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检索条件"任意字段=19th International Conference on Algorithmic Learning Theory"

共 540 条记录，以下是21-30 订阅

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Resource Allocation of Multi-user Workloads in Cloud and Edge Data-Centers Using Reinforcement learning 19

Resource Allocation of Multi-user Workloads in Cloud and Edg...

引用

19th international conference on Network and Service Management (CNSM) - Network and Service Management in the Era of Generative AI and Digital Twins

作者： Jimenez, Julian Soto, Paola De Vleeschauwer, Danny Chang, Chia-Yu De Bock, Yorick Latre, Steven Camelo, Miguel Univ Antwerp imec Dept Comp Sci IDLab Sint Pietersvliet 7 B-2000 Antwerp Belgium Nokia Bell Labs Copernicuslaan 50 B-2018 Antwerp Belgium

ISBN: (纸本)9783903176591

Cloud and edge Data-center (DC) are designed to allocate computing resources dynamically to users based on the agreed Service Level Agreement (SLA). However, the ever-increasing demand for beyond 5G services necessitates an efficient workload management. A key challenge in this regard is auto-scaling, a dynamic process that adjusts computing resources to meet fluctuating system demands, optimizing resource utilization and cost efficiency. Traditional auto-scaling algorithms, which rely on fixed thresholds or control-theory, may face limitations in modern DC which are characterized by diverse, dynamic, and multi-user workloads. In this paper, we propose a Reinforcement learning (RL)-based controller that extends the capacity of the state-of-the-art RL-based auto-scalers to the multi-user workload scenario. We compare the proposed RL agent against the well-known Proportional-Integral (PI) controller and a threshold (thD)-based controller in a multi-user workload scenario in terms of created Cloud-native Network Functions (CNFs) and peak latency performed in a discrete event simulator.

关键词： Auto-scaling Workload Management Reinforcement learning Cloud-Native Network Function

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Chord Generation Based on Transformer and Color Harmony theory 19

Chord Generation Based on Transformer and Color Harmony Theo...

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19th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Li, Shuyu Hong, Yun Wei, Xinyue Wu, Tingyu Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China

ISBN: (纸本)9798350304398

Artificial intelligence has been widely used in the field of music and achieved good results. As an important concept in music, chord generation is one of the basic tasks in composition. the application of machine learning in music is of great significance to human's understanding of artificial intelligence and music, and artificial intelligence technology can also enrich people's research in music. In this paper, a chord generation model is constructed based on Transformer to further dig into the internal structure of chords and consider chords from the aspect of chord notes, hoping to generate more delicate and flexible chords. At the same time, combined with the color harmony theory, the evaluation method and mapping method meeting the rules of music theory are constructed, hoping to get better output effect through a simple model, and the template mapping method is used to improve the actual effect and extensibility of the model. © 2023 IEEE.

关键词： Music

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A Review On Game theoretical Incentive Mechanism For Federated learning 19

A Review On Game Theoretical Incentive Mechanism For Federat...

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19th IEEE-India-Council international conference (INDICON)

作者： Warrier, Lekha C. Ragesh, G. K. Indian Inst Informat Technol Dept CSE Kottayam Kerala India Indian Inst Informat Technol Dept Cyber Secur Kottayam Kerala India

ISBN: (纸本)9781665473507

Rapid growth of training data has led the Artificial Intelligence industry to the distributed learning paradigms. Federated learning has recently gained attention for the privacy preserved distributed learning activities. this collaborative training method facilitates training of data where it originates and secure aggregation of all the local models at the central server. In order to inspire the clients for the active and long run participation in the learning activity, appropriate incentive mechanisms has been developed. In this paper, a systematic review of incentive mechanisms for federated learning based on game theory concept has been studied.

关键词： federated learning incentives game theory stackelberg game non-cooperative game

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Adaptive Model Aggregation for Decentralized Federated learning in Vehicular Networks 19

Adaptive Model Aggregation for Decentralized Federated Learn...

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19th international conference on Network and Service Management (CNSM) - Network and Service Management in the Era of Generative AI and Digital Twins

作者： Movahedian, Mahtab Dolati, Mahdi Ghaderi, Majid Univ Calgary Dept Comp Sci Calgary AB Canada Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

ISBN: (纸本)9783903176591

Decentralized federated learning (DFL) enables collaborative training of machine learning models without sharing sensitive data. As such, its application in vehicular networks has gained significant attention. At the core of DFL is the so-called model aggregation, in which each vehicle combines its locally trained model with those received from its neighboring vehicles to generate an updated model that, over time, converges to a global model shared by all vehicles. However, due to high mobility and wireless communication, vehicle-to-vehicle communication is lossy. As a result, a vehicle may receive its neighboring vehicle models only partially. A technical challenge in DFL is how to efficiently utilize such partial models to speed up model training without increasing the training overhead. In this paper, we present an Adaptive Model Aggregation (AMA) algorithm to address this challenge. Our algorithm runs asynchronously on each vehicle and uses a threshold to decide whether to use a partially received model for aggregation. We show that due to the mobility of vehicles, a static threshold is not sufficient and subsequently develop an algorithm based on the contextual multi-arm bandit theory to adaptively compute an optimal threshold for each vehicle based on the dynamics of the network. We evaluate the performance of AMA in realistic environments that include different mobility patterns. Our results show that AMA can decrease DFL aggregation overhead by 83% without reducing the training accuracy compared to non-adaptive aggregation.

关键词： Vehicles

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Lifelong learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World learning 24

Lifelong Learning and Personalization in Long-Term Human-Rob...

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19th Annual ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Irfan, Bahar Stafa, Mariacarla Bobu, Andreea Churamani, Nikhil KTH Royal Inst Technol Stockholm Sweden Univ Naples Parthenope Naples Italy Boston Dynam AI Inst Boston MA USA Univ Cambridge Cambridge England

ISBN: (纸本)9798400703232

the complex and largely unstructured nature of real-world situations makes it challenging for conventional closed-world robot learning solutions to adapt to such interaction dynamics. these challenges become particularly pronounced in long-term interactions where robots need to go beyond their past learning to continuously evolve with changing environment settings and personalize towards individual user behaviors. In contrast, open-world learning embraces the complexity and unpredictability of the real world, enabling robots to be "lifelong learners" that continuously acquire new knowledge and navigate novel challenges, making them more context-aware while intuitively engaging the users. Adopting the theme of "open-world learning", the fourth edition of the "Lifelong learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)"1 workshop seeks to bring together interdisciplinary perspectives on real-world applications in human-robot interaction (HRI), including education, rehabilitation, elderly care, service, and companionship. the goal of the workshop is to foster collaboration and understanding across diverse scientific communities through invited keynote presentations and in-depth discussions facilitated by contributed talks, a break-out session, and a debate.

关键词： Lifelong learning Continual learning Personalization Adaptation Human-Robot Interaction Open-World learning Workshop

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19th international conference on Design Science Research in Information Systems and Technology, DESRIST 2024

19th International Conference on Design Science Research in ...

引用

19th international conference on Design Science Research in Information Systems and Technology, DESRIST 2024

ISBN: (纸本)9783031611742

the proceedings contain 30 papers. the special focus in this conference is on Design Science Research in Information Systems and Technology. the topics include: Overcoming Rebound Effects: A Process Blueprint for Circular Systems Design;Conceptualizing Multi-party AI Reliance for Design Research;Wasn’t Expecting that – Using Abnormality as a Key to Design a Novel User-Centric Explainable AI Method;designing a Large Language Model-Based Coaching Intervention for Lifestyle Behavior Change;a Design-Principle-Friendly Conceptual Model of Observational Crowdsourcing;design Principles for Machine learning Based Clinical Decision Support Systems: A Design Science Study;theory-Driven Design of a Negotiation Canvas for Reaching Win-Win Agreements;a Typology of Knowledge Creation in Design Science Research Projects;classifying Design Science Research in Terms of Types of Reasoning from an Epistemological Perspective;visualizing Argumentation for Research Problem and Research Design;toward a Method for Design Science Research Meta-Studies to Improve the Reusability of Design Principles;dialectical Tensions in Design theorizing: Exploring the Selection, Use, and Development of Kernel theory;design Science Research as a Guide for Innovative Higher Education Teaching: Towards an Application-Oriented Extension of the Proficiency Model;let’s Chat to Negotiate: Designing a Conversational Agent for learning Negotiation Skills;no Need to Cry over Spilt Milk: A Workflow for Regenerating Graph Data Using Robotic Process Automation;towards a Smarter Tomorrow: A Design Science Perspective on Building a Smart Campus IoT Data Platform;bridging Enterprise Knowledge Management and Natural Language Processing - Integration Framework and a Prototype;Generating Synthetic LiDAR Point Cloud Data for Object Detection Using the Unreal Game Engine.

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Integrating machine learning and IoT into apiary management to optimize bee health and production 19

Integrating machine learning and IoT into apiary management ...

引用

19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Vatskel, Vladimir Biloshchytskyi, Andrii Neftissov, Alexandr Kuchanskyi, Oleksandr Andrashko, Yurii Biloshchytska, Svitlana Sachenko, Illia Astana IT University Astana010000 Kazakhstan Department of Information Technologies Kyiv National University of Construction and Architecture Kiev01001 Ukraine Department of Information Systems and Technology Taras Shevchenko National University of Kyiv Kyiv01001 Ukraine Department of System Analysis and Optimization Theory Uzhhorod National University Uzhhorod88000 Ukraine

this article discusses integrating machine learning and IoT technologies into bee apiary management systems. the research aims to optimize bee conditions, improve their health and increase honey production. Using machine learning, large amounts of data collected by hive sensors are analyzed to predict and prevent various bee health problems effectively. Particular attention is paid to methods of controlling temperature in hives through pulse-width modulation technology, which provides accurate and energy-efficient regulation of temperature conditions. A method for predicting temperature in hives for effective apiary management is also proposed. © 2024 the Authors. Published by Elsevier B.V.

关键词： beekeeping energy efficiency Internet of things machine learning pulse width modulation sustainable agriculture temperature control

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19th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2023

19th IFIP WG 12.5 International Conference on Artificial Int...

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19th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2023

ISBN: (纸本)9783031341106

the proceedings contain 129 papers. the special focus in this conference is on Artificial Intelligence Applications and Innovations. the topics include: Generating Synthetic Vehicle Speed Records Using LSTM;Measuring the State-Observation-Gap in POMDPs: An Exploration of Observation Confidence and Weighting Algorithms;predicting Colour Reflectance with Gradient Boosting and Deep learning;the Importance of the Current Input in Sequence Modeling;towards Historical Map Analysis Using Deep learning Techniques;analysis of the Lingering Effects of Covid-19 on Distance Education;exploring the Power of Failed Experiences in Case-Based Reasoning for Improved Decision Making;improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing;a Sharpe Ratio Based Reward Scheme in Deep Reinforcement learning for Financial Trading;mind the Gap: Addressing Incompleteness Challenge in Case-Based Reasoning Applications;Sentiment Analysis of Tweets on Online Education during COVID-19;image Classification Using Class-Agnostic Object Detection;Ince-PD Model for Parkinson’s Disease Prediction Using MDS-UPDRS I & II and PDQ-8 Score;medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records;Predicting ALzheimer's Disease with AI and Brain Imaging Data;Pre-trained Model Robustness Against GAN-Based Poisoning Attack in Medical Imaging Analysis;semi-supervised Brain Tumor Segmentation Using Diffusion Models;A Methodology for Emergency Calls Severity Prediction: From Pre-processing to BERT-Based Classifiers;a Temporal Metric-Based Efficient Approach to Predict Citation Counts of Scientists;algorithmic Forex Trading Using Q-learning;Comparing Vectorization Techniques, Supervised and Unsupervised Classification Methods for Scientific Publication Categorization in the UNESCO Taxonomy;decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling;exploring Pairwise Spatial Relationships for Actions Recognition and

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19th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2023

19th IFIP WG 12.5 International Conference on Artificial Int...

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19th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2023

ISBN: (纸本)9783031341700

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Impact of learning Data Statistics on the Performance of a Recommendation System Based on MovieLens Data 1

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19th international conference on Dependability of Computer Systems (DepCoS-RELCOMEX)

作者： Kuzelewska, Urszula Falkowski, Michal Bialystok Tech Univ Wiejska 45a PL-15351 Bialystok Poland

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

Recommendation systems are an efficient way to improve user satisfaction on the internet and personalise web services. they use various algorithms to analyse large amounts of data and offer customers the most useful products. the accuracy of the offered propositions is the most important feature of all recommendation systems, which is closely related to the principle of the methods. However, the input data also influences their performance. the objective of this study is to determine whether there is a correlation between data statistics, such as density, shape and skewness, and the performance of the algorithm as measured by RMSE. Additionally, we examined the coverage of both items and users with respect to the input data. the verification was performed using classical item and user-based methods with cosine similarity.

关键词： Recommender systems Evaluation of recommender systems Data statistics

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