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arXiv 2023年

作者： Saber, Ahmad Mohammad Youssef, Amr Svetinovic, Davor Zeineldin, Hatem El-Saadany, Ehab Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Concordia Institute for Information Systems Engineering MontrealQC Canada

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works which proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves. © 2023, CC BY-NC-SA.

关键词： Distributed power generation

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New Intrusion Detection System to Protect MANET Networks Employing Machine Learning Techniques

New Intrusion Detection System to Protect MANET Networks Emp...

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2021 International Conference of Modern Trends in information and Communication Technology Industry, MTICTI 2021

作者： Khalifa, Marwa Mohammed Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Computer Engineering / Information Technologies Turkey University of Altinbaş School of Science and Engineering Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%. © 2021 IEEE.

关键词： Support vector machines

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Explaining Learned Reward Functions with Counterfactual Trajectories

arXiv

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arXiv 2024年

作者： Wehner, Jan Oliehoek, Frans Siebert, Luciano Cavalcante CISPA Helmholtz Center for Information Security Germany Department of Intelligent Systems Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Learning rewards from human behavior or feedback is a promising approach to aligning AI systems with human values but fails to consistently extract correct reward functions. Interpretability tools could enable users to understand and evaluate possible flaws in learned reward functions. We propose Counterfactual Trajectory Explanations (CTEs) to interpret reward functions in Reinforcement Learning by contrasting an original and a counterfactual trajectory and the rewards they each receive. We derive six quality criteria for CTEs and propose a novel Monte-Carlo-based algorithm for generating CTEs that optimizes these quality criteria. To evaluate how informative the generated explanations are to a proxy-human model, we train it to predict rewards from CTEs. CTEs are demonstrably informative for the proxy-human model, increasing the similarity between its predictions and the reward function on unseen trajectories. Further, it learns to accurately judge differences in rewards between trajectories and generalizes to out-of-distribution examples. Although CTEs do not lead to a perfect prediction of the reward, our method, and more generally the adaptation of XAI methods, are presented as a fruitful approach for interpreting learned reward functions and thus enabling users to evaluate them. © 2024, CC BY.

关键词： Contrastive Learning

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Low Power Network-on-Chip Architecture Design Technique

Low Power Network-on-Chip Architecture Design Technique

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Tejas Musale Arun Ganti Ankur Gogoi Kanchan Manna Department of Electrical and Electronics Engineering BITS Pilani India Department of Computer Science and Information Systems BITS Pilani India Galgotias University Uttar Pradesh India

ISBN: (数字)9798331539672

ISBN: (纸本)9798331539689

In the evolving landscape of many-core architectures, the Network on Chip (NoC) emerges as a pivotal inter-connection framework, accommodating the escalating number of cores on a single chip. Despite its widespread adoption, power consumption remains a critical challenge, significantly influenced by factors such as topology, bit toggling, and routing algorithms, with bit-switching power being a predominant concern. In this work, we have proposed an innovative technique in the cores aimed at reducing the power consumption of the NoC. To calculate our method's area, time, and power consumption, we have synthesized it on a Xilinx Virtex-7 FPGA board. We have modified the Noxim open-source simulator to validate our approach using synthetic traffic. Empirical results demonstrate a 29.64% reduction in dynamic power consumption and a 29.09% improvement in switching activity for the average case (Mode 1) traffic scenario.

关键词： Power demand Network topology Network-on-chip Switches Very large scale integration Logic gates Routing Topology Field programmable gate arrays

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Raising Awareness of Location information Vulnerabilities in Social Media Photos using LLMs

arXiv

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arXiv 2025年

作者： Ma, Ying Zhang, Shiquan Yang, Dongju Sarsenbayeva, Zhanna Knibbe, Jarrod Goncalves, Jorge School of Computing and Information Systems University of Melbourne Melbourne Australia School of Computer Science University of Sydney Sydney Australia School of Electrical Engineering and Computer Science University of Queensland St Lucia Australia

Location privacy leaks can lead to unauthorised tracking, identity theft, and targeted attacks, compromising personal security and privacy. This study explores LLM-powered location privacy leaks associated with photo sharing on social media, focusing on user awareness, attitudes, and opinions. We developed and introduced an LLM-powered location privacy intervention app to 19 participants, who used it over a two-week period. The app prompted users to reflect on potential privacy leaks that a widely available LLM could easily detect, such as visual landmarks & cues that could reveal their location, and provided ways to conceal this information. Through in-depth interviews, we found that our intervention effectively increased users’ awareness of location privacy and the risks posed by LLMs. It also encouraged users to consider the importance of maintaining control over their privacy data and sparked discussions about the future of location privacy-preserving technologies. Based on these insights, we offer design implications to support the development of future user-centred, location privacy-preserving technologies for social media photos. © 2025, CC BY.

关键词： Differential privacy

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Communication-efficient and Decentralized Federated Minimax Optimization

Communication-efficient and Decentralized Federated Minimax ...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Sajjad Ghiasvand Amirhossein Reisizadeh Mahnoosh Alizadeh Ramtin Pedarsani Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

As distributed learning applications like Federated Learning, the Internet of Things (IoT), and Edge Computing expand, addressing their limitations becomes crucial. We approach decentralized learning across a network of communicating clients or nodes, focusing on two primary challenges: data heterogeneity and adversarial robustness. To address these, we introduce a decentralized minimax optimization method incorporating two key components: local updates and gradient tracking. Minimax optimization serves as a fundamental tool for adversarial training, ensuring robustness. Local updates are vital in Federated Learning (FL) to alleviate the communication bottleneck, while gradient tracking is necessary to demonstrate convergence amid data heterogeneity. Our analysis of the proposed algorithm, Dec-Fed Track, in nonconvex-strongly-concave minimax optimization demonstrates its convergence to a stationary point. Additionally, numerical experiments support our theoretical results.

关键词： Training computer aided instruction Federated learning Distance learning Optimization methods Focusing Robustness Internet of Things Convergence Edge computing

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CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans

CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Pl...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Chowdhury, Deepanjali Roy, Ahana Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Texas A&m University Department of Electrical and Computer Engineering College Station United States Howard County Public School System MD United States Bowie State University Department of Computer Science Bowie United States University of Maryland Baltimore County Department of Information Systems Baltimore United States

ISBN: (纸本)9781665453813

Managing a work-life balance has always been challenging, especially after the recent trend of working from home, which has made maintaining one's fitness and diet regime strenuous. Failing to adhere to a fitness and diet plan has been shown to cause long-term effects on a person's health including obesity and shortened lifespans. Currently, people plan their fitness and diet plans around their schedule, but these tasks are challenging to maintain. Users tend to give up these plans due to a lack of time and planning. To help with better planning for a fitness and diet regime, we propose, CHARLIE, a chatbot that works around a user's schedule to intelligently recommend diets and fitness goals based on their calendar. CHARLIE is a recommendation system that suggests the users' a certain meal and fitness activities based on the amount of time they have left in their day and the number of calories they lose. © 2023 IEEE.

关键词： Recommender systems

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Incremental-Dissipativity-Based Synchronization of Interconnected systems with MIMO Nonlinear Operators

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IFAC-PapersOnLine 2023年第2期56卷 1853-1858页

作者： Tao Liu David J. Hill Jun Zhao Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong SAR Shenzhen Institute of Research and Innovation University of Hong Kong Shenzhen China Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia College of Information Science and Engineering Northeastern University Shenyang China

This paper considers a type of dynamical networks with incrementally dissipative multiple inputs and multiple outputs nonlinear operators that are interconnected both internal and external interconnections. A novel quadratic form of incremental dissipativity is used to investigate synchronization of such a network from an input-output approach point of view. It is shown that the synchronization problem is equivalent to a relative stability issue of a large-scale interconnected nonlinear system with dissipative subsystems. Therefore, traditional dissipativity theory is used for the synchronization analysis.

关键词： Incremental dissipativity synchronization multiple-input multiple-output operator interconnected systems stability of nonlinear systems

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Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence

arXiv

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arXiv 2024年

作者： Le, Thao Miller, Tim Zhang, Ruihan Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO/Data61 Australia

This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Weight of Evidence (WoE) for each hypothesis in the decision-making process. We apply and evaluate this tool in the skin cancer domain by building a web-based application that allows users to upload a dermatoscopic image, select a hypothesis and analyse their decisions by evaluating the provided evidence. Further, we demonstrate the effectiveness of Visual Evaluative AI on different concept-based explanation approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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On the Capacity of Vehicular Communications 31

On the Capacity of Vehicular Communications

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2023 31st National Conference with International Participation, TELECOM 2023

作者： Sofotasios, Paschalis C. Yoo, Seong Ki Simmons, Nidhi Cotton, Simon L. Valkama, Mikko Khalifa University Center for Cyber Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Tampere University Department of Electrical Engineering TampereFI-33101 Finland Coventry University Centre for Future Transport and Cities Faculty of Engineering Environment and Computing United Kingdom Queen's University Belfast Institute of Electronics Communications and Information Technology BelfastBT3 9DT United Kingdom

ISBN: (纸本)9798350303292

The μ / inverse gamma and μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications. © 2023 IEEE.

关键词： Vehicle to vehicle communications

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