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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Alami, Amine Boumhidi, Jaouad Chakir, Loqman Sidi Mohammed Ben Abdellah University of Fes LISAC Laboratory Department of Computer Science Faculty of Sciences Dhar El Mehraz Fes Morocco

ISBN: (纸本)9798350350180

DL techniques have increased the efficiency of decision making in different areas. However, in the case of the presence of uncertainties in the data or in the environment, decision-making requires the explainability of the model, especially for high-stakes decision making such as medical image analysis area. This paper focuses on medical image classification CNN based deep learning models and aims to apply and compare three popular explainable AI approaches LIME, SHAP and *** results on a Pneumonia and Alzheimer's datasets for disease detection show that the Grad-CAM method seems to outperform LIME and SHAP and able to enhance the interpretability of DL models, identify automatically the most important features that contribute to the model's decision. © 2024 IEEE.

关键词： Contrastive Learning

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On the convergence and improvement of stochastic normalized gradient descent

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science China(Information sciences) 2021年第3期64卷 105-117页

作者： Shen-Yi ZHAO Yin-Peng XIE Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing University

Non-convex models, like deep neural networks, have been widely used in machine learning applications. Training non-convex models is a difficult task owing to the saddle points of models. Recently,stochastic normalized gradient descent(SNGD), which updates the model parameter by a normalized gradient in each iteration, has attracted much attention. Existing results show that SNGD can achieve better performance on escaping saddle points than classical training methods like stochastic gradient descent(SGD).However, none of the existing studies has provided theoretical proof about the convergence of SNGD for non-convex problems. In this paper, we firstly prove the convergence of SNGD for non-convex ***, we prove that SNGD can achieve the same computation complexity as SGD. In addition, based on our convergence proof of SNGD, we find that SNGD needs to adopt a small constant learning rate for convergence guarantee. This makes SNGD do not perform well on training large non-convex models in practice. Hence, we propose a new method, called stagewise SNGD(S-SNGD), to improve the performance of SNGD. Different from SNGD in which a small constant learning rate is necessary for convergence guarantee,S-SNGD can adopt a large initial learning rate and reduce the learning rate by stage. The convergence of S-SNGD can also be theoretically proved for non-convex problems. Empirical results on deep neural networks show that S-SNGD achieves better performance than SNGD in terms of both training loss and test accuracy.

关键词： non-convex problems stochastic normalized gradient descent computation complexity

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A GPGPU-Based Algorithm Acceleration System for ECG Signal Processing 7

A GPGPU-Based Algorithm Acceleration System for ECG Signal P...

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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Huang, Tianyu Wang, Zhijiong Hu, Guannan Pun, Sio Hang Li, Hung Chun Vai, Mang I Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Zhuhai China Institute of Microelectronics Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China

ISBN: (纸本)9798331529314

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. This system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. These features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data. © 2024 IEEE.

关键词： Noninvasive medical procedures

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets 39

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

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Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

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Neural Computing and Applications 2025年第16期37卷 9295-9308页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

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Understanding Disclosure and Support for Youth Mental Health in Social Music Communities

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Proceedings of the ACM on Human-computer Interaction 2023年第CSCW1期7卷 1-32页

作者： Jin, Yucheng Cai, Wanling Chen, Li Dai, Yuwan Jiang, Tonglin Hong Kong Baptist University Department of Computer Science Hong Kong Peking University School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health Beijing China

Online music platforms that include social networking features sometimes become supportive social communities where young people can disclose their emotional distress and receive support. However, few studies have examined young people's disclosure in social music communities or the support they provide or receive. In this study, which focuses on a large online music platform as a research site, we used mixed methods to analyze young users' comments (N = 163) and the associated replies (N = 2,732) related to their psychological distress (e.g., depression, anxiety, stress, and loneliness). We found that the main types of comments involved experience sharing, and these comments often invoked peer support in the form of encouragement, caring, or self-disclosure. We also conducted an interview study with 13 young users of our research site to understand their perceptions of and motives for engaging in disclosure and support. The interviewees stated that music-induced and comment-induced emotional resonance was the main reason for their disclosure and support. Finally, we discussed the implications of our findings for designing a supportive social music community to benefit youth mental health. © 2023 ACM.

关键词： Social networking (online)

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ChatGPT: potential, prospects, and limitations

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 6-11页

作者： Jie ZHOU Pei KE Xipeng QIU Minlie HUANG Junping ZHANG School of Computer Science Fudan UniversityShanghai 200433China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433China

Recently,OpenAI released Chat Generative Pre-trained Transformer(ChatGPT)(Schulman et al.,2022)(https://***),which has attracted considerable attention from the industry and academia because of its impressive *** is the first time that such a variety of open tasks can be well solved within one large language *** better understand ChatGPT,we briefly introduce its history,discuss its advantages and disadvantages,and point out several potential ***,we analyze its impact on the development of trustworthy artificial intelligence,conversational search engine,and artificial general intelligence.

关键词： artificial limitations intelligence

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Position: Building Guardrails for Large Language Models Requires Systematic Design 41

Position: Building Guardrails for Large Language Models Requ...

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41st International Conference on Machine Learning, ICML 2024

作者： Dong, Yi Mu, Ronghui Jin, Gaojie Qi, Yi Hu, Jinwei Zhao, Xingyu Meng, Jie Ruan, Wenjie Huang, Xiaowei Department of Computer Science University of Liverpool United Kingdom State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China WMG University of Warwick Warwick United Kingdom Institute of Digital Technologies Loughborough University London United Kingdom

As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies. Guardrails, which filter the inputs or outputs of LLMs, have emerged as a core safeguarding technology. This position paper takes a deep look at current open-source solutions (Llama Guard, Nvidia NeMo, Guardrails AI), and discusses the challenges and the road towards building more complete solutions. Drawing on robust evidence from previous research, we advocate for a systematic approach to construct guardrails for LLMs, based on comprehensive consideration of diverse contexts across various LLMs applications. We propose employing sociotechnical methods through collaboration with a multi-disciplinary team to pinpoint precise technical requirements, exploring advanced neural-symbolic implementations to embrace the complexity of the requirements, and developing verification and testing to ensure the utmost quality of the final product. Copyright 2024 by the author(s)

关键词： Guard rails

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New spotted hyena intelligent algorithm for solving small-scale CVRP problem

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of science and Technology (Natural science Edition) 2024年第2期52卷 77-83页

作者： Wang, Xiaofeng Mo, Chunhui Zhang, Lin Yang, Lan Country Department of Computer Science North Minzu University Yinchuan750021 China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

A new spotted hyena intelligent optimizer (ISHO) algorithm incorporating multi-strategy improvement was proposed for the characteristics of the capacitated vehicle routing problem (CVRP).A combination of K-means clustering and greedy selection was used for the initialization method. Based on the standard spotted hyena optimizer, the spiral exploration mechanism in the whale optimization algorithm was incorporated to enhance the global search capability of the ***, a variational operator, two crossover operators and reverse operation methods were *** spiral exploration was combined with the cross variational operator to redefine the spotted hyena optimizer in discrete ***, the computational results of ISHO in four different types of a total of 24 international benchmark cases were experimentally compared and which was analyzed with other intelligent optimization *** results show that the ISHO algorithm effectively avoids the drawbacks of falling into local optimum and low quality solutions, and has certain superiority for solving small-scale CVRP problems. © 2024 Huazhong University of science and Technology. All rights reserved.

关键词： K-means clustering

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CA-FedRC: Codebook Adaptation Via Federated Reservoir Computing in 5G NR

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9995-9999页

作者： Ye, Ziqiang Liao, Sikai Gao, Yulan Fang, Shu Xiao, Yue Xiao, Ming Zammit, Saviour University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm10044 Sweden University of Malta Department of Communications and Computer Engineering Msida2080 Malta

With the burgeon deployment of the fifth-generation new radio (5 G NR) networks, the codebook plays a crucial role in enabling the base station (BS) to acquire the channel state information (CSI). Different 5 G NR codebooks incur varying overheads and exhibit performance disparities under diverse channel conditions, necessitating codebook adaptation based on channel conditions to reduce feedback overhead while enhancing performance. However, existing methods of 5 G NR codebooks adaptation require significant overhead for model training and feedback or fall short in performance. To address these limitations, this letter introduces a federated reservoir computing framework designed for efficient codebook adaptation in computationally and feedback resource-constrained mobile devices. This framework utilizes a novel series of indicators as input training data, striking an effective balance between performance and feedback overhead. Compared to conventional models, the proposed codebook adaptation via federated reservoir computing (CA-FedRC), achieves rapid convergence and significant loss reduction in both speed and accuracy. Extensive simulations under various channel conditions demonstrate that our algorithm not only reduces resource consumption of users but also accurately identifies channel types, thereby optimizing the trade-off between spectrum efficiency, computational complexity, and feedback overhead. © 1967-2012 IEEE.

关键词： 5G mobile communication systems

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