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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

共 20290 条记录，以下是291-300 订阅

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Representative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive software Systems 57

Representative Dataset Generation Framework for AI-based Fai...

引用

57th Hawaii International conference on System Sciences (HICSS)

作者： Abboush, Mohammad Knieke, Christoph Rausch, Andreas Tech Univ Clausthal Inst Software & Syst Engn Clausthal Zellerfeld Germany

ISBN: (纸本)9780998133171

Recently, thanks to its ability of extracting knowledge from historical data, the data-driven approach has been widely used in various phases of the system development life cycle. In real-time system validation, remarkable achievements have been accomplished in developing an intelligent failure analysis based on historical data. However, despite its superiority over other conventional approaches, e.g., model-based and signal-based, the availability of representative datasets persists as a major challenge. thus, for different engineering applications, new solutions to generate representative faulty data in different forms should be explored. therefore, in this study, a novel approach based on Hardware-in-the-Loop (HIL) simulation and real-time Fault Injection (FI) method is proposed to generate and collect data samples under single and simultaneous faults for Machine Learning (ML) applications during system validation phases. the developed framework can generate not only sequential data, but also textual data including fault logs. the results show the applicability of the proposed framework in simulating and capturing the system behaviour under faults within the system components.

关键词： HIL testing fault injection automotive software systems development failure analysis machine learning

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Evaluating the Performance of PEFT and LoRA Approaches in Transformer-based Architecture for Handwritten Character Recognition 9

Evaluating the Performance of PEFT and LoRA Approaches in Tr...

引用

9th International conference on Computer Science and engineering, UBMK 2024

作者： Savci, Pinar Das, Bihter Das, Resul Arcelik A. S. Sütlüce Karaaǧaç St. No:2-6 Beyoglu Istanbul34445 Turkey Firat University Technology Faculty Department of Software Engineering Turkey

ISBN: (纸本)9798350365887

Handwritten character recognition poses significant challenges due to the variability in writing styles, inconsistencies in character shapes, and noise in the data. In this study, it was conducted a comparative analysis of Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) approaches against a traditional Convolutional Neural Network (CNN) for handwritten character recognition. the approaches have been evaluated using the A-Z Handwritten Alphabets dataset based on accuracy, training loss, validation loss, and training time. the PEFT and LoRA approaches were fine-tuned using pre-trained transformers, while the CNN model was trained from scratch. Our results demonstrated that the LoRA approach of the Google/vit-base-patch16-224-in21k achieved the highest accuracy of 100% with the shortest training time of approximately 58 minutes. Conversely, the traditional CNN model, while competitive in accuracy (98.28%), required longer training times. the study highlights the efficiency and adaptability of PEFT and LoRA approaches in resource-constrained environments and contributes to the literature by offering a detailed performance comparison. these insights can guide the selection of appropriate model architectures for various handwritten character recognition applications. © 2024 IEEE.

关键词： Convolutional neural networks

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the Impact of Feature Selection on Malware Classification Using Chi-Square and Machine Learning 9

The Impact of Feature Selection on Malware Classification Us...

引用

9th International conference on Computer and Communication engineering, ICCCE 2023

作者： Rasheed, Areeg Fahad Zarkoosh, M. Al-Azzawi, Sana Sabah Al-Nahrain University College of Information Engineering Baghdad Iraq Software Engineering Computiq Baghdad Iraq Luleå University of Technology Eislab Machine Learning Luleå Sweden

ISBN: (纸本)9798350325218

the Internet of things (IoT) is a network of physical objects, automobiles, household appliances, and other items that are integrated with sensors, software, and connections to gather and share data via the Internet. the rapid proliferation of the Internet of things devices has ushered in a wave of new security challenges, specifically in the realm of malware detection and these challenges necessitate innovative solutions. Consequently, the primary objective of this study is to develop an advanced malware detection system leveraging machine learning algorithms in tandem with natural language processing techniques like tokenization and vectorization, in addition to a feature selection method named Chi-Square. the proposed method is tested using the IoTPot dataset and compared with recent research in the field, where it outperformed the current work with respect to the accuracy, F1-score, recall, and precision. Furthermore, the proposed method was compared with time-based consulting and demonstrated superior performance with NLP and Chi-square than without, making it more suitable for resources constrained by such IoT systems. We also provide the code for the proposed method to foster transparency.1.1https://***/AREEG94FAHAD/chisqaure © 2023 IEEE.

关键词： Internet of things

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A software Failure Mode Analysis Method based on Test knowledge Graph 9

A Software Failure Mode Analysis Method Based on Test Knowle...

引用

9th International conference on Dependable Systems and their Applications, DSA 2022

作者： Yang, Wansheng Hu, Chi Ma, Siyou Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

software failure mode analysis (SFMA) is an important process for analyzing failure propagation by identifying possible software failure points, and then summarizing software failure modes so as to improve software quality. However, the traditional SFMA is carried out mainly by software safety analysis method, which possesses heavy workload, low efficiency, and over-dependence on the experience of safety analysts. In this paper, a SFMA is proposed method based on knowledge graph. It collects the failure factors, failure mechanism and failure performance of the software under test into a fixed Resource Description Framework (RDF) triple store by establishing a knowledge graph of data assets in the software testing process. then, the method of text clustering is applied to realize the automatic analysis of software failure mode. this method mainly covers three steps: 1) Extract failure factors - the RDF data of the descriptive text of various entity nodes related to software defect reports from the software test knowledge graph;2) Combine the RDF triples into a normalizing text in a unified format, an easy way of further natural language processing;3) Generate software defects with similar characteristics and similar failure behaviors into 'defect modes' or 'failure modes' by using clustering algorithms. the empirical study has proven that the SFMA proposed in this paper presents a novel, feasible solution for processing extremely large-scale data. © 2022 IEEE.

关键词： Failure modes

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Digital Twin System for River Network based on Graph Neural Network 36

Digital Twin System for River Network Based on Graph Neural ...

引用

36th International conference on software engineering and knowledge engineering, SEKE 2024

作者： Jin, Zishuo Ye, Feng Zhang, Peng Hohai University NanJing China Jiangsu water Conservancy Engineering Planning Office NanJing China

ISBN: (纸本)1891706594

River network management presents a significant challenge in the field of water conservancy, and the subsequent advancements in digital technology offer a promising avenue for addressing this challenge. However, the utilization of cutting-edge technologies like digital twin and deep learning within the water conservancy domain remains largely experimental, hampered by the absence of applicable models, authentic scenarios, and viable solutions. To address this, we propose a river network prediction and response system based on digital twin. this system leverages an improved graph attention network to discern spatial correlations within the detection data of individual sites, integrating bidirectional long short-term memory neural network for predictive analysis. Concurrently, we have developed a suite of dynamic visualization tools designed to streamline real-time situation analysis for users. through the establishment of an interactive digital twin river network system, we furnish a fresh strategic framework for addressing water conservancy challenges. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Rivers

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A Review of Blockchain-based Privacy Computing Research 10

A Review of Blockchain-based Privacy Computing Research

引用

10th IEEE International conference on Cyber Security and Cloud Computing and 9th IEEE International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yang, Yang Jin, Kai Liang, Wei Liu, Yaqin Li, Yuhui Hosam, Osama Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350312461

Blockchain technology offers unique advantages in terms of decentralization, transparency, and de-anonymization. However, it also poses challenges to user anonymity and data privacy protection. Consequently, researchers have employed advanced cryptographic primitives to enhance the privacy and anonymity of blockchain-based privacy payments, as well as to extend privacy payment methods to more general forms of privacy computing. Nevertheless, relying on high-level cryptographic primitives and emerging technologies, these solutions have proven challenging for academic and industrial personnel to understand and apply. therefore, we introduce the principle mechanisms of zero-knowledge proofs and homomorphic encryption and their typical algorithms, analyze and summarize recent research in blockchain privacy computing across several dimensions, and briefly present their potential applications. © 2023 IEEE.

关键词： Smart contract

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Leveraging SDN: Empirical Evaluation of Privacy Enriched Data Communication over VANET Using software Defined Network Assistance 9

Leveraging SDN: Empirical Evaluation of Privacy Enriched Dat...

引用

9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Saranya, G. Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India

ISBN: (纸本)9798350365092

In the realm of modern transportation systems, Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling efficient communication among vehicles and infrastructure elements for enhanced road safety and traffic management. However, the inherent vulnerabilities in VANETs, such as privacy breaches and security threats, necessitate robust frameworks to mitigate risks and ensure secure data transmission. this paper presents a comprehensive architecture for Privacy-Enriched Data Communication over VANETs using software-Defined Network (SDN) assistance. the proposed framework integrates key components including vehicles, Roadside Units (RSUs), and SDN controllers to facilitate secure and privacy-aware communication. Vehicles act as data sources, employing pseudonyms and encryption mechanisms to protect user privacy. RSUs serve as access points, collecting and relaying data while enforcing access control policies. SDN controllers provide centralized management, dynamically allocating resources and enforcing privacy policies through encryption, pseudonymization, and access control mechanisms. Communication protocols such as IEEE 802.11p and LTE enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, while the OpenFlow protocol facilitates communication between SDN controllers and network devices. Additionally, trust management mechanisms evaluate the trustworthiness of network entities based on behavioral analysis, reliability metrics, and security considerations, ensuring the integrity and resilience of the network. By amalgamating SDN-based management with privacy enhancement mechanisms and trust management protocols, the proposed framework offers a robust solution for addressing privacy and security challenges in VANETs, thereby fostering safer and more secure transportation ecosystems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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theoretical Basis and Development of Short-Wave Underwater Radio Communication Systems 9

Theoretical Basis and Development of Short-Wave Underwater R...

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9th IEEE All-Russian Microwave conference, RMC 2024

作者： Tomilin, Aleksandr Panchelyuga, Victor Panchelyuga, Mariya National Research Tomsk Polytechnic University Department of Mechanical Engineering Tomsk Russia Institute of Theoretical and Experimental Biophysics RAS Pushchino Russia

ISBN: (纸本)9798331540944

the results of full-scale experiments on the transmission of high-frequency voice radio signals in an aquatic environment using ball antennas are presented. based on the generalized theory, the process of propagation of a free spherically symmetric electromagnetic wave in an electrically conductive medium is described. the main idea is to determine and use the potential component of the magnetic field, which is usually excluded by calibrations. the principles of organizing voice communication with mobile underwater objects are formulated. the technical aspects of creating standard transceiver complexes for underwater radio communication are discussed. © 2024 IEEE.

关键词： Maxwell equations

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DR-DRL: Distributed Redundant Task Offloading based on Reinforcement Learning 9

DR-DRL: Distributed Redundant Task Offloading based on Reinf...

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9th International conference on Information Science, Computer Technology and Transportation, ISCTT 2024

作者： Lan, Shizhan Lin, Hongcai Liang, Huaqian Chen, Shan Wang, Zhenyu School of Software Engineering South China University of Technology Guangzhou China China Mobile Group Guangxi branch Co. Ltd Nanning China

ISBN: (纸本)9798350388435

Edge computing enhances task reliability by employing redundant task executions across edge nodes. Conventional decentralized task offloading strategies, based on heuristics and game theory, either focus on optimization or are based on unrealistic assumptions. Moreover, existing Deep Reinforcement Learning (DRL) task offloading approaches underperform due to discrepancies between simulated environments and real systems, and overlook task redundancy, thus failing to meet reliability requirements. this paper proposes a DRL-based approach to offload distributed redundant tasks, namely DR-DRL (Decentralized and Redundant DRL), to solve the redundancy problem of distributed task offloading under edge computing. Experimental results show that DR-DRL has about 8% higher task success rate than other benchmark methods. ©2024 IEEE.

关键词： Deep reinforcement learning

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software Testing Effort Estimation based on Machine Learning Techniques: Single and Ensemble Methods 16

Software Testing Effort Estimation Based on Machine Learning...

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16th International conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Hosni, Mohamed Medarhri, Ibtissam Carrillo de Gea, Juan Manuel MOSI Research Team LM2S3 ENSAM Moulay Ismail University of Meknes Morocco MMCS Research Team LMAID ENSMR Rabat Morocco Department of Informatics and Systems Faculty of Computer Science University of Murcia Spain

ISBN: (纸本)9789897587160

Delivering an accurate estimation of the effort required for software system development is crucial for the success of any software project. However, the software development lifecycle (SDLC) involves multiple activities, such as software design, software build, and software testing, among others. software testing (ST) holds significant importance in the SDLC as it directly impacts software quality. Typically, the effort required for the testing phase is estimated as a percentage of the overall predicted SDLC effort, typically ranging between 10% and 60%. However, this approach poses risks as it hinders proper resource allocation by managers. Despite the importance of this issue, there is limited research available on estimating ST effort. this paper aims to address this concern by proposing four machine learning (ML) techniques and a heterogeneous ensemble to predict the effort required for ST activities. the ML techniques employed include K-nearest neighbor (KNN), Support Vector Regression, Multilayer Perceptron Neural Networks, and decision trees. the dataset used in this study was obtained from a well-known repository. Various unbiased performance indicators were utilized to evaluate the predictive capabilities of the proposed techniques. the overall results indicate that the KNN technique outperforms the other ML techniques, and the proposed ensemble showed superior performance accuracy compared to the remaining ML techniques. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive Learning

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