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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Md. Wafiur Rahman Israt Anjum Soad As Hamim Mahi Md Faisal Karim Abdul Ahad Md. Hasan Imam Bijoy Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Electrical Engineering and Computer Science The University of Toledo Toledo USA Department of Electrical Engineering and Computer Science North South University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The wholly wholesale transition into remote and hybrid workspaces is characteristic of modern-day work settings. As more and more firms adopt such flexible models, it is important to note, their implications on employee wellness and productivity. This paper uses machine learning techniques in order to understand the effects of mental distancing and hybridization on various key results such as the mental state of the employees, balance between their work and private life, separation from the society, as well as productivity levels. With a sample size of 5000 employees collect from kaggle, we determine the advantages and disadvantages these work models present. In other regards, geographical larger shifts create practical benefits including greater flexibility and autonomy plus productivity. However, such dramatic shifts create higher work-related stress, greater social tension and isolation, as well as challenges with workplace culture. AI, or more precisely machine learning, uncovers concepts in relation to work models in this paper, fico that enable firms to better personate their constituents and the workforce. Reasonable conclusions could be drawn so as flexible working arrangements benefit in reducing work-life conflict however improved organizational structure within the companies is needed to have such benefits due. Mental health as one of the resources, alongside technology will assist in such transition. This paper is a step forward for HR practitioners.

关键词： Productivity Computational modeling Employment Focusing Mental health Machine learning Companies Data collection Remote working Data models

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Hardware-Based Randomized Encoding for Sensor Authentication...

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2022 IEEE Texas Power and Energy Conference, TPEC 2022

作者： Hutto, Kevin Grijalva, Santiago Mooney, Vincent School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering School of Computer Science Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781665479028

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems. © 2022 IEEE.

关键词： SCADA systems

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Analysis of Trie-Based Lan File Search Engine

Analysis of Trie-Based Lan File Search Engine

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Shanmugasundaram, C. Umamaheswari, C. Suriyaa, V. Tr, Kartikeyan Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering India St. Thomas College of Arts and Science Department of Computer Science with Data Science India Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350315127

Searching and Accessing files in a computer device is one of the most important and basic task performed by the computer today. Each Operating System has a dedicated search bar for this, But however, In a network encompassing a multitude of systems, retrieving a specific file from various systems can pose a formidable task. Put differently, users frequently find it challenging to recollect the precise location where they've stored a particular file. This undertaking offers a user-friendly application interface wherein the user inputs the file's name, subsequently receiving both the precise file path and the IP address of the computer device within the network as output. We can access the file by clicking on the path generated. Accessing Files from multiple systems in a network is quite difficult and a Slow process and there are not many Applications which serve this purpose. We aim to overcome this problem by providing an efficient Trie Data Structure based Searching Algorithm to search and access files in a network from any node in the Local Area Network. © 2023 IEEE.

关键词： Local area networks

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Accurate Distinction of Weak and Strong AC Grids for Emerging Hierarchical-Infeed LCC-UHVDC Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 772-777页

作者： Hao Xiao Xianzhong Duan Yi Zhang Department of Electrical and Computer Engineering University of ManitobaWinnipeg R3T 2N2ManitobaCanada State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security and High Efficiency Key LaboratorySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina RTDS Technologies Inc. Winnipeg R3T 2E1ManitobaCanada

Distinction of weak and strong AC grids for emerging hierarchical-infeed LCC-UHVDC systems is important for planning and operation departments. However, accuracy of earlier distinction methods is limited as they were developed by empirical reasoning without rigorous theoretical analysis. Hence in this letter, hierarchical-infeed interactive effective short-circuit ratio (HIESCR) index is first used for strength evaluation of HIDC systems with complex inter-inverter interactions considered. Boundary HIESCR (BHIESCR) is also introduced in the proposed distinction method of weak and strong AC grids. That is, weak (or strong) AC grids are, respectively, identified when HIESCR is less (or greater) than BHIESCR. Second, it is shown BHIESCR remains almost unchanged as 3.0 versus various system parameters and rated operation variables based on rigorous theoretical analysis. This salient feature makes the proposed method more accurate than earlier methods. Finally, the proposed method is validated by simulations based on the PSCAD/EMTDC program.

关键词： Hierarchical-Infeedinteractive effective short-circuit ratio hierarchical-infeed LCC-UHVDC systems inter-inverter interactions strong AC grids weak AC grids

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Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Novel NBA Fantasy League driven by Engineered Team Chemistry...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Arkanath, Ganesh Gupta, Nishad Kurban, Hasan Sharma, Parichit Madhavan, K.R. Buxton, Elham Khorasani Dalkilic, Mehmet M. Texas A&m University at Qatar Electrical and Computer Engineering Doha Qatar Indiana University Computer Science Department BloomingtonIN United States Indiana University Data Science Program BloomingtonIN United States University of Illinois Computer Science Department SpringfieldIL United States

ISBN: (纸本)9798350324457

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position;(2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA's API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan. © 2023 IEEE.

关键词： big data data-mining feature-engineering nba-fantasy-league python-flask

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Physically Unclonable and Reconfigurable Circuits for IP Protection: Opportunities and Challenges

Physically Unclonable and Reconfigurable Circuits for IP Pro...

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IEEE computer Society Annual Symposium on VLSI

作者： Mrittika Chowdhury Mahmudul Hasan Tamzidul Hoque Md Sakib Hasan Dept. of Electrical and Computer Engineering University of Mississippi Dept. of Electrical Engineering and Computer Science University of Kansas

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Solutions to protect Hardware Intellectual Property (IP) are crucial to safeguard against various hardware security threats, including IP piracy, theft, and reverse engineering. Logic locking, a prominent design transformation technique, aims to mitigate these attacks by introducing additional logic into the circuit to enable the locking of design functionality and hiding of the design intent based on a secret key. However, most existing logic locking schemes are vulnerable to a range of invasive and non-invasive attacks that can unlock the design, compromising their effectiveness. In this paper, we summarize these major threats and discuss a potential approach of replacing conventional logic gates with analog chaos-based gates to mitigate these threats. The novel chaos-based locking mechanism offers unclonability and reconfigurability, making it challenging for attackers to apply the well-known attack vectors, including SAT-based attacks, physical attacks, and side-channel attacks. We also discuss the challenges of integrating this novel approach compared to an existing locking method and provide a discussion on future research directions to address them.

关键词： Surveys Hardware security Reverse engineering Side-channel attacks Logic gates Very large scale integration Vectors

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Research on Pupil Segmentation Network Based on Improved TransUNet 8

Research on Pupil Segmentation Network Based on Improved Tra...

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8th International Conference on computer Graphics and Virtuality, ICCGV 2025

作者： Jianting, Liu Huazhong, Xiang Zexi, Zheng Dawei, Zhang Songlin, Zhuang College of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781510689213

To address the critical challenge of pupil segmentation accuracy in medical image analysis, this paper proposes an innovative pupil segmentation method based on an enhanced TransUNet deep neural network. The proposed architecture introduces three key improvements: a novel multi-kernel self-selective attention mechanism that optimizes local feature extraction through weighted fusion of different convolution modules;a modified cross-attention Transformer that enables efficient parallel processing of spatial information in both horizontal and vertical dimensions;and an advanced feature fusion module that effectively integrates global and local information through multi-scale feature weighting. Extensive experiments on the OpenEDS dataset demonstrate the method's superior performance, achieving state-of-the-art results with average precision of 97.7%, average recall of 97.41%, and average Dice coefficient of 98.15%. These significant improvements in segmentation accuracy not only advance the field of medical image analysis but also provide a robust foundation for applications in ophthalmological diagnosis, human-computer interaction, and cognitive science research. The proposed methodology offers valuable insights into designing efficient attention mechanisms for medical image segmentation tasks, contributing to both theoretical understanding and practical applications in healthcare imaging systems. © 2025 SPIE.

关键词： Deep neural networks

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Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4322-4343页

作者： Al-Maliki, Shawqi Qayyum, Adnan Ali, Hassan Abdallah, Mohamed Qadir, Junaid Hoang, Dinh Thai Niyato, Dusit Al-Fuqaha, Ala Division College of Science and Engineering Doha34110 Qatar Information Technology University Lahore54000 Pakistan University of New South Wales Sydney Sydney2052 Australia Qatar University Department of Computer Science and Engineering College of Engineering Doha34110 Qatar University of Technology Sydney School of Electrical and Data Engineering Sydney2052 Australia Nanyang Technological University College of Computing and Data Science 639798 Singapore

Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples - input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community. © 2020 IEEE.

关键词： Taxonomies

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