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International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD)

作者： Elisa Maeko Dustin Van Der Haar Academy of Computer Science and Software Engineering University of Johannesburg Johannesburg South Africa Academy of Computer Science and Software Engineering name University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This research aims to aid public service organizations in selecting effective multi-modal authentication technology to mitigate authentication risks and facilitate informed decision-making, preventing financial burdens associated with unsuccessful system implementations. It has become a financial burden for organisations to deploy inappropriate technology in an environment that ultimately goes unused and may lead to unsuccessful system implementations. Different biometric attributes, characteristics, qualities, and criteria - the environment (organisation), budget and business requirements, specifications, design, system performance, the information technology infrastructure, operations, and post-deployment functions - must be compared. The research methodology also encompasses the execution of a survey, concomitant with factor analysis, which serves as the cornerstone for assessing the model. The model evaluation survey, conducted with participants aged 18 to 55, garnered a response rate of 99.4% from respondents who positively consented to participate in the study. In order to analyse and evaluate the proposed model, this research incorporates a spectrum of intricate statistical techniques, including descriptive statistics, factor analysis, chi-square for goodness of fit, Mardia's coefficient, multivariate kurtosis, and path model analysis. This study contributes to the body of knowledge by studying the human factors that impact public awareness, perception, and the usability of multi-modal biometrics. The formalised model proposed in the study will be of value to the public services environment that may need to deploy multi-modal biometric authentication technologies to minimise future failed deployments.

关键词： Surveys Analytical models Biological system modeling System performance Authentication Organizations Kurtosis

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Towards an active cyber defence framework for smmes in developing countries 22

Towards an active cyber defence framework for smmes in devel...

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22nd European Conference on Cyber Warfare and Security, ECCWS 2023

作者： Ntingi, Nombeko Toit, Jaco Du Solms, Sebastian Von Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781914587702

Small, medium, and micro enterprises (SMMEs) are obliged to adopt digital technologies to render services to their clients and remain competitive. The COVID-19 global crisis has accelerated the cyberfication of systems and services. The move to digital platforms has afforded SMMEs opportunities to offer their services to a broader geographical area. However, this has also presented opportunities for cybercriminals to invade the digital infrastructure. Adopting digital transformation has put SMMEs in a vulnerable position since they need to manage their cybersecurity while lacking the necessary skills and ICT infrastructure. The inability of SMMEs to defend themselves against cyberattacks compels them to outsource their security needs to external security service providers. These external security service providers offer security services based on a hierarchical operating model. Essential security services are offered at a lower level. If the paying clients require advanced security services, they may be provided as an add-on to the contractual agreement resulting in additional cost. This paper explores the active cyber defence (ACD) approach to enhance cybersecurity defence while minimising service costs. Therefore, the primary objective and outcome of this paper are to identify some of the essential drivers that will contribute towards developing the active cyber defence framework for SMMEs in developing countries. For purposes of clarity, essential drivers are the gaps highlighted during the literature review and will be referred to as "essential drivers" throughout the paper. The essential drivers, together with suggested recommendations, will be consolidated. The essential drivers were drawn from existing literature by going through peer-reviewed academic papers and company whitepapers. To achieve the primary objective, we need to establish whether SMMEs are utilising the services of external security service providers. The external security service providers wi

关键词： Developing countries

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SupervisedImmuneNet: Training Artificial Immune Networks using a Supervised Learning Approach for Improved Multi-Class Classification 6

SupervisedImmuneNet: Training Artificial Immune Networks usi...

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6th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2023

作者： Sithungu, Siphesihle Philezwini Ehlers, Elizabeth Marie Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9798400709067

The most common application of artificial immune networks (AINs) is on unsupervised learning tasks. This is due to the fact that AINs are inspired by the adaptive immune system, which consists of a network of antibodies that self-organises to form a memory of external antigens. The self-organising nature of AINs makes them a natural approach for solving problems involving learning and adapting to patterns or structures present in a dataset to form an abstract representation. Training AINs in this fashion means that the dataset need not have class labels because the typical aim of the learning process is not to perform classification. However, there have been attempts to use AINs for classification tasks by considering the resulting clusters of antibodies as representative of the classes present in a dataset. This has also been done when applying AINs to the task of recognising handwritten characters. However, in all the approaches found in the literature, the common method was to leave the task of discovering classes to the AINs. Doing so is contrary to how other models are trained to do classification tasks where data samples are provided along with their class labels to guide the learning process. Therefore, this paper presents a novel supervised learning approach to training AINs for multi-class classification. The proposed approach was tested on the MNIST handwritten digits dataset and achieved a classification accuracy of 99.45%. © 2023 ACM.

关键词： Antibodies

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A Cyber counterintelligence competence framework: Developing the job roles 22

A Cyber counterintelligence competence framework: Developing...

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22nd European Conference on Cyber Warfare and Security, ECCWS 2023

作者： Sithole, Thenjiwe Toit, Jaco Du Von Solms, Sebastian H. Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781914587702

In recent years, there have been intensifying cyber risks and volumes of cyber incidents prompting a significant shift in the cyber threat landscape. Both nation-state and non-state actors are increasingly resolute and innovative in their techniques and operations globally. These intensifying cyber risks and incidents suggest that cyber capability is inversely proportional to cyber risks, threats and attacks. Therefore, this confirms an emergent and critical need to adopt and invest in intelligence strategies, predominantly cyber counterintelligence (CCI), which is a multi-disciplinary and proactive measure to mitigate risks and counter cyber threats and cyber-Attacks. Concurrent with the adoption of CCI is an appreciation that requisite job roles must be defined and developed. Notwithstanding the traction that CCI is gaining, we found no work on a clear categorisation for the CCI job roles in the academic or industry literature surveyed. Furthermore, from a cybersecurity perspective, it is unclear which job roles constitute the CCI field. This paper stems from and expands on the authors prior research on developing a CCI Competence Framework. The proposed CCI Competence Framework consists of four critical elements deemed essential for CCI workforce development. In order of progression, the Framework s elements are: CCI Dimensions (passive-defensive, active-defensive, passive-offensive, active-offensive), CCI Functional Areas (detection, deterrence, deception, neutralisation), CCI Job Roles (associated with each respective Functional Area), and Tasks and Competences (allocated to each job role). Pivoting on prior research on CCI Dimensions and CCI Functional Areas, this paper advances a proposition on associated Job Roles in a manner that is both intelligible and categorised. To this end, the paper advances a five-step process that evaluates and examines Counterintelligence and Cybersecurity Job Roles and functions to derive a combination of new or existing Job Role

关键词： Cybersecurity

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

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Tsinghua science and Technology 2023年第1期28卷 69-81页

作者： Xingguo Jiang Aidong Sun Yan Sun Hong Luo Mohsen Guizani School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Institute of Food safety and Nutrition Jiangsu Academy of Agricultural SciencesNanjing 210000China Computer Science and Engineering Department Qatar UniversityDoha 2713Qatar

As the smart grid develops rapidly,abundant connected devices offer various trading *** raises higher requirements for secure and effective data *** centralized data management does not meet the above ***,smart grid with conventional consortium blockchain can solve the above ***,in the face of a large number of nodes,existing consensus algorithms often perform poorly in terms of efficiency and *** this paper,we propose a trust-based hierarchical consensus mechanism(THCM)to solve this ***,we design a hierarchical mechanism to improve the efficiency and ***,intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance ***,we propose a trust evaluation method to improve the election process of ***,we implement a prototype system to evaluate the performance of *** results demonstrate that the consensus efficiency is improved by 19.8%,the throughput is improved by 12.34%,and the storage is reduced by 37.9%.

关键词： consortium blockchain consensus algorithm trust evaluation method smart grid Internet of Things(IoT)

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Introduction to the Special Issue on Formal Methods and Models for System Design

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Brandt, Jens Saha, Indranil Zhang, Lijun Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

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Multi-Person Respiration Monitoring Leveraging Commodity Wi-Fi Devices

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计算机科学技术学报（英文版） 2025年第1期40卷 229-251页

作者：伊恩泽牛凯张扶桑高睿杨罗骏张大庆 1School of Computer Science Peking University Beijing 100871 China 2Beijing Xiaomi Mobile Software Co. Ltd. Beijing 100085 China 3State Key Laboratory of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing 100190 China 4School of Computer Science and Engineering Nanyang Technological University Singapore 639798 Singapore 5Telecom SudParis Institut Polytechnique de Paris Paris 91000 France

Monitoring respiration is an important component of personal health *** recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-based mul-ti-person respiration sensing mainly extract individual's respiration rate in the frequency domain using the fast Fourier transform(FFT)or multiple signal classification(MUSIC)method,leading to the following limitations:1)largely ineffec-tive in recovering breaths of multiple persons from received mixed signals and in differentiating individual breaths,2)un-able to acquire the time-varying respiration pattern when the subject has respiratory abnormity,such as apnea and chang-ing respiration rates,and 3)difficult to identify the real number of subjects when multiple subjects share the same or simi-lar respiration *** address these issues,we propose Wi-Fi-enabled MUlti-person SEnsing(WiMUSE)as a signal pro-cessing pipeline to perform respiration monitoring for multiple persons ***,as a pioneering time domain approach,WiMUSE models the mixed signals of multi-person respiration as a linear superposition of multiple waveforms,so as to form a blind source separation(BSS)*** effective separation of the signal sources(respira-tory waveforms)further enables us to quantify the differences in the respiratory waveform patterns of multiple subjects,and thus to identify the number of subjects along with their respective respiration *** implement WiMUSE on commodity Wi-Fi devices and conduct extensive experiments to demonstrate that,compared with the approaches based on the FFT or MUSIC method,90％error of respiration rate can be reduced by more than 60％.

关键词： respiration monitoring multi-person sensing Wi-Fi sensing

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MaGIC: Multi-modality Guided Image Completion 12

MaGIC: Multi-modality Guided Image Completion

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hao Yu, Yongsheng Luo, Tiejian Fan, Heng Zhang, Libo School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Software Chinese Academy of Sciences China Department of Computer Science University of Rochester United States Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signals into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoiding the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image segmentation

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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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