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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Traditional power system is facing challenges demanding new operational requirements to meet targets of Net Zero Emissions by 2050. Aggregators are playing progressively important role in the demand response (DR) electricity market but are often riddled with deep level of market monopoly and lack of transparency/secrecy. Emerging real-time information technology (IT) applications and novel modelling of digital twins (DT) of individual electricity assets are challenging this position by promising improvements and openness that allows TSO direct access to available demand side flexibility. Additionally, DT technologies are facilitating how demand response services are delivered to end-users by allowing individual assets participation at the atomic level. In this study, the application potentials of assets’ DT in participating in the demand response electricity market was examined. Again, an overview of DT applications for DR was conducted. The novelty of this study is highlighted in development of new approach that facilitates individual end-user assets’ contribution to demand response efforts. The research identifies key useful questions that might serve as inspiration for stakeholders and policy-makers to further close existing gaps in the field of DT, smart-grid and demand response.

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Role of masks in mitigating viral spread on networks

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Physical Review E 2023年第1期108卷 014306-014306页

作者： Yurun Tian Anirudh Sridhar Chai Wah Wu Simon A. Levin Kathleen M. Carley H. Vincent Poor Osman Yağan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 USA Thomas J. Watson Research Center IBM Yorktown Heights New York 10598 USA Department of Ecology & Evolutionary Biology Princeton University Princeton New Jersey 08544 USA Software and Societal Systems School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: The probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and tradeoffs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Last, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence.

关键词： Cascades Epidemic Percolation phase transition Spreading Multilayer & multiplex networks Poisson degree distribution networks Random graphs Social networks SIR model Spreading models Uncorrelated percolation

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering Information filters Information age Reliability

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Discovering Complex Correlations Among Multiple IoT Devices in Smart Environments

Discovering Complex Correlations Among Multiple IoT Devices ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Andrew D'Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Air Force Research Laboratory Rome NY USA

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.

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VoiceGuard: An Effective and Practical Approach for Detecting and Blocking Unauthorized Voice Commands to Smart Speakers

VoiceGuard: An Effective and Practical Approach for Detectin...

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International Conference on Dependable systems and Networks (DSN)

作者： Xuening Xu Chenglong Fu Xiaojiang Du E. Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems UNC Charlotte Charlotte NC USA Information Directorate Air Force Research Laboratory Rome NY USA

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Energy consumption of on-device machine learning models for IoT intrusion detection

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Internet of Things (Netherlands) 2023年 21卷

作者： Tekin, Nazli Acar, Abbas Aris, Ahmet Uluagac, A. Selcuk Gungor, Vehbi Cagri Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University Miami 33174 FL United States Department of Software Engineering Erciyes University Kayseri 38280 Turkey Department of Computer Engineering Abdullah Gul University Kayseri 38080 Turkey

Recently, Smart Home systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy consumption. To the best of our knowledge, no study has been conducted to analyze the energy consumption of ML-based IDS. Therefore, in this paper, we perform a comparative analysis of on-device ML algorithms in terms of energy consumption for IoT intrusion detection applications. For a thorough analysis, we study the training and inference phases separately. For training, we compare the cloud computing-based ML, edge computing-based ML, and IoT device-based ML approaches. For the inference, we evaluate the TinyML approach to run the ML algorithms on tiny IoT devices such as Micro Controller Units (MCUs). Comparative performance evaluations show that deploying the Decision Tree (DT) algorithm on-device gives better results in terms of training time, inference time, and power consumption. © 2022 Elsevier B.V.

关键词： Energy consumption Intrusion detection IoT On-device machine learning Smart home

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Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

SSRN

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SSRN 2023年

作者： Li, Ping Zhang, Yu Yuan, Li Xiao, Huaxin Lin, Binbin Xu, Xianghua School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electrical and Computer Engineering Peking University Beijing China College of Systems Engineering National University of Defense Technology Changsha China College of Computer Science Zhejiang University Hangzhou China

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high © 2023, The Authors. All rights reserved.

关键词： Pixels

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detection in Water Distribution Plant

A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detecti...

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Innovations in Intelligent systems and Applications Conference

作者： Ali Kivanc Sahin Bora Cavdar Ramazan Ozgur Dogan Selen Ayas Busra Ozgenc Mustafa Sinasi Ayas Electrical and Electronics Engineering Erzurum Technical University Erzurum Türkiye Energy Systems Engineering Karadeniz Technical University Trabzon Türkiye Software Engineering Gümüshane University Gümüşhane Türkiye Computer Engineering Karadeniz Technical University Trabzon Türkiye Electrical and Electronics Engineering Karadeniz Technical University Trabzon Türkiye

To reduce potential threats to public health and economic losses, water distribution systems, which are critical parts of industrial control systems, require accurate and effective anomaly detection. The successful incorporation of machine learning models into anomaly detection procedures has been made possible by recent developments in computer science. In this research, a hybrid CNN-LSTM framework for anomaly detection in water distribution systems is presented using Python programming language in the JupyterLab environment. The performance of the proposed CNN-LSTM network model is carefully evaluated on the publicly available WADI dataset, which contains several types of attacks for a water distribution testbed, using a statistical window-based anomaly detection approach. The experimental results show acceptable precision, recall and F1 scores of 0.8619, 0.7041, and 0.775, respectively, demonstrating the efficiency of the framework in detecting anomalies in the water distribution system.

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