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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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A Scheme for Authentication and Traceability of 5G Mobile Terminal Based on IPv6 and Blockchain

A Scheme for Authentication and Traceability of 5G Mobile Te...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xinsheng Ji Ying Zhao Xingxing Liao Jie Yang Chunxiao Wang Department of Computer Science and Technology Tsinghua University Beijing China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China Purple Mountain Laboratories Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

To satisfy the service-side requirements for network layer authentication and traceability of users, this paper proposes a method for mobile terminal authentication and identity tracing based on IPv6 and blockchain. Our method introduces a new verification function (VEF) to verify the interface identification between the terminal and the 5G core network (5GC), thereby effectively enhancing the security of terminal access. Furthermore, considering the privacy protection of user identity information to prevent attackers from deducing encrypted content, it is necessary to periodically update encryption keys. Focusing on the practical deployment of VEFs on a network-wide scale and server-proximal for authentication and traceability, a time-varying key sharing scheme based on blockchain is proposed. The experimental section analyzes the communication performance and security of the proposed scheme, showing that our scheme does not significantly degrade the original network performance while providing substantial security gains.

关键词： Wireless communication Privacy 5G mobile communication Authentication Signal processing User experience Blockchains Synchronization Noise measurement Protection

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Robots in education: Influence of Regulatory Focus Theory

Robots in education: Influence of Regulatory Focus Theory

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

The Covid-19 pandemic has massively developed the use of distance learning. The limits of this practice have gradually come to light, both for students and for teachers. It is now crucial to design alternative solutions to overcome the shortcomings of videoconferencing in terms of involvement, concentration, learning, and equity. Social robots are increasingly used as tutors in the educational context and help improve teaching efficiency. Many psychology-based principles have been applied in education to guide instructional strategies, motivate students, and create a positive and productive learning environment. In this work, we use Regulatory Focus Theory (RFT), which categorizes an individual’s motivation into two types: Promotion and Prevention. Promotion-focused individuals are motivated by the potential for growth and achievement, whereas prevention-focused individuals are motivated by the potential for avoiding negative outcomes. Based on RFT, we aim to explore if and how the regulatory-focused behavior of the tutor robot can affect participants’ learning outcomes. In this work, a language learning scenario was designed with two conditions: (1) a robot tutor with promotion-focused behavior, (2) a robot tutor with prevention-focused behavior. The results are encouraging and support that promotion robot tutor can increase the learning efficiency of promotion participants and prevention robot tutor will enhance the learning interest of prevention participants.

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Candidate Evaluation with Multimodal Data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Design of Highly Reliable Fault Backtracking Calculation for Space Applications 15

Design of Highly Reliable Fault Backtracking Calculation for...

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15th International Conference on computer Research and Development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Beijing Chongqing China Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

The design architecture of backtracking calculation control processing for space large manipulator control is proposed. SRAM-Type FPGA is used to build the hardware full three-mode computing core, and the processor architecture is designed based on FPGA. The register value and cache access value of the single instruction calculation process of the three computing processors are used to compare the three modes in the anti-fuse FPGA. At the same time, the results of three computing processors were compared by SPARC CPU. Fault backtracking calculation control, when a single computing module occurs an error, first detect the instruction execution position of the faulty module, and then carry out register-level data recovery for the faulty module, and restore to the instruction before the fault to accelerate the operation to complete the 'backtracking' process, until the other two modules complete the calculation synchronization. According to the theoretical derivation and analysis, it is clear that under the linear increasing detection window, the maximum time of fault recovery is only related to the three-mode ratio transmission rate. According to this conclusion, the hardware digital logic design of the key modules of fault detection and recovery is carried out. The overall structure is simple and easy to implement, digital logic only occupies less than 100,000 logic gates. Compared with traditional methods, the fault recovery efficiency is improved by at least 90%. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Enhancing Security in OpenID Connect Single Sign-On (SSO) systems: A Comprehensive Vulnerability Analysis and Mitigation Approach 4

Enhancing Security in OpenID Connect Single Sign-On (SSO) Sy...

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Al-Shabi, Mohamed Bennour, Akram Al-Sarem, Mahamed Khalaf, Osama Ibrahim Sikder, Rajesh Algburi, Sameer Department of management Information system Taibah University Medina Saudi Arabia "LAMIS" Laboratory Echahid Chikh Larbi Tebessi University Tebessa Algeria College of Computer Science and Engineering Taibah University Medina Saudi Arabia Al-Nahrain University Jadriya Baghdad Iraq Information Technology University of the Cumberlands United States Al-Kitab University College of Engineering Techniques Kirkuk Iraq

ISBN: (纸本)9781839539954

Single Sign-On (SSO) systems have become popular for simplifying the login process by allowing users to authenticate with a single identity provider (IDP). However, the widespread use of these systems raises concerns about user privacy, as IDPs like Google or Facebook can gather extensive information about user behavior on the web. This poses a significant challenge for privacy-conscious users who wish to limit the disclosure of their web access behavior to third-party organizations. In this paper, we present a comprehensive and large-scale practical study focused on the OpenID Connect protocol, a widely used SSO protocol. Our analysis delves into the various stages of the protocol's operation, highlighting its security flaws and vulnerabilities. Additionally, we conduct a systematic examination of the privacy implications associated with user access to SSO systems. We provide a detailed account of how easily user information can be accessed, shedding light on the potential risks. © The Institution of engineering & Technology 2023.

关键词： Differential privacy

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A Machine Learning Algorithm For Developing A Smart Health system

A Machine Learning Algorithm For Developing A Smart Health S...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Shemily P John P Anju S. Barath Kumar P. Vijayaraghavan Nishesh Nigam G. Amirthayogam General Studies Unit University College of Bahrain Kingdom of Bahrain Department of Computer Science and Business System (csbs) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Biotechnology Karpagam Academy of Higher Education Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science & Engineering (cse) IES Institute of Technology and Management Bhopal M.P India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The merging of revolutionary technologies like deep learning has greatly affected the healthcare sector, changing diagnosis and decision-making processes. This study tries to explore the role of neural networks in Digital Health research by examining 192 records drawn from the database used by Scopus. Employing a carefully made search term, the study tries to identify nations that excel in book output, primary research subjects, top funding backers, and common research terms in this domain. Notably, the debut paper on AI in smart health appeared in 2011, signalling the origins of a sizeable surge of academic output. India has emerged as the leader in performing study in this area, showing a remarkable growth trend. Furthermore, the research shows that Access from the IEEE is the top magazine, having the largest number of papers within this area. This extensive review serves as an important tool for scholars, policy makers, and healthcare workers, giving deep insights into the development of artificial intelligence in Smart Health study. The results show a positive trend, demonstrating that artificial intelligence in Connected Health is set for substantial increase in the future. The creativity and sincerity of this study highlight its importance in developing knowledge and defining the future environment of healthcare innovation.

关键词： Technological innovation Ethics Machine learning algorithms Databases Reviews Smart healthcare Neural networks

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Recommendation systems in Education : A Bibliometric Analysis

Recommendation Systems in Education : A Bibliometric Analysi...

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International Conference on Business and Industrial Research (ICBIR)

作者： Hendra Mayatopani Dina Fitria Murad Nor Adnan Yahaya School of Computing University Malaysia of Computer Science & Engineering Selangor Malaysia Information System Pradita University Tangerang Indonesia Information Systems Department Binus Online Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration.

关键词： Deep learning Filtering Collaborative filtering Education Bibliometrics Market research Information age International collaboration Recommender systems Optimization

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