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Traceable ring signature schemes based on SM2 digital signature algorithm and its applications in the data sharing scheme

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Frontiers of computer Science 2024年第2期18卷 251-253页

作者： Yongxin ZHANG Hong LEI Bin WANG Qinghao WANG Ning LU Wenbo SHI Bangdao CHEN Qiuling YUE R&D Department SSC Holding CompanyLtd.Chengmai 571924China School of Cyberspace Security(School of Cryptography) Hainan UniversityHaikou 570228China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Computer Science and Technology Xidian UniversityXi'an 710071China Blockchain Research Group Oxford-Hainan Blockchain Research InstituteChengmai 571924China

1 Introduction Identity privacy concerns hinder data sharing by casting doubt on the safeguarding of personal information,eroding trust,and impeding the willingness of individuals and organizations to exchange their data[1,2].The traceable ring signatures(TRSs)addresses the contradiction between identity privacy and regulation[3],no scheme has been developed thus far that is based on SM2,the Chinese cryptographic public key algorithm standard,without relying on centralized trust.

关键词： SM2 signature scheme

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An Intranet Defense Capability Assessment Method for Network security Solutions Based on ATT&CK Framework 2nd

An Intranet Defense Capability Assessment Method for Network...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： An, Qi Zhu, Zifeng Li, Shudong Han, Weihong Wu, Xiaobo Li, Shumei Cyberspace Institute of Advanced Technology GuangzhouUniversity Guangzhou510006 China Department of New Networks Peng Cheng Laboratory Shenzhen518000 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Computer College Jilin Normal University Siping136000 China

ISBN: (纸本)9789819745180

Faced with the growing threat of intranet attacks and numerous network security solutions, the defensive capabilities of network security solutions need to be evaluated in order to reasonably select a network security solution. However, the existing assessment of the defense capability of network security solutions mainly focuses on anti-DDoS devices, and lacks the assessment of the defense capability of the intranet, and it is complicated to obtain the values of assessment indicators during the assessment process. In addition, it is also difficult to obtain ideas for defense improvement of network security solutions. In the face of this situation, we propose an intranet defense capability assessment method for network security solutions based on the ATT&CK framework from an attack perspective. This method introduces the ATT&CK framework into the assessment of the intranet defense capability of network security solutions, constructs an indicator system with comprehensiveness and good scalability, and involves tactics, techniques and sub-technologies in the enterprise framework used by 138 APT organizations in the ATT&CK framework as objective data for indicator assignment, balancing the influence of attackers’ subjective experiences and preferences on the assessment results, and this method can easily obtain ideas to improve the intranet defense capability of network security solutions. The effectiveness of the proposed evaluation method is verified by conducting simulation experiments at a network range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intranets

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Dual Branch PnP Based Network for Monocular 6D Pose Estimation

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Intelligent Automation & Soft Computing 2023年第6期36卷 3243-3256页

作者： Jia-Yu Liang Hong-Bo Zhang Qing Lei Ji-Xiang Du Tian-Liang Lin Department of Computer Science and Technology Huaqiao UniversityXiamen361000China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen361000China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen361000China College of Mechanical Engineering and Automation Xiamen361000China

Monocular 6D pose estimation is a functional task in the field of com-puter vision and *** recent years,2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based ***,for monocular 6D pose estimation,these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the per-spective-n-point(PnP)*** is still a difference in the distance from the expected estimation *** obtain a more effective feature representation result,edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on 6D pose regression and comparing the effectiveness of the intermediate ***,although the transformation matrix is composed of rotation and translation matrices from 3D model points to 2D pixel points,the two variables are essentially different and the same network cannot be used for both variables in the regression ***,to improve the effectiveness of the PnP algo-rithm,this paper designs a dual-branch PnP network to predict rotation and trans-lation ***,the proposed method is verified on the public LM,LM-O and YCB-Video *** ADD(S)values of the proposed method are 94.2 and 62.84 on the LM and LM-O datasets,*** AUC of ADD(-S)value on YCB-Video is *** experimental results show that the performance of the proposed method is superior to that of similar methods.

关键词： 6D pose monocular RGB edge enhancement dual-branch PnP 2D-3D correspondence

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Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

Personalized Educational Video Evaluation Combining Student'...

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IEEE International Conference on Systems, Man and cybernetics

作者： Jinta Weng Haoyu Dong Yifan Deng Yue Hu Hao Wu Heyan Huang School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Cyber Engineering Guangzhou University China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important computer Science, engineering, and Decision Sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural Science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Logistic Regression for Personalized Hypertension Risk Profiling in Cloud-Integrated Healthcare Systems

Logistic Regression for Personalized Hypertension Risk Profi...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Saravanan, S. Ramya, S. Monikapreethi, S.K. Kavididevi, Venkatesh Kovarasan, Rajesh Kambattan Sujatha, S. Department of Electrical and Electronics Engineering Muthayammal Engineering College Tamil Nadu Namakkal India Department of Cyber Security Paavai Engineering College Tamil Nadu Namakkal India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

Advanced analytics for personalized chronic disease risk profiles are becoming possible as healthcare systems combine cloud-based technology. This work applies logistic regression (LR) models to cloud-integrated healthcare systems for personalized hypertension risk profiling. A large dataset of heterogeneous patient records was used to train and evaluate the LR model, including clinical characteristics and lifestyle variables. To assess the LR model's predictive accuracy in identifying high-risk hypertension patients and to provide a framework for personalized risk stratification that can be seamlessly integrated into cloud-based healthcare systems. The model's performance was rigorously cross-validated and compared to risk assessment methods. The LR approach predicted hypertension risk better than older techniques. Healthcare professionals and patients found the cloud integration of the personalized risk profiling system convenient, scalable, and accessible. It shows that cloud-based analytics may improve preventive healthcare by personalizing treatments to risk profiles. LR techniques can optimize personalized hypertension risk management in cloud-integrated systems, advancing data-driven healthcare. © 2024 IEEE.

关键词： Cloud Integration Healthcare Systems Hypertension Risk Personalized Medicine Predictive Modeling

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Turmeric leaf disease detection using optimized deep learning model

Turmeric leaf disease detection using optimized deep learnin...

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2nd International Conference on computer Vision and Internet of Things (ICCVIoT 2024)

作者： R. Selvaraj M. S. Geetha Devasena T. Satheesh K. Karpagavadivu F. Ravindaran Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Artificial Intelligence and Data Science Dr.N.G.P. Institute of Technology Coimbatore India Computer Science and Engineering (Cyber Security) Karpagam college of Engineering Coimbatore India

ISBN: (数字)9781837242535

Turmeric is popular for its medicinal, culinary, and non-culinary uses. Turmeric cultivation is affected due to disease in their leaves. Farmers conventionally identify the leaf diseases and provide pesticides to avoid further loss. However, manual identification of leaf disease over large farms is time-consuming and requires human effort. Also, in several types of leaf disease, the specific type that occurs in the plant should be distinguished; thus, proper knowledge of farming is required. To simplify this and to know the type of disease, smart agricultural practices are followed worldwide. One of the intelligent techniques adopted in farming is leaf disease detection. By providing the filed image to a learning algorithm, the type of disease can be detected accurately without manual interventions. ML and DL are used in this smart agricultural practice. To improve the performance further, an optimized deep learning model is presented in this research work. A Novel gazelle optimization algorithm is combined with deep learning MobileNetV3 network to attain enhanced disease detection performance. Experimentation on our own dataset with 5000 samples, including leaf spot, leaf blight, leaf rot, and curl disease-affected leaves, the proposed model performance is evaluated and observed with a maximum accuracy of 96.8%, a precision of 97.1% over conventional disease detection models.

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Identification and Categorization of Unusual Internet of Vehicles Events in Noisy Audio

Identification and Categorization of Unusual Internet of Veh...

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IEEE Conference on Vehicular technology (VTC)

作者： Farkhund Iqbal Ahmad Abbasi Abdul Rehman Javed Gautam Srivastava Zunera Jalil Thippa Reddy Gadekallu Department of Cyber Security Zayed University UAE Department of Cyber Security Air University Islamabad Pakistan Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The volume of multimedia data produced by various smart devices has increased dramatically with the advent of new digital technologies, including in the Internet of Vehicles (IoV). It has become more difficult to extract valuable insights from multimedia data due to several challenges during data analysis. The main problem is the need to quickly and precisely identify abnormalities in multimedia data. This research presents an unusual occurrence of the audio forensics database named UOAFDB and a practical method for identifying and categorizing unusual occurrences in audio files. To study the detection of abnormal audio and the classification of rare sound (e.g., car crash—machine gun, explosion) events for audio forensics, we construct a large audio dataset containing ten rare special events (anomalies) with 15 different background environmental settings (e.g., beach, restaurant, and train). The suggested method determines the optimal amount of features using the best feature extraction methodology available by extracting Mel-frequency cepstral coefficients (MFCCs) features from the audio signals of the newly formed dataset. Modern deep learning algorithms use these features as input to assess performance. Additionally, we apply deep learning methods to the most recent and best available dataset and obtain promising outcomes. The experimental findings demonstrate promising results on the UOAFDB dataset.

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A Comprehensive Survey of Joint Radar-Communication Technologies and their future Applications in Healthcare Sector

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IEEE Access 2024年 1-1页

作者： Al-Salehi, Abdulrahman Sufyan, Mubarak Mohammed Al-Ezzi Malik, Aqdas Naveed Al-Asaly, Mahfoudh Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Networks and cyber security AlJanad University Of Science and Technology Taiz Yemen Department of Electrical Engineering University of Engineering and Technology G.T Road Lahore Pakistan STC’s Artificial Intelligence Chair College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

The integration of joint radar and communication technologies has emerged as a transformative approach with promising applications across various sectors, including healthcare. This paper presents a comprehensive examination of the current state of the art in this field, including design considerations, technical challenges, and key research directions. Furthermore, this paper compares the current review with previous literature reviews in the field and emphasizes its comprehensiveness and indepth examination of the key issues and trends in the field of unified radar and communication systems. The ultimate goal of this paper is to provide researchers and practitioners with a comprehensive and up-to-date understanding of the current state of this field and its future research directions. Authors

关键词： Health care

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