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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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CHAR: Composite Head-body Activities Recognition with A Single Earable Device

CHAR: Composite Head-body Activities Recognition with A Sing...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Peizhao Zhu Yongpan Zou Wenyuan Li Kaishun Wu College of Computer Science and Software engineering Shenzhen University

The increasing popularity of earable devices stimulates great academic interest to design novel head gesture-based interaction technologies. But existing works simply consider it as a singular activity recognition problem. This is not in line with practice since users may have different body movements such as walking and jogging along with head gestures. It is also beneficial to recognize body movements during human-device interaction since it provides useful context information. As a result, it is significant to recognize such composite activities in which actions of different body parts happen simultaneously. In this paper, we propose a system called CHAR to recognize composite head-body activities with a single IMU sensor. The key idea of our solution is to make use of the inter-correlation of different activities and design a multi-task learning network to extract shared and specific representations. We implement a real-time prototype and conduct extensive experiments to evaluate it. The results show that CHAR can recognize 60 kinds of composite activities (12 head gestures and 5 body movements) with high accuracies of 97.0% and 89.7% in user- dependent and independent cases, respectively.

关键词： Pervasive computing Performance evaluation Legged locomotion Headphones Prototypes Activity recognition Multitasking

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Improved Time-Saving Face Detection in Video 9

Improved Time-Saving Face Detection in Video

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9th International engineering Conference on Sustainable Technology and Development, IEC 2023

作者： Qadir, Jihad Anwar Jalal, Shayan Ihsan Jameel, Samer Kais Al-Talabani, Abdulbasit University of Raparin College of Basic Education Department of Computer Science Kurdistan Region Rania Iraq Koya University Faculty of Engineering Department of Software Engineering Kurdistan Region Koya Iraq

ISBN: (纸本)9798350335064

Human face detection from video sequences is a difficult problem in computer vision. Face detection is the process of determining the location of a face or faces in each frame of a video. Face detection in real-time videos is an ongoing under-investigated research problem. This paper proposes a new method for face detection in videos that significantly reduces the time required to detect faces. The method targets a Search Area Boundary (SAB) in the next frames that are determined based on the position of the detected faces in the current frame. The model will look for the face in a box that is slightly larger than the SAB from the previous frame. This means that it will not search the entire video frame for the face, so it will take less time to find the face. After a few frames, the method will begin searching the entire frame for new faces. The results show that the proposed method can save up to 73.5% of the time, especially when the SAB is small, with an accuracy degradation of no more than 7% in the best case. © 2023 IEEE.

关键词： Face recognition

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Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations 2

Interpretable Pulmonary Disease Diagnosis with Graph Neural ...

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2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2023

作者： Li, Jiahong Chen, Yiyuan Wang, Yichi Ye, Yiqiang Sun, Min Ren, Hao Cheng, Weibin Zhang, Haodi College of Computer and Software Engineering Guangzhou China Cornell University Dept. Computer Science New York United States Shenzhen University College of Computer and Software Engineering Shenzhen China China Mobile Information Technology Co. Limited Shenzhen China Guangdong Second Provincial General Hospital Institute for Healthcare Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350373257

With recent fast development of artificial intelligence and related techniques, computer-aided diagnosis is increasingly emerging. During the process of automated diagnosis, the importance of explainable decision making has grown significantly. Traditional black-box models have often hindered the practical implementation of automated diagnosis algorithms. To address these challenges, we propose a novel framework that seamlessly integrates prototype learning with Graph Neural Networks (GNN) while also providing post-hoc explanations. On one hand, our model achieves intrinsic interpretability by reasoning based on the similarity calculations with prototypes for each disease. On the other hand, it employs counterfactual reasoning on graphs to pinpoint the most significant features for post-hoc explanations, supporting the diagnosis process. Building on these strengths, we integrate vision-language models to effectively harness multimodal patient information, thereby capturing a more detailed understanding of their medical condition. Our experiments on real Chinese EMRs of Pulmonary diseases demonstrate that our method not only delivers precise diagnoses but also accurately identifies medical findings substantiating the diagnoses. © 2023 IEEE.

关键词： Pulmonary diseases

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Unraveling user perceptions and biases: A comparative study of ML and DL models for exploring twitter sentiments towards ChatGPT

Journal of Engineering Research

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Journal of engineering Research 2025年第2期13卷 1658-1665页

作者： Mohammed Rashad Baker Anil Utku Software Department College of Computer Science and Information Technology University of Kirkuk 36001 Kirkuk Iraq Department of Computer Engineering Faculty of Engineering Munzur University 62100 Tunceli Turkey

ChatGPT is a powerful chatbot that used to generate human-like text. Chat GPT, developed by OpenAI, can answer many questions honestly, like a personal teacher who knows almost everything. It can perform various tasks such as question and answer, solving mathematical equations, writing text, debugging, and translating between languages. Compared to traditional chatbots, ChatGPT has been adopted by many users since its introduction. Some users feel that ChatGPT will override most content creation professions. This study analyzed users’ feelings about ChatGPT by analyzing tweets shared about ChatGPT. For this purpose, a hybrid Deep Learning (DL) model was developed using Convolutional Neural Networks (CNN) and Bi- Long Short-Term Memory (Bi-LSTM) models. The study has been compared with a number of DL and Machine Learning algorithms, LSTM, Bi-LSTM, CNN, Gated Recurrent Unit, Random Forests and Support Vector Machines. The experimental outcomes demonstrated that, the FastText-trained CNN-Bi-LSTM model exceeded the other models in terms of accuracy, reaching 96.59%.

关键词： Sentiment Analysis ChatGPT Deep Learning CNN Bi-LSTM

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Time Series Fusion Model for Predicting Electric power tools and equipment 6

Time Series Fusion Model for Predicting Electric power tools...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Xue, Liang Ling, Xinghong Yang, Lei Yang, Yang Computing Science and Artificial Intelligence College Suzhou City University Suzhou China College of Computer Science and Software Engineering Hohai University Nanjing China Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare Suzhou City University Suzhou China

ISBN: (纸本)9798331541699

To effectively address the issue of inadequate accuracy and timeliness in industrial data classification, this paper proposes a fusion classification model based on time series (DT-RF-MLP) for loss prediction of electrical work equipment. Firstly, data information is extracted from the dataset and features are constructed based on the data information to enhance the diversity of data features. Secondly, redundant and highly correlated features are removed by quantifying the correlation degree between the features to simplify the model inputs. On this basis, a fusion classification model is constructed and comparative experiments are conducted with a single model to validate the advantages of the fusion model in terms of prediction performance. The experimental results demonstrate that the model markedly enhances prediction accuracy while concurrently reducing the duration of the model run. © 2024 IEEE.

关键词： Prediction models

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Decompose-Compose Feature Augmentation for Imbalanced Crack Recognition in Industrial Scenarios

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IEEE Transactions on Automation science and engineering 2025年

作者： Chen, Zhuangzhuang Xu, Chengqi Hu, Tao Wang, Li Chen, Jie Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China University of Southern California Department of computer science Los AngelesCA90007 United States

Automated crack recognition has achieved remarkable progress in the past decades as a critical task in structure health monitoring, to ensure safety and durability in many industrial scenarios. However, imbalanced crack recognition remains challenging due to the scarcity of crack samples and the consequential limited diversity. To resolve this, Artificial Intelligence Generated Content (AIGC) has been gradually adopted to generate synthetic data and reduce reliance on large amounts of labeled crack samples. This paper assumes that a crack sample in the feature space can be regarded as a combination of crack and background semantics. Then, the decompose-compose feature augmentation framework (DeCo) is proposed to perform crack data synthesis in the feature space by randomly composing crack and background semantic-relevant features. Specifically, the contrastive learning-based decomposing loss is proposed to enforce two encoders to separately learn crack and background semantics from crack samples with the theoretical guarantee. After that, an effective cross-instance feature union strategy is proposed to synthesize diverse crack samples by composing the crack-relevant features from a crack sample and background-relevant features across other training samples. To address the limited availability of related benchmarks, we collect INPP2022 and IRC2022 datasets from real-world applications in nuclear power plants and road pavement. Experimental results show that DeCo performs favorably against state-of-the-art competitors in imbalanced crack recognition tasks. © 2004-2012 IEEE.

关键词： Nuclear fuels

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A Deep Reinforcement Learning Perspective on Adaptive Federated Dropout 20

A Deep Reinforcement Learning Perspective on Adaptive Federa...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Sun, Tonghui Huang, Yan Xie, Zhenzhen Pang, Junjie Chen, Xingyun Cai, Zhipeng Qingdao University College of Computer Science and Technology Qingdao266000 China Kennesaw State University College of Computing and Software Engineering AtlantaGA United States Shandong University School of Computer Science and Technology Qingdao266237 China Georgia State University Department of Computer Science AtlantaGA United States

ISBN: (纸本)9798350324334

Federated learning is a distributed learning solution that achieves high-quality machine learning models while ensuring privacy and collaboration among various end devices. However, different kinds of end devices can lead to an unstable training process due to limited and dynamic communication and computation resources. Federated Dropout is an important technique for mitigating such resource bottlenecks by randomly removing a fixed percentage of activations of model components. However, most of the existing FL dropout methods cannot effectively utilize the distinguished characteristics of different clients or fail to automatically adapt to end device heterogeneity. In this paper, we thus propose Adaptive Federated Dropout with Reinforcement Learning (AFD-RL). AFD-RL employs reinforcement learning technology to adapt to the clients' unbalanced computation and communication resources and provide a personalized dropout strategy for each client. By automatically selecting suitable model components for each client in each communication round, AFD-RL achieves better accuracy with fewer communication rounds. Extensive experiments validate the effectiveness of the proposed approach in improving training efficiency and inference accuracy. © 2023 IEEE.

关键词： Reinforcement learning

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An effective fault localization approach for Verilog based on enhanced contexts

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Frontiers of computer science 2024年第5期18卷 223-225页

作者： Zhuo ZHANG Ya LI Lei XIA Jianxin XUE Jiang WU Xiaoguang MAO School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingbo 315000China No.83 Army Joint and Truma Disease Treatment Centre of PLA Xinxiang 453000China Department of Software Engineering Shanghai Second Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

New computer architecture innovationswith diverse functionalities and comprehensive features continue to emerge incessantly,resulting in a rising trend of incorporating a larger number of circuit devices into these products[1].In the case of a sophisticated and expansive integrated circuit chip,the presence of defective or malfunctioning components can significantly impact the overall performance of the *** situation may even result in costly repercussions.

关键词： Verilog computer circuit.

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Optimal Illumination Distance Metrics for Person Re-identification 21st

Optimal Illumination Distance Metrics for Person Re-identif...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Chao Wang, Zhongyuan Hu, Ruimin Wang, Xiaochen Zhou, Wen National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China College of Information Engineering Nanjing University of Finance Economics Nanjing China

ISBN: (纸本)9789819601240

Person re-identification (Re-ID) has been widely used in public security and surveillance. Due to the influence of different shooting times and locations, can lead to lighting variations in the images captured by the camera, and the lighting variations seriously affect the Re-ID performance. Existing methods utilize images under different lighting conditions to transform to uniform lighting conditions to eliminate the effect of lighting on Re-ID. However, these methods ignore the fact that different illumination metric distances have different recognition performances. Therefore, we propose the optimal illumination distance metric, by analyzing different light distances for pedestrians in different light conditions, we find that there exists an optimal light condition in which the distance between image pairs is smaller than the distance in other light conditions. The distance under the optimal lighting condition is used in the retrieval process as the distance between its final sorted image pairs. To verify the effectiveness of our method, Market-1501-IA and Duke-MTMC-IA datasets are constructed, and a large number of experiments are conducted under different lighting conditions to verify the existence of the optimal lighting conditions and the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting

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