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8th International Conference on Information Technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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TASRI: Toward Traffic-Aware, Sustainable and Reliable ISL Provisioning for LEO Satellite Constellation Networking

TASRI: Toward Traffic-Aware, Sustainable and Reliable ISL Pr...

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International Workshop on Quality of Service

作者： Long Chen Yi Ching Chou Hengzhi Wang Feng Wang Haoyuan Zhao Hao Fang Sami Ma Feilong Tang Linghe Kong Jiangchuan Liu School of Computing Science Simon Fraser University Canada College of Computer Science and Software Engineering Shenzhen University China Department of Computer and Information Science The University of Mississippi USA Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Inter-Satellite Links (ISLs) are key for worldwide communication and efficient use of space networks in the future 6G network. However, they face challenges in sustainability and reliability. Reducing ISLs saves energy and extends battery life, which is critical since satellite batteries are hard to replace. More ISLs, however, can make the system more reliable but at the cost of higher energy use, especially problematic when traffic is uneven, speeding up battery wear. To tackle this dilemma, we for the first time develop a Traffic-Aware, Sustainable and Reliable ISL provisioning (TASRI) framework for LEO satellite constellation networks. In TASRI, ISLs can be flexibly switched on and off to better accommodate various traffic conditions as well as reliability and sustainability. We formulate the ISL provisioning problem based on the sustainability-oriented weight model and then propose an on-demand topology evolving algorithm. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability with considerably fewer ISLs.

关键词： Satellite constellations Satellites Network topology Simulation Low earth orbit satellites Switches Batteries

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

arXiv

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arXiv 2024年

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

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%. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Quantum Machine Learning for Security Data Analysis

Quantum Machine Learning for Security Data Analysis

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AI IoT Congress (AIIoT), World

作者： Dhanunjai Bandi Yong Shi Hossain Shahriar Dan Lo Kun Suo Hongmei Chi Kai Qian College of Computing and Software Engineering Kennesaw State University USA Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA

In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.

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A STUDY ON INTERIOR Γ-HYPERFILTERS IN ORDERED Γ-SEMIHYPERGROUPS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第1期84卷 71-80页

作者： Chen, C. Kosari, S. Omidi, S. Davvaz, B. Akhoundi, M. Institute of Software School of Electronics Engineering and Computer Sciences Peking University Beijing100871 China Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Ministry of Education Iran Department of Education in Tehran Tehran Iran Department of Mathematics Yazd University Yazd Iran Clinical Research Development Unit of Rouhani Hospital Babol University of Medical Sciences Babol Iran

In this paper, interior Γ-hyperfilters of ordered Γ-semihypergroups are de- fined and various types of them are studied. Results related to productional ordered Γ- semihypergroups were investigated. Moreover, we investigate some properties of the in- verse images of strong interior Γ-hyperfilters in ordered Γ-semihypergroups. Finally, we discuss the relationship between two fundamental notions of ordered Γ-semihypergroup, the several types of interior Γ-hyperfilters and the (completely) prime interior Γ-hyperideals. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： (completely) prime I-Γ-hyperfilter Ordered Γ-semihypergroup Strong I-Γ-hyperfilter Weak I-Γ-hyperfilter

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Orchestrating Sustainable and Service-Differentiable Satellite Networking: A Federated Cross-Orbit Approach

Orchestrating Sustainable and Service-Differentiable Satelli...

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International Workshop on Quality of Service

作者： Yi Ching Chou Long Chen Feng Wang Hengzhi Wang Xiaoqiang Ma Sami Ma Jiangchuan Liu School of Computing Science Simon Fraser University Canada Department of Computer and Information Science The University of Mississippi USA College of Computer Science and Software Engineering Shenzhen University China Department of CSIS Douglas College Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Satellite networks are believed to become an indispensable component in the forthcoming 6G network and beyond. The surging demands attract numerous satellite network operators into this market to compete, yet also cooperate via resource sharing for cost and performance improvement, which is similar to the growth trajectory of how the Internet becomes the network of networks. Hence, we envision a federated network of satellite networks (shortened as federated satellite network) in this paper, where satellite network operators will eventually federate with each other to achieve a win-win situation. However, the yet-to-come federated satellite network faces two unique challenges: sustainability and dynamic topology. As such, we propose a sustainable and service-differentiable framework named Federated Cross-orbit Satellite Network (FCSN). Different from most existing solutions which focused on the Internet or simple cooperation among satellites, the FCSN orchestrates network resources in the dynamic topology to improve sustainability, through service-differentiable offloading in the resource-limited scenario. We formulate the sustainability-oriented federated offloading problem based on the utility and cost models tailored for the FCSN and propose an efficient hardware-budget constrained auction algorithm with a bounded approximation ratio. Finally, we design a truthful and rational payment scheme to motivate the construction of the FCSN. Extensive simulation results based on real-world deployments show that our solution significantly improves sustainability and delay, making it one step further toward the vision of the federated network of satellite networks.

关键词： Satellites Costs Network topology Simulation Approximation algorithms Topology Internet

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A Systematic Literature Review on Large Language Models for Automated Program Repair

arXiv

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arXiv 2024年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Ma, Yuxiang Sun, Weisong Yang, Yun Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Technologies Swinburne University of Technology Australia

Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), a rapidly increasing number of APR techniques have been proposed, significantly facilitating software development and maintenance and demonstrating remarkable performance. However, due to ongoing explorations in the LLM-based APR field, it is challenging for researchers to understand the current achievements, challenges, and potential opportunities. This work provides the first systematic literature review to summarize the applications of LLMs in APR between 2020 and 2024. We analyze 127 relevant papers from LLMs, APR and their integration perspectives. First, we categorize existing popular LLMs that are applied to support APR and outline three types of utilization strategies for their deployment. Besides, we detail some specific repair scenarios that benefit from LLMs, e.g., semantic bugs and security vulnerabilities. Furthermore, we discuss several critical aspects of integrating LLMs into APR research, e.g., input forms and open science. Finally, we highlight a set of challenges remaining to be investigated and the potential guidelines for future research. Overall, our paper provides a systematic overview of the research landscape to the APR community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://***/iSEngLab/AwesomeLLM4APR. © 2024, CC BY.

关键词： Repair

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Towards Enhanced Verification in Process Mining: A Ctmc-Based Probabilistic Model Checking Approach

SSRN

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SSRN 2024年

作者： Mangi, Fawad Ali Su, Guoxin Zhang, Minjie Liu, Li School of Computing and Information Technology University of Wollongong Wollongong2522 Australia Department of Computer Systems Engineering Mehran University of Engineering and Technology Sindh Jamshoro76062 Pakistan School of Big Data and Software Engineering Chongqing University Chongqing400030 China

Process mining is a rapidly growing research field that uses data science techniques to analyse business processes and extract insights about their performance, efficiency, and compliance. The process mining approach begins with process discovery, which involves creating process models based on event logs;and depending on the type of models discovered, the correctness of models needs to be guaranteed. Verification is paramount to ensure the accuracy and reliability of discovered process models in process mining, the current research lacks to model the stochastic nature of process mining models. This paper introduces a novel technique for verification of process mining models, leveraging continuous time Markov chain (CTMC) model of probabilistic model checking technique. The proposed method aims to address the inherent uncertainties and dynamic nature of real-world processes, providing a more robust and comprehensive verification framework compared to traditional methods. The technique employs CTMC to represent the stochastic behavior of processes, allowing for the incorporation of time and probability in the analysis of process models. This approach enables the evaluation of process models under various conditions and scenarios, considering the probabilistic aspects and temporal characteristics inherent in business processes. The paper presents a detailed methodology of the proposed technique, illustrating its application through a case study of loan application process performed in a financial institution, and empirical evaluations. © 2024, The Authors. All rights reserved.

关键词： Model checking

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PET: Multi-agent Independent PPO-based Automatic ECN Tuning for High-Speed Data Center Networks

arXiv

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arXiv 2024年

作者： Cheng, Kai Wang, Ting Du, Xiao Du, Shuyi Cai, Haibin The Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education The Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200050 China The Department of Houston International Institute Dalian Maritime University Liaoning Dalian116026 China

Explicit Congestion Notification (ECN)-based congestion control schemes have been widely adopted in high-speed data center networks (DCNs), where the ECN marking threshold plays a determinant role in guaranteeing a packet lossless DCN. However, existing approaches either employ static settings with immutable thresholds that cannot be dynamically self-adjusted to adapt to network dynamics, or fail to take into account many-to-one traffic patterns and different requirements of different types of traffic, resulting in relatively poor performance. To address these problems, this paper proposes a novel learning-based automatic ECN tuning scheme, named PET, based on the multi-agent Independent Proximal Policy Optimization (IPPO) algorithm. PET dynamically adjusts ECN thresholds by fully considering pivotal congestion-contributing factors, including queue length, output data rate, output rate of ECN-marked packets, current ECN threshold, the extent of incast, and the ratio of mice and elephant flows. PET adopts the Decentralized Training and Decentralized Execution (DTDE) paradigm and combines offline and online training to accommodate network dynamics. PET is also fair and readily deployable with commodity hardware. Comprehensive experimental results demonstrate that, compared with state-of-the-art static schemes and the learning-based automatic scheme, our PET achieves better performance in terms of flow completion time, convergence rate, queue length variance, and system robustness. © 2024, CC BY.

关键词： Mammals

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