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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Yan, Yang Jin, Rize Wang, Qiuyan Sun, Shimin Tianjin University of Technology and Education School of Information Technology and Engineering Tianjin China Tiangong University School of Software Tianjin China Tiangong University School of Computer Science and Technology Tianjin China

ISBN: (纸本)9798350325485

Zero correlation zone (ZCZ) sequences have been widely applied to quasi-synchronous code division multiple access system (QS-CDMA) and therefore have been extensively studied in recent years. In this paper, ZCZ sequence sets are constructed. The presented ZCZ sequence sets have the following properties: 1) each sequence in the constructed sequence set is perfect;2) each set is an optimal ZCZ sequence set with parameters (N2,N,N);3) the maximum inter-set cross-correlation of the given multiple sequence sets meets the Sarwate bound. © 2023 IEEE.

关键词： Set theory

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No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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WiHunter: Enabling Real-time Small Object Detection via Wireless Sensing 31

WiHunter: Enabling Real-time Small Object Detection via Wire...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zou, Xiang Liu, Jianwei Han, Jinsong Xi, Wei Wang, Zhi School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Technology Zhejiang University China School of Software Engineering Xi'an Jiaotong University China

ISBN: (纸本)9798350399738

Rodent infestation is a great danger to human society, continuously threatening food safety and inducing disease spread. Existing methods to deal with rodent infestation are mainly based on passive bait traps and poisoning. These methods lack timeliness and effectiveness due to the missing of real-time detection. In this paper, we develop WiHunter, a new wireless sensing system to discover small objects (e.g., rat). Our idea is to exploit reflection signal effect of wireless channels induced by the movement of small objects around the receiver antenna. However, existing wireless sensing works usually employ customized or costly device-dependency Network Interface Cards(NIC), which are impractical to be densely deployed in reality. We implement WiHunter with several CSI-enabled standalone IoT nodes. We show how such devices enable moving small object detection via WiFi signal. The rationale behind this is 1) thanks to the widespread deployments of WiFi infrastructures and IoT devices, the WiFi signal covers almost every location of the corner, 2) the signal amplitude of each device is related to the small object near the receiver antenna. This ability gives us the opportunity to sense object as small as a rat. We implement WiHunter with ESP32 microcontroller on Espressif IoT Development Framework (both of them are cheap commodity off-the-shelf (COTS) devices) and design a practical small object intrusion detection system. Comprehensive and real-world experiments demonstrate that our system is effective in detecting the presence of small objects with an average accuracy of 92.1%. © 2023 IEEE.

关键词： Rats

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An Overview of Security Threats, Attack Detection and Defense for Large-Scale Multi-Agent Systems (LSMAS) in Internet of Things (IoT)

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 70-81页

作者： Cai, Jijing Wen, Long Feng, Hailin Fang, Kai Chen, Junxin Wei, Wei Wang, Wei Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Macau University of Science and Technology School of Innovation Engineering Macau999078 China Dalian University of Technology School of Software Dalian116621 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China

With the global advancement of the Internet of Things (IoT), large-scale multi-agent systems (LSMAS) technology has been increasingly adopted across various industries. Despite the widespread use of IoT, vulnerabilities in its software and hardware components pose significant challenges to ensuring security. To address the security concerns faced by LSMAS, this article explores its application in IoT domains such as smart grids, smart manufacturing, and smart healthcare. By examining the structural layers of IoT-the perception layer, network layer, and application layer-this paper analyzes the specific security threats encountered by each layer, along with the defense strategies designed to counter these attacks. Furthermore, we categorize and compare both traditional and AI-based attack detection methods, dividing the latter into machine learning-based, deep learning-based, and transfer learning-based approaches. This analysis highlights the strengths and weaknesses of the various attack detection methods currently deployed in LSMAS, identifying the existing challenges they face. Finally, we outline potential trends for the future development of LSMAS in IoT, offering research directions for experts in related fields. © 2023 IEEE.

关键词： Multi agent systems

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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EMARec: a sequential recommendation with exponential moving average

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Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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Dynamic Multimedia Encryption Using a Parallel File System Based on Multi-Core Processors 4

Dynamic Multimedia Encryption Using a Parallel File System B...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Simi Margarat, G. Malathi, S. Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Grt Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350393354

A new parallel file system and multi-core processor-based dynamic multimedia encryption method is presented in this study. Multimedia encryption efficiency and security were the main goals, addressing massive data sets and high-speed processing. We created a dynamic encryption algorithm for multimedia content, supported by a strong parallel file system and tuned for multi-core processing. Multiple tests showed significant improvements in encryption and decryption times, retaining short durations for real-time applications. After encryption, the system maintained multimedia content quality and accuracy with data integrity rates nearing 100%. Balanced CPU utilization showed efficient resource use and processor load management. The system's excellent throughput efficiency showed its capacity to process enormous amounts of data. These results indicate that parallel file systems and multi-core CPUs may solve multimedia encryption problems. Adaptable and scalable, the system supports many multimedia formats and sizes. This paper proposes a multimedia encryption system that improves performance data security and integrity, paving the way for safe multimedia processing advances. © 2024 IEEE.

关键词： Cryptography

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GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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computer Modeling in engineering & Sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

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CardioNetFusion: A Deep Learning and Explainable AI Integrated System for Cardiovascular Disease Detection 24

CardioNetFusion: A Deep Learning and Explainable AI Integrat...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Raja, Gunasekaran Essaky, Selvam Muthu, Surya Raj Suresh, Babith Sarish Cho, Jaehyuk Yoo, Seohyun Easwaramoorthy, Sathishkumar Veerappampalayam Anna University NGNLab Department of Computer Technology Chennai India Jeonbuk National University Department of Software Engineering Division of Electronics and Information Engineering Jeonju-si Korea Republic of Sunway University School of Engineering and Technology Malaysia

ISBN: (纸本)9798331530631

Early detection of Cardiovascular Diseases (CVD) plays a vital role in effective treatment and management. However, traditional methods for analyzing ECG signals are often limited in both accuracy and interpretability. In this work, we introduce a novel model, CardioNetFusion, designed to address these limitations by incorporating advancements in Deep Learning (DL) and Explainable AI (XAI). The model leverages the strengths of Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 to improve the robustness of ECG signal interpretation. To further enhance transparency and provide healthcare professionals with actionable insights, saliency maps are utilized to visualize model predictions. Additionally, by integrating an Application Programming Interface (API) within an IoT-based sensor fusion framework, the model supports real-time cardiovascular health monitoring. Achieving a classification accuracy of 98.7% in detecting arrhythmias, CardioNetFusion surpasses existing methodologies and offers a practical, reliable solution for early CVD diagnosis and management. Traditional methods of ECG analysis often have limitations in terms of diagnostic accuracy and interpretability. This paper proposes a CardioNetFusion model to address the challenges of conventional methods of ECG data analysis by leveraging advances in Deep Learning (DL) and Explainable AI (XAI). The proposed CardioNetFusion model integrates Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 models to improve the robustness of ECG signal interpretation. Additionally, we utilized saliency maps to enhance model transparency and provide clear, actionable insights for healthcare professionals. Integrating Application Programming Interface (API) in the IoT-based sensor fusion approach to our proposed model facilitates seamless real-time monitoring of cardiovascular health. With an impressive 98.7% accuracy in arrhythmia classification, CardioNetFusion outperformed existing methods and ensured reliab

关键词： Convolutional neural networks

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