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检索条件"任意字段=21st International Conference on Distributed Computing and Intelligent Technology"

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21st international conference on distributed computing and intelligent technology, ICDCIT 2025

21st International Conference on Distributed Computing and I...

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21st international conference on distributed computing and intelligent technology, ICDCIT 2025

ISBN: (纸本)9783031814037

The proceedings contain 21 papers. The special focus in this conference is on distributed computing and intelligent technology. The topics include: Invited Paper: Application of Physics-Based and Data-Driven Approaches for Drug-Like Property Prediction;invited Paper: distributed Computability: A Few Results Masters students Should Know;ioT-Based Service Allocation in Edge computing Using Game Theory;Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments;a "Symbolic" Representation of Object-Nets;optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge;mobile Agents on Chordal Graphs: Maximum Independent Set and Beyond;faster Leader Election and Its Applications for Mobile Agents with Parameter Advice;a Novel Protocol for Mitigating Wormhole Attack in Multi-hop Payment Channels;unleashing Multicore strength for Efficient Execution of Blockchain Transactions;Enhancing QR Decomposition: A GPU-Based Approach to Parallelizing the Householder Algorithm with CUDA streams;source Sets in Temporal Graphs;team Formation Based on the Degree Distribution of the Social Networks;inVideo Search: Scene Description Clustering and Integrating Image and Audio Captioning for Enhanced Video Search;does Data Balancing Impact stutter Detection and Classification?;deep Learning-Driven Person Re-identification: Leveraging Color Space Transformations;an Ensembled Parking Space Classifier Across Diverse Weather Conditions;classification of Impacted Teeth from Panoramic Radiography Using Deep Learning;HJCFL: Hashcash and Jaya-Based Communication Efficient Federated Learning.

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Invited Paper: distributed Computability: A Few Results Masters students Should Know 21st

Invited Paper: Distributed Computability: A Few Results Mast...

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21st international conference on distributed computing and intelligent technology

作者： Raynal, Michel Univ Rennes CNRS INRIA IRISA F-35042 Rennes France Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China

ISBN: (纸本)9783031814037;9783031814044

As today Informatics is more and more (driven) eaten by its applications, it becomes more and more important to know what can be done and what cannot be done. For a long time now, this is well known in sequential computing. But today the world becomes more and more distributed, and consequently more and more applications are distributed. As a result, it becomes important, or even crucial, to understand what is distributed computing and which are its power and its limits. This article is a step in this direction when looking from an agreement-oriented fault-tolerance point of view.

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Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments 21st

Exploring Hidden Behaviors in OpenMP Multi-threaded Applicat...

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21st international conference on distributed computing and intelligent technology

作者： Bhowmik, Biswajit Girish, K. K. Mishra, Pawanesh Mishra, Rishi Natl Inst Technol Dept Comp Sci & Engn Maharshi Sushrut CAS Lab BRICS Lab Mangalore Karnataka India

ISBN: (纸本)9783031814037;9783031814044

In high-performance computing (HPC), multi-threaded applications using OpenMP face complex challenges in identifying hidden performance issues, often due to resource conflicts, software inefficiencies, and hardware anomalies. These subtle issues can significantly degrade performance and reduce system reliability. This paper introduces an innovative approach designed to address these concealed issues in OpenMP multi-threaded applications. The proposed method integrates a Random Forest classifier with anthropomorphic diagnosis to effectively identify and diagnose performance-affecting problems. The approach has demonstrated a remarkable ability to detect 90% of performance-affecting issues that are often obscured within complex HPC environments.

关键词： High Performance computing (HPC) OpenMP Random Forest Anthropomorphic Diagnosis Performance Issues System Efficiency

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IoT-Based Service Allocation in Edge computing Using Game Theory 21st

IoT-Based Service Allocation in Edge Computing Using Game Th...

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21st international conference on distributed computing and intelligent technology

作者： Agrawal, Kushagra Goktas, Polat Sahaol, Biswajit Swain, Sujata Bandyopadhyay, Anjan KIT Deemed Be Univ Sch Comp Engn Bhubaneswar India Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031814037;9783031814044

The rapid growth of the Internet of Things (IoT) has created a pressing need for efficient service allocation methods to manage the multitude of connected devices. Edge computing has become essential to fulfill the low-latency and high-bandwidth demands of IoT applications. This paper investigates the use of game theory as a framework for optimizing service allocation in edge computing environments. By treating the interactions between IoT devices and edge servers as a strategic game, we propose strategies to achieve optimal allocation and resource utilization. Our approach tackles key challenges such as minimizing latency, improving energy efficiency, and balancing load. Experimental results indicate that game-theoretic methods greatly improve the performance and scalability of IoT systems in edge computing, positioning them a promising solution for future applications.

关键词： Edge computing Internet of Things (IoT) Game Theory

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Decision System in a Building to Coordinate the Energy Forecasting Tasks 21st

Decision System in a Building to Coordinate the Energy Forec...

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21st international Symposium on distributed computing and Artificial Intelligence

作者： Ramos, Daniel Faria, Pedro Vale, Zita Polytech Porto LASI Intelligent Syst Associate Lab GECAD Res Grp Intelligent Engn & Comp Adv Innovat Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Nowadays, energy buildings have a huge impact in society regarding the active role in the management of energy consumption. Hence, building owners are required to avoid energy losses and improve energy efficiency as high as possible. Therefore, it is required to plan an optimization strategy to buy and sell energy in the market ahead of time. To formulate this optimization plan, building owners require the work of specialists responsible for processing, training, forecasting, and evaluation tasks regarding the prediction of energy consumption data from a building for a specific target of time. Therefore, a multiagent-system is needed to allow the cooperation of various agents including the building owner, forecast provider, data structurer and error analysis. Moreover, forecasting algorithms such as artificial neural networks should be taken into consideration in order to process large quantities of energy consumption data during the training and forecasting phases.

关键词： agents artificial neural networks building owner energy building

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Optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge 21st

Optimal Dispersion in Triangular Grids: Achieving Efficiency...

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21st international conference on distributed computing and intelligent technology

作者： Pandit, Himani Pandit, Supantha Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India

ISBN: (纸本)9783031814037;9783031814044

In the dispersion problem, a group of k <= n mobile robots, initially placed on the vertices of an anonymous graph G with n vertices, must redistribute themselves so that each vertex hosts no more than one robot. We address this challenge on an anonymous triangular grid graph, where each vertex can connect to up to six adjacent vertices. We propose a distributed deterministic algorithm that achieves dispersion on an unoriented triangular grid graph in O(root n) time, where n is the number of vertices. Each robot requires O(log n) bits of memory. The time complexity of our algorithm and the memory usage per robot are optimal. This work builds on previous studies by Kshemkalyani et al. [WALCOM 2020 [17]] and Banerjee et al. [ALGOWIN 2024 [3]]. Importantly, our algorithm terminates without requiring prior knowledge of n and resolves a question posed by Banerjee et al. [ALGOWIN 2024 [3]].

关键词： distributed algorithms Mobile robots Dispersion Triangular grid Deterministic algorithms

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FuzzTheREst: An intelligent Automated Black-Box REstful API Fuzzer 21st

FuzzTheREST: An Intelligent Automated Black-Box RESTful API ...

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21st international Symposium on distributed computing and Artificial Intelligence

作者： Dias, Tiago Maia, Eva Praca, Isabel Porto Sch Engn ISEP Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy testing is a dynamic analysis software testing technique that consists of feeding faulty input data to a System Under Test (SUT) and observing its behavior. Specifically regarding black-box REstful API testing, recent literature has attempted to automate this technique using heuristics to perform the input search and using the HTTP response status codes for classification. However, most approaches do not keep track of code coverage, which is important to validate the solution. This work introduces a black-box REstful API fuzzy testing tool that employs Reinforcement Learning (RL) for vulnerability detection. The fuzzer operates via the OpenAPI Specification (OAS) file and a scenarios file, which includes information to communicate with the SUT and the sequences of functionalities to test, respectively. To evaluate its effectiveness, the tool was tested on the Petstore API. The tool found a total of six unique vulnerabilities and achieved 55% code coverage.

关键词： Fuzzy Testing REstful APIs Vulnerability Detection Code Security Artificial Intelligence

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Automated Machine Learning and Explainable Artificial Intelligence in Predictive Maintenance: An MLJAR Framework Review 21st

Automated Machine Learning and Explainable Artificial Intell...

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21st international Symposium on distributed computing and Artificial Intelligence

作者： Mota, Bruno Faria, Pedro Ramos, Carlos Polytech Porto LASI Intelligent Syst Associate Lab GECAD Res Grp Intelligent Engn & Comp Adv Innovat Rua Dr Antonio Bernardino Almeida 431 P-4200072 Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Predictive Maintenance (PdM) through Machine Learning (ML) has become an essential strategy for industries to reduce redundant maintenance activities. Even so, many companies lack the technical knowledge to implement these PdM systems, and more often than not, do not trust ML models for their lack of transparency and interpretability. To mitigate these issues, the present paper explores and implements an Automated Machine Learning (AutoML) and Explainable Artificial Intelligence (XAI) framework designated as MLJAR. The framework is evaluated for its AutoML and XAI capabilities in a widely used synthetic PdM dataset in the literature. Promising results were found as the framework was able to outperform most works in the literature by up to 26.6% in recall score, with the only work surpassing MLJAR by up to 3.2%, yet having the drawbacks of 58.3% worse precision score, and no AutoML or XAI capabilities. Overall, the MLJAR framework was able to, on average, provide 13.2% and 2.2% better scores in recall and accuracy, respectively.

关键词： Automated Machine Learning Explainable Artificial Intelligence Predictive Maintenance

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Enhancing QR Decomposition: A GPU-Based Approach to Parallelizing the Householder Algorithm with CUDA streams 21st

Enhancing QR Decomposition: A GPU-Based Approach to Parallel...

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21st international conference on distributed computing and intelligent technology

作者： Eshwar, Uppu Chatterjee, Soumyajit Peri, Sathya Indian Inst Technol Hyderabad Hyderabad 502285 Telangana India

ISBN: (纸本)9783031814037;9783031814044

Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional sequential implementations of the Householder algorithm face significant limitations in terms of performance and scalability when applied to large matrices. To overcome these constraints, this paper explores the parallelization of the Householder QR algorithm on Graphics Processing Units (GPUs) using CUDA, a parallel computing platform and programming model developed by NVIDIA. Our method ensures the availability of critical intermediate data, distinguishing it from standard libraries like cuSOLVER, which modify the processing order and often discard important intermediate computations. By leveraging CUDA streams, we achieve enhanced parallelism without compromising the integrity of the algorithm's sequence or the accessibility of intermediate data. Our performance analysis reveals that our implementation achieves efficiency comparable to cuSOLVER, making it a viable option. This study not only presents a novel implementation but also extends the potential for GPU-accelerated linear algebra procedures to benefit a wider range of scientific and engineering applications.

关键词： QR Decomposition Parallel computing General Purpose GPU programming CUDA streams

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Adaptive Traffic Light Control Through Reinforcement Learning Based on Sensor Integration 21st

Adaptive Traffic Light Control Through Reinforcement Learnin...

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21st international Symposium on distributed computing and Artificial Intelligence

作者： Uribe-Chavert, Pedro Lopez-Cuerva, Luis Posadas-Yague, Juan-Luis Poza-Lujan, Jose-Luis Univ Politecn Valencia Doctoral Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Informat Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Res Inst Ind Comp & Automat Camino VeraSn Valencia 46022 Spain

ISBN: (纸本)9783031820724;9783031820731

This paper explores intelligent traffic light management advancements, focusing on controlling intersection traffic opening times. The decision-making process is influenced by factors such as traffic density. The information for these decisions is gathered from sensors placed on the streets, whose accuracy can vary. Data collected are processed to aid control agents in decision-making. The paper proposes an intersection control algorithm that operates under the assumption of lacking sensorisation. To balance raw sensor data, control nodes implement a reinforced learning algorithm to select the most suitable combination of sensors to improve traffic parameters. The paper also introduces a method for calculating traffic density by combining sensors with imprecise data. This research contributes to intelligent traffic management by providing a novel approach to intersection control and traffic density calculation.

关键词： intelligent Traffic Light Management Reinforced Learning Algorithm Traffic Density Calculation

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