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检索条件"任意字段=Knowledge-Based Software Engineering Conference"

共 17481 条记录，以下是611-620 订阅

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A Novel Model Using Multiple Bagging Ensemble Method For Measuring, Inferring and Predicting the Quality of Continuous Assessment Question Papers 14

A Novel Model Using Multiple Bagging Ensemble Method For Mea...

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14th International conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Senthil Velan, S. Preethika Reddy, S. Talwar, Preethi Bheemshankar Gomathi, R.D. CMR Institute of Technology Information Science and Engineering Bengaluru India Kongu Engineering College Department of English Tamil Nadu India

ISBN: (纸本)9798350335095

Predictive Machine Learning techniques provide mechanisms for the computing machines to analyze and understand the knowledge inherently embedded in the given dataset. This unique technique can be effectively used in understanding the quality of the question papers compiled for Continuous Assessment Tests (CATs). Understanding the quality of the question papers requires a systematic and software engineering-based approach. In this research work, a few of the CAT question papers have been carefully picked and used for applying the proposed methodology. Well, defined set of features, properties, and quality attributes have been applied in the proposed model and mapped into each other for providing a better understanding of the question paper's quality. Using metrics the analysis of quality for the given CAT paper is attempted. based on the application of the model, it was able to clearly predict and understand the quality levels of the given set of question papers. © 2023 IEEE.

关键词： software engineering

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Analysis of Porous Tibia Implant Designs based on Triply Periodic Minimal Surface Scaffolds for Different Activities 6

Analysis of Porous Tibia Implant Designs Based on Triply Per...

引用

6th IEEE Novel Intelligent and Leading Emerging Sciences conference, NILES 2024

作者： Eltlhawy, Basma Fouda, Noha Eldesouky, Ibrahim Pyramids Higher Institute of Engineering and Technology Department of Mechatronics Engineering Giza Egypt Mansoura University Faculty of Engineering Department of Production and Mechanical Design Engineering Mansoura Egypt Mansoura University Faculty of Engineering Production and Mechanical Design Engineering Department Egypt New Administrative Capital Faculty of Engineering Mechanical Engineering Department Cairo Egypt

ISBN: (纸本)9798350378511

The knee j oint is loaded differently during daily activities. Recovery and ability to practice different activities after total knee replacement (TKR) surgery still trouble most patients. Additively manufactured porous implants are promising candidates for matching natural bone performance. Detailed knowledge about the mechanical behavior of porous implants for tibia- TKR applications is essential for preclinical selection of implants. The current study analyzed three different implant designs based on triply periodic minimal surfaces (TPMS): schwarz, diamond, and gyroid. Models were analyzed using finite element analysis under dynamic loadings of most frequent activities;up-stairs and down-stairs, stand-up and sit-down, stance, and bend, walking and jogging to study the stress distribution in the bone and the implants. The results obtained in this study showed that jogging was the most difficult daily physical activity. Among the studied TPMS structures, the schwarz-stem implant showed the best mechanical performance. © 2024 IEEE.

关键词： Knee prostheses

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Machine Learning-assisted Test Records Analysis during the Real-Time Validation of Automotive software Systems based on HIL Simulation 58

Machine Learning-assisted Test Records Analysis during the R...

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58th Hawaii International conference on System Sciences, HICSS 2025

作者： Abboush, Mohammad Knieke, Christoph Rausch, Andreas Technische Universität Clausthal Institute for Software and Systems Engineering Germany

ISBN: (纸本)9780998133188

In the automotive industry, according to ISO 26262, comprehensive testing is conducted to ensure software systems quality over various phases of the V-model. However, at the system integration and testing phase, a significant amount of time series data is analyzed manually based on expert knowledge to identify the nature of the failure occurring, which is a costly process in terms of time, effort, and difficulty. This study proposes a novel Machine Learning (ML)-assisted failure analysis approach for the real-time validation of automotive software systems (ASSs). Specifically, based on a representative critical faults dataset, intelligent data-driven ML-based Fault Detection and Diagnosis (FDD) models are developed, including LSTM and Denoising Autoencoder (DAE) + k-means techniques for known and unknown sensor-related faults, respectively. The novelty of these models lies in their ability to identify single and simultaneous faults in noisy conditions. To assess the efficacy of the developed model, a dataset from a virtual test drive platform with a high-fidelity entire vehicle model was employed. The evaluation outcomes illustrate the superiority of the proposed model for known faults classification in comparison to other state-of-the-art methods, with an average F1-score of 91.85%. Furthermore, the integration of DAE with k-means exhibited a high clustering performance against noise with a low MSE and DB, i.e., 0.044 and 0.68, respectively. Consequently, the proposed approach facilitates the real-time validation of the ASSs in an efficient manner enhancing the safety and reliability. © 2025 IEEE Computer Society. All rights reserved.

关键词： software testing

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EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support

EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educat...

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Li, Qiaomu Xie, Ying Chakravarty, Sumit Lee, Dabae Kennesaw State University College of Computing and Software Engineering MariettaGA30060 United States Kennesaw State University Department of Instructional Technology MariettaGA30060 United States

ISBN: (纸本)9798350362480

In general, educational support with Large Language Models (LLMs) faces challenges in knowledge organization, expertise integration, and contextual adaptation. So, we present EduMAS, a novel multi-agent framework that coordinates specialized agents with graph-based knowledge navigation. Our framework introduces three key innovations: (1) Specialized Agents that provide expertise in different learning aspects to solve decomposed subtasks professionally;(2) Graph Navigator for graph-based knowledge extraction and selection to improve the quality of responses;(3) The Emotional Awareness mechanism for better contextual adaptation. Through comprehensive experiments on college-level physics education and evaluated by six state-of-the-art LLMs, EduMAS demonstrates significant improvements over the baseline model in complex concept integration, cross-disciplinary understanding, and theory-to-application translation. Ablation studies further validate the contribution of each framework component, Specialized Agents and Graph Navigator play important roles in performance improvement. Our work provides strong support for LLM-powered multi-agent system in AI-assisted education. © 2024 IEEE.

关键词： Multi agent systems

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12th International conference on Model and Data engineering, MEDI 2023

12th International Conference on Model and Data Engineering,...

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12th International conference on Model and Data engineering, MEDI 2023

ISBN: (纸本)9783031493324

The proceedings contain 27 papers. The special focus in this conference is on Model and Data engineering. The topics include: Social Recommendation Using Deep Auto-encoder and Confidence Aware Sentiment Analysis;deep Learning based on TensorFlow and Keras for Predictive Monitoring of Business Process Execution Delays;OntoFD: A Generic Social Media Fake News Ontology;Finding a Second Wind: Speeding Up Graph Traversal Queries in RDBMSs Using Column-Oriented Processing;ontology Matching Using Multi-head Attention Graph Isomorphism Network;Investigating the Perceived Usability of Entity-Relationship Quality Frameworks for NoSQL Databases;fuzzy HealthIoT Ontology for Comorbidity Treatment;advancing Brain Tumor Segmentation via Attention-based 3D U-Net Architecture and Digital Image Processing;breast Cancer Detection based DenseNet with Attention Model in Mammogram Images;a Formal Metamodel for software Architectures with Composite Components;AI-LMS: AI-based Long-Term Monitoring System for Patients in Pandemics: COVID-19 Case Study;cardiovascular Anomaly Detection Using Deep Learning Techniques;real-Time Mitigation of Trust-Related Attacks in Social IoT;hybrid Data-Driven and knowledge-based Predictive Maintenance Framework in the Context of Industry 4.0;Enhancing Semantic Image Synthesis: A GAN-based Approach with Multi-Feature Adaptive Denormalization Layer;towards an Effective Attribute-based Access Control Model for Neo4j;GRU-based Forecasting Model for Energy Production and Consumption: Leveraging Random Forest Feature Importance;localizing Non-functional Code Bugs in User Interfaces Using Deep Learning Techniques;a Floating-Point Numbers Theory for Event-B;Model-based Testing Approach for EIP-1559 Ethereum Smart Contracts;execution Planning for Aggregated Search in the Web of Data: A Free-Metadata Approach;Discovering Relationships Between Heterogeneous Declarative Mappings for RDF knowledge Graph;Exploring Synthetic Noise Algorithms for Real-World Similar Data Gen

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Teaching and Promoting Engagement with OSS: Yet Another Experience Report 23

Teaching and Promoting Engagement with OSS: Yet Another Expe...

引用

37th Brazilian Symposium on software engineering, SBES 2023, held in conjunction with the Brazilian conference on software: Theory and Practice, CBSoft 2023

作者： Flach, Christina Von Feitosa, Daniela Soares Federal University of Bahia Institute of Computing Salvador Bahia Brazil

ISBN: (纸本)9798400707872

OSS-based learning refers to using open source software (OSS) and their sociotechnical practices in the pedagogical context. Several educators reported its benefits and barriers within different contexts, goals, and areas of knowledge, bringing evidence that it is a feasible approach to address software engineering Education challenges. Some appealing factors to adopting OSS-based learning are the availability of the software source code and workflows, access to the OSS community, and information about its development and evolution, which may improve the learning of SE concepts and practices and foster students' engagement in real-world projects. Observing students as they work towards hard and soft skills, understanding, engaging with, and eventually contributing to an OSS project is a rewarding part of the routine of educators who have recognized the benefits of OSS-based learning and adopted it in their classes. We report our experience after delivering a "hands-on"course to introduce graduate students to OSS projects and their sociotechnical practices. Some graduate students were higher education instructors in other institutions. We present the course design and details of its execution, followed by a reflection based on students' feedback and our perceptions of gains and pains. Overall, students valued the course and were highly motivated to explore OSS, especially those who worked as educators. Most of the feedback concerning the course methodology was positive, but some students requested more information about the weekly lesson plans in advance. We hope this experience report helps to demystify OSS, inspire educators to adopt OSS projects in their courses, and foster instructors' and students' engagement with OSS projects. © 2023 ACM.

关键词： Open source software

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Fine-grained Commit-level Vulnerability Type Prediction by CWE Tree Structure 23

Fine-grained Commit-level Vulnerability Type Prediction by C...

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45th IEEE/ACM International conference on software engineering (ICSE)

作者： Pan, Shengyi Bao, Lingfeng Xia, Xin Lo, David Li, Shanping Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Huawei Shenzhen Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781665457019

Identifying security patches via code commits to allow early warnings and timely fixes for Open Source software (OSS) has received increasing attention. However, the existing detection methods can only identify the presence of a patch (i.e., a binary classification) but fail to pinpoint the vulnerability type. In this work, we take the first step to categorize the security patches into fine-grained vulnerability types. Specifically, we use the Common Weakness Enumeration (CWE) as the label and perform fine-grained classification using categories at the third level of the CWE tree. We first formulate the task as a Hierarchical Multi-label Classification (HMC) problem, i.e., inferring a path (a sequence of CWE nodes) from the root of the CWE tree to the node at the target depth. We then propose an approach named TREEVUL with a hierarchical and chained architecture, which manages to utilize the structure information of the CWE tree as prior knowledge of the classification task. We further propose a tree structure aware and beam search based inference algorithm for retrieving the optimal path with the highest merged probability. We collect a large security patch dataset from NVD, consisting of 6,541 commits from 1,560 GitHub OSS repositories. Experimental results show that TREEVUL significantly outperforms the best performing baselines, with improvements of 5.9%, 25.0%, and 7.7% in terms of weighted F1-score, macro F1-score, and MCC, respectively. We further conduct a user study and a case study to verify the practical value of TREEVUL in enriching the binary patch detection results and improving the data quality of NVD, respectively.

关键词： software Security Vulnerability Type CWE

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AMF: A Field to Realize the Values of Agile Manifesto through Retrospectives 29

AMF: A Field to Realize the Values of Agile Manifesto throug...

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29th Asia-Pacific software engineering conference (APSEC)

作者： Kawaguchi, Ikkaku Hayase, Yasuhiro Kobayashi, Kyohei Watanabe, Chiemi Univ Tsukuba Ibaraki Japan Deloitte Tohmatsu Consulting LLC Tokyo Japan Tsukuba Univ Technol Ibaraki Japan

ISBN: (纸本)9781665455374

In recent decades, companies and other organizations have been increasingly focusing on agile software development methods, and Scrum has been increasingly introduced into software development projects. However, teams in the early stages of Scrum adoption tend to focus on adhering to development processes and practices, often neglecting the four essential values declared in the Agile Manifesto. This paper proposes a field of retrospective called Agile Manifesto Farm (AMF) as a method to promote awareness of the four values and to progressively formalize and establish the knowledge obtained through the activities of each sprint. As a case study, the proposed method is applied in a project-based learning class.

关键词： Agile software Development Scrum Retrospective

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A Visualization Model for Classification of Human Personality through Bio-electric current Pattern Analysis

A Visualization Model for Classification of Human Personalit...

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Kim, Janghwan Jang, Woosung Kim, R. Young Chul Hongik Univ Dept Software & Commun Engn Sejong South Korea

ISBN: (纸本)9798350361513;9798350372304

Modern medicine is studied based on standardized figures obtained through Medical and scientific methods with one equality of all humans. The disease's measurement, analysis, and treatment are quantified through this. Traditional Korean medicine, on the other hand, aims to maintain a healthy human life by solving the fundamental problem of disease based on each different one of all humans. However, traditional Korean medicine may not fit in modern times due to its long accumulation of prior knowledge-based records. We are working in a software engineering lab. Because there are no medical doctors, we focus on the number of values of human bioelectricity. We propose a method of identifying the human constitution with how to apply bioelectricity pattern signals based on traditional Korean medical theory. Through the proposed method, it will expect to care for health conditions. With this visualization, we may possibly identify the abnormal area of the human body with internal organs, such as liver, heart, lung spleen, kidney. After recovering health, we may also recognize to become the normal condition of him/her.

关键词： Bio-electric current Pattern Eight Constitutional Medicine (ECM) Sassang Constitutional Medicine (SCAT) Ying and Yang Theory

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DeepRank: Test Case Prioritization for Deep Neural Networks 35

DeepRank: Test Case Prioritization for Deep Neural Networks

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Li, Wei Zhang, Zhiyi Jian, Yifan Liu, Chen Huang, Zhiqiu Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Marxism Yangzhou University Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China

Deep neural networks (DNNs) have been widely used in safety-critical fields such as autonomous driving and medical diagnosis. However, DNNs are easily disturbed to make wrong decisions, which may lead to loss of life or property. Therefore, it is vital to test DNN adequately. In practice, to reveal the incorrect behavior of DNN and improve its robustness, testers usually need massive labeled data to test and optimize DNN. However, labeling test inputs to detect the correctness of DNN predictions is an expensive and time-consuming task that even affects the efficiency of DNN testing. To relieve the labeling-cost problem, we propose DeepRank, a test case prioritization technique based on cross-entropy loss. The key idea of DeepRank is that the higher the loss value of a test case relative to the DNN, the more likely it is to be mispredicted and the more conducive it is to improve the robustness of the DNN through retraining. Therefore, the cross-entropy loss value can be used for test case prioritization. We experimentally validate our approach on two datasets and three DNNs models. The experimental results demonstrate that DeepRank is significantly better than existing test case prioritization methods regarding fault-revealing capability and retraining effectiveness. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep neural networks

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