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Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Abbas Thajeel Rhaif Alsahlanee Abha Kiran Rajpoot Gaurav Agrawal Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of CSE(AIML) KIET Group of Institution Ghaziabad India Department of CSE(AI) KIET Group of Institution Ghaziabad India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

In this groundbreaking study, we delve into the pivotal role of the Vision Transformer (V-iT) in the realm of deepfake detection, seeking to unravel its effectiveness in navigating the intricate landscape of manipulated visual content. As image-generating technologies advance, the proliferation of Dfakes poses profound challenges to the integrity of digital media, sparking concerns about misinformation and eroding trust in visual communication. Against this backdrop, V-iT emerges as a cutting-edge solution, leveraging its unique ability to comprehend intricate patterns in visual data. Our research is anchored in a meticulous methodology, involving comprehensive experiments designed to assess V-iT's performance in deepfake detection. The results, spanning 10 diverse samples, reveal consistently high accuracy, precision, recall, and F1 rate values. V-iT's capacity to achieve accurate classifications, minimize false positives, and sensitively detect manipulated content underscores its robustness in addressing the complexities of deceptive media practices. This paper goes beyond a mere validation of V-iT's capabilities, offering a nuanced understanding of its performance metrics. The high precision values emphasize V-iT's accuracy in identifying manipulated content, while exceptional recall values highlight its sensitivity in capturing true positive instances. The balanced F1 rate provides a harmonious measure of V-iT's overall effectiveness, demonstrating its potential as a dependable tool in upholding the authenticity of visual communication.

关键词： Deepfakes Visualization computer vision Accuracy Sensitivity Visual communication Weapons Resists Transformers Robustness

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Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity

arXiv

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arXiv 2022年

作者： Allard, Maxime Smith, Simón C. Chatzilygeroudis, Konstantinos Lim, Bryan Cully, Antoine Adaptive and Intelligent Robotics Lab Imperial College London United Kingdom Computer Engineering and Informatics Department University of Patras Greece

In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned skills. A high diversity of skills increases the chances of a robot to succeed at overcoming new situations since there are more potential alternatives to solve a new task. However, finding and storing a large behavioural diversity of multiple skills often leads to an increase in computational complexity. Furthermore, robot planning in a large skill space is an additional challenge that arises with an increased number of skills. Hierarchical structures can help reducing this search and storage complexity by breaking down skills into primitive skills. In this paper, we introduce the Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural repertoire to learn diverse skills and leverages them to make the robot adapt quickly in the physical world. We show that the hierarchical decomposition of skills enables the robot to learn more complex behaviours while keeping the learning of the repertoire tractable. Experiments with a hexapod robot show that our method solves a maze navigation tasks with 20% less actions in simulation, and 43% less actions in the physical world, for the most challenging scenarios than the best baselines while having 78% less complete failures. © 2022, CC BY.

关键词： Robot programming

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Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach

arXiv

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arXiv 2025年

作者： Zhao, Changyuan Liu, Guangyuan Xiang, Bin Niyato, Dusit Delinchant, Benoit Du, Hongyang Kim, Dong In College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

With advancements in physical power systems and network technologies, integrated Cyber-Physical Power Systems (CPPS) have significantly enhanced system monitoring and control efficiency and reliability. This integration, however, introduces complex challenges in designing coherent CPPS, particularly as few studies concurrently address the deployment of physical layers and communication connections in the cyber layer. This paper addresses these challenges by proposing a framework for robust sensor placement to optimize anomaly detection in the physical layer and enhance communication resilience in the cyber layer. We model the CPPS as an interdependent network via a graph, allowing for simultaneous consideration of both layers. Then, we adopt the Log-normal Shadowing Path Loss (LNSPL) model to ensure reliable data transmission. Additionally, we leverage the Fiedler value to measure graph resilience against line failures and three anomaly detectors to fortify system safety. However, the optimization problem is NP-hard. Therefore, we introduce the Experience Feedback Graph Diffusion (EFGD) algorithm, which utilizes a diffusion process to generate optimal sensor placement strategies. This algorithm incorporates cross-entropy gradient and experience feedback mechanisms to expedite convergence and generate higher reward strategies. Extensive simulations demonstrate that the EFGD algorithm enhances model convergence by 18.9% over existing graph diffusion methods and improves average reward by 22.90% compared to Denoising Diffusion Policy Optimization (DDPO) and 19.57% compared to Graph Diffusion Policy Optimization (GDPO), thereby significantly bolstering the robustness and reliability of CPPS operations. © 2025, CC BY.

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Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge computing, its Applications, and Future Research Trajectories

arXiv

引用

arXiv 2024年

作者： Yang, Ning Chen, Shuo Zhang, Haijun Berry, Randall The Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW72BX United Kingdom The Department of Computing and Communication Engineering Beijing University of Science and Technology Beijing100083 China The Department of Electrical and Computer Engineering Northwestern University Chicago60208 United States

Mobile Edge computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Optimized Combinatorial Approach For Stock Market Prediction Considering User Preferences

An Optimized Combinatorial Approach For Stock Market Predict...

引用

Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： P Iyappan K Anuradha Revathi R Sathish Kumar L D Saravanan M Maragatharajan Vellore Institute of Technology Vellore Tamil Nadu India School of Computer Applications Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Computer Science Karpagam Academy Of Higher Education Coimbatore Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Kotrikalan Madhya Pradesh India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The economy is often seen as the engine that propels a nation forward. It regulates the distribution of resources by keeping an eye on the complex relationship between production and consumption. To put it simply, the economic system is there to meet the demands of all the people and businesses that make up the economy. The stock market is an important part of any economic understanding since it is the setting in which activities such as the purchase and sale of shares and the issue of new shares by different types of limited liability companies (both public and private) take place in relation to market research and exchanges. Because it is so difficult for the average person to predict when stock prices will go up or down, it is critical to find the best stocks on the market and put their money into them. When it comes to intelligently and reliably meeting user demands, however, conventional processes fall short. An optimized stock prediction system that takes user preferences and demands into account is presented in this study as a new way to manage retrieval. This technique is crucial for properly satisfying user requirements. Essential aspects such as supply-demand dynamics, inflation, geopolitical events, technological advancements, natural catastrophes, company performance, and the efficiency of government data may be dynamically gathered using this technique. All of these things add together to make stock price swing predictions. Machine learning algorithms are crucial for stock prediction because of the massive amounts of data that must be collected, processed, cleaned, and analyzed. The proposed method, ML + LSTM, yields 96% accuracy. Comparing the existing methods, which are KNN, decision tree, and random forest, gives accuracy like 80, 92, and 94 percentages. In addition, the article highlights the use of ML techniques including k-nearest neighbour, decision tree, and random forest. Important financial indicators, including common factors, sector factors,

关键词： Deep learning Machine learning algorithms Heuristic algorithms Prediction algorithms Reliability Decision trees Stock markets

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi

Smart Home Automation with IoT Sensor Systems for Energy Con...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Srinivasan Jagannathan V. Deepa B. Sakthi Karthi Durai M. Sundar Rajan J. Jeya A Celin Sasikala Dhamodaran Department of Computer Applications Madanapalle Institute of Technology & Science Madanapalle PG and Research Department of Computer Science Tiruppur Kumaran College for Women Tiruppur Tamil Nadu India Computer Science and Engineering Kalasalingam Academy of Research and Education Faculty of Electrical and Computer Engineering Arbaminch Institute of Technology Arbaminch University Ethiopia Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil Viruthunagar NIMS School of Computing and Artificial Intelligence NIMS University Jaipur Rajasthan India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency.

关键词： Automation Protocols Energy conservation Zigbee Lighting Smart homes Sensor systems Real-time systems Reliability Wireless fidelity

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When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study

arXiv

引用

arXiv 2024年

作者： Xiong, Yang Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Xiong, Zehui Liang, Ying-Chang Mao, Shiwen Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore Computer Science and Design Pillar University of Technology and Design Singapore Chengdu611731 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework’s application in networking. We begin by exploring RAG’s applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs’ structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG’s enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications. © 2024, CC BY.

关键词： Knowledge graph

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Hierarchical Quality-Diversity for Online Damage Recovery

arXiv

引用

arXiv 2022年

作者： Allard, Maxime Smith, Simón C. Chatzilygeroudis, Konstantinos Cully, Antoine Adaptive and Intelligent Robotics Lab Imperial College London United Kingdom Computer Engineering and Informatics Department University of Patras Greece

Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical damages in a few minutes. These adaptation capabilities are directly linked to the behavioural diversity in the repertoire. The more alternatives the robot has to execute a skill, the better are the chances that it can adapt to a new situation. However, solving complex tasks, like maze navigation, usually requires multiple different skills. Finding a large behavioural diversity for these multiple skills often leads to an intractable exponential growth of the number of required solutions. In this paper, we introduce the Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural repertoire to learn diverse skills and leverages them to make the robot more adaptive to different situations. We show that the hierarchical decomposition of skills enables the robot to learn more complex behaviours while keeping the learning of the repertoire tractable. The experiments with a hexapod robot show that our method solves maze navigation tasks with 20% less actions in the most challenging scenarios than the best baseline while having 57% less complete failures. © 2022, CC BY.

关键词： Robots

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CARA: A Hybrid Framework Integrating Swarm AI Agents and Knowledge Graphs for Advanced LLM Reasoning

CARA: A Hybrid Framework Integrating Swarm AI Agents and Kno...

引用

Advancements in computing (ICAC), International Conference on

作者： Nirodya Pussadeniya Ruchira Wijesinghe Udaya Wijenayake Bhagya Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments.

关键词： Adaptation models Adaptive systems Decision making Training data Knowledge graphs Linguistics Cognition Artificial intelligence Standards Context modeling

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BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search

arXiv

引用

arXiv 2023年

作者： Khalid, Shah Khusro, Shah Alam, Aftab Wahid, Abdul Pakistan School of Computer Science Jiangsu University China Department of Computer Science University of Peshawar Pakistan Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

The enormous growth of research publications has made it challenging for academic search engines to bring the most relevant papers against the given search query. Numerous solutions have been proposed over the years to improve the effectiveness of academic search, including exploiting query expansion and citation analysis. Query expansion techniques mitigate the mismatch between the language used in a query and indexed documents. However, these techniques can suffer from introducing non-relevant information while expanding the original query. Recently, contextualized model BERT to document retrieval has been quite successful in query expansion. Motivated by such issues and inspired by the success of BERT, this paper proposes a novel approach called QeBERT. QeBERT exploits BERT-based embedding and Citation Network Analysis (CNA) in query expansion for improving scholarly search. Specifically, we use the context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance Feedback (PRF) fashion. Initial experimental results on the ACL dataset show that BERT-embedding can provide a valuable augmentation to query expansion and improve search relevance when combined with CNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

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