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检索条件"任意字段=IEEE International Conference on Software - Science, Technology and Engineering 2005"

共 39361 条记录，以下是41-50 订阅

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Discovering Explainability Requirements in ML-Based software 24

Discovering Explainability Requirements in ML-Based Software

引用

46th international conference on software engineering: Companion, ICSE-Companion 2024

作者： Sporsem, Tor Norwegian University of Science and Technology SINTEF Trondheim Norway

ISBN: (纸本)9798400705021

As the demand for Machine Learning (ML)-based software continues to grow across various industries such as healthcare, automotive, energy, and banking, there is an increasing need for explainability requirements. Domain experts such as doctors must have confidence in ML-based software to integrate them into their professional practices. This requires developers to simultaneously develop clear explanations of how these Machine Learning models work as they build the systems. While numerous philosophies and techniques for eliciting user requirements in software systems have been extensively studied within Requirements engineering (RE), scholars argue that we need new approaches tailored to elicit explainability requirements. This PhD research aims to conduct empirical studies examining emerging methodologies and philosophies for identifying explainability requirements. The objective is to connect theoretical insights and practical approaches adopted by practitioners in this rapidly evolving field. © 2024 ieee Computer Society. All rights reserved.

关键词： Requirements engineering

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AVIATE: Exploiting Translation Variants of Artifacts to Improve IR-based Traceability Recovery in Bilingual software Projects 24

AVIATE: Exploiting Translation Variants of Artifacts to Impr...

引用

39th ACM/ieee international conference on Automated software engineering (ASE)

作者： Sun, Kexin Ren, Yiding Kuang, Hongyu Gao, Hui Ma, Xiaoxing Rong, Guoping Shao, Dong Zhang, He Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Traceability plays a vital role in facilitating various software development activities by establishing the traces between different types of artifacts (e.g., issues and commits in software repositories). Among the explorations for automated traceability recovery, the IR (Information Retrieval)-based approaches leverage textual similarity to measure the likelihood of traces between artifacts and show advantages in many scenarios. However, the globalization of software development has introduced new challenges, such as the possible multilingualism on the same concept (e.g., "(sic)" vs. "attribute") in the artifact texts, thus significantly hampering the performance of IR-based approaches. Existing research has shown that machine translation can help address the term inconsistency in bilingual projects. However, the translation can also bring in synonymous terms that are not consistent with those in the bilingual projects (e.g., another translation of "(sic)" as "property"). Therefore, we propose an enhancement strategy called AVIATE that exploits translation variants from different translators by utilizing the word pairs that appear simultaneously across the translation variants from different kinds artifacts (a.k.a. consensual biterms). We use these biterms to first enrich the artifact texts, and then to enhance the calculated IR values for improving IR-based traceability recovery for bilingual software projects. The experiments on 17 bilingual projects (involving English and 4 other languages) demonstrate that AVIATE significantly outperformed the IR-based approach with machine translation (the state-of-the-art in this field) with an average increase of 16.67 in Average Precision (31.43%) and 8.38 (11.22%) in Mean Average Precision, indicating its effectiveness in addressing the challenges of multilingual traceability recovery.

关键词： Traceability recovery Cross-lingual information retrieval Biterm

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Enhancing Cybersecurity through Advanced Keylogging Techniques 2

Enhancing Cybersecurity through Advanced Keylogging Techniqu...

引用

2nd ieee international conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Sanjay, K. Ratheesh, J.K. Johin, R.R. Krishnamoorthy, A. Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (纸本)9798350368109

As the viruses are increasing efficiently, our project aims to develop a keylogger software. The purpose of this software is to continuously monitor and assess the activities of employees, and to address security concerns that may arise for both children and professionals who can encounter and install unwanted software. Using the Java programming language, keylogger is crafted for specialized testing purposes within this project. This software is a solution for monitoring the activities of employees and children. This program is tested in a controlled environment to attain its efficiency. The outcomes suggest that the software functions effectively, indicating the areas for potential enhancement in future research endeavors. © 2024 ieee.

关键词： Cyber attacks

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Research on Resource Scheduling of Computing Power Network Based on Deep Q-Network Algorithm 3

Research on Resource Scheduling of Computing Power Network B...

引用

3rd international conference on Cloud Computing, Big Data Application and software engineering, CBASE 2024

作者： Wang, Zhijun Zhou, Jingbo High Performance Computing Center Changchun Normal University Changchun China School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331506582

The resource scheduling of computing power networks is a complex project, and the scheduling strategies used will directly affect the efficiency of subsequent processing of computing tasks. This article focuses on the resource scheduling problem of computing power networks, using the DQN algorithm in deep reinforcement learning to optimize resource utilization, resource mobilization rate, and reduce task loss rate, providing a solution. © 2024 ieee.

关键词： Scheduling algorithms

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FASTFIXER: An Efficient and Effective Approach for Repairing Programming Assignments 24

FASTFIXER: An Efficient and Effective Approach for Repairing...

引用

39th ACM/ieee international conference on Automated software engineering (ASE)

作者： Liu, Fang Liu, Zhenwei Zhao, Qianhui Jiang, Jing Zhang, Li Li, Ge Sun, Zian Li, Zhongqi Ma, Yuchi Beihang Univ Sch Comp Sci & Engn State Key Lab Complex & Crit Software Environm Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Huawei Cloud Comp Technol Co Ltd Shenzhen Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fix the bugs in programming assignments, where the Large Language Models (LLMs) based approaches have shown promising results. Given the growing complexity of identifying and fixing bugs in advanced programming assignments, current fine-tuning strategies for APR are inadequate in guiding the LLM to identify bugs and make accurate edits during the generative repair process. Furthermore, the autoregressive decoding approach employed by the LLM could potentially impede the efficiency of the repair, thereby hindering the ability to provide timely feedback. To tackle these challenges, we propose FASTFIXER, an efficient and effective approach for programming assignment repair. To assist the LLM in accurately identifying and repairing bugs, we first propose a novel repair-oriented fine-tuning strategy, aiming to enhance the LLM's attention towards learning how to generate the necessary patch and its associated context. Furthermore, to speed up the patch generation, we propose an inference acceleration approach that is specifically tailored for the program repair task. The evaluation results demonstrate that FASTFIXER obtains an overall improvement of 20.46% in assignment fixing when compared to the state-of-the-art baseline. Considering the repair efficiency, FASTFIXER achieves a remarkable inference speedup of 16.67x compared to the autoregressive decoding algorithm.

关键词： Automated Program Repair Large Language Models Programming Education Inference Acceleration

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An OAM Mode Purity Compensation Method for Antenna Array with Elements Offset

An OAM Mode Purity Compensation Method for Antenna Array wit...

引用

2024 ieee international conference on Computational Electromagnetics, ICCEM 2024

作者： Li, Yating Cheng, Xiang Chen, Pei-Yao Zong, Xianzheng Sun, Sheng School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China The Tenth Research Institute China Aerospace Science and Technology Corporation Beijing China

ISBN: (纸本)9798350383317

In addressing the issue of decreased mode purity caused by the radial offset of array elements in orbital angular momentum waves (OAM) antenna array, a compensation method based on amplitude and phase modulation is proposed. The analysis leads to the determination that amplitude optimization is employed to achieve effective compensation. Experimental result validates the effectiveness of the proposed compensation method. © 2024 ieee.

关键词： Antenna arrays

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Federated Unlearning with Multiple Client Partitions 59

Federated Unlearning with Multiple Client Partitions

引用

59th Annual ieee international conference on Communications (ieee ICC)

作者： Zhu, Weipeng Jia, Juncheng Luo, Bing Lin, Xiaodong Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China Duke Kunshan Univ Data Sci Res Ctr Suzhou Peoples R China Univ Guelph Sch Comp Sci Guelph ON Canada

ISBN: (纸本)9781728190549

Federated learning (FL) has recently received more and more attention in the joint field of distributed machine learning (ML) and privacy computing. Similar to the traditional ML systems, there exists the need of effective and efficient unlearning algorithms to unlearn certain training data from the FL model. The traditional machine unlearning algorithms have limitations for the FL systems, since the data of clients are both private and non-IID. In this paper, we propose a new algorithm for federated unlearning called FedUMP to improve the model performance and accelerate the unlearning process. Its main idea is to first create multiple different client partition strategies, each of which divides the clients into several subsets. Then we independently train subset models for all client subsets and aggregate the results of subset models for predictions. Furthermore, we propose a retraining acceleration method to reduce the time consumption with multiple partitions, and a partition strategy design method to search for good partition strategies efficiently. Extensive experiments on various datasets and model architectures demonstrate that FedUMP improves both model performance and unlearning speed.

关键词： Federated learning

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Empowering Human-Machine Communication in Code Generation with NLP 5

Empowering Human-Machine Communication in Code Generation wi...

引用

5th ieee international conference on Recent Trends in Computer science and technology, ICRTCST 2024

作者： Aswal, Upendra Singh Rao, Pappala Mohan Supreeth, B.R. Manikandan, G. Srinivasan, R. Tiwari, Mohit Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Andhra University College Of Engineering AU College Of Engineering Computer Science and System Engineering AP Visakhapatnam530003 India Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Department of Computer Science Ullal Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Thandalam India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi India

ISBN: (纸本)9798350351378

The symbiotic interaction between people and computers in the realm of software development is experiencing a fundamental transition, spurred by improvements in Natural Language Processing (NLP). This study investigates how natural language processing (NLP) is changing the software development process by facilitating better human-machine collaboration throughout the coding phase. Data collection, NLP model creation, and assessment are all part of the rigorous process used in our work, which yields important results. The possibility for improving code quality and efficiency while also democratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to minimize bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds empirical insights but also imagines a future in which software development is more inclusive, efficient, and morally responsible. From improved natural language processing models to real-time feedback loops and multimodal techniques, the future of this transforming discipline promises to further influence the landscape of software development. © 2024 ieee.

关键词： software design

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Evaluating door quality using CAE analysis and a LabView-controlled pneumatic system 2

Evaluating door quality using CAE analysis and a LabView-con...

引用

2nd ieee international conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Pramod, Sredha Beena Sai Neeraj, Kuruvella Vineetha, M. Sreedharan, Pramod Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mechanical Engineering India SRM Institute of Science and Technology Department of DSBS India

ISBN: (纸本)9798350368109

In this paper, we have developed a door analysis program which is pneumatically mechanised controlled by Lab- View programming to analyze the performance of the door. The impact test evaluates the validity of the structure, the ease of use, and the sustainability of the door, and mimics the actual situation of the flakes. A mechanism by pneumatic system that simulates and controls the force and impact of a door being closed forcefully. LabView software automates the force required for slamming, duration and frequency of the slam test. The evaluation procedure is further enhanced by the addition of computer-aided engineering (CAE) analysis to test the strength of the doors. © 2024 ieee.

关键词： software testing

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AgriSense: Crop Advisor 9

AgriSense: Crop Advisor

引用

9th international conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Maheshwari, A. Madhava, S. Sethu Dinesh, T. College of Engineering & Technology Srm Institute of Science And Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798350365092

Advanced software in regenerative farming assists cultivators in managing crop information, such as cycle, reaping, and soil management. Sensors assess soil conditions. This information aids in developing a system that provides crop suggestions based on variables like crop specifics, soil makeup, climate, temperature, soil acidity, and precipitation. Machine learning models, such as Logistic Regression, Random Forest, and Decision Tree, were utilized on data from the Indian Chamber of Food and Agriculture on Kaggle. Findings highlight that PyTorch Tabular Fit yields the most accurate results at 99% precision and faster model construction. © 2024 ieee.

关键词： Crops

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