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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Hu, Min Gao, Dongming Wang, Xiaohua Tang, Yiming Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373820

Remote Photoplethysmography (rPPG) is a technique for remotely measuring physiological signals such as (HR). Compared to traditional contact-based measurements, HR estimation based on video recording of human face offers significant convenience and wide-ranging application prospects. This paper proposes an end-to-end model named VSTCFANet. Firstly, a video segment attention module (VSA) is constructed to mitigate redundancy in facial videos. The facial videos are divided into multiple subsegments and passed through corresponding subpaths to extract spatial and temporal information. Secondly, a time-channel frequency attention module (TCFA) is designed to extract features in the channel frequency domain. The channel frequency domain features of the video segments are aggregated to enhance the model's robustness, thereby reducing the effection caused by changes in distance and head movements. Additionally, an emotion recognition module is built to utilize the obtained rPPG signals to identify the emotion of experimental subjects. Lastly, two loss functions are employed to mitigate the interference from other physiological signals on the rPPG signals. Extensive experiments are conducted on the PURE, COHFACE, and MAHNOB-HCI datasets to validate the effectiveness of the proposed model, the experimental results show that the proposed model has made significantly better performances in measurement accuracy than the state-of-the-art baselines. © 2024 IEEE.

关键词： Emotion Recognition

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Research on network traffic intrusion detection based on GAN 9

Research on network traffic intrusion detection based on GAN

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Xi, Chiming Wang, Hui Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai China

ISBN: (纸本)9798350363043

With the rapid growth of computer network devices around the world, network threats are increasing, and securing the network environment has become an urgent problem. However, the current network traffic dataset is generally characterized by unbalanced data distribution, in which certain anomalous traffic types have a relatively small number of samples, which causes the detection model to ignore traffic types with a small number of samples, thus reducing the overall accuracy of the model. To address this problem, this paper proposes an improved intrusion detection method based on generative adversarial networks (GAN). By designing a regularized bi-discriminator structure to improve the generation quality of the generator. In addition, a random mask enhancement module is integrated after the generator to supplement the detailed information of data features. Our designed GAN model is applied to Support Vector Machine (SVM) and Long Short-Term Memory Network (LSTM), the experiment results show that it significantly improves the detection accuracy. © 2024 IEEE.

关键词： Generative adversarial networks

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Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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Multidimensional daily sleep monitoring system based on IoT technology 4

Multidimensional daily sleep monitoring system based on IoT ...

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4th International Conference on Green Communication, Network, and Internet of Things, CNIoT 2024

作者： Liu, Wenfeng Fan, Zhicheng Xu, Zhuanjia Deng, Fuliang Wang, Wei Yang, Mengdie Li, Lanhui School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510685710

In today’s fast-paced, high-pressure society, sleep disorders are increasingly prevalent. Long-term, daily sleep monitoring is essential for maintaining human health. To achieve multidimensional, convenient, and reliable sleep monitoring, this study introduces a novel IoT-based system designed for long-term tracking of individual sleep patterns. The system captures data across three dimensions-sleep environment, objective sleep data, and subjective sleep experiences-using environmental sensors, wearable devices, and a smartphone app for self-reporting. Data is processed, stored, managed, and visualized via a cloud platform. Field experiments with volunteers were conducted to validate the system’s effectiveness, demonstrating its precision and stability. The system enables comprehensive and long-term sleep monitoring, tracks nightly changes in user sleep experiences, and identifies environmental factors strongly associated with sleep depth. This provides critical insights for improving sleep quality and highlights the system’s broad application potential. © 2024 SPIE.

关键词： Sleep research

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Video Based Cricket Shot Classification using modified LRCN Approach

Video Based Cricket Shot Classification using modified LRCN ...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Krithiga, R. Ramesh, Balaji Sriraam, C. Lionel Donato, L. Rojesh, S. School of Computer Science and Engineering Vellore Institute of Technology TamilNadu Chennai India

ISBN: (纸本)9798331540821

In recent years, advancements in Artificial Intelligence (AI) have caused major transformations in the realm of cricket analysis. From player performance evaluation to strategic decision-making, AI technologies have revolutionized how cricket is understood and played. In this paper, we focus on the task of classifying cricket shots using video data, leveraging deep learning methodologies. The proposed model aims to classify cricket videos into five distinct shot categories, utilizing the CricShot10 dataset originally comprising ten shot classes. With approximately 100-150 video clips per shot type, we streamline the classification task by selecting five fundamental shots for analysis. To accomplish this, we propose a modified architecture of the popular architecture combining Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, commonly referred to as Long-Term Recurrent Convolutional Network (LRCN). Additionally, we integrate techniques such as MaxPooling2D, Dropout, Flatten, and Dense layers to enhance the model's performance and robustness. Through this proposal, we not only contribute to the field of cricket analysis but also demonstrate the efficacy of deep learning approaches in video-based sports analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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Text Watermarking-Based Federated Learning Backdoor Attack Detection Scheme 6

Text Watermarking-Based Federated Learning Backdoor Attack D...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Zuo, Yiming Yang, Zheng Chen, Dongdong Gu, Ke Jiang, Luyao Changsha University of Science & Technology School of Computer and Communication Engineering ChangSha China

ISBN: (纸本)9798350388374

With the widespread application of federated learning across various domains, backdoor attacks pose a serious threat to the security of models. In this paper, we proposes a text watermarking-based federated learning backdoor attack detection scheme. The core idea of the scheme is to use the aggregation server to embed and verify the text watermark in the process of global model aggregation, so as to achieve tracking and security protection of the global model. Secondly, in order to deal with the situation that malicious attackers may forge text watermarks, we also deployed a backdoor attack detection and defense based on local gradient symbols on the aggregation server. The scheme detects the gradient information uploaded by the local device, and only when the gradient symbols of most local models are consistent, can it be included in the aggregation process of the global model to ensure the security of the model. Finally, the experimental results verify the effectiveness of this method. © 2024 IEEE.

关键词： Federated learning

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A Unimodal Valence-Arousal Driven Contrastive Learning Framework for Multimodal Multi-Label Emotion Recognition 24

A Unimodal Valence-Arousal Driven Contrastive Learning Frame...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Wenjie Yu, Jianfei Xia, Rui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400706868

Multimodal Multi-Label Emotion Recognition (MMER) aims to identify one or more emotion categories expressed by an utterance of a speaker. Despite obtaining promising results, previous studies on MMER represent each emotion category using a one-hot vector and ignore the intrinsic relations between emotions. Moreover, existing works mainly learn the unimodal representation based on the multimodal supervision signal of a single sample, failing to explicitly capture the unique emotional state of each modality as well as its emotional correlation between samples. To overcome these issues, we propose a Unimodal Valence-Arousal driven contrastive learning framework (UniVA) for the MMER task. Specifically, we adopt the valence-arousal (VA) space to represent each emotion category and regard the emotion correlation in the VA space as priors to learn the emotion category representation. Moreover, we employ pre-trained unimodal VA models to obtain the VA scores for each modality of the training samples, and then leverage the VA scores to construct positive and negative samples, followed by applying supervised contrastive learning to learn the VA-aware unimodal representations for multi-label emotion prediction. Experimental results on two benchmark datasets MOSEI and M3ED show that the proposed UniVA framework consistently outperforms a number of existing methods for the MMER task. The source code is publicly released at https://***/NUSTM/UniVA. © 2024 ACM.

关键词： Contrastive Learning

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MAPPO based Single-hop Task Offloading with Dynamic Vehicle Prediction in RSU-assisted IoV 10

MAPPO based Single-hop Task Offloading with Dynamic Vehicle ...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhao, Wei Yang, Dongling Weng, Tangjie Xu, Xinwei Shao, Xun Liu, Zhi Anhui University of Technology School of Computer Science and Technology China Anhui University of Technology School of Management Science and Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan The University of Electro-Communications Department of Computer and Network Engineering Japan

ISBN: (纸本)9798331520861

In the realm of Internet of Vehicles (IoV), Roadside Units (RSUs) play a vital role due to their advanced sensing and computing capabilities. They support Mobile Edge Computing (MEC) by enabling real-time data processing and reducing latency and energy consumption. Given the large volume of data and the limited computing capacity of RSUs, tasks are often offloaded to other nodes, such as the cloud, other RSUs, or vehicles. However, due to the environment dynamic caused by vehicle mobility, the set of neighboring nodes for each RSU frequently changes, and variations in neighboring nodes are not perceived timely. In such cases, RSUs need to predict changes in neighboring nodes to ensure effective task offloading. Moreover, since each RSU can only perceive the surrounding environment, it is difficult for a single node to make optimal offloading decisions for each task independently. To address these issues, this paper proposes a distributed task offloading scheme that incorporates vehicle trajectory prediction. First, each RSU uses the vehicle trajectories predicted by the Gated Recurrent Unit (GRU) model to determine neighboring vehicles and update the set of neighboring nodes. Then, to enable each RSU to select a suitable computing node and minimize computation delay and energy consumption for the task, we adopt a distributed approach where each RSU independently makes offloading decisions based on local observations. To achieve this goal, we establish a 01 mathematical model and transform it into a Markov Decision Process (MDP). Subsequently, we propose a solution based on Multi-Agent Proximal Policy Optimization (MAPPO). Through extensive simulation experiments, we demonstrate that the scheme effectively reduces both long-term average processing delay and energy consumption. © 2024 IEEE.

关键词： MAPPO neighboring vehicle prediction RSU task offloading

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