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IEEE Conference on Decision and Control

作者： Aris Kanellopoulos Filippos Fotiadis Kyriakos G. Vamvoudakis Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method.

关键词： Robust control Actuators Simulation Switches Control systems Stability analysis Security Optimization

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SirLLM: Streaming Infinite Retentive LLM

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

As Large Language Models (LLMs) become increasingly prevalent in various domains, their ability to process inputs of any length and maintain a degree of memory becomes essential. However, the one-off input of overly long texts is limited, as studies have shown that when input lengths exceed the LLMs' pre-trained text length, there is a dramatic decline in text generation capabilities. Moreover, simply extending the length of pre-training texts is impractical due to the difficulty in obtaining long text data and the substantial memory consumption costs this would entail for LLMs. Recent efforts have employed streaming inputs to alleviate the pressure of excessively long text inputs, but this approach can significantly impair the model's long-term memory capabilities. Motivated by this challenge, we introduce Streaming Infinite Retentive LLM (SirLLM), which allows LLMs to maintain longer memory during infinite-length dialogues without the need for fine-tuning. SirLLM utilizes the Token Entropy metric and a memory decay mechanism to filter key phrases, endowing LLMs with both long-lasting and flexible memory. We designed three distinct tasks and constructed three datasets to measure the effectiveness of SirLLM from various angles: (1) DailyDialog;(2) Grocery Shopping;(3) Rock-Paper-Scissors. Our experimental results robustly demonstrate that SirLLM can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. When having a coversation, "A sir could forget himself," but SirLLM never does! Our code is publicly available at https://***/Zoeyyao27/SirLLM. © 2024, CC BY.

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing Data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. Data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing 22

PredART: Towards Automatic Oracle Prediction of Object Place...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Tahmid Rafi Xueling Zhang Xiaoyin Wang Department of Computer Science University of Texas at San Antonio USA Department of Software Engineering Rochester Institute of Technology USA Department of Computer Science University of Texas at San Antonio United States of America

ISBN: (纸本)9781450394758

While the emerging Augmented Reality (AR) technique allows a lot of new application opportunities, from education and communication to gaming, current augmented apps often have complaints about their usability and/or user experience due to placement errors of virtual objects. Therefore, identifying noticeable placement errors is an important goal in the testing of AR apps. However, placement errors can only be perceived by human beings and may need to be confirmed by multiple users, making automatic testing very challenging. In this paper, we propose PredART, a novel approach to predict human ratings of virtual object placements that can be used as test oracles in automated AR testing. PredART is based on automatic screenshot sampling, crowd sourcing, and a hybrid neural network for image regression. The evaluation on a test set of 480 screenshots shows that our approach can achieve an accuracy of 85.0% and a mean absolute error, mean squared error, and root mean squared error of 0.047, 0.008, and 0.091, respectively.

关键词： Virtual Objects Placement Error Augmented Reality

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PEMT-CoSim: A Co-Simulation Platform for Packetized Energy Management and Trading in Distributed Energy systems

PEMT-CoSim: A Co-Simulation Platform for Packetized Energy M...

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2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022

作者： Li, Yuanliang Hou, Luyang Du, Hang Yan, Jun Liu, Yuhong Ghafouri, Mohsen Zhang, Peng Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada School of Computer Science Beijing University of Posts and Telecommunications Beijing China Santa Clara University Department of Computer Engineering Santa ClaraCA United States College of Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The integration of Internet-of-Things, artificial intelligence, and other emerging technologies is driving the transition from traditional electricity consumers to energy prosumers in smart grids. To encourage flexible participation of prosumers in decentralized/distributed power management and trading, packetized energy (PE) technology is developed to manage prosumers' load and distributed energy resources in a request-reply way by encapsulating the energy into modulated, routable, and trackable electric packets of fixed duration and/or power for PE management (PEM) and trading (PET). To facilitate the research on PE and investigate PE-related problems in real-world environments, this paper proposes a co-simulation platform called PEMT-CoSim based on the Transactive Energy Simulation Platform (TESP) for PEM and PET, which integrates PE into the management for prosumers in distribution systems by developing a dedicated Application Programming Interfaces (API) for PEM and PET. We validate our simulation platform through PEM and PET case studies to showcase how to manage the power packages with prosumers' active participation in distributed energy systems. We also make the proposed platform open-source to encourage researches in PE for smart grids. © 2022 IEEE.

关键词： Simulation platform

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Indoor Navigation Using Ultra Wide Band

Indoor Navigation Using Ultra Wide Band

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Intelligent Computing and Control for engineering and Business systems (ICCEBS)

作者： Divya B T Veeramakali S Revathi Harisharajan R R Adhirooban D Ebishdon G V Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

Indoor positioning is a significant and intriguing topic in navigation systems that present numerous use cases for investigation. Researchers are exploring technologies capable of providing accurate locations in indoor spaces where GNSS positioning systems are ineffective. In the numerous solutions available for indoor location services, Ultra-wideband (UWB) technology was chosen for its ability to provide high-precision positioning information for multiple points in real-time. This paper presents a UWB indoor positioning system that utilizes time of flight (TOF) algorithm. At times, time difference of arrival (TDOA) algorithms compute range measurements, this facilitates the trans-receiver (TAG) plane position algorithm's location determination. UWB technology was chosen because it is the latest technology available on Android 12 and further also offers high accuracy, resistance to interference and minimal signal reflection from walls. UltraWide Band(UWB) localization encounters various obstacles, such as multipath distortions, additional signal delays, time discrepancies in clocks, signal interference, and the computational resources necessary to determine the user's location.

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Fast Second-order Method for Neural Networks under Small Treewidth Setting

Fast Second-order Method for Neural Networks under Small Tre...

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IEEE International Conference on Big Data

作者： Xiaoyu Li Jiangxuan Long Zhao Song Tianyi Zhou Department of Computer Science Stevens Institute of Technology Hoboken NJ School of Software Engineering South China University of Technology Guangzhou China The Simons Institute for the Theory of Computing University of California Berkeley Berkeley CA Department of Computer Science University of Southern California Los Angeles CA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n 3 ) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ 2 ) respectively, where m = Ω(n 4 ) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ 2 ).

关键词： Training Knowledge engineering Costs Neural networks Machine learning Big Data Computational efficiency Matrix decomposition Optimization Convergence

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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LGHAE: Local and Global Hyper-relation Aggregation Embedding for Link Prediction 9th

LGHAE: Local and Global Hyper-relation Aggregation Embedding...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Yuan, Peikai Qi, Zhenheng Sun, Hui Liu, Chao College of Computer Science and Technology Harbin Engineering University Harbin China Institute of Systems Engineering Academy of Military Sciences Beijing100089 China

ISBN: (纸本)9789819959709

The Knowledge Graph (KGs) have profoundly impacted many research fields. However, there is a problem of low data integrity in KGs. The binary-relational knowledge graph is more common in KGs but is limited by less information. It often has less content to use when predicting missing entities (relations). The hyper-relational knowledge graph is another form of KGs, which introduces much additional information (qualifiers) based on the main triple. The hyper-relational knowledge graph can effectively improve the accuracy of predicting missing entities (relations). The existing hyper-relational link prediction methods only consider the overall perspective when dealing with qualifiers and calculate the score function by combining the qualifiers with the main triple. However, these methods overlook the inherent characteristics of entities and relations. This paper proposes a novel Local and Global Hyper-relation Aggregation Embedding for Link Prediction (LGHAE). LGHAE can capture the semantic features of hyper-relational data from local and global perspectives. To fully utilize local and global features, Hyper-InteractE, as a new decoder, is designed to predict missing entities to fully utilize local and global features. We validated the feasibility of LGHAE by comparing it with state-of-the-art models on public datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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A Game-Theoretic Approach for Pricing and Determining Quality Levels of Cybersecurity Products Under an Exogenous Information-Sharing Program 19th

A Game-Theoretic Approach for Pricing and Determining Qualit...

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19th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2022

作者： Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Price and quality level of products are two important decisions of any business. This paper provides equilibrium solutions for these decisions of two players for a cybersecurity ecosystem, including a solution provider and an information provider. We assume that end users join a cybersecurity ecosystem based on the prices and qualities of the solution provider’s and information provider’s products;so that, the increasing of the prices or/and decreasing the qualities will reduce the number of end-users of that ecosystem. Also, it is assumed that there is an exogenous information-sharing program under which the effect of quality of the information provider’s product on the cybersecurity level depends on the level of information-sharing. Under this information-sharing program, the equilibrium solutions of prices and qualities of these two players are given. Also, some results and insights are given with a numerical example. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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