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International Conference on Electronic Engineering and Informatics (EEI)

作者： Jinlan Zheng Ruiqi Zhou Xingxing Ju Xin Han Dianwei Wang Key Laboratories of Sensing and Application of Intelligent Optoelectronic System in Sichuan Provincial Universities Sichuan University of Arts and Science Dazhou China College of Electronics and Information Engineering Sichuan University Chengdu China College of Mathematics Sichuan University of Arts and Science Dazhou Sichuan China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an Shaanxi China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In order to address the mixed variational inequalities, this study proposes a proximal neurodynamic technique with predefined-time stability. Our approach incorporates adjustable parameters, differentiating it from traditional fixed-time stability methods and increasing its versatility and applicability. Unlike fixed - time and finite-time stability techniques, the proposed method demonstrates the ability to reach a unique solution within a predetermined time limit. The effectiveness and feasibility of the proposed approach are verified through simulations, confirming the theoretical derivations.

关键词： Neurodynamics Stability analysis Hardware Informatics Optimization

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The Power of Bamboo: On the Post-Compromise security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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YTCNet: A Real-time Algorithm for Parcel Damage Detection with Rich Features and Attention

YTCNet: A Real-time Algorithm for Parcel Damage Detection wi...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhi Chen Cuifeng Du Yuyu Zhou Haoxuan Guan Xiujie Huang Zhefu Li Changjiang Liu Xiaotian Zhuang Xingyu Zhu Quanlong Guan Jinan University Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory China Cetc Potevio Science & Technology Co. Ltd Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving China Guangdong Testing Institute of Product Quality Supervision China Beijing JD Zhenshi Information Technology Co. Ltd Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this study, we tackle the challenge of parcel damage detection and present YTCNet to improve both accuracy and real-time performance. We enhance the Yolov5 algorithm by integrating C3TR modules into the architecture to capture more comprehensive feature information. Furthermore, we introduce the CBAM attention module to focus on critical areas in parcel images, enhancing the model’s feature extraction capabilities. By effectively combining C3TR and CBAM modules, we successfully developed an efficient parcel damage detection algorithm. Our experimental results demonstrate the superior performance of YTCNet in accuracy and real-time processing compared to various object detection models across multiple parcel damage scenarios. Our research significantly contributes to the logistics supply chain industry by aligning with IoT development. It enables practical applications to more effectively identify parcel damage, thereby enhancing customer satisfaction.

关键词： YOLO Industries Accuracy Supply chains Transportation Feature extraction Real-time systems

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Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

arXiv

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing data processing Vectors Space exploration Resource management Speech processing Mutual information

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Hard Sample Matters a Lot in Zero-Shot Quantization

Hard Sample Matters a Lot in Zero-Shot Quantization

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Huantong Li Xiangmiao Wu Fanbing Lv Daihai Liao Thomas H. Li Yonggang Zhang Bo Han Mingkui Tan South China University of Technology Information Technology R&D Innovation Center of Peking University Changsha Hisense Intelligent System Research Institute Co. Ltd School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Hong Kong Baptist University Key Laboratory of Big Data and Intelligent Robot Ministry of Education PengCheng Laboratory

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data.

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An autoencoder-like nonnegative matrix co-factorization for improved student cognitive modeling 24

An autoencoder-like nonnegative matrix co-factorization for ...

引用

Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Shenbao Yu Yinghui Pan Yifeng Zeng Prashant Doshi Guoquan Liu Kim-Leng Poh Mingwei Lin College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University UK Intelligent Thought and Action Lab School of Computing University of Georgia Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)9798331314385

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

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Efficient Adaptive Label Refinement for label noise learning

引用

Neurocomputing 2025年 639卷

作者： Zhang, Wenzhen Cheng, Debo Lu, Guangquan Zhou, Bo Li, Jiaye Zhang, Shichao School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China School of Computer Science and Technology Hainan University Hainan Haikou570228 China Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Guangxi Hechi546300 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China

Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing complex manually designed strategies, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in models that are overly sensitive to hyperparameters and challenging to train. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples, proposing a simple yet highly applicable method called Adaptive Label Refinement (ALR). First, inspired by label refurbishment techniques, we update the original hard labels to soft labels using the model's predictions to reduce the risk of fitting incorrect labels. Then, by introducing entropy loss, the model adaptively ’harden’ the high-confidence soft labels based on the confidence differences, guiding the model to focus more on learning from clean samples. This approach is simple and efficient, requiring no auxiliary datasets and minimal hyperparameter tuning, making it more adaptable than existing methods that require careful adjustments for different datasets. We validate ALR's effectiveness through experiments on benchmark datasets with artificial label noise (CIFAR-10/100) and real-world datasets with inherent noise (ANIMAL-10N, Clothing1M, WebVision). The results show that ALR outperforms state-of-the-art methods. © 2025

关键词： Deep neural networks

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Minimum Time Extrema Estimation for Large-Scale Radio-Frequency Identification systems

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Journal of Computer Science & Technology 2020年第5期35卷 1099-1114页

作者： Xiao-Jun Zhu Li-Jie Xu Xiao-Bing Wu Bing Chen College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Jiangsu Key Laboratory of Dig Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210049China Wireless Research Centre University of CanterburyChristchurch 8041New Zealand

We consider the extrema estimation problem in large-scale radio-frequency identification(RFID)systems,where there are thousands of tags and each tag contains a finite *** objective is to design an extrema estimation protocol with the minimum execution *** the standard binary search protocol wastes much time due to inter-frame overhead,we propose a parameterized protocol and treat the number of slots in a frame as an unknown *** formulate the problem and show how to find the best parameter to minimize the worst-case execution ***,we propose two rules to further reduce the execution *** first is to find and remove redundant *** second is to concatenate a frame from minimum value estimation with a frame from maximum value estimation to reduce the total number of *** show that,in a typical scenario,the proposed protocol reduces execution time by 79%compared with the standard binary search protocol.

关键词： radio-frequency identification(RFID)system maximum value estimation minimum value estimation time efficient protocol

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Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

引用

arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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