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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Evaluation User Acceptance of Mobile Virtual Reality Application for English Learning 1

Evaluation User Acceptance of Mobile Virtual Reality Applica...

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1st International Conference on Information system and Information technology, ICISIT 2022

作者： Chandra, Albert Yakobus Prasetyaningrum, Putri Taqwa Suria, Ozzi Santosa, Paulus Insap Nugroho, Lukito Edi Pratama, Irfan Universitas Mercu Buana Yogyakarta Department of Information System Faculty of Information Technology Indonesia Universitas Gadjah Mada Department of Electrical Engineering and Information Technology Faculty of Engineering Yogyakarta Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

This research develops virtual reality that integrates media elements such as sound, animation, and video with virtual world to create an immersive user experience. It is aimed to provide more interactivities and interesting features in supporting learning activities, such as using Cardboard to observe the virtual world and interact with virtual objects. Previously, a prototype was built to evaluate (high school) students' interests in using virtual reality technology as a supporting media to learn English language subject at high schools in Yogyakarta. Earlier research shows that students agreed to use of virtual reality prototype had been useful to support their learning activities. In this present research, a mobile application prototype is developed and evaluated using TAM methodology in order to understand the user behavior towards the application. The application of TAM in the field of learning and teaching for various learning domains implemented to understand the acceptance from the users while using Virtual Reality-based learning media. The TAM model It is a model can explain that the user's point of view will determine their attitude in accepting Virtual Reality-based learning applications. The evaluation results from this study showed that respondent is enjoyable to use this application (4.37 mean point) and 3.98 (mean point) feels that Virtual reality application is useful for learning English. © 2022 IEEE.

关键词： Virtual reality

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Failure Detection from the Knocking Sounds Using Convolutional Neural Network 38

Failure Detection from the Knocking Sounds Using Convolution...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Areerob, Punyapat Sum, Rithea Khongprasongsiri, Chanon Boonto, Sudchai King Mongkut's University of Technology Thonburi Department of Control System and Instrumentation Engineering Bangkok10140 Thailand National Astronomical Research Institute Thailand

ISBN: (纸本)9798350302196

Toilet quality assurance is a crucial process in ensuring lavatories meet rigorous performance, durability, and hygiene standards. The current standard of Maximum Performance (MaP) testing faces challenges, leading researchers to explore innovative approaches such as sound source classification for quality assurance. This approach involves collecting a diverse dataset of lavatory sounds and extracting relevant acoustic features. Deep learning models, particularly convolutional neural networks (CNNs), are trained on these features to accurately classify sound sources. The trained models were evaluated and compared by considering metrics such as classification accuracy, computational complexity, and model parameters. This paper performs these tests and chooses the most effective model to enhance the quality assurance process for toilets. Incorporating sound source classification techniques has several benefits, including the optimization of testing processes, non-intrusive performance assessment, and efficient resource utilization through targeted testing and troubleshooting. By improving the standards of lavatory quality, this approach ensures enhanced performance, durability, and hygiene of lavatories. © 2023 IEEE.

关键词： Convolution

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Smart Traffic Control system for Dubai: A Simulation Study Using YOLO Algorithms

Smart Traffic Control System for Dubai: A Simulation Study U...

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2023 IEEE International Conference on Electro Information technology, eIT 2023

作者： Al Mamlook, Rabia Emhamed Zahrawi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Shresth, Sujeet University of Al-Zawiya Industrial Engineering and Engineering Management Libya Trine University Indiana United States British University in Dubai Engineering and Information Technology Dubai United Arab Emirates Yarmouk University Department of Computer Science Information Technology and Computer Science Irbid211633 Jordan Wayne State University Department of Industrial and System Engineering DetroitMI48201 United States

ISBN: (纸本)9781665493765

Dubai's growing population and public transportation have led to an increase in vehicular traffic and associated challenges. To tackle these issues, there is a rising interest in using machine learning (ML)techniques to improve the city's traffic control system. This study aims to explore the potential of ML in enhancing traffic management and creating a sustainable urban environment. A novel approach to traffic management in Dubai that combines AI and ML algorithms has been proposed, with the potential to significantly improve traffic flow and safety. A simulation study on a smart traffic control system that utilizes YOLO algorithms for real-time vehicle detection and counting is presented. The system optimizes traffic light timings and balances traffic load among different roads to reduce congestion and improve traffic flow. The simulation results demonstrate that the system is highly effective in adapting to changing traffic conditions and reducing congestion. The study concludes that the use of ML algorithms such as YOLO has the potential to revolutionize traffic management in urban areas, leading to a more efficient and sustainable transportation system. Further research and development in this area could bring significant benefits to both motorists and the environment. © 2023 IEEE.

关键词： Smart city

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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Computer systems Science & engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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HLMEA: Unsupervised Entity Alignment Based on Hybrid Language Models 39

HLMEA: Unsupervised Entity Alignment Based on Hybrid Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Xiongnan Wang, Zhilin Chen, Jinpeng Yang, Liu Oh, Byungkook Hwang, Seung-Won Li, Jianqiang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Alibaba Group China School of Computer Science Beijing University of Posts and Telecommunications China Xiangjiang Laboratory China School of Computer Science and Engineering Central South University China Computer Science & Engineering Konkuk University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Entity alignment (EA) is crucial for integrating knowledge graphs (KGs) constructed from diverse sources. Conventional unsupervised EA approaches attempt to eliminate human intervention but often suffer from accuracy limitations. With the rise of large language models (LLMs), leveraging their capabilities for EA presents a promising direction. However, it introduces new challenges: formulating the LLM-based EA problem and extracting the background knowledge in LLMs to realize EA without human intervention. This paper proposes HLMEA, a novel hybrid language model-based unsupervised EA method. HLMEA formulates the EA task into a filtering and single-choice problem and synergistically integrates small language models (SLMs) and LLMs. Specifically, SLMs filter candidate entities based on textual representations generated from KG triples. Then, LLMs refine this selection to identify the most semantically aligned entities. An iterative self-training mechanism allows SLMs to distill knowledge from LLM outputs, enhancing the EA ability of hybrid language models in subsequent rounds cooperatively. We also conducted extensive experiments on benchmark datasets to evaluate HLMEA's performance. The results demonstrate that HLMEA significantly outperforms unsupervised and even supervised EA baselines, proving its potential for scalable and effective EA across large KGs. The code and data are available at https://***/xnjin-ai/HLMEA. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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IoT-Enabled RFID in Supply Chain Management: A Comprehensive Survey and Future Directions

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Journal of Computer and Communications 2024年第11期12卷 207-223页

作者： Jannatul Ferdousmou Mani Prabha MD Omar Farouk MD Samiun Hasan Mahmud Sozib Al Modabbir Zaman Department of Business Administration International American University Los Angeles CA USA Department of Industrial System Engineering School of Engineering &Technology (SET) Asian Institute of Technology (AIT) Pathum Thani Thailand Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

Radio frequency identification (RFID) has emerged as a pivotal technology in supply chain management (SCM), significantly enhancing its efficiency and effectiveness. When integrated with the internet of things (IoT) to form RFID-IoT, this technology brings transformative advancements to SCM, enabling automated sensing, pervasive computing, and ubiquitous data access across the entire supply chain, from manufacturers and distributors to retailers and consumers. This integration facilitates real-time identification and monitoring of products, enhances various processes, improves logistic tracking, and ensures better product quality management. Despite its promising benefits, the adoption of RFID-IoT in SCM faces several challenges, including technical complexities, data security concerns, and high implementation costs. However, the future potential of RFID-IoT technology remains substantial. It is anticipated that further integration with other emerging technologies, such as block chain and artificial intelligence, will lead to more comprehensive and robust SCM solutions, offering unprecedented levels of transparency, efficiency, and automation in supply chain operations.

关键词： Radio Frequency Identification Supply Chain Management Internet of Things Logistic Tracking Data security Wireless Sensor Networks Block Chain Artificial Intelligence RFID-IoT Integration Real-Time Supply Chain Monitoring Inventory Management Automation Logistics Optimization

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Fruit Fly Optimization Technique Based Regulator for LFC of Conventional Power system with the Integration of Plugin Electric Vehicles 5

Fruit Fly Optimization Technique Based Regulator for LFC of ...

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5th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2023

作者： Kalyan, Ch. Naga Sai Srikanth Goud, B. Reddy, Ch. Rami Bajaj, Mohit Tulsky, Vladimir N. Kamel, Salah Vasireddy Venkatadri Institute of Technology Electrical and Electronics Engineering Guntur522 508 India Electrical and Electronics Engineering Anurag University Venkatapur Ghatkesar Medchal Hyderabad500 088 India Malla Reddy Engineering College Electrical and Electronics Engineering Maisammaguda Secunderabad500 100 India Department of Electrical Engineering Dehradun248002 India Moscow Power Engineering Institute Electrical Power System Department Moscow111250 Russia Aswan University Faculty of Engineering Department of Electrical Engineering Aswan81542 Egypt

ISBN: (纸本)9798350399523

In this paper, a recent soft computing approach of fruit fly optimization technique (FFOT) based tilt-integral-derivative plus filter (TIDN) regulator is enforced to attain the frequency stabilization in the two-area conventional power system (TACPS). The TACPS integrates the Hydro-thermal units in each area and carried out the dynamical analysis for 10% step load disturbance (SLD) in area-1. The supremacy of the adopted FFOT is demonstrated with other approaches of moth flame optimization (MFO) and flower pollination algorithm (FPA). The TACPS is considered with the communication time delays (CTDs) as the realistic constraint to conduct the system dynamic analysis close to practical nature. Further, the TACPS is integrated with the plugin electric vehicles (PEVs) in area-1 to attain the enhancement in system dynamic performance. © 2023 IEEE.

关键词： Fruits

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Trajectory planning method with grinding compensation strategy for robotic propeller blade sharpening application

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Journal of Manufacturing Processes 2023年 86卷 294-310页

作者： Cheng, Yong-Sheng Yen, Shih-Hsiang Bedaka, Amit Kumar Shah, Syed Humayoon Lin, Chyi-Yeu Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei106 Taiwan Taiwan Building Technology Center National Taiwan University of Science and Technology Taipei106 Taiwan

A propeller is the main part of a water-borne carrier's drive system. Geometrical shape accuracy and sharpness on the cutting side of the propeller's blade play vital roles to minimize fluid resistance and improve transmission efficiency. For Jet Ski and small boats, the significant thickness variation along the blade edge is embedded in the lost-wax casting process. The automatic trajectory planning for the robotic propeller blade's sharpening operation is challenging due to its complicated curved shape and the tiny gaps between two connected blades. The trajectory planning for robotic grinding based on the edge thickness distribution of the standard model may result in over- or under-cutting due to the highly inconsistent shape deformation caused by the casting process. To address the aforementioned challenges, this study proposes a fully automated robot grinding system. The proposed method combines a novel trajectory planning approach based on a parametric and point cloud model with a 3D scanning on edge-based trajectory adjustment approach. Moreover, to improve the grinding accuracy, a laser-vision sensor-based detection algorithm is also developed for grinding compensation to counteract the geometrical variation caused by the casting process. The effectiveness of the proposed system is validated through numerous experimental trials using a 6-DOF industrial robot. The research findings showcase that the proposed technique is feasible, stable, and effective for robotic propeller blade edge sharpening operations. © 2023

关键词： Grinding (machining)

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Prototype for Current and Voltage Monitoring of Distribution Transformer Based on WiFi

Prototype for Current and Voltage Monitoring of Distribution...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Yudianto, Firman Herlambang, Teguh Adinugroho, Mukhtar Susanto, Fajar Annas Wulan, Tri Deviasari Farid, Imam Wahyudi Unusa Faculty of Business Economics and Digital Technology Department of Information System Indonesia Unusa Faculty of Business Economics and Digital Technology Department of Management Indonesia Its Vocational Faculty Department of Electrical Automation Engineering Indonesia

ISBN: (纸本)9798350309225

A transformer is a key component in the electrical power distribution system. To maintain the continuity and reliability of the power system, it is essential to ensure the reliability of the energy distribution system. Factors affecting the quality of electrical energy include current, voltage, and temperature. With an increase in load, the transformer's temperature and current can rise, and the increase in current can be influenced by excessive load, thus raising the transformer's temperature. The temperature rise can also be influenced by the environmental conditions inside the transformer room. Excessive temperature in the transformer room can lead to explosions. Therefore, this thesis research aims to design a prototype monitoring device based on WiFi for parameters such as current, voltage, and temperature, to provide real-time information about the transformer's condition. The results of this research show that the prototype monitoring device for the transformer provides values very close to manual calculations, with the largest error percentages being 2.18% for current measurement, 1.04% for voltage measurement, and 3.3% for temperature measurement. This prototype monitoring device also provides information to the user when the current value in the circuit reaches 2A, making it easier for the user to identify potential transformer faults before they lead to damage. The transformer's efficiency ranges from 99.54% to 99.82%. Maintenance is required for the transformer as it approaches high temperatures to ensure optimal transformer performance. © 2023 IEEE.

关键词： Temperature measurement

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