检索结果-内蒙古大学图书馆

MODEL CHECKING FOR MULTI-AGENT SYSTEMS MODELED BY EPISTEMIC PROCESS CALCULUS

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yu, Qixian Cao, Zining Hui, Zong Zhou, Yuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Faculty of Computer and Software Engineering Huaiyin Institute Of Technology Huaian223001 China

This paper presents a comprehensive framework for modeling and verifying multi-agent systems. The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the syntax and semantics to capture the essential features of agent behavior interactions and epistemic states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal Logic incorporating epistemic operators to express complex properties related to agent epistemic state. To verify ATLE specifications, this paper presents a model checking algorithm that systematically explores the state space of a multi-agent system and evaluates the satisfaction of the specified properties. Finally, a case study is given to demonstrate the method. © 2025, CC BY.

关键词： Multi agent systems

Measurement of Performance Parameters in the GPON Optical Network 23

学校读者我要写书评

暂无评论

Measurement of Performance Parameters in the GPON Optical Ne...

23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Trubák, Kamil Štípal, Jiří Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

With the increasing number of end users that are using multimedia services, demand for access network high bitrate systems with sufficient quality of services also increases. However, this might not always be ensured by the telecom operators, as they must perform network optimization concerning Quality of Service (QoS) and multimedia data transmission. In this work, we focused on Gigabit Passive Optical Network (GPON) performance testing with the help of various tools (iPerf, RFC 6349 or ITU-T Y.1564). The Grafana software tool is used to monitor data streams. Measurements were made to limit the downstream bitrate of up to 20 end users at 100 Mbit/s. Based on the repeated measurements, an aggregation curve is modelled indicating the available bitrate with respect to the network load. © 2025 SPIE.

关键词： Internet protocols

Medical Expert System to Diagnose the Most Common Psychiatric Diseases 21

学校读者我要写书评

暂无评论

Medical Expert System to Diagnose the Most Common Psychiatri...

7th International Conference on engineering and MIS, ICEMIS 2021

作者： Alsagheer, Eman A. Rajab, Hamzah A. Elnajar, Khadiga M.Y. Department of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Department of Software Engineering Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (纸本)9781450390446

Expert Systems are programs that employ artificial intelligence and mimic the performance of human experts in a certain field by gathering and capturing expert information. In this paper, we present a rule-based expert system to provide the diagnosis for the most common psychiatric diseases to facilitate the diagnosis process. This system diagnoses four types of psychiatric diseases Anxiety Disorder (generalized anxiety, panic disorder, OCD, and phobic disorder), Stress-related disorder, Depression, and Schizophrenia using the International Classification of Diseases 10th Revision. The knowledge used in this system is acquired from domain experts, and relevant medical books and documents to capture knowledge that is valid as much as possible. Then, the acquired knowledge is demonstrated using a diagnostic chart, and decision table that represents the signs and symptoms involved in the diagnosis. A rule-based expert system and production rules that represent the domain knowledge are built using SWI-PROLOG Editor. The system is based on backward chaining which starts with possible goals and attempts to collect information that confirms it. The testing result shows the average accuracy of the system register 80% in diagnosing patients with psychiatric diseases. © 2021 ACM.

关键词： Expert systems

Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Yunke Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science Faculty of Engineering The University of Sydney Australia

Adversarial imitation learning has become a widely used imitation learning framework. The discriminator is often trained by taking expert demonstrations and policy trajectories as examples respectively from two categories (positive vs. negative) and the policy is then expected to produce trajectories that are indistinguishable from the expert demonstrations. But in the real world, the collected expert demonstrations are more likely to be imperfect, where only an unknown fraction of the demonstrations are optimal. Instead of treating imperfect expert demonstrations as absolutely positive or negative, we investigate unlabeled imperfect expert demonstrations as they are. A positive-unlabeled adversarial imitation learning algorithm is developed to dynamically sample expert demonstrations that can well match the trajectories from the constantly optimized agent policy. The trajectories of an initial agent policy could be closer to those non-optimal expert demonstrations, but within the framework of adversarial imitation learning, agent policy will be optimized to cheat the discriminator and produce trajectories that are similar to those optimal expert demonstrations. Theoretical analysis shows that our method learns from the imperfect demonstrations via a self-paced way. Experimental results on MuJoCo and RoboSuite platforms demonstrate the effectiveness of our method from different aspects. Copyright © 2023, The Authors. All rights reserved.

关键词： Demonstrations

Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes

学校读者我要写书评

暂无评论

Fed-CVLC: Compressing Federated Learning Communications with...

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui John C. S. Lui Jiangchuan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR School of Computing Science Simon Fraser University Canada

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy; yet the communication between the PS and scattered clients can be a severe bottleneck. Model compression algorithms, such as quantization and sparsification, have been suggested but they generally assume a fixed code length, which does not reflect the heterogeneity and variability of model updates. In this paper, through both analysis and experiments, we show strong evidences that variable-length is beneficial for compression in FL. We accordingly present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response of the dynamics of model updates. We develop optimal tuning strategy that minimizes the loss function (equivalent to maximizing the model utility) subject to the budget for communication. We further demonstrate that Fed-CVLC is indeed a general compression design that bridges quantization and sparsification, with greater flexibility. Extensive experiments have been conducted with public datasets to demonstrate that Fed-CVLC remarkably outperforms state-of-the-art baselines, improving model utility by 1.50%-5.44%, or shrinking communication traffic by 16.67%-41.61%.

关键词： Training Data privacy Codes Quantization (signal) Federated learning Data models Servers

Enhancing Diversity in Multi-objective Feature Selection

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ebrahimi, Sevda Makrehchi, Masoud Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada

Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly enhancing model performance, interpretability, and resource efficiency across diverse domains. In population-based optimization methods, the generation of diverse individuals holds utmost importance for adequately exploring the problem landscape, particularly in highly multi-modal multi-objective optimization problems. Our study reveals that, in line with findings from several prior research papers, commonly employed crossover and mutation operations lack the capability to generate high-quality diverse individuals and tend to become confined to limited areas around various local optima. This paper introduces an augmentation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is achieved through two key components: the genuine initialization method and the substitution of the worst individuals with new randomly generated individuals as a re-initialization approach in each generation. The proposed multi-objective feature selection method undergoes testing on twelve real-world classification problems, with the number of features ranging from 2,400 to nearly 50,000. The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population’s quality and, consequently, enhances the performance of the multi-objective *** Codes 68T05 (Primary), 68T20, 68W20, 68W40, 90C29, 90C27, 62H30, 62H25 (Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

Multi-objective Binary Coordinate Search for Feature Selection

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is computationally highly demanding on many real-world datasets and requires a very efficient algorithm to reach a set of optimal features with a limited number of fitness evaluations. For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. To the best of our knowledge, the proposed algorithm in this paper is the first multi-objective coordinate search algorithm. In this method, we generate new individuals by flipping a variable of the candidate solutions on the Pareto front. This enables us to investigate the effectiveness of each feature in the corresponding subset. In fact, this strategy can play the role of crossover and mutation operators to generate distinct subsets of features. The reported results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets, particularly when the computing budget is limited. Moreover, this simple hyper-parameter-free algorithm can solve feature selection much faster and more efficiently than NSGA-II. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature Selection

Streamlining Attendance with Voice Recognition via Gaussian Mixture Model

学校读者我要写书评

暂无评论

Streamlining Attendance with Voice Recognition via Gaussian ...

IT and Industrial Technologies (ICIT), International Conference on

作者： Noor Ali Khadija Ali Fatimah Ali Aaminah Ali Nisar Ali Raja Hashim Ali A-Levels Lahore Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Faculty of Engineering and Applied Science University of Regina Regina Canada Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi Pakistan Department of Business University of Europe for Applied Sciences Potsdam Germany

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GIK Institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an AI-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study.

关键词： Biometrics Training Accuracy Translation Virtual assistants Speech recognition Transforms Security Gaussian mixture model Graphical user interfaces

A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

学校读者我要写书评

暂无评论

IEEE Transactions on Consumer Electronics 2024年

作者： Aldosary, Abdallah Tanveer, Muhammad Ahmad, Musheer Maghrabi, Louai A. Ahmed, Emad A. Hussain, Amir El-Latif, Ahmed A. Abd Department of Computer Engineering Prince Sattam bin Abdulaziz University Riyadh Wadi Addwasir11991 Saudi Arabia University of Management and Technology Department of Computer Science Lahore54770 Pakistan Jamia Millia Islamia Department of Computer Engineering New Delhi110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia South Valley University Faculty of Computers and Information Department of Computer Science Qena Egypt Edinburgh Napier University United Kingdom Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive ' ASCON' with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAFMCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost. © 1975-2011 IEEE.

关键词： ASCON Authenticated Encryption Authentication Mobile Crowdsourcing Network Scyther Security