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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bagade, Jayashree V. Dari, Sukhvinder Singh Dhivrani, Rekha Chowhan, Santosh Ravivarman, G. Janakiraman, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Symbiosis International Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats. © 2024 IEEE.

关键词： Cyber Instructions Malicious Procedures Threats

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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

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IEEE Internet of Things Journal 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT's accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations

Boosting Spike Camera Image Reconstruction from a Perspectiv...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Rui Zhao Ruiqin Xiong Jing Zhao Jian Zhang Xiaopeng Fan Zhaofei Yu Tiejun Huang School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing Peking University School of Electronic and Computer Engineering Peking University School of Computer Science and Technology Harbin Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

As a bio-inspired vision sensor with ultra-high speed, spike cameras exhibit great potential in recording dynamic scenes with high-speed motion or drastic light changes. Different from traditional cameras, each pixel in spike cam-eras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity. In this process, multiple factors impact the imaging, including the photons' Poisson arrival, thermal noises from circuits, and quantization effects in spike readout. These factors intro-duce fluctuations to spikes, making the recorded spike in-tervals unstable and unable to reflect accurate light intensi-ties. In this paper, we present an approach to deal with spike fluctuations and boost spike camera image reconstruction. We first analyze the quantization effects and reveal the unbi-ased estimation attribute of the reciprocal of differential of spike firing time (DSFT). Based on this, we propose a spike representation module to use DSFT with multiple orders for fluctuation suppression, where DSFT with higher or-ders indicates spike integration duration between multiple spikes. We also propose a module for inter-moment feature alignment at multiple granularities. The coarser alignment is based on patch-level cross-attention with a local search strategy, and the finer alignment is based on deformable convolution at the pixel level. Experimental results demon-strate the effectiveness of our method on both synthetic and real-captured data. The source code and dataset are avail-able at https://***/ruizhao26/BSF.

关键词： Fluctuations Quantization (signal) Firing Estimation Vision sensors Cameras Search problems

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks 3

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Shekhar, Jayant Rama Prabha, K.P. Prashanth, S. Umamaheswari, K. Rahul, S.G. Gayathri, K. Sharda School of Engineering and Technology Sharda University Department of Cse UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Excel Engineering College Department of Aeronautical Engineering Tamil Nadu Namakkal India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543358

Real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria. © 2025 IEEE.

关键词： Traffic congestion

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Multi-Label Feature Selection for High-Dimensional Biological data via Global Relevance and Redundancy Optimization Based on JS Divergence

Multi-Label Feature Selection for High-Dimensional Biologica...

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International Conference on Information science and Technology (ICIST)

作者： Man Yang Yibo Wang Yadi Wang Xiaoding Guo Huiyu Mu Hangjun Che Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Computer Science and Technology School of International Business Henan University Zhengzhou China College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

In recent years, multi-label feature selection has been widely used in fields such as bioinformatics, information retrieval, and multimedia annotation. Most of the previous multi-label feature selection methods are directly transformed from the traditional single-label feature selection methods, or they cannot make full use of label information. As a result, the selected feature subset involves features that are redundant or irrelevant to label information. Moreover, most algorithms do not use discretization when processing continuous data sets, so they cannot effectively eliminate the interference of abnormal data. In order to solve these problems, based on the GRRO (Global Relevance and Redundancy Optimization) algorithm, the JS divergence is used to better measure the overall similarity between two distributions and introduces data discretization preprocessing operations for continuous data sets. The experimental results after experimental verification on ten typical high-dimensional biological data sets show that the GRRO-JS algorithm is superior to the traditional multi-label feature selection method and the GRRO algorithm in terms of accuracy and efficiency and has high practical value.

关键词： Correlation Accuracy Termination of employment Interference Parallel processing Feature extraction Biology Time measurement Time complexity Optimization

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Distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning 29

Distributed Smart Multihome Energy Management Based on Feder...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Peng, Liwei He, Fan Hasegawa, Go Cheng, Yanfen Shao, Xun School of Computer Science and Artificial Intelligence Wuhan University of Technology China Research Institute of Electrical Communication Tohoku University Japan Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan

ISBN: (纸本)9798350330717

In recent years, there's been a surge in the popularity and affordability of distributed power generation equipment, such as photovoltaic systems (PV) and energy storage systems. At present, however, most solutions target individual user's energy management. Given the varied energy consumption habits, networking neighboring users and managing energy as a unified system could boost efficiency. Yet, this comes with challenges: unpredictable dynamic demands, significant computational loads, and concerns over data privacy. To tackle these challenges, we introduce a management system that merges deep reinforcement learning (DRL) with federated learning (FL) techniques, named PDDPG-FL. In this setup, each home possesses an agent responsible for decisions like charging/discharging and trading energy with other users. For every agent, we employ a priority-aware deep deterministic policy gradient (PDDPG) algorithm. This not only addresses fluctuating demand adeptly but also offers computational advantages over the conventional DDPG algorithm. Moreover, by incorporating the FL framework, agents can collaborate without risking data privacy breaches. Simulation results show that PDDPG-FL can reduce dependency on main supply grids by up to 9.7% and offers a more streamlined computational process. © 2023 IEEE.

关键词： deep reinforcement learning energy trade Federated learning home energy management system (HEMS)

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A Blockchain-based Predictive Maintenance Scheme for Smart Agriculture

A Blockchain-based Predictive Maintenance Scheme for Smart A...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Mohil Patel Sudeep Tanwar Anish Jindal Naif Alshammari Haris Pervaiz Hasan Ahmed Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India Department of Computer Science University of Durham United Kingdom School of Computing and Communications Lancaster University UK School of Computer Science and Electronic Engineering University of Essex UK

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

With technology invading all the sectors, the agriculture sector is not alien to technological developments and advancements. The use of IoT, robotics, sensors, artificial intelligence, etc., has resulted in the concept of Smart Agriculture, which aims to decrease human effort and increase the quantity and quality of the products. A lot of machinery is being used in smart agriculture and its maintenance is an important aspect for continued productivity and efficiency. Implementing a predictive maintenance scheme will reduce the high cost in terms of money and time and decrease unexpected faults and breakdowns. Motivated from the aforementioned discussion, in this paper, we propose a blockchain-based predictive maintenance scheme that utilizes the data collected by sensors to monitor and predict the condition of machines. The proposed scheme that uses K-Nearest Neighbours, Random Forest and LSTM models have been used and their performance has been compared based on accuracy, precision, recall, and F1-score. Results show that Random Forest achieved an F1-score of 89%, which is the best performance among all the models and can be effectively utilized for this purpose.

关键词： Smart agriculture Productivity Costs Crops Robot sensing systems Prediction algorithms Maintenance Monitoring Random forests Predictive maintenance

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Efficient data-aware Distance Comparison Operations for High-Dimensional Approximate Nearest Neighbor Search

arXiv

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arXiv 2024年

作者： Deng, Liwei Chen, Penghao Zeng, Ximu Wang, Tianfu Zhao, Yan Zheng, Kai University of Electronic Science and Technology of China China University of Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

High-dimensional approximate k nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods have unique ways of generating candidates, they all share the same DCO process. In this study, we focus on accelerating the process of DCOs that dominates the time cost in most existing AKNN algorithms. To achieve this, we propose an data-Aware Distance Estimation approach, called DADE, which approximates the exact distance in a lower-dimensional space. We theoretically prove that the distance estimation in DADE is unbiased in terms of data distribution. Furthermore, we propose an optimized estimation based on the unbiased distance estimation formulation. In addition, we propose a hypothesis testing approach to adaptively determine the number of dimensions needed to estimate the exact distance with sufficient confidence. We integrate DADE into widely-used AKNN search algorithms, e.g., IVF and HNSW, and conduct extensive experiments to demonstrate the superiority. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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