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IEEE Symposium on High-Performance computer Architecture

作者： Jiaqi Zhai Xuanhua Shi Kaiyi Huang Chencheng Ye Weifang Hu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication.

关键词： Quantization (signal) High dimensional data Memory management Graphics processing units Bandwidth Real-time systems Sparse matrices Recommender systems Field programmable gate arrays Optimization

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

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

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Jincheng Zhou Yue Li Yujie Shen Dan Tang Hongliang Cai Jing Gong Ningchao Bai 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: (数字)9798331541729

ISBN: (纸本)9798331541736

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.

关键词： Correlation Disasters Redundancy Reconstruction algorithms Vectors Manufacturing Cryptography data mining data communication Computational complexity

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Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach

arXiv

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arXiv 2025年

作者： Wang, Yanmeng Ji, Wenkai Zhou, Jian Xiao, Fu Chang, Tsung-Hui School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge, but its performance can be severely hindered by unreliable wireless transmission and inherent data heterogeneity among clients. Existing solutions primarily address these challenges by incorporating wireless resource optimization strategies, often focusing on uplink resource allocation across clients under the assumption of homogeneous client-server network standards. However, these approaches overlooked the fact that mobile clients may connect to the server via diverse network standards (e.g., 4G, 5G, Wi-Fi) with customized configurations, limiting the flexibility of server-side modifications and restricting applicability in real-world commercial networks. This paper presents a novel theoretical analysis about how transmission failures in unreliable networks distort the effective label distributions of local samples, causing deviations from the global data distribution and introducing convergence bias in FL. Our analysis reveals that a carefully designed client selection strategy can mitigate biases induced by network unreliability and data heterogeneity. Motivated by this insight, we propose FedCote, a client selection approach that optimizes client selection probabilities without relying on wireless resource scheduling. Experimental results demonstrate the robustness of FedCote in DNN-based classification tasks under unreliable networks with frequent transmission failures. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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Large Language Model Supply Chain: A research Agenda

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ACM Transactions on Software engineering and Methodology 2025年第5期34卷

作者： Wang, Shenao Zhao, Yanjie Hou, Xinyi Wang, Haoyu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of these models have given rise to a multifaceted supply chain that presents unique challenges across infrastructure, foundation models, and downstream applications. This article provides the first comprehensive research agenda of the LLM supply chain, offering a structured approach to identify critical challenges and opportunities through the dual lenses of software engineering (SE) and security and privacy (S&P). We begin by establishing a clear definition of the LLM supply chain, encompassing its components and dependencies. We then analyze each layer of the supply chain, presenting a vision for robust and secure LLM development, reviewing the current state of practices and technologies, and identifying key challenges and research opportunities. This work aims to bridge the existing research gap in systematically understanding the multifaceted issues within the LLM supply chain, offering valuable insights to guide future efforts in this rapidly evolving domain. © 2025 Association for Computing Machinery. All rights reserved.

关键词： Large Language Models LLM Supply Chain

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Hard-Label Black-Box Attacks on 3D Point Clouds

arXiv

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arXiv 2024年

作者： Liu, Daizong Tao, Yunbo Zhou, Pan Hu, Wei Wangxuan Institute of Computer Technology Peking University No. 128 Zhongguancun North Street Beijing China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

With the maturity of depth sensors in various 3D safety-critical applications, 3D point cloud models have been shown to be vulnerable to adversarial attacks. Almost all existing 3D attackers simply follow the white-box or black-box setting to iteratively update coordinate perturbations based on back-propagated or estimated gradients. However, these methods are hard to deploy in real-world scenarios (no model details are provided) as they severely rely on parameters or output logits of victim models. To this end, we propose point cloud attacks from a more practical setting, i.e., hard-label black-box attack, in which attackers can only access the prediction label of 3D input. We introduce a novel 3D attack method based on a new spectrum-aware decision boundary algorithm to generate high-quality adversarial samples. In particular, we first construct a class-aware model decision boundary, by developing a learnable spectrum-fusion strategy to adaptively fuse point clouds of different classes in the spectral domain, aiming to craft their intermediate samples without distorting the original geometry. Then, we devise an iterative coordinate-spectrum optimization method with curvature-aware boundary search to move the intermediate sample along the decision boundary for generating adversarial point clouds with trivial perturbations. Experiments demonstrate that our attack competitively outperforms existing white/black-box attackers in terms of attack performance and adversary quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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How graph neural networks learn: lessons from training dynamics 24

How graph neural networks learn: lessons from training dynam...

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Proceedings of the 41st International Conference on Machine Learning

作者： Chenxiao Yang Qitian Wu David Wipf Ruoyu Sun Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI Shanghai Jiao Tong University Amazon Web Services School of Data Science The Chinese University of Hong Kong Shenzhen and Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster.

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ENSIOT: A Stacking Ensemble Learning Approach for IoT Device Identification

ENSIOT: A Stacking Ensemble Learning Approach for IoT Device...

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International Workshop on Quality of Service

作者： Kangli Niu Shenghao Liu Lingzhi Yi Xianjun Deng Suning Chen Laurence T. Yang Minmin Cheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Safety Engineering Zhongnan University of Economics and Law

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In order to resist network attacks on IoT devices, identifying IoT devices is the first step for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship between traffic characteristics and devices. However, the form of selected traffic features are too singular without considering device behavioral characteristics and the classifier used is too specific with simple structure in these methods. This paper proposes a stacking ensemble learning approach for IoT device identification, ENSIOT, which fully considering the behavioral characteristics of devices and integrating the advantages of various machine learning methods to achieve efficient identification of IoT devices. Firstly, in the process of traffic processing, our method selects features from activity cycles, port numbers, signalling patterns, and cipher suites. Then, in model integration, many machine learning methods are used as base models to learn features selected, and output preliminary recognition results. Finally, the meta model learns the relationship between label and the recognition results of each base model and outputs the final device identification result. This stacking structure stacks the base models and the meta model to make a classifier with strong identification and generalization ability. Incremental learning is used to improve identification accuracy when traffic pattern changing. Comparative experiments are conducted on two datasets of UNSW and TMA-2021. The experimental results verify the effectiveness of ENSIOT, which achieve the accuracy of over 98% on two dataset and bring a noticeable improvement in terms of both accuracy and macro F1 score.

关键词： Ciphers Accuracy Incremental learning Stacking Resists Traffic control Internet of Things

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Reversible data hiding scheme for encrypted JPEG bitstreams using adaptive RZL rotation

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Frontiers of Information Technology & Electronic engineering 2024年第10期25卷 1353-1369页

作者： Yongning GUO Guodong SU Zhiqiang YAO Wang ZHOU School of Big Data and Artificial Intlligence Fujian Polytechnic Normal UniversityFuzhou 350300China College of Mathematics and Informatics Fujian Normal UniversityFuzhou 350117China Engineering Research Center for ICH Digitalization and Multi-Source Information Fusion(Fujian Polytechnic Normal University) FujianProvinceUniversityFuzhou350300China School of Computer and Sofiware Engineering Xihua UniversityChengdu 610039China

Joint Photographic Experts Group(JPEG)format is extensively used for images in many practical applications due to its excellent compression ratio and satisfactory image *** compelling concerns about the invasion of privacy,this paper proposes an effective reversible data hiding scheme for encrypted JPEG bitstreams,to provide security and privacy for both secret messages and valuable ***,a format-compatibility and file size preserving encryption algorithm is applied to encipher the plaintext JPEG image into a noise-like ***,we present an effective reversible data hiding scheme in encrypted JPEG bitstreams using adaptive RZL rotation,where the secret messages are concealed with the sequence of RZL *** the authorized user receives the marked encrypted JPEG bitstreams,the error-free extraction of secret messages and the lossless recovery of the original plaintext JPEG image can be accomplished *** experiments are conducted to show that,compared to some state-of-the-art schemes,the proposed scheme has a superior performance in terms of embedding capacity,while keeping file size preservation and format compatibility.

关键词： Joint Photographic Experts Group(JPEG) Reversible data hiding Embedding capacity File size preservation Format compatibility

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