by the camera is removed, and the picture is corrected. Secondly, the position of the target is detected by the YOLOv4 algorithm and the pixels of the target in the picture is obtained. Finally, through the binocular vision algorithm, camera imaging principle and the propagation characteristics of light on the interface of different medium, the spatial position of the target in the world coordinate system is obtained. © 2021 ACM.

关键词： Binoculars

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Evolutionary Reinforcement Learning: A Survey

arXiv

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arXiv 2023年

作者： Bai, Hui Cheng, Ran Jin, Yaochu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Faculty of Technology Bielefeld University Bielefeld33615 Germany Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, several critical challenges remain, such as brittle convergence properties caused by sensitive hyperparameters, difficulties in temporal credit assignment with long time horizons and sparse rewards, a lack of diverse exploration, particularly in continuous search space scenarios, challenges in credit assignment in multi-agent RL, and conflicting objectives for rewards. Evolutionary computation (EC), which maintains a population of learning agents, has demonstrated promising performance in addressing these limitations. This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL). We categorize EvoRL methods according to key research areas in RL, including hyperparameter optimization, policy search, exploration, reward shaping, meta-RL, and multi-objective RL. We then discuss future research directions in terms of efficient methods, benchmarks, and scalable platforms. This survey serves as a resource for researchers and practitioners interested in the field of EvoRL, highlighting the important challenges and opportunities for future research. With the help of this survey, researchers and practitioners can develop more efficient methods and tailored benchmarks for EvoRL, further advancing this promising cross-disciplinary research field. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Labeled Graph Partitioning Scheme for Distributed Edge Caching

SSRN

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SSRN 2023年

作者： Wang, Pengfei Li, Shiqi Sun, Geng Zhou, Changjun Gao, Chengxi Qiu, Sen Tao, Tiwei Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Changchun130012 China School of Computer Science and Technology Zhejiang Normal University Jinhua321000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data results in low cache hit rates and high data access latency. This limitation stems from existing graph partitioning schemes that overlook the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we propose LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generates frequent query pattern according to the user’s historical query subgraphs, and then performs labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers has a high hit rate for frequent user queries while satisfying minimum edge-cut. Evaluation on real datasets from various application domains demonstrates that LGPE achieves approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performs well in terms of edge cuts in special graph cases. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Methods and tools for automatic or semi-automatic recognition of selected emotions using machine learning algorithms

Methods and tools for automatic or semi-automatic recognitio...

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Progress in Applied Electrical engineering (PAEE)

作者： Piotr Schneider Dariusz Mikołajewski Anna Bryniarska Magdalena Igras-Cybulska Artur Cybulski Walery Marcinowicz Maciej Janiszewski Aleksandra Kawala-Sterniuk Department of Neuroinformatics and Biomedical Engineering Institute of Computer Science Maria Curie-Sklodowska University Lublin Poland Hishoo Sp. z o.o Gdynia Poland Faculty of Computer Science Kazimierz Wielki University Bydgoszcz Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

Automatic or semi-automatic recognition of selected emotions using Machine Learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its ‘invasiveness’ (e.g. by attaching sensors to the user’s body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： Emotion recognition Machine learning algorithms Accuracy Video on demand Speech recognition Machine learning Feature extraction Recording Convolutional neural networks Web sites

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StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning

arXiv

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arXiv 2023年

作者： Jing, Peiguang Liu, Xianyi Wang, Ji Wei, Yinwei Nie, Liqiang Su, Yuting School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Information Technology Monash University Victoria Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Emotion distribution learning has gained increasing attention with the tendency to express emotions through images. As for emotion ambiguity arising from humans’ subjectivity, substantial previous methods generally focused on learning appropriate representations from the holistic or significant part of images. However, they rarely consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, we propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL, which interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. Specifically, we consider exploring the intra- and inter-layer correlations among GRAM-based stylistic representations, and meanwhile exploit an adversary-constrained high-order attention mechanism to capture potential interactions between subtle visual parts. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art methods. The implementation is released at: https://***/liuxianyi/StyleEDL. © 2023, CC BY.

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H∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

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IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Marine Engineering Keelung202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Membership functions

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Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?

arXiv

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arXiv 2022年

作者： Liu, Yue Tantithamthavorn, Chakkrit Li, Li Liu, Yepang Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Machine learning (ML)-based Android malware detection has been one of the most popular research topics in the mobile security community. An increasing number of research studies have demonstrated that machine learning is an effective and promising approach for malware detection, and some works have even claimed that their proposed models could achieve 99% detection accuracy, leaving little room for further improvement. However, numerous prior studies have suggested that unrealistic experimental designs bring substantial biases, resulting in over-optimistic performance in malware detection. Unlike previous research that examined the detection performance of ML classifiers to locate the causes, this study employs Explainable AI (XAI) approaches to explore what ML-based models learned during the training process, inspecting and interpreting why ML-based malware classifiers perform so well under unrealistic experimental settings. We discover that temporal sample inconsistency in the training dataset brings over-optimistic classification performance (up to 99% F1 score and accuracy). Importantly, our results indicate that ML models classify malware based on temporal differences between malware and benign, rather than the actual malicious behaviors. Our evaluation also confirms the fact that unrealistic experimental designs lead to not only unrealistic detection performance but also poor reliability, posing a significant obstacle to real-world applications. These findings suggest that XAI approaches should be used to help practitioners/researchers better understand how do AI/ML models (i.e., malware detection) work-not just focusing on accuracy improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： Android malware

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Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment 2

Energy-Efficient Load Balancing Technique to optimize Averag...

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2nd International Conference on Physics and Applied sciences, ICPAS 2021

作者： Jeyalaksshmi, S. Anita Smiles, J. Akila, D. Mukherjee, Dibyendu Obaid, Ahmed J. Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India Chennai India Department of Computer Science and Engineering Brain Ware University India Faculty of Computer Science and Mathematics University of Kufa Iraq

Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power;ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS). © Published under licence by IOP Publishing Ltd.

关键词： Quality of service

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On Security-Constrained Bidding of Shared Transportation Hubs for Renewable Fuels and Refined Oil Participating Within Energy, Regulation and Pipeline Logistics Markets

On Security-Constrained Bidding of Shared Transportation Hub...

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IEEE Student Conference on Electric Machines and Systems

作者： Shengshi Wang Hongyi Li Danji Huang Kun Li Shichang Cui Xiaomeng Ai Jiakun Fang Jinyu Wen State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China School of Engineering Cardiff University Cardiff UK Department of Electrical and Computer Engineering Iowa State University Ames United States Department of Electrical and Computer Engineering University of Macau Macau China PetroChina ShenZhen New Energy Research Institute Co. Ltd. Shenzhen China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

This paper investigates the security-constrained bidding strategy of the emerging shared transportation hubs for renewable fuels and refined oil (STH-RRs) participating within energy, regulation and pipeline logistics markets. Firstly, to excavate more flexible capability for the power system as well as to increase the revenue of the STH-RR operation company, the model for STH-RR participating in regulation market is established. Secondly, the security-constrained two-stage bidding model is first proposed for STH-RRs to balance the revenue across energy, regulation and pipeline logistics markets. Particularly, to guarantee secure operation of STH-RRs and meet the delivery demand, the rigorous multi-product transportation constraints are considered in both stages. Case studies based on the modified real-world multi-product pipelines in South China validate the proposed method, with the revenue and turnover increase of 14% and 4.7%, respectively, compared with conventional methods.

关键词： Renewable energy sources Uncertainty Oils Pipelines Transportation Regulation Fuels Frequency control Logistics Testing

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