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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Najafzadeh, Mehdi Gholami, Mostafa University of Science and Technology of Mazandaran Department of Electrical and Computer Engineering Behshahr Iran

This paper reviews the methods to identify unauthorized cryptocurrency mining customers in distribution networks. Hence, various methods and models were compared. Also, the research conducted in the field of cryptocur... 详细信息

ISBN: (纸本)9781665476232

关键词： Data mining

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Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers’ Coding Practices with Insecure Suggestions from Poisoned AI Models

Poisoned ChatGPT Finds Work for Idle Hands: Exploring Develo...

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IEEE Symposium on Security and Privacy

作者： Sanghak Oh Kiho Lee Seonhye Park Doowon Kim Hyoungshick Kim Department of Electrical and Computer Engineering Sungkyunkwan University Republic of Korea Department of Electrical Engineering and Computer Science University of Tennessee USA

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

AI-powered coding assistant tools (e.g., ChatGPT, Copilot, and IntelliCode) have revolutionized the software engineering ecosystem. However, prior work has demonstrated that these tools are vulnerable to poisoning attacks. In a poisoning attack, an attacker intentionally injects maliciously crafted insecure code snippets into training datasets to manipulate these tools. The poisoned tools can suggest insecure code to developers, resulting in vulnerabilities in their products that attackers can exploit. However, it is still little understood whether such poisoning attacks against the tools would be practical in real-world settings and how developers address the poisoning attacks during software development. To understand the real-world impact of poisoning attacks on developers who rely on AI-powered coding assistants, we conducted two user studies: an online survey and an in-lab study. The online survey involved 238 participants, including software developers and computer science students. The survey results revealed widespread adoption of these tools among participants, primarily to enhance coding speed, eliminate repetition, and gain boilerplate code. However, the survey also found that developers may misplace trust in these tools because they overlooked the risk of poisoning attacks. The in-lab study was conducted with 30 professional developers. The developers were asked to complete three programming tasks with a representative type of AI-powered coding assistant tool (e.g., ChatGPT or IntelliCode), running on Visual Studio Code. The in-lab study results showed that developers using a poisoned ChatGPT-like tool were more prone to including insecure code than those using an IntelliCode-like tool or no tool. This demonstrates the strong influence of these tools on the security of generated code. Our study results highlight the need for education and improved coding practices to address new security issues introduced by AI-powered coding assistant tools.

关键词： Surveys Codes Chatbots Encoding Software Security Task analysis

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GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction

GPR-SCSANet: Unequal-Length Time Series Normalization with S...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sun, Fangling Liang, Chuang Adali, Tülay Zhang, Daoqiang Jiang, Rongtao Calhoun, Vince D. Qi, Shile Nanjing University of Aeronautics and Astronautics Department of Computer Science and Technology Nanjing China University of Maryland Department of Computer Science and Electrical Engineering BaltimoreMD United States Yale School of Medicine Department of Radiology and Biomedical Imaging New Haven United States AtlantaGA United States

ISBN: (纸本)9798350386226

Functional magnetic resonance imaging (fMRI), as a non-invasive method to reveal brain function alterations, frequently yields time series with unequal lengths in real-world scenarios, which may arise from factors such as motion artifacts, participant state, and differing scan protocols. This variability conflicts with the traditional methods relying on isometric inputs, which poses a significant challenge for the downstream applications such as brain age prediction. To address this challenge, we introduced Gaussian Process Regression (GPR) to normalize the length of time series and proposed split-channel residual convolution (SC) and self-attention mechanisms (SA) to perform brain age estimation, called GPR-SCSANet. Results showed that the proposed framework, GPR-SCSANet, is able to fully utilize the inherent information and learn richer feature representations from unequal-length fMRI time courses, which significantly improved the prediction accuracy across 3 brain atlases and 5 prediction models. The results demonstrated the effectiveness and robustness of the proposed GPR-SCSANet, showcasing the potential for broader applications in brain age prediction task. © 2024 IEEE.

关键词： Time series

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A Statistical Evaluation of Indoor Lorawan Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis

A Statistical Evaluation of Indoor Lorawan Environment-Aware...

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European Conference on Networks and Communications (EuCNC)

作者： Nahshon Mokua Obiri Kristof Van Laerhoven Department of Electrical Engineering and Computer Science University of Siegen Germany

ISBN: (数字)9798350391800

ISBN: (纸本)9798350391817

Modeling path loss in indoor LoRaWAN technology deployments is inherently challenging due to structural obstructions, occupant density and activities, and fluctuating environmental conditions. This study proposes a two-stage approach to capture and analyze these complexities using an extensive dataset of 1,328,334 field measurements collected over 6 months in a single-floor office at the University of Siegen's Hölderlinstraße Campus, Germany. First, we implement a multiple linear regression (MLR) model that includes the traditional propagation metrics (distance, structural walls) and an extension with proposed environmental variables (relative humidity, temperature, carbon dioxide (CO 2 ), particulate matter, and barometric pressure). Using analysis of variance (ANOVA), we demonstrate that adding these environmental factors can reduce unexplained variance by 42.32%. Secondly, we examine residual distributions by fitting five candidate probability distributions: Normal, Skew-Normal, Cauchy, Student's $t$ , and Gaussian Mixture Models (GMM) with 1 to 5 components. Our results show that a four-component GMM captures the residual heterogeneity of indoor signal propagation most accurately, significantly outperforming single-distribution approaches. Given 6G's push for ultra-reliable, context-aware communications, our analysis shows that environment-aware modeling can substantially improve LoRaWAN network design in dynamic indoor IoT deployments.

关键词： 6G mobile communication Temperature measurement Analytical models Temperature Linear regression LoRaWAN Propagation losses Environmental factors Probability distribution Analysis of variance

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A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

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computers, Materials & Continua 2023年第9期76卷 2959-2978页

作者： Muhammad Firdaus Harashta Tatimma Larasati Kyung-Hyune Rhee Department of Artificial Intelligence Convergence Pukyong National UniversityBusan48513Korea School of Electrical Engineering and Informatics Institut Teknologi BandungBandung40132Indonesia College of Information Technology and Convergence Division of Computer Engineering and AIPukyong National UniversityBusan48513Korea

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

关键词： Edge intelligence federated learning differential privacy blockchain vehicular networks

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Tour Spot Recommendation System via Content-Based Filtering 16

Tour Spot Recommendation System via Content-Based Filtering

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Muneer, Mishal Rasheed, Uzair Khalid, Sadia Ahmad, Muhammad University of Lahore Department of Computer Science and It Lahore Pakistan College of Electrical and Mechanical Engineering Nust Department of Computer and Software Engineering Islamabad Pakistan University of Engineering and Technology Department of Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665464772

Online recommendation systems have gained prominence. The online recommendation systems provide a better and faster way to choose Tour Spots for traveling and transactions. Recommender systems are powerful emerging technologies that help consumers to find the best places for touring according to their interests. The recommendation system recommends the most appropriate destinations for the end users. Internet travel portals also compete with each other regularly by taking into consideration several characteristics. Recommendation systems are one of the best methods to raise income and attract consumers. Established systems retrieved irrelevant information that resulted in low customer satisfaction and disappointment of customers. In this research, the Tour Spot recommendation system through content-based filtering is proposed and designed. It recommends the best picnic spot according to the budget and interest of the user. The proposed system improves the tourist experience and recommends the best place according to the things they would like;most significantly it is budget friendly as it suggests different places according to the interests and needs of the user. Data collection, including user information, consolidated user engagement reports, and tour spot data, is the primary challenge of implementing a travel recommendation system. User information originates mainly from information entered by the user during the registration process. When the system has the data of user likes and dislikes, then the system compares the user preferences to the dataset that the author makes for the tour recommendation systems in the dataset first identifies all the features that must be considered at the time of selection of travel destination. This Article also considers user likes and dislikes and the previous history of users to recommend a similar tour spot. © 2022 IEEE.

关键词： Recommender systems

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SOLSA: Neuromorphic Spatiotemporal Online Learning for Synaptic Adaptation

SOLSA: Neuromorphic Spatiotemporal Online Learning for Synap...

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Asia and South Pacific Design Automation Conference

作者： Zhenhang Zhang Jingang Jin Haowen Fang Qinru Qiu Department of Electrical Engineering and Computer Science Syracuse University

Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its high computation and memory cost as well as extended latency. In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation (SOLSA), which is specifically designed for online learning of SNNs composed of Leaky Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft reset. The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses. Compared to the BPTT algorithm, SOLSA has much lower memory requirement and achieves a more balanced temporal workload distribution. Moreover, SOLSA incorporates enhancement techniques such as scheduled weight update, early stop training and adaptive synapse filter, which speed up the convergence and enhance the learning performance. When compared to other non-BPTT based SNN learning, SOLSA demonstrates an average learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA achieves a 5% higher average learning accuracy with a 72% reduction in memory cost.

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Computational investigation of the structural and electrolyte properties of the extended family of lithium (thio)boracite materials: Li4B7O12Cl and beyond

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Physical Review Materials 2024年第6期8卷 065401-065401页

作者： D. Cory Lynch Yan Li Pieremanuele Canepa N. A. W. Holzwarth Department of Physics Department of Materials Science and Engineering Department of Electrical and Computer Engineering Texas Center for Superconductivity at the

Motivated by previous investigations that showed very promising high ionic conductivities within relatively stable framework structures, we report a systematic first-principles study of the extended family of lithium (thio)boracites, consisting of eight chemical compositions. Three of the compositions—Li4B7O12Cl, Li4Al3B4O12Cl, and Li6B7S13Cl—are comparable to synthesized and analyzed materials reported in the experimental literature. The five additional compositions—Li4B7S12Cl, Li4Al3B4S12Cl, Li6B7O13Cl, Li6Al3B4O13Cl, and Li6Al3B4S13Cl—are predicted from the computational modeling and analysis presented in this paper. For each material, idealized ordered rhombohedral, cubic, or monoclinic ground-state structures are determined. Through various methodologies including thermodynamic, voltage window, and harmonic phonon analyses, stability is assessed for all eight Li (thio)boracite-derived compositions. Based on the dominant energetics of density functional theory, an analysis of the thermodynamically accessible phases predicts stability for Li4B7O12Cl only. The analysis of the voltage windows of these materials suggests that the sulfur materials are much more reactive in contact with lithium metal than their oxygen counterparts. This reactivity problem has been identified in other highly conducting solid electrolytes and various mitigation methods discussed in the literature look promising. Within the harmonic approximation, phonon analysis predicts that all eight materials are dynamically stable in their ground-state structures. Future investigations will focus on the performance of the family of lithium (thio)boracites, including ionic conductivity predictions and ion migration mechanisms.

关键词： Batteries First-principles calculations Ionic transport Structural properties

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Enhanced Stability of Microgrids based on Advanced Virtual Rotor Control and Vanadium Redox Flow Batteries 50

Enhanced Stability of Microgrids based on Advanced Virtual R...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Abbou, Hossam E.A. Benzoubir, Mohammed E. Hachemi, Ahmed Delassi, Abdelmoumene Arif, Salem Trabelsi, Mohamed Vahedi, Hani Bauer, Pavol Electrical Engineering Departement LACoSERE laboratory University of Amar Telidji Laghouat03000 Algeria Electrical Engineering Lab University of Kasdi Merbah Ouargla Algeria Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait City27235 Kuwait Department of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9781665464543

This paper presents an innovative control strategy to enhance the stability of interconnected Microgrids (MGs) with low inertia and high penetration levels of Renewable Energies (REs). The proposed control strategy encompasses a new virtual droop control mechanism that emulates the primary control of synchronous generators for enhanced system stability. Additionally, a weighted Proportional-Integral (PI) controller is used to mitigate the adverse effects of measurement delays caused by Phase-Locked Loop (PLL) dynamics. Furthermore, a feedback integral loop is introduced to improve the efficiency and lifespan of Vanadium Redox Flow Batteries (VRFBs) enabling swift and precise power delivery while reducing steady-state errors. Finally, a new fractional-order virtual inertia control (VIC) is introduced to leverage the fractional derivatives and enhance the system's frequency response. The presented simulation results demonstrate the effectiveness of the proposed control approach in improving the frequency response and power exchange dynamics across interconnected MGs under various operating scenarios. © 2024 IEEE.

关键词： Microgrids

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A Deep Learning Approach for Sustainable and Secure Operations of Cloud Data Centres for Optimising the Energy Efficiency 4

A Deep Learning Approach for Sustainable and Secure Operatio...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sathish, K. Arya, Hemlata Kaliappan, S. Sivakumar, S. Arigela, Arun Kumar Venkatesh, R. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India Department of Computer Science & Engineering Madhya Pradesh Gwalior India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Saravanampatti Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electrical and Electronics Engineering Tamilnadu Avadi Chennai India A UGC Autonomous Institute Department of CSIT Telangana Hyderabad India Department of Physics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350385793

This study focuses on enhancing the sustainability and efficiency of hospital data centers through the deployment of machine learning algorithms. Support Vector Machines (SVM), Decision Trees (DT), Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN) are applied to forecast server workloads and dynamically optimize resource allocation. SVM emerges as the most accurate model at 96.5%, leading a suite of models that effectively avoid server overloads and allow efficient load sharing. The research extends its influence to the optimization of important hardware parameters, including temperature, humidity, power consumption, and energy efficiency. Each approach contributes differently to enhance the performance of the server infrastructure reliability. The study continues with a thorough examination of energy efficiency gains over an 8-hour period, offering vital insights into the models' dynamic influence. Ultimately, this study develops a complete framework for sustainable and adaptable healthcare data management, revealing the potential of machine learning to modernize hospital infrastructure and meet increasing operational difficulties. © 2024 IEEE.

关键词： Data Center Optimization Energy Efficiency Healthcare Machine Learning

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