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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Srinivasan, Sruthi Dewan, M. Ali Akber School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing and Information Systems Athabasca University Faculty of Sceinece and Technology AB Canada

ISBN: (纸本)9781665456371

Online learning has gained traction over recent years, especially since online education has become more widespread. However, it comes with its own set of challenges of which high dropout is still a major one. Identifying at-risk learners at an early stage is pivotal to offering personalized attention that can potentially prevent them from dropping out from the online courses. This work proposes two methods to analyze students' progress in an online course and subsequently identify dropout prone students. The first method performs fusion of course activity features by concatenating previous weeks' features before training. The second method extracts course activity features from the start date of the courses to a current week instead of concatenating as the first method does. A set of machine learning models and an ensemble framework were trained and tested on these two types of feature sets. On evaluating the models, the benchmark dataset KDDCup15 has been used, where the first method yielded an F1-score 91% while the second method yielded a score 92%. It was observed that both feature fusion methods produce comparable results although we expected that the concatenation of the features over time would produce better results. We also found that using features over a longer duration of time can help in achieving better performance. Further, ensemble model consistently outperformed the base classifiers. © 2022 IEEE.

关键词： Students

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Inductive Position Sensors based on Coupling of Coils on Printed Circuit Boards for Demanding Automotive Applications

arXiv

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arXiv 2025年

作者： Kuntz, Stefan Gerlach, Gerald Fella, Sina Robert Bosch GmbH Abstatt Germany Faculty of Electrical and Computer Engineering Dresden University of Technology Dresden Germany

Rotor position feedback is required in many industrial and automotive applications, e.g. for field-oriented control of brushless motors. Traditionally, magnetic sensors, resolvers or optical encoders are used to measure the rotor position. However, advances in inductive sensing concepts enable a low-cost, high-precision position measurement principle which is robust against magnetic stray fields exceeding 4000 A/m. The operating principle is based on the coupling of a transmitter coil with several receiver coils in the megahertz frequency range. The coils are part of a printed circuit board (PCB) which also comprises circuitry for demodulation and signal processing. The transmitter coil induces eddy currents in an electrically conductive passive coupling element, which provides position-dependent amplitude modulation. The voltage induced in the receiver coils encodes the rotor angle information, typically in quadrature signals. The coupling element requires no rare-earth materials and can be made of stainless steel, for instance. The PCB-based design of the sensor offers considerable flexibility in optimizing its performance. By tailoring the coil geometry and arrangement, accuracy, air gap and overall sensor dimensions can be adjusted to meet a broad range of application-specific requirements. A sensor design sample exhibits a mechanical angle error less than 0.02∘ (0.1∘ electrical) in both, finite-element simulation and test bench measurement, with good agreement. © 2025, CC BY-NC-ND.

关键词： Inductive sensors

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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection

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IEEE Access 2025年 13卷 102109-102132页

作者： Onur Polat Ali Ayid Ahmad Saadin Oyucu Enes Algül Ferdi Doäan Ahmet Aksöz Department of Computer Engineering Bingöl University Bingöl Türkiye Department of Electrical Engineering College of Engineering University of Kirkuk Kirkuk Iraq Department of Computer Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Engineering Adıyaman Üniversitesi Adiyaman Türkiye Department of Electrical and Electronics Engineering Kayseri University Kayseri Türkiye

This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. CICIoT 2023 is used as the dataset. ADAM optimization algorithm with cross-entropy loss is used to eliminate overfitting and training is performed. Within the scope of the study, the proposed model is compared by applying it with six deep learning architectures (hybrid CNN-LSTM, Non-Local Neural Network (NLNN), Residual Attention Network (RAN), Dual Attention Network (DANet), Transformer-CNN and Attentional CNN). The obtained results show that the proposed CNN-LSTM model outperforms other complex architectures and achieves a high test accuracy of 99.23%. It has demonstrated remarkable performance according to precision, recall and F1 evaluation metrics in detecting distributed denial of service (DDoS) and denial of service (DoS) attacks. The proposed model successfully identifies Mirai botnet variants and fragmentation-based attacks. Although other models, Transformer-CNN (98.81%) and DANet (98.07%), provide high performance, they fall behind the superior temporal modeling capabilities of CNN-LSTM. When the obtained findings are examined, they highlight the relative strengths of various deep learning approaches for IoT security applications. The performances of the implemented deep learning models reached accuracies exceeding 96%, demonstrating the importance of IoT-based SCADA systems against evolving cyber threats. The study revealed the superior successes of deep learning-based approaches for IoT security.

关键词： Security SCADA systems Internet of Things Malware Convolutional neural networks Protocols Long short term memory Feature extraction Access control Real-time systems

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Data-Driven Model for Heat Load Prediction in Buildings Connected to District Heating Networks

Data-Driven Model for Heat Load Prediction in Buildings Conn...

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International Multidisciplinary Conference on computer and Energy Science (SpliTech)

作者： Alaeddine Hajri Roberto Garay-Marinez Ana M. Macarulla Mohamed Amin Ben Sassi Department of Computer Systems Engineering Mediterranean Institute of Technology Tunis Tunisia Faculty of Engineering Institute of Technology University of Deusto Avda. Universidades Spain

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

In this study we investigate the heat load patterns in one building using multi-step forecasting model. We combine the Autoregressive models that use multiple eXogenous variables (ARX) with Seasonally adaptable Time of Week and Climate dependent models (S-TOW-C), to obtain a robust and accurate regression model that we called S-TOW-C-ARX used in time series forecasting. Based on the experiment results, it has been shown that the proposed model is suitable for short term heat load forecasting. The best forecasting performance is achieved in winter term where the prediction values are from 4 to 20 % away from the targets, which are commonly seen as very good values.

关键词： Adaptation models Accuracy Load forecasting District heating Buildings Predictive models Data models

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Performance Evaluation of Intrusion Detection System with SVM

Performance Evaluation of Intrusion Detection System with SV...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ricky Aurelius Nurtanto Diaz I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Bali Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection Systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset.

关键词： Support vector machines Performance evaluation Accuracy Metaheuristics Intrusion detection Machine learning Network security Intelligent systems Cybernetics

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Influence of Local Lag on Reaction Force in Networked Virtual Environment with Haptic Sense

Influence of Local Lag on Reaction Force in Networked Virtua...

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computer Applications (ICCA), IEEE Conference on

作者： Khaing Zar Win Yutaka Ishibashi Khaing Htet Win Faculty of Computer Science University of Computer Studies Yangon Yangon Myanmar Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Faculty of Computer Systems and Technologies University of Computer Studies Yangon Yangon Myanmar

ISBN: (数字)9798350353624

ISBN: (纸本)9798350353631

This paper investigates the influence of local lag on the reaction forces in a networked virtual maze system incorporating haptic feedback by experiment. In the experiment, participants move a box from the starting position to the target point by using Raising method with haptic device. We measure the average operation time and the average of average reaction force for three different moving velocities and three distinct local lags. Experimental results reveal that as the moving velocity increases and the local lag becomes higher, the two measures become larger. Thus, we illustrate that there exists a strong relationship among the average operation time, the average of average reaction force, moving velocity, and local lag, through multiple regression analysis.

关键词： Force measurement Force Virtual environments computer applications Particle measurements Control systems Time measurement

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A New ZVS Interleaved Flyback Converter 12

A New ZVS Interleaved Flyback Converter

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mojaveri, Shokouh Shabani Delshad, Majid Heydarian-Forushani, Ehsan Branch Islamic Azad University Faculty of Engineering Isfahan Iran Qom University of Technology Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

This paper presents a new auxiliary circuit for interleaved flyback converters to create soft switching conditions in the element in addition the semiconductor in the converter structure prevents the voltage spike across the switches. Also, due to the use of the ripple cancellation technique, the input current has decreased. The proposed interleaved flyback converter has been completely analyzed, a 100W laboratory sample has been developed to confirm its performance, and practical results have been provided to verify the proper performance of the circuit. © 2025 IEEE.

关键词： Flyback converter Interleaved Ripple cancellation Zero voltage switching

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Protect Your Secrets: Understanding and Measuring Data Exposure in VSCode Extensions

Protect Your Secrets: Understanding and Measuring Data Expos...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Yue Liu Chakkrit Tantithamthavorn Li Li Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Engineering Beihang University China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Recent years have witnessed the emerging trend of extensions in modern Integrated Development Environments (IDEs) like Visual Studio Code (VSCode) that significantly enhance developer productivity. Especially, popular AI coding assistants like GitHub Copilot and Tabnine provide conveniences like automated code completion and debugging. While these extensions offer numerous benefits, they may introduce privacy and security concerns to software developers. However, there is no existing work that systematically analyzes the security and privacy concerns, including the risks of data exposure in VSCode extensions. In this paper, we investigate on the security issues of cross-extension interactions in VSCode and shed light on the vulner-abilities caused by data exposure among different extensions. Our study uncovers high-impact security flaws that could allow adversaries to stealthily acquire or manipulate credential-related data (e.g., passwords, API keys, access tokens) from other extensions if not properly handled by extension vendors. To measure their prevalence, we design a novel automated risk detection framework that leverages program analysis and natural language processing techniques to automatically identify potential risks in VSCode extensions. By applying our tool to 27,261 real-world VSCode extensions, we discover that 8.5 % of them (i.e., 2,325 extensions) are exposed to credential-related data leakage through various vectors, such as commands, user input, and configurations. Our study sheds light on the security challenges and flaws of the extension-in-IDE paradigm and provides suggestions and recommendations for improving the security of VSCode extensions and mitigating the risks of data exposure.

关键词： Productivity Visualization Privacy Codes Passwords Software Vectors Natural language processing Security Software development management

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Architecture Dataware Housing and Related Issues

Architecture Dataware Housing and Related Issues

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Jain, Upma Alabdeli, Haider Bansal, Saloni Deepak, A. Singla, Atul Veena, C.H. Kaushik, Rohit Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Computer Engineering and Applications GLA University Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India Faculty of Management SRM Institute of Science and Technology NCR Campus India

ISBN: (纸本)9798350355338

In today's fast-paced business environment, making informed decisions is crucial for success. To achieve this, decision-makers are increasingly turning to data-oriented and business intelligence databases. As a result, data warehousing is becoming a popular tool for managing and analyzing information. In essence, data warehousing is an operational system that processes an organization's day-to-day transactions and stores present and historical data from various sources in a centralized computer system. This enables organizations to gain insights and make informed decisions based on their data. In this study, the architecture, classification, and challenges associated with data warehousing have been explored to provide a thorough understanding of this essential technology. © 2024 IEEE.

关键词： Data warehouses

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Turbulence Modeling Through Deep Learning: An In-Depth Study of Wasserstein GANs 4

Turbulence Modeling Through Deep Learning: An In-Depth Study...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Alghamdi, Wajdi Mayakannan, S. Sivasankar, G.A. Singh, Jagendra Ravi Naik, B. Venkata Krishna Reddy, Ch. King Abdulaziz University Faculty of Computing and Information Technology Department of Information Technology Jeddah21589 Saudi Arabia Vidyaa Vikas College of Engineering and Technology Tiruchengode Mechanical Engineering Tamilnadu Namakkal India Kit - Kalaignar Karunanidhi Institute of Technology Department of Aeronautical Engineering Coimbatore641402 India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Great Noida India St. Martin's Engineering College Department of Mechanical Engineering Telangana Secunderabad India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350300888

This research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein Gans (WGANs) to produce localized disturbances. Network features including the learning rate and loss function are examined as they pertain to the performance of the WGANs during training on turbulent data gleaned from high-resolution Direct Numerical Simulations (DNS). DNS input data and the generated turbulent structures are proven to agree qualitatively well. The projected turbulent fields are evaluated quantitatively and statistically. © 2023 IEEE.

关键词： Turbulence

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