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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Jin, Xin Chen, Shiyu Nanjing University of Science and Technology School of Mechanical Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is everywhere in people's lives. It controls users' activities in social networks. Understanding the mechanism of interactions between users in social networks can benefit various applications, such as recommendations. Traditional recommendation systems based on collaborative filtering suppose that the behaviors of users are independent of each other and ignore the influence relationships among users. However, different historical behaviors and social relationships of users result in different influences among users. First, the complete user behavior sequences are fed into a recurrent neural network to capture long term patterns of user interest in items. Secondly, a self-attention network is used to obtain short lived interests of users. Then the outputs of the recurrent neural network as queries are fed into another self-attention network to obtain user representations that combine transient effect of and long term patterns of users' interests, thereby predicting the ratings of users to items. Finally, the proposed recommendation system is used to recommend products that need to be promoted to the maximum number of users. The proposed method is verified to outperform the state-of-the-art algorithms on three real-world datasets. © 2024 IEEE.

关键词： Chatbots

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Virtual Reality Applications for Social Anxiety Treatment: A Comprehensive Literature Review 2

Virtual Reality Applications for Social Anxiety Treatment: A...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Agarwal, Prerna Arya, Pradeep Kumar Gupta, Bharat Shrivastava, Pranav Tuli, Kareena Hussain, Saquib School of Computer Science Engineering and Technology Bennett University UP Greater Noida201310 India Department of Computer Science & Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9798331504106

Social anxiety is a psychological disorder distinguished by a profound apprehension of social environments, leading to irrational anxiety among individuals. This excessive fear of social situations affects a considerable number of young people worldwide. This comprehensive literature analysis investigates the remedy of social anxiety with help of virtual reality (VR) in. It explores the extent and limitations of gamifying treatment solutions for social anxiety. This approach enables individuals to engage in social exposure therapy conveniently. It assists in enhancing their home-based exercise routine and reducing the likelihood of relapses. A detailed assessment of the frequency of social anxiety disorder, common therapeutic approaches, and the limitations associated with these conventional treatments is provided. Subsequently, the prospective applications of VR-based exposure therapy to improve social competencies among individuals suffering from social anxiety are detailed. The review places particular emphasis on the Indian demographic. This focus aims to address the distinctive challenges and prospects within this population due to the extensive cultural diversity. ©2024 IEEE.

关键词： Virtual environments

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Deep Learning Approach to Emotion Recognition by Facial Expressions: A Review Paper 5

Deep Learning Approach to Emotion Recognition by Facial Expr...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Shubhanshi Shukla, Shipra Amity School Of Engineering And Technology Computer Science And Engineering Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330861

Emotion recognition by facial expression is a challenging task that has gotten much attention in recent years. Deep neural networks are used to extract pertinent information from facial photographs and categorise them into various emotional categories in deep learning approaches, which have proven their potential in this area The article examines the most recent advances in deep learning for recognising emotions from facial expressions. The model architecture and training methods are explored, and their effectiveness on benchmark datasets is assessed. Also discuss the challenges and prospects for this field, including the need for large and diverse datasets, the interpretability of models, and the requirement to eliminate any bias in training data. Overall, deep learning algorithms for emotion recognition using facial expressions have shown considerable promise and can be applied in a variety of applications, such as virtual assistants, entertainment, and healthcare. © 2023 IEEE.

关键词： Emotion Recognition

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Automatic Motorcyclist Helmet Rule Violation Detection using YOLOv5

Automatic Motorcyclist Helmet Rule Violation Detection using...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Chheda, Meet Kapoor, Nupur Desai, Khushi Gupta, Rajeev Kumar School of Technology Computer Engineering Pandit Deendayal Energy University Gandhinagar India

ISBN: (纸本)9798350374933

Road traffic injuries rank among the leading causes of death worldwide, with motorcyclists accounting for a significant proportion of these fatalities. According to the World Health Organization, helmets are effective in reducing the risk of head injuries by approximately 69% and fatalities by about 42%. Despite these benefits, compliance with helmet laws remains inconsistent, underscoring the need for enhanced enforcement strategies. This paper presents the development of an Automatic Motorcyclist Helmet Rule Violation Detection system that employs the advanced YOLOv5 model to address this challenge. The system is designed to detect motorcyclists without helmets in real-time and simultaneously capture their vehicle number plates, thus aiding law enforcement in the effective enforcement of helmet laws. By automating the detection and reporting process, the system reduces the reliance on manual monitoring and helps ensure more consistent and unbiased enforcement across various road conditions and traffic volumes. Additionally, the real-time functionality of the system allows for immediate action to be taken against violators, potentially deterring future non-compliance and fostering a culture of safety among riders. Through this technology-driven approach, our project contributes to ongoing efforts in leveraging cutting-edge technology to improve road safety and reduce the incidence of motorcycle-related accidents and injuries, aligning with global initiatives for safer roads. © 2024 IEEE.

关键词： Highway accidents

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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

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Neural Computing and Applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Blockchain-Based Trust Model for Inter-Domain Routing

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computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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Biological Template Protection Based on Multikey Cryptographic Decomposition Features 9

Biological Template Protection Based on Multikey Cryptograph...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Wang, Jing Jiang, Haowen Yang, Yuqi Xin, Rundong University of Science and Technology School of Computer and Communication Engineering School of Civil Engineering Changsha410114 China University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798350383706

As more and more privacy protection mechanisms are introduced, facial recognition is gradually being optimized in terms of homo-morphic computing power. However, certain aspects have been overlooked, such as the reversibility of facial features due to key leakage and the degradation of accuracy when keys are exchanged to update the template library. To address this problem, we propose a feature grouping method that aims to improve the efficiency of ciphertext operation. This approach involves a multi-key encryption strategy for facial features that ensures their irreversibility. Experimental validation supports both the irreversibility and the ability to update within this framework. When rigorously tested, this scheme shows increased security compared to single-key frameworks because part of the key is withheld, making it difficult for attackers to obtain comprehensive facial feature data. In addition, even when using keys of the same length, attackers are faced with increased time expenditure. With regard to the upgradability of systems, experimental findings have shown for the first time that the loss of accuracy associated with homomorphic encryption algorithms is within tolerable limits. It is noteworthy that our proposed method has an even smaller loss in accuracy. When assessing the performance and precision of the algorithm, we achieved an impressive accuracy rate of 97.425% with processing times of 0.302 seconds per face. The benefits of our feature grouping method become more clear as the face template library grows. It outperforms non-grouping methods by more than 4 seconds on nearly 1,000 face libraries. © 2023 IEEE.

关键词： Face recognition

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Wi-SFDAGR: WiFi-Based Cross-Domain Gesture Recognition via Source-Free Domain Adaptation

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IEEE Internet of Things Journal 2025年第13期12卷 24159-24173页

作者： Yan, Huan Zhang, Xiang Huang, Jinyang Feng, Yuanhao Li, Meng Wang, Anzhi Ou, Weihua Wang, Hongbing Liu, Zhi Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

WiFi Channel State Information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors such as data privacy protection hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this paper, we propose Wi-SFDAGR, a WiFi-based Source-free Domain Adaptation Gesture Recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods. © 2014 IEEE.

关键词： Differential privacy

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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Efficient Authentication System Using Wavelet Embeddings of Otoacoustic Emission Signals

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computer Systems Science & engineering 2023年第5期45卷 1851-1867页

作者： V.Harshini T.Dhanwin A.Shahina N.Safiyyah A.Nayeemulla Khan Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security *** our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by the human cochlea in response to an external sound stimulus,as a biometric *** are robust to falsification attacks,as the uniqueness of an individual’s inner ear cannot be *** this study,we use both the raw 1D TEOAE signals,as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform(CWT).We use 1D and 2D Convolutional Neural Networks(CNN)for the former and latter,respectively,to derive the feature *** corresponding lower-dimensional feature maps are obtained using principal component analysis,which is then used as features to build classifiers using machine learning techniques for the task of person identification.T-SNE plots of these feature maps show that they discriminate well among the *** the various architectures explored,we achieve a best-performing accuracy of 98.95%and 100%using the feature maps of the 1D-CNN and 2D-CNN,respectively,with the latter performance being an improvement over all the earlier *** performance makes the TEOAE based person identification systems deployable in real-world situations,along with the added advantage of robustness to falsification attacks.

关键词： Person identification system cochlea:transient evoked otoacoustic emission wavelet transform convolutional neural network

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