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Secure Cloud Data Storage System Using Hybrid Paillier–Blowsh Algorith

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computers, Materials & Continua 2021年第4期67卷 779-798页

作者： Bijeta Seth Surjeet Dalal Dac-Nhuong Le Vivek Jaglan Neeraj Dahiya Akshat Agrawal Mayank Mohan Sharma Deo Prakash K.D.Verma SRM University Delhi-NCRSonipatHaryana131029India Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Information Technology Duy Tan UniversityDanang 550000Vietnam Graphic Era Hill University DehradunUttarakhand248171India Amity University Haryana122412India Principal Software Lead Zillow Inc.USA School of Computer Science&Engineering Faculty of EngineeringSMVD UniversityKakryalKatra182320India Material Science Research Laboratory Department of PhysicsS.V.CollegeAligarh202001India

Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically recongured in order to deliver optimum resource utilization by exploiting the pay-per-use ***,concerns around security have been an impediment in the extensive adoption of the cloud computing *** this regard,advancements in cryptography,accelerated by the wide usage of the internet worldwide,has emerged as a key area in addressing some of these security *** this document,a hybrid cryptographic protocol deploying Blowsh and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption Standard(AES)and Rivest Shamir Adleman(RSA)*** for secure data storage protocol in two phases have been *** proposed hybrid protocol endeavors to improve the power of cloud storage through a decrease in computation time and ciphertext *** have been carried out with Oracle Virtual Box and Fog server used on an Ubuntu 16.04 *** grouping of asymmetric and homomorphic procedures has demonstrated enhanced *** usage has helped in decreasing the storage space and computation *** analysis in terms of computation overhead and quality of service parameters like loads of parameters with and without attacks,throughput,and stream length for different modes of block cipher mode has been carried *** analysis has been carried out by utilizing the Hardening Index as an audit parameter using Lynis ***,for halting the aforementioned approaches and for regulating trafc,rewall protection has been generated in the chosen hybrid ***,enhancements in the performance of the Paillier and Blowsh hybrid scheme with and without compression compared to the existing schemes using RSA and AES procedures have been demonstrated.

关键词： Cryptography blowsh homomorphic paillier cloud computing

A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Manna, Suvojit Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malayasia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Ultimo Australia

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

ExpFinder: An Ensemble Expert Finding Model Integrating N-gram Vector Space Model and µCO-HITS

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arXiv 2021年

作者： Kang, Yong-Bin Du, Hung Mohammad Forkan, Abdur Rahim Jayaraman, Prem Prakash Aryani, Amir Sellis, Timos Department of Media and Communication Swinburne University of Technology Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia Social Innovation Research Institute Faculty of Business and Law Swinburne University of Technology Australia

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder, a new ensemble model for expert finding, that integrates a novel N-gram vector space model, denoted as nVSM, and a graph-based model, denoted as µCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N-gram words, and ExpFinder incorporates nVSM into µCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%. © 2021, CC BY.

关键词： Information retrieval

Design and Implementation of Enterprise Recruitment Mini Program

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Journal of Cyber Security 2021年第3期3卷 125-132页

作者： Yu Xue Nan Wei Junyang Han Chishe Wang Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Since the 21st century,the Internet has been updated and developed at an alarming *** the same time,WeChat applets are constantly improving and introducing new *** an enterprise recruitment system based on WeChat applets for the majority of job seekers and recruiter users,provide job seekers with easy-to-reach employment opportunities,and provide a convenient and clear screening environment for job *** front-end part of the applet is developed using WeChat developer tools,and the back-end system is developed using *** Spring Boot+Spring MVC framework,implemented in Java *** is managed using MySql *** function of this company’s recruitment applet is similar to the ordinary traditional native recruitment *** achieves basic functions such as job search,job search,collection of jobs,delivery of resumes,viewing of the job search process,recruitment of job information,screening of job resumes,notification of interviews,etc.

关键词： Mini-program recruitment WeChat developer tools MyEclipse

Analysis and Design of University Teaching Equipment Management Information System

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Journal of Cyber Security 2021年第3期3卷 177-185页

作者： Yu Xue Xiang Jiao Chishe Wang Huanlun Chen Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Teaching equipment management is an important factor for colleges and universities to improve their teaching level,and its management level directly affects the service life and efficiency of teaching *** in recent years,our university recruitment of students scale is increasing year by year,the size of the corresponding teaching equipment is also growing,therefore to develop a teaching equipment management information system is necessary,not only can help universities to effective use of the existing teaching resources,also can update scrap equipment,related equipment maintenance,and build a good learning environment to students and to the improvement of the teaching quality of colleges and universities play a reliable safeguard *** paper first introduces some common development tools,and then analyzes the user functional requirements and data requirements of the system,and analyzes the feasibility of the system development from many aspects,finally based on B/S mode,using Java language,JSP technology and MySQL database design and implementation of a teaching equipment management information *** main functional modules of the system include equipment basic information management,equipment loan and return information management,equipment maintenance information management,equipment scrap information management,the interface of each functional module is shown in the paper.

关键词： Information systems teaching equipment management system analysis

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

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arXiv 2024年

作者： Ma, Jiating Zhou, Yipeng Li, Qi Sheng, Quan Z. Cui, Laizhong Liu, Jiangchuan The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Shenzhen518060 China The School of Computing Faculty of Science and Engineering Macquarie University Macquarie ParkNSW2119 Australia The Institute for Network Sciences and Cyberspace Tsinghua University Beijing100190 China The Beijing National Research Center for Information Science and Technology Beijing100190 China The School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients’ privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines. © 2024, CC BY.

关键词： Federated learning

Structure Function Based Transform Features for Behavior-Oriented Social Media Image Classification 5th

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Structure Function Based Transform Features for Behavior-Ori...

5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Krishnani, Divya Shivakumara, Palaiahnakote Lu, Tong Pal, Umapada Ramachandra, Raghavendra International Institute of Information Technology Naya Raipur Naya RaipurChhattisgarh India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783030414030

Social media has become an essential part of people to reflect their day to day activities including emotions, feelings, threatening and so on. This paper presents a new method for the automatic classification of behavior-oriented images like Bullying, Threatening, Neuroticism-Depression, Neuroticism-Sarcastic, Psychopath and Extraversion of a person from social media images. The proposed method first finds facial key points for extracting features based on a face detection algorithm. Then the proposed method labels face regions as foreground and other than face region as background to define context between foreground and background information. To extract context, the proposed method explores Structural Function based Transform (SFBT) features, which study variations on pixel values. To increase discriminating power of the context features, the proposed method performs clustering to integrate the strength of the features. The extracted features are then fed to Support Vector Machines (SVM) for classification. Experimental results on a dataset of six classes show that the proposed method outperforms the existing methods in terms of confusion matrix and classification rate. © Springer Nature Switzerland AG 2020.

关键词： Classification (of information)

Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results

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arXiv 2024年

作者： Li, Boyang Ying, Xinyi Li, Ruojing Liu, Yongxian Shi, Yangsi Li, Miao Zhang, Xin Hu, Mingyuan Wu, Chenyang Zhang, Yukai Wei, Hui Tang, Dongli Zhao, Jian Jin, Lei Xiao, Chao Ling, Qiang Lin, Zaiping Sheng, Weidong Peng, Chenxu Ke, Wei He, Yuhang Yang, Huoren Liu, Lingjie Liu, Ziqi Shi, Zelin Liu, Yunpeng Yu, Chuang Zhao, Jinmiao Xiang, Heng Yang, Shan Li, You Wang, Guoqing Li, Tianyu Zhou, Minghang Xie, Dongyu Lan, Chenxi Qiao, Chaofan Gao, Yupeng Liu, Yongxu Feng, Wei Ma, Zhihao Chen, Deping Song, Xiaopeng Yang, Jiuping Qiu, Zhaobing Ni, Rixiang Lin, Ye Luo, Changhai Zheng, Shuyuan Huang, Baojin Zhou, Xiaoqi Guo, Qingshan Zhang, Xuyang Li, Xiuhong Wu, Dangxuan Ma, Jian Zeng, Haodong Wang, Luyao Fu, Qiang Dai, Yimian Kou, Renke Song, Jian Feng, Changfeng Xiao, Mengxuan Xiong, Zihao Huang, Hong Liu, Yingxu Zhao, Quanyi National University of Defense Technology China China Telecom Institute of AI China Northwestern Polytechnical University China Beijing University of Posts and Telecommunications China Zhejiang Supcon Information Co. Ltd. China School of Physics Xi’an Jiaotong University China College of Artificial Intelligence Faculty of Electronic and Information Engineering Xi’an Jiaotong University China School of Software Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China The Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Sichuan Zhongke Langxing Photoelectric Technology Co. Ltd. China Center for Future Media University of Electronic Science and Technology of China China Hangzhou Institute of Technology Xidian University China School of Electronic Engineering Xidian University China School of Mechanical Engineering and Automation Fuzhou University China Beijing Insitute of Technology Chongqing Innovation Center China Xinjiang University China Army Engineering University PLA Shijiazhuang Campus China Nanjing University of Science and Technology China Henan University of Technology China Chongqing University China

In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource. © 2024, CC BY-NC-SA.

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