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Biosignal Contrastive Representation Learning for Emotion Recognition of Game Users

IEEE Transactions on Games 2024年第2期17卷 1-15页

作者： Li, Rongyang Ding, Jianguo Ning, Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing China Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels from game users. However, unsupervised BRL in ERGU faces challenges, including overfitting caused by limited data and performance degradation due to unbalanced sample distributions. Faced with the above challenges, we propose a novel method of biosignal contrastive representation learning (BCRL) for ERGU, which not only serves as a unified representation learning approach applicable to various modalities of biosignals but also derives generalized biosignals representations suitable for different downstream tasks. Specifically, we solve the overfitting by introducing perturbations at the embedding layer based on the projected gradient descent (PGD) adversarial attacks and develop the sample balancing strategy (SBS) to mitigate the negative impact of the unbalanced sample on the performance. Further, we have conducted comprehensive validation experiments on the public dataset, yielding the following key observations: 1) BCRL outperforms all other methods, achieving average accuracies of 76.67%, 71.83%, and 63.58% in 1D-2C Valence, 1D-2C Arousal and 2D-4C Valence/Arousal, respectively;2) The ablation study shows that both the PGD module (+7.58% in accuracy on average) and the SBS module (+14.60% in accuracy on average) have a positive effect on the performance of different classifications;3) BCRL model exhibits the certain generalization ability across the different games, subjects and classifiers. IEEE

关键词： Emotion Recognition

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Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

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computers, Materials & Continua 2023年第6期75卷 6161-6184页

作者： Zhen Wang Qiu-yu Zhang Ling-tao Meng Yi-lin Liu School of Computer and Communication Lanzhou University of TechnologyLanzhou730050China

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval *** research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption ***,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep ***,the central similarity is used to quantify and construct the deep hash sequence of *** Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable ***,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted *** experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user *** retrieval accuracy is improved by at least 37%compared to traditional hashing *** the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.

关键词： Content-based image retrieval deep supervised hashing central similarity quantification searchable encryption Paillier homomorphic encryption

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An Efficient Encrypted Speech Retrieval Based on Unsupervised Hashing and B+ Tree Dynamic Index

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computers, Materials & Continua 2023年第7期76卷 107-128页

作者： Qiu-yu Zhang Yu-gui Jia Fang-Peng Li Le-Tian Fan School of Computer and Communication Lanzhou University of TechnologyLanzhou730050China

Existing speech retrieval systems are frequently confronted with expanding volumes of speech *** dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’real-time retrieval *** study proposes an efficient method for retrieving encryption speech,using unsupervised deep hashing and B+ tree dynamic index,which avoid privacy leak-age of speech data and enhance the accuracy and efficiency of *** cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan(DGHV)Fully Homomorphic Encryption(FHE)technique,which encrypts the original *** addition,this research employs Residual Neural Network18-Gated Recurrent Unit(ResNet18-GRU),which is used to learn the compact binary hash codes,store binary hash codes in the designed B+tree index table,and create a mapping relation of one to one between the binary hash codes and the corresponding encrypted *** B+tree index technology is applied to achieve dynamic index updating of the B+tree index table,thereby satisfying users’needs for real-time *** experimental results on THCHS-30 and TIMIT showed that the retrieval accuracy of the proposed method is more than 95.84%compared to the existing unsupervised hashing *** retrieval efficiency is greatly *** to the method of using hash index tables,and the speech data’s security is effectively guaranteed.

关键词： Encrypted speech retrieval unsupervised deep hashing learning to hash B+tree dynamic index DGHV fully homomorphic encryption

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Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors

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Chinese Journal of Electronics 2024年第6期33卷 1515-1525页

作者： Anhua MA Su PAN Weiwei ZHOU Broadband Wireless Communication College Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing Tech University

This paper proposes a Markov decision process based service migration algorithm to satisfy quality of service(QoS) requirements when the terminals leave the original server. Services were divided into real-time services and non-real-time services, each type of them has different requirements on transmission bandwidth and latency,which were considered in the revenue function. Different values were assigned to the weight coefficients of QoS parameters for different service types in the revenue and cost functions so as to distinguish the differences between the two service types. The overall revenue was used for migration decisions, rather than fixed threshold or instant *** Markov decision process was used to maximize the overall revenue of the system. Simulation results show that the proposed algorithm obtained more revenue compared with the existing works.

关键词： Energy consumption Costs Markov decision processes Simulation Quality of service Market research Cost function Real-time systems Energy efficiency Servers

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Central Tendency Feature Selection (CTFS): a novel approach for efficient and effective feature selection in intrusion detection systems

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Multimedia Tools and Applications 2025年 1-22页

作者： Bouke, Mohamed Aly Abdullah, Azizol Udzir, Nur Izura Samian, Normalia Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 Serdang Malaysia

In the digital era, the escalation of data generation and cyber threats has heightened the importance of network security. Machine Learning-based Intrusion Detection Systems (IDS) play a crucial role in combating these threats, yet there is a significant gap in their feature selection methodologies. These traditional methods often fail to handle large datasets, leading to performance limitations efficiently. To advance this crucial aspect of IDS, we propose the novel Central Tendency-Based Feature Selection (CTFS) method. Our empirical investigation, utilizing the widely acknowledged UNSWNB15 and NSLKDD cybersecurity datasets, reveals that CTFS not only outperforms established feature selection methods like Univariate Selection, Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Gini Index in terms of accuracy, achieving up to 95%, and recall, reaching up to 99%, but it also substantially enhances execution efficiency. Specifically, CTFS demonstrates a remarkable reduction in feature selection time to approximately 0.1 s for UNSWNB15 and 0.026 s for NSLKDD, outmatching the more time-intensive RFE, which recorded 569.7 s and 24.9 s, respectively. These findings showcase CTFS as a potent solution, balancing high performance with computational efficiency, a key consideration in time-sensitive security applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cybersecurity

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An Energy Distribution Correlation Judgment Method for Interrupted Sampling Repeater Jamming Suppression

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Progress In Electromagnetics Research B 2024年 105卷 59-78页

作者： Li, Ji Su, Fan Wang, Wei Yan, Rui Li, Jialiang College of Computer and Communication Engineering Changsha University of Science & Technology Changsha410000 China

Interrupted Sampling Repeater Jamming (ISRJ) can produce several false targets through intermittent sampling and forwarding of the intercepted signals. The paper proposes an interference identification and suppression method based on Short-Time Fourier Transform-Energy Distribution Correlation Judgment (STFT-EDCJ) to lessen the impact of the false targets mixed in echo pulses. Firstly, the method obtains the energy distribution of echoes in the time-frequency domain employing the short-time Fourier transform, extracts the time slice of higher energy targets through energy peak detection, and then calculates the Pearson correlation coefficient (PCC) of the energy distribution in the frequency domain of each target time slice to construct the Target PCC Datasets (TPCCD). Secondly, it distinguishes between the real target and false targets after echo pulse pressure by the range and specificity of TPCCD. Finally, it uses mapping the time domain position of the false targets to suppress interference. The abundant simulation results verify the proposed method’s effectiveness, and the Monte Carlo simulation demonstrates the method’s effectiveness under ISRJ models. © (2023), (Electromagnetics Academy). All Rights Reserved.

关键词： Monte Carlo methods

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Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 405-414页

作者： Hao Liu Fengwei Liang Tianyu Hu Jichao Hong Huimin Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Mechanical Engineering University of Science and Technology BeijingBeijingChina IEEE

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

关键词： Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Local-global dynamic correlations based spatial-temporal convolutional network for traffic flow forecasting

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High technology Letters 2024年第4期30卷 370-379页

作者： ZHANG Hong GONG Lei ZHAO Tianxin ZHANG Xijun WANG Hongyan College of Computer and Communication Lanzhou University of TechnologyLanzhou 730050P.R.China

Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional *** model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of *** experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.

关键词： traffic flow forecasting graph convolutional network(GCN) temporal convolu-tional network(TCN) attention mechanism(AM)

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Robust federated contrastive recommender system against targeted model poisoning attack

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Science China(Information Sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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