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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3227 条记录，以下是291-300 订阅

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Federated Reinforcement learning for Sharing Experiences Between Multiple Workers

Federated Reinforcement Learning for Sharing Experiences Bet...

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2023 international conference on machine learning and Cybernetics, ICMLC 2023

作者： Feng, Wenliang Liu, Han Peng, Xiaogang College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Reinforcement learning has been successfully applied in various fields, such as games and robots. However, there are still some issues in the traditional reinforcement learning paradigm that involves one agent per environment, such low efficiency in data generation and insufficient amount of training data. In this paper, we propose a federated reinforcement learning framework that utilizes multiple agents to generate data for simultaneous training of models. Specifically, the proposed framework allows the best performing agent to share its learning experience with other agents for improving the learning performance and protecting the privacy of agents. The proposed framework is applied to the Cart-pole, Acrobot and LunarLander environments in OpenAI Gym, and the results show that a significant improvement of the learning performance can be achieved by adopting our framework. © 2023 IEEE.

关键词： Reinforcement learning

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Optimizing Collective Building Management through a machine learning-based Decision Support System 4

Optimizing Collective Building Management through a Machine ...

引用

4th international Informatics and software engineering conference, IISEC 2023

作者： Guvencli, Mert Kiran, Halil Dogan, Erkan Dag, Hasan Ozyuruyen, Burcu Cakar, Tuna Apsiyon Informatics Sys. Inc. Software-Team Lead Istanbul Turkey Apsiyon Informatics Sys. Inc. Product-Team Lead Istanbul Turkey Apsiyon Informatics Sys. Inc. Software-Management CPO Istanbul Turkey Kadir Has University Informatics Management Systems Istanbul Turkey Apsiyon Informatics Sys. Inc. R and D and Innovation Incentives Sen. Sp. Istanbul Turkey MEF University Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

This study presents the design, implementation, and evaluation of a Decision Support System (DSS) developed for Collective Building Management. Given the potential advantages of machine learning techniques in this domain, the research explores how these techniques can be used to improve collective building management. The dataset consists of 824,932 records and 15 attributes, after preprocessing the data to fill in missing values with the median. The random forest algorithm was chosen for model training and achieved a performance rate of 71.2%. This model can be used to optimize decision processes in collective building management. The proposed prototype is notable for its ability to automatically generate operational plans. In conclusion, machine learning-based DSSs are effective tools for collective building management. © 2023 IEEE.

关键词： Decision support systems

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Proceedings - 2023 IEEE/ACIS 21st international conference on software engineering Research, Management and Applications, SERA 2023

Proceedings - 2023 IEEE/ACIS 21st International Conference o...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

ISBN: (纸本)9798350345889

The proceedings contain 72 papers. The topics discussed include: construction and application of knowledge graph for food therapy;determining the most significant metadata features to indicate defective software commits;documentation practices in agile software development: a systematic literature review;rule-based representation learning for traditional Chinese medicine knowledge graph;experimental evaluation of adversarial attacks against natural language machine learning models;mobile user analysis considering collaboration with financial services;commit message can help: security patch detection in open source software via transformer;texture and orientation-based feature extraction for robust facial expression recognition;scientific organization of blood donation camp through lexicographic optimization and taxicab path computation;research and application of content-based image hash retrieval algorithm;and cloud-based digital twins storage in emergency healthcare.

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A mobile package recommendation method based on grid search combined with XGBoost model 6

A mobile package recommendation method based on grid search ...

引用

6th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Zhang, Chengxin Zhang, Shengnan Shenyang University of Technology China

ISBN: (纸本)9781510668249

The package recommendation has always been an important issue in the marketing of mobile operators, and machine learning provides a new solution for operators. Aiming at the problem that too many training times of dirty data in the existing methods lead to the reduction of prediction accuracy and the tedious manual setting of model parameters, this paper propose a model combining grid search and XGBoost, and use the exhaustive search method to find the parameter value with the highest accuracy in the validation set within the parameter range of the given XGBoost. Compared with Random Forest and XGBoost default parameter values, experiments show that the proposed model has higher prediction accuracy and can effectively avoids the error caused by manual adjustment of parameters. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Data modeling machine learning Education and training Random forests Performance modeling Decision trees

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Data Quality for software Vulnerability Datasets 23

Data Quality for Software Vulnerability Datasets

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

作者： Croft, Roland Babar, M. Ali Kholoosi, M. Mehdi Univ Adelaide CREST Sch Comp Sci Adelaide SA Australia Cyber Secur Cooperat Res Ctr Joondalup Australia

ISBN: (纸本)9781665457019

The use of learning-based techniques to achieve automated software vulnerability detection has been of long-standing interest within the software security domain. These data-driven solutions are enabled by large software vulnerability datasets used for training and benchmarking. However, we observe that the quality of the data powering these solutions is currently ill-considered, hindering the reliability and value of produced outcomes. Whilst awareness of software vulnerability data preparation challenges is growing, there has been little investigation into the potential negative impacts of software vulnerability data quality. For instance, we lack confirmation that vulnerability labels are correct or consistent. Our study seeks to address such shortcomings by inspecting five inherent data quality attributes for four state-of-the-art software vulnerability datasets and the subsequent impacts that issues can have on software vulnerability prediction models. Surprisingly, we found that all the analyzed datasets exhibit some data quality problems. In particular, we found 20-71% of vulnerability labels to be inaccurate in real-world datasets, and 17-99% of data points were duplicated. We observed that these issues could cause significant impacts on downstream models, either preventing effective model training or inflating benchmark performance. We advocate for the need to overcome such challenges. Our findings will enable better consideration and assessment of software vulnerability data quality in the future.

关键词： software vulnerability data quality machine learning

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Evaluation of Natural Language Used to software Engineer based on Chatbot

Evaluation of Natural Language Used to Software Engineer bas...

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2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Balamurugan, J. Subramanyam, Naguru Mani, D. Sharada Basi Reddy, A. St. Joseph's College of Engineering Department of Mba Chennai India Audisankara College of Engineering and Technology Department of Computer Science and Engineering Gudur India Qis College of Engineering and Technology Vengamukkapalem Department It Ongole India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350317060

The field of natural language processing (NLP), which aims to represent and analyze human language digitally, has recently attracted much interest. machine learning has found several uses, including translation, spam filtering, data mining, stigmatization, medicine, and answering questions online. After introducing the background and development of NLP, it breaks down into four distinct periods by describing the various stages of NLP and the building blocks of Natural Language Generation (NLG). The numerous applications of NLP, current trends, and problems are then presented, the state of the art in great depth. At the end of the paper, it explores several existing NLP datasets, models, and assessment measures. © 2023 IEEE.

关键词： machine learning

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Metamorphic Testing of machine Translation Models using Back Translation

Metamorphic Testing of Machine Translation Models using Back...

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4th IEEE/ACM international Workshop on Deep learning for Testing and Testing for Deep learning (DeepTest) / international conference on software engineering (ICSE)

作者： Gao, Wentao He, Jiayuan Pham, Van-Thuan Univ Melbourne Melbourne Australia RMIT Univ Melbourne Australia

ISBN: (纸本)9798350338126

machine translation software has been widely adopted in recent years. The recent advance in deep learning research has massively improved the accuracy and fluency of the translated output. However, incorrect translations may still occur, which causes misunderstandings, and even more detrimental consequences when applying these systems for crucial applications, such as translating legal and medical documents. This calls for methods that can test the correctness of machine translation software efficiently and effectively. In this paper, we propose a method that uses back-translation as a reference for machine translation testing, minimizing the knowledge and use of the NLP tools in the target language so that the same workflow can be applied to test systems translating English to multiple languages. We build a metamorphic testing method using our proposed concept called contextual referentially transparent input (CRTI). A CRTI is a piece of text that should have a similar meaning under a certain context in any given language. Our method detects inconsistency between a CRTI in the original sentence and the back-translation to report translation errors. To evaluate our method, we translate 200 sentences using Google Translate. Our method reports 57 suspicious issues with a precision of 74% in Chinese translation and 22 suspicious issues with a precision of 82% in Vietnamese translation.

关键词： metamorphic testing machine translation testing back-translation

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Building a Deep learning Model to Facilitate software Project Risk Assessment

Building a Deep Learning Model to Facilitate Software Projec...

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2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Kolikipogu, Ramakrishna Rastogi, Parag Victorseelan, D. Singh, Pavitar Parkash Department of Information Technology Hyderabad India Subharti Institute of Technology & Engineering Swami Vivekanand Subharti University Department of Computer Science & Engineering Meerut India Nehru College of Management Department of Mathematics Coimbatore India Lovely Professional University Department of Management Phagwara India

ISBN: (纸本)9798350317060

software companies strive to create software projects of superior quality by leveraging the most optimal global resources at the most competitive cost. We implemented a methodology to attain global software development (GSD). Utilizing a method that involves collaborating on projects across various geographically dispersed locations, commonly called distributed development, is recommended. Companies encounter multiple challenges when they endeavour to implement Global software Development (GSD) due to the inherent characteristics of GSD and its distinctions from conventional methodologies. Constructing a structure for machine learning to aid technology project risk analysis. The study's results indicate that 15 key factors significantly impact software projects in the context of GSD. Experimental analysis reveals that logistic regression and random forest models yield the most favourable outcomes, achieving 88% and 80% accuracy, respectively. Both models have a 71% and 72% area under the curve © 2023 IEEE.

关键词： Risk assessment

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Novel Approach for software Reliability Analysis Controlled with Multifunctional machine learning Approach 4

Novel Approach for Software Reliability Analysis Controlled ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： William, P. Gupta, Manish Chinthamu, Narendar Shrivastava, Anurag Kumar, Indradeep Kakoli Rao, A. Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Lovely Professional University Division of Research and Development Phagwara India Cto Candidate DallasTX United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Department of CSE Greater Noida India

ISBN: (纸本)9798350300093

Reliability engineering is distinguished from other fields by its focus on software. Models that forecast when things will go wrong are used to evaluate the reliability of a piece of software. Real-world issues might arise when it comes to reliability. Many computational algorithms have been developed in order to provide simple, trustworthy, and fast answers. Even though it doesn't work, 'Quality' is an important part of any programme that is often missing from software solutions. Using defect prediction models and object-oriented metrics, problem classes in software are found and used to measure its quality. Modeling software failure data and incorporating class-specific metrics peculiar to defective classes are used in this work to conduct an experimental investigation of defective classes. machine learning and object-oriented metrics are used to do this. It is known how likely the classes of Marian Jureczko (MJ) Data sets are to go wrong. After looking at the Marian Jureczko Data set, it has been shown that RF gives the best accuracy and ROCAUC values. The RF model that was built is great. © 2023 IEEE.

关键词： software reliability

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Toward More Effective Deep learning-based Automated software Vulnerability Prediction, Classification, and Repair 23

Toward More Effective Deep Learning-based Automated Software...

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

作者： Fu, Michael Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350322637

software vulnerabilities are prevalent in software systems and the unresolved vulnerable code may cause system failures or serious data breaches. To enhance security and prevent potential cyberattacks on software systems, it is critical to (1) early detect vulnerable code, (2) identify its vulnerability type, and (3) suggest corresponding repairs. Recently, deep learning-based approaches have been proposed to predict those tasks based on source code. In particular, software vulnerability prediction (SVP) detects vulnerable source code;software vulnerability classification (SVC) identifies vulnerability types to explain detected vulnerable programs;neural machine translation (NMT)-based automated vulnerability repair (AVR) generates patches to repair detected vulnerable programs. However, existing SVPs require much effort to inspect their coarse-grained predictions;SVCs encounter an unresolved data imbalance issue;AVRs are still inaccurate. I hypothesize that by addressing the limitations of existing SVPs, SVCs and AVRs, we can improve the accuracy and effectiveness of DL-based approaches for the aforementioned three prediction tasks. To test this hypothesis, I will propose (1) a finer-grained SVP approach that can point out vulnerabilities at the line level;(2) an SVC approach that mitigates the data imbalance issue;(3) NMT-based AVR approaches to address limitations of previous NMT-based approaches. Finally, I propose integrating these novel approaches into an open-source software security framework to promote the adoption of the DL-powered security tool in the industry.

关键词： Cybersecurity software Vulnerability software Security

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