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2023 Wave Electronics and its Application in information and Telecommunication Systems, WECONF 2023

作者： Bogatyrev, V.A. Bogatyrev, S.V. Sivov, V.V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer Technology Saint-Petersburg Russia

ISBN: (纸本)9798350348293

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node. © 2023 IEEE.

关键词： Fault tolerance

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Patterns for Improving User Contribution 23

Patterns for Improving User Contribution

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Mohammad Daud Haiderzai Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9798400700408

User contributions is an important part in software development, the pursuit of identifying effective means of user contributions in software development is a crucial area of interest for software companies and their development teams. Determining the most opportune moments, locations, and methods for such contributions presents a significant challenge for organizations, which necessitates the exploration of generic and proven solutions. Applying patterns, established and observed solutions may provide the answers to such challenges and similar ones. This paper offers insights into the practical implementation of seven newly discovered organizational patterns in a real-life environment. The patterns are comprehensively described, accompanied by illustrative examples, and subsequently applied in actual practices.

关键词： organizational patterns software development software engineering

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Resnet Video 3D for Gait Retrieval: A Deep Learning Approach to Human Identification 23

Resnet Video 3D for Gait Retrieval: A Deep Learning Approach...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Vuong, Gia-Huy Tran, Minh-Triet Faculty of Information Technology University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam Faculty of Information Technology Software Engineering Lab University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798400708916

Gait, the distinctive way a person walks, is a useful biometric trait for various applications such as crime prevention, forensic identification, and social security. Gait retrieval, which aims to find the person who matches a given gait, is an active research area, its research has drawn a significant increase. However, learning discriminative temporal features from gait data is difficult due to the subtle variations in the spatial domain of the silhouette. Recent deep learning methods have demonstrated their effectiveness for gait retrieval by learning more robust features from raw video data. In this paper, we propose a baseline network based on ResNet video R3D-18, which can capture both spatial and temporal information from the data, to address the gait retrieval problem. Our experimental results show that our optimized backbone network can extract powerful vector representations of gait and achieve high performance in retrieving the person who matches the gait from the database. On CASIA-B dataset, we obtain Rank-1 accuracy of 97.09% and Rank-10 accuracy of 99.27% under normal walking condition. The source code will be available at. © 2023 ACM.

关键词： Deep learning

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Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Nik Ahmad, Nik Azlina Lokman, Anitawati Mohd Abdullah, Munaisyah Hakim Suhaimi, Ahmad Iqbal Universiti Kuala Lumpur Malaysian Institute of Information Technology Software Engineering Section Kuala Lumpur Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Shah Alam Malaysia

ISBN: (纸本)9798350302684

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs. © 2023 IEEE.

关键词： Product design

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Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

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Iran Journal of Computer Science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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Research and development of two-factor authentication methods using neural networks 4

Research and development of two-factor authentication method...

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4th IEEE International Conference on Smart information Systems and Technologies, SIST 2024

作者： Darbek, Temirkhan Mukasheva, Assel Bissembayev, Alibek Gnatyuk, Sergiy Mukammejanova, Dinargul The Department of Information Systems and Cybersecurity Almaty university of power engineering and telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The School of Information Technology and Engineering Kazakh British Technical University Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering at National Aviation University National Aviation University Kyiv Ukraine The Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (纸本)9798350374865

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital. © 2024 IEEE.

关键词： biometric authentication deep learning digital security identification Neural networks

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments 3

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ismail Alkhallas, Muhannad Imhmed Mohamed, Abdalslam S Che Ismail, Rizalafande Syed Idrus, Syed Zulkarnain Salem Saed, Halifah Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Perlis Kangar01000 Malaysia Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Aljufra University Faculty of Information Technology Aljufra Libya Arau02600 Malaysia Department of Communication Perlis Kangar01000 Malaysia Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya

ISBN: (纸本)9798350319897

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students. © 2023 IEEE.

关键词： Virtual reality

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Sample efficient identity testing and independence testing of quantum states 12

Sample efficient identity testing and independence testing o...

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12th Innovations in Theoretical Computer Science Conference, ITCS 2021

作者： Yu, Nengkun Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959771771

In this paper, we study the quantum identity testing problem, i.e., testing whether two given quantum states are identical, and quantum independence testing problem, i.e., testing whether a given multipartite quantum state is in tensor product form. For the quantum identity testing problem of D(Cd) system, we provide a deterministic measurement scheme that uses O(dΕ22) copies via independent measurements with d being the dimension of the state and Ε being the additive error. For the independence testing problem D(Cd1 ⊗ Cd2 ⊗ · · · ⊗ Cdm) system, we show that the sample complexity is Θ(~ Πmi=1Ε2di) via collective measurements, and O(Πmi=1Ε2d2i) via independent measurements. If randomized choice of independent measurements are allowed, the sample complexity is Θ(d3Ε2/2) for the quantum identity testing problem, and Θ(~ Πmi=1Ε2d3 i/2) for the quantum independence testing problem. © Nengkun Yu.

关键词： Quantum optics

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