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Journal of Uncertain systems 2024年第2期17卷

作者： Abdullah, Lazim Zulkifli, Norsyahida Awang, Noor Azzah Hashim, Hazwani Management Science Research Group Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus 21030 Malaysia Mathematical Sciences Department Faculty of Science Universiti Teknologi Malaysia Johor Bahru 81310 Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Technologi MARA Shah Alam 40450 Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Technologi MARA Kelantan Branch Machang 18500 Malaysia

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are pivotal methods in multi-criteria decision-making, addressing causal relationships among criteria and ranking alternatives, respectively. This paper introduces a novel approach, integrating DEMATEL and TOPSIS under the framework of single-valued neutrosophic set (SVNS) to handle indeterminacy in decision-making. Applied to a knowledge management strategy case, the method utilizes eight criteria and four alternatives derived from the literature. The process involves DEMATEL for expert weight determination and direct-relation matrix creation through linguistic variable influence. Subsequently, TOPSIS is employed to rank alternatives based on distance measures. The combined method identifies the ‘Human Resource department’ as the most crucial in knowledge management strategy. This integrated approach facilitates organizations in pinpointing vital criteria and alternatives for effective knowledge management. Comparative analyses with existing methods are also presented. © World Scientific Publishing Company.

关键词： Decision-making DEMATEL knowledge management single-valued neutrosophic set TOPSIS

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Investigations on Cyber Security Vulnerability using Distribution Analysis

Investigations on Cyber Security Vulnerability using Distrib...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Leo John Baptist Andrews Annamalai Alagappan D Sarathkumar M. Fathima Sampath Kumar Venkatachary R Rajeshkanna Raymon Antony Raj Department of Information Technology Faculty of Engineering and Technology Botho University Gaborone Botswana Department of Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana Department of Electrical and Electronics Engineering Kongu Engineering College Erode District Tamilnadu India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Grant Thornton Gaborone Botswana Department of EEE Thamirabarani Engineering College Tirunelveli Tamil Nadu India

Internet communication is used as a mode of engagement by businesses, organizations, and even nations in a variety of contexts. As a result, both the quantity of data produced and the importance of the information that can be derived from it are growing. Multiple safeguards are required in order to ensure that the accessibility, integrity, and confidentiality of company information and assets are maintained. When it comes to protecting the privacy of a company and its reputation, adopting countermeasures and minimizing the damage caused by irreversible cyberattacks are both very crucial. It is possible to utilize both active and passive attack tactics, as well as social engineering tests, to evaluate the weaknesses of a corporation’s cyber security. The identification and ranking of vulnerabilities both play an active part in the process of raising the level of corporate awareness, as well as closing shortfalls and enhancing measures. In this investigation, ethics committee reports and confidentiality agreements were followed in order to do a cyber security vulnerability assessment on seven separate organizations. These organizations represented the governmental sector, the corporate sector, and the civil society. In order to evaluate the effectiveness of the firms' cyber security infrastructures, security scans such as port, web, application-based system and network penetration tests, distributed denial of service attacks, and social engineering were carried out. The Common Vulnerability Scoring System (CVSS) was used in order to change the base score of each institution and to identify the vulnerabilities that were present. Enterprise Networks, Cyber Security Analysis, Cyber Attacks, Penetration, and Vulnerability are some of the Keywords that can be found in this article.

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Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

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IEEE Access 2025年 13卷 95837-95850页

作者： Ouahabi, Mohammed Said Benyounes, Abdelhafid Barkat, Said Ihammouchen, Syphax Rekioua, Toufik Rabehi, Abdelaziz El-Kenawy, El-Sayed M. Alharbi, Amal H. University of M’sila Electrical Engineering Laboratory Faculty of Technology M’sila28000 Algeria University of Bejaia Laboratoire de Technologie industrielle et de l’Information Bejaia06000 Algeria University of Djelfa Telecommunications and Smart Systems Laboratory PO Box 3117 Djelfa17000 Algeria PO Box 33349 Isa Town Bahrain Applied Science Private University Applied Science Research Center Amman Jordan Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of 1μs from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies. © 2013 IEEE.

关键词： Identification (control systems)

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Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network

arXiv

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arXiv 2024年

作者： Cai, Yunyi Wang, Weiyi Yu, Lanlan Wang, Ruixiang Sun, Gui-Quan Kummer, Allisandra G. Ventura, Paulo C. Lv, Jiancheng Ajelli, Marco Liu, Quan-Hui College of Computer Science Sichuan University Chengdu China Department of Mathematics North University of China Taiyuan China Complex Systems Research Center Shanxi University Taiyuan China Laboratory for Computational Epidemiology and Public Health Department of Epidemiology and Biostatistics School of Public Health Indiana University Bloomington BloomingtonIN United States

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%-88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depend on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve prep

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Text-guided cross-position attention for image analysis: Case of medical image

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computers in Biology and Medicine 2025年 193卷 110297-110297页

作者： Lee, Go-Eun Choi, Sang Tae Kim, Seon Ho Chung, Jaewoo Cho, Jungchan Choi, Sang-Il Department of Computer Science and Engineering Dankook University Yongin16890 Korea Republic of Division of Rheumatology Department of Internal Medicine Chung-Ang University College of Medicine Seoul06973 Korea Republic of Integrated Media Systems Center University of Southern California Los Angeles United States College of Medicine Dankook University Cheonan31116 Korea Republic of Department of Computing Gachon University Seongnam13120 Korea Republic of

In this study, we propose a novel text-guided cross-position attention module that aims to apply multimodality text and images to position attention in medical image segmentation. To match the dimension of the text feature to that of the image feature map, learnable parameters are multiplied by text features and combined with multimodal semantics via cross-attention. This allows the model to learn the dependencies between various text and image characteristics. The proposed model demonstrated superior performance in comparisons with other image-only or image-text data medical models. The module was applied to an active sacroiliitis diagnosis system and brain midline shift analysis by keypoint detection. In the first application, a region of interest (ROI) was segmented to classify inflammation of the sacroiliac joints. In the second application, the module was extended to a keypoint detection task that applied a Gaussian heatmap to detect the points required to determine the brain midline shift. Experimental results show that the proposed module can provide improved results in both applications. © 2025

关键词： Semantic Segmentation

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Development of a Grid-Connected Microinverter Based on the Integration of the CUK Converter with a Switchgear Structure

Development of a Grid-Connected Microinverter Based on the I...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： A.S.F Subhamathi Anuradha. T A. V. Suganya K. Shanthi P. Umamaheswari Jose Anand A Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamilnadu Department of Electrical and Electronics Engineering KCG College of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Muthukumaran Institute of Technology Kanchipuram Chennai Department of Computer Science and Business Systems Veltechmultech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering KCG College of Technology Karapakkam Chennai

The main focus of this research work is on designing the dimensions of the proposed DC-DC and DC-AC converters. Since it is an unprecedented DC-DC converter, it is important to enable the DC-DC converter to enable simulations in PSIM software. In addition, the size of the proposed DC-AC converter was provided for the purpose of making simulations and specifying the actual components for implementing the 180 W prototype, with the current flowing into the phase. In addition, simulation results are obtained, which show that it is possible to find the maximum power point of the PV module by implementing the MPPT algorithm without having to measure the current of the PV module.

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SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection

SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep S...

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2021 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2021

作者： Wang, Hengli Fan, Rui Cai, Peide Liu, Ming The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering China College of Electronic and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China

ISBN: (纸本)9781665417143

Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this task. In particular, SNE-RoadSeg, our previously proposed method based on a surface normal estimator (SNE) and a data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace detection. However, SNE-RoadSeg is computationally intensive, and it is difficult to execute in real time. To address this problem, we introduce SNE-RoadSeg+, an upgraded version of SNE-RoadSeg. SNE-RoadSeg+ consists of 1) SNE+, a module for more accurate surface normal estimation, and 2) RoadSeg+, a data-fusion DCNN that can greatly minimize the trade-off between accuracy and efficiency with the use of deep supervision. Extensive experimental results have demonstrated the effectiveness of our SNE+ for surface normal estimation and the superior performance of our SNE-RoadSeg+ over all other freespace detection approaches. Specifically, our SNE-RoadSeg+ runs in real time, and meanwhile, achieves the state-of-the-art performance on the KITTI road benchmark. Our project page is at https://***/sne-roadseg-plus. © 2021 IEEE.

关键词： Data fusion

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Using Natural Language Processing to Predict Costume Core Vocabulary of Historical Artifacts

arXiv

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arXiv 2022年

作者： Muralikrishnan, Madhuvanti Hilal, Amr Miller, Chreston Smith-Glaviana, Dina Computer Science Dept Virginia Tech BlacksburgVA United States Informatics Lab Virginia Tech BlacksburgVA United States Apparel Housing and Resource Management Dept Virginia Tech BlacksburgVA United States The Department of Computer and Systems Engineering Alexandria University Egypt

Historic dress artifacts are a valuable source for human studies. In particular, they can provide important insights into the social aspects of their corresponding era. These insights are commonly drawn from garment pictures as well as the accompanying descriptions and are usually stored in a standardized and controlled vocabulary that accurately describes garments and costume items, called the Costume Core Vocabulary. Building an accurate Costume Core from garment descriptions can be challenging because the historic garment items are often donated, and the accompanying descriptions can be based on untrained individuals and use a language common to the period of the items. In this paper, we present an approach to use Natural Language Processing (NLP) to map the free-form text descriptions of the historic items to that of the controlled vocabulary provided by the Costume Core. Despite the limited dataset, we were able to train an NLP model based on the Universal Sentence Encoder to perform this mapping with more than 90% test accuracy for a subset of the Costume Core vocabulary. We describe our methodology, design choices, and development of our approach, and show the feasibility of predicting the Costume Core for unseen descriptions. With more garment descriptions still being curated to be used for training, we expect to have higher accuracy for better generalizability. Copyright © 2022, The Authors. All rights reserved.

关键词： Social aspects

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Building a Patient-Centered Virtual Hospital Ecosystem Using Both Access Control and CNN-Based Models

Building a Patient-Centered Virtual Hospital Ecosystem Using...

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IEEE International Conference on Big Data

作者： Sara A. Alsalamah Shada Alsalamah Walaa N. Ismail Hessah Alsalamah Chang-Tien Lu Department of Computer Science Virginia Tech Falls Church VA USA College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Riyadh KSA College of Computer and Information Sciences King Saud University Riyadh KSA Department of Digital Health and Innovation Science Division World Health Organization Geneva Switzerland Department of Management Information Systems Al Yamamah University Riyadh KSA College of Engineering and Architecture Al Yamamah University KSA

ISBN: (纸本)9781665480468

Virtual hospitals empower traditional hospitals to deliver more accessible, affordable, and comprehensive patient-centered (PC) care services. However, the legacy information systems of traditional hospitals are ill-equipped to support the needs of virtual hospitals. We propose a holistic virtual hospital ecosystem design that addresses these issues. We have developed two models. The first is a VHealth-CNN model that extracts PC knowledge from multi-sourced biomedical big data by (1) extracting disease health-related features; (2) structuring the relevant health-related features as per the pre-identified factors; (3) training a convolutional neural network (CNN) double-layer structure, where we select significant health-related features in the first layer, and classify the positively and negatively correlated features in the second one; and (4) generating disease class outputs representing the PC knowledge. The second model is a granular VHealth-AC model that seamlessly grants healthcare practitioners at a hub hospital remote access to PC knowledge at the right point of care. We have deployed a granular 5-tier PC information classification scheme to enforce information security rules across hospitals. In addition, we examined the feasibility of the proposed design through a tele-monitoring service experimental case study for predicting obesity, hypertension, and diabetes. The experimental results show that the proposed model predicts obesity, hypertension, and diabetes diagnoses with 91.3%, 93.5%, and 95% accuracy, respectively. Finally, our ecosystem design should encourage the adoption of virtual hospitals and the adoption of virtual healthcare services as a new norm.

关键词： Knowledge engineering Hypertension Obesity Hospitals Biological system modeling Ecosystems Big Data

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Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base

arXiv

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arXiv 2024年

作者： Wang, Yuchen Lin, Yin-Shan Huang, Ruixin Wang, Jinyin Liu, Sensen School of Architecture University of Hawaii at Manoa 2410 Campus Road HonoluluHI96822 United States Khoury College of Computer Science Northeastern University 360 Huntington Ave BostonMA02115 United States Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science Stony Brook University 100 Nicolls Rd Stony BrookNY11794 United States Department of Electrical and Systems Engineering Washington University in St. Louis 1 Brookings Dr St. LouisMO63130 United States

This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research. © 2024, CC BY-NC-ND.

关键词： Human computer interaction

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