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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Liu, Tong Xu, Dongyang Zhang, Tiantian Zhang, Shilong Chen, Jinhua Yu, Keping Leung, Victor C.M. Graduate School of Science and Engineering Hosei University Japan School of Information and Communications Engineering Xi'an Jiaotong University China Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Computer Science and Software Engineering Shenzhen University China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9798331521165

The accelerated development of the Industrial Internet of Things (IoT) has significantly enhanced the computational efficiency and processing capabilities of Wireless Consumer Application Networks (WCAN). Despite these advancements, the performance of WCAN remains constrained by the inherent limitations in computational capacity and energy resources of wireless devices. To address these issues, this paper proposes a wireless powered-mobile edge computing system with Reconfigurable Intelligent Surface (RIS) assistance for WCAN, where wireless consumer devices can not only harvest energy from access point, but also offload computing tasks to edge server with the help of RIS. Aiming to maximize the system computation rate, a non-convex problem is developed and a block optimization with deep reinforcement learning (BO-DRL) scheme is proposed. First, the RIS phase shifts in the Wireless Power Transfer (WPT) stage and offloading stage are optimized using closed-form solutions and the gradient ascent algorithm, respectively. Subsequently, the offloading strategies are optimized using deep reinforcement learning, and a convex optimization algorithm is employed to obtain the time allocation for the WPT and offloading stages. Numerical results illustrate that the BO-DRL algorithm greatly enhances computation rate by 20% compared to without RIS. © 2025 IEEE.

关键词： Deep reinforcement learning

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EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG

EEG-EMG FAConformer: Frequency Aware Conv-Transformer for th...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, ZhengXiao Cai, Minghong Li, Letian Tian, Siyuan Dai, Ren-Jie Tongji University College of Electronic and Information Engineering Shanghai China Tongji University School of Software Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9798350386226

Motor pattern recognition paradigms are the main forms of Brain-computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent ChannelSpecific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEGEMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability. © 2024 IEEE.

关键词： Electromyography

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing 4

A High-capacity Information Hiding Scheme Based on Blakley S...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhou, Jincheng Li, Yue Shen, Yujie Tang, Dan Cai, Hongliang Gong, Jing Bai, Ningchao School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (纸本)9798331541729

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing. © 2024 IEEE.

关键词： Euclidean division Information hiding Modulus function Secret sharing

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Short-Time Traffic Flow Prediction Based on a Combined Model of EMD and LSTM 4

Short-Time Traffic Flow Prediction Based on a Combined Model...

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4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023

作者： Zhao, Qihan Lou, Lidu Ouyang, Bo School of Computer Science and Engineering Central South University Changsha China School of Software Engineering South China University Hengyang China

ISBN: (纸本)9798350314526

In traffic management, accurate forecasting of short-Term traffic patterns is of utmost importance to achieve optimal performance and efficiency of road networks. This research proposes a prediction technique for short-Term traffic flow, which utilizes empirical modal decomposition (EMD) and long short-Term memory neural networks (LSTM). Firstly, the traffic flow sequence is decomposed into a series of relatively stable subseries using EMD, minimizing the impact of various trend data interactions. Secondly, to improve model training efficiency, normalization is applied separately to each subseries. Subsequently, an LSTM-based time-series prediction model is built for each subseries, which enhances the model's predictive accuracy. Finally, the forecasted values of short-Term traffic flow are obtained by aggregating the prediction outcomes of each subseries. The simulation results demonstrate that the proposed method more accurately predicts the traffic flow change trend and achieves higher stability than conventional prediction techniques. © 2023 IEEE.

关键词： Long short-term memory

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract) 40

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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Faithfulness Measurable Masked Language Models 41

Faithfulness Measurable Masked Language Models

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41st International Conference on Machine Learning, ICML 2024

作者： Madsen, Andreas Reddy, Siva Chandar, Sarath Mila Montreal Canada Computer Engineering and Software Engineering Department Polytechnique Montreal Montreal Canada Computer Science and Linguistics McGill University Montreal Canada Facebook CIFAR AI Canada Canada CIFAR AI Canada

A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model performance. However, token masking introduces out-of-distribution issues, and existing solutions that address this are computationally expensive and employ proxy models. Furthermore, other metrics are very limited in scope. This work proposes an inherently faithfulness measurable model that addresses these challenges. This is achieved using a novel fine-tuning method that incorporates masking, such that masking tokens become in-distribution by design. This differs from existing approaches, which are completely model-agnostic but are inapplicable in practice. We demonstrate the generality of our approach by applying it to 16 different datasets and validate it using statistical in-distribution tests. The faithfulness is then measured with 9 different importance measures. Because masking is in-distribution, importance measures that themselves use masking become consistently more faithful. Additionally, because the model makes faithfulness cheap to measure, we can optimize explanations towards maximal faithfulness;thus, our model becomes indirectly inherently explainable. Copyright 2024 by the author(s)

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Faizan Ayub, Muhammad Li, Xiong Mahmood, Khalid Shamshad, Salman Kumar Das, Ashok Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Qilu University of Technology Shandong Academy of Sciences 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 Industrial Network and Information System Security Jinan250353 China

Smart grids (SGs) rely on home area networks (HANs) and neighborhood area networks (NANs) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the physical unclonable function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol’s resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol’s effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol’s enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Explainable AI: A Way to Achieve Trustworthy AI 10

Explainable AI: A Way to Achieve Trustworthy AI

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Li, Yanlu Xiao, Yufeng Gong, Yinyan Zhang, Ronglin Huo, Yingzi Wu, Yanbing Hunan Key Laboratory for Service Computing Novel Software Technology Xiangtan411201 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350389524

AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI's decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI. © 2024 IEEE.

关键词： Machine learning

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QoSCAPE: QoS-Centric Adaptive Path engineering with Blockchain-Enabled Reinforcement Learning

QoSCAPE: QoS-Centric Adaptive Path Engineering with Blockcha...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Karakus, Murat Guler, Evrim Ayaz, Furkan Uludag, Suleyman Department of Software Engineering Ankara University Ankara06830 Turkey Department of Computer Engineering Bartin University Bartin74110 Turkey Department of Computer Science University of Michigan-Flint FlintMI48502 United States

ISBN: (纸本)9798350379433

In the evolving landscape of effective network management, path engineering, also known as traffic engineering, optimization and selection remain a critical challenge. This paper presents an innovative framework that integrates software Defined Networking (SDN), Blockchain technology (BC), and Reinforcement Learning (RL) to enhance the efficiency and security of network path optimization. SDN's centralized control enables dynamic network traffic management, while BC ensures secure, transparent, and immutable logging of network transactions, fostering trust and accountability. Reinforcement learning, capable of learning and adapting from environmental interactions, is employed to dynamically optimize routing decisions. Our proposed framework, QoSCAPE: QoS-Centric Adaptive Path engineering with Blockchain-Enabled Multi-Agent Reinforcement Learning, leverages the programmability of SDN to collect real-time network state information and the decentralization of BC to secure this data. The numerical results underscore the superior efficiency of QoSCAPE, which consistently achieves near-total request success rates within milliseconds, significantly outperforming traditional HRA and DRA methods in rapidly fulfilling network service demands in terms of Path Setup Time (PST) and Requests Serviced (RS) metrics, yet notably surpasses all other approaches by minimizing Network Message Overhead (NMO). Its optimization in reducing message volume ensures efficient resource usage and preserves network scalability, distinguishing it as a superior choice for multi-ISP routing frameworks. © 2024 IEEE.

关键词： Efficiency

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