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

共 17430 条记录，以下是541-550 订阅

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Agile Effort Estimation: Have We Solved the Problem Yet? Insights from the Replication of the GPT2SP Study 31

Agile Effort Estimation: Have We Solved the Problem Yet? Ins...

引用

31st IEEE International conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Tawosi, Vali Moussa, Rebecca Sarro, Federica Department of Computer Science London United Kingdom

ISBN: (纸本)9798350330663

Replication studies in software engineering are indispensable for ensuring the reliability, generalizability, and transparency of research findings. They contribute to the cumulative growth of knowledge in the field and promote a scientific approach that benefits both researchers and practitioners. In this article, we report our experience replicating a recently published work proposing a Transformer-based approach for Agile Story Point Estimation' dubbed GPT2SP. GPT2SP was proposed with the intent of addressing the three limitations of a previous Deep Learning-based approach dubbed Deep-SE, and the results reported in the original study set GPT2SP as the new state-of-the-art. However, when we used the GPT2SP source code made publicly available by the authors of the original study, we found a bug in the computation of the evaluation measure and the reuse of erroneous results from previous work, which had unintentionally introduced biases in the GPT2SP's performance evaluation. In this study, we report on the results we obtained after fixing the issues present in the original study, which reveal that their results were in fact unintentionally inflated due to these issues and that despite advancements, challenges remain in providing accurate effort estimations for agile software projects. © 2024 IEEE.

关键词： software design

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Exploring Compressed Sensing for Proficient Cryptocurrency Mining 28th

Exploring Compressed Sensing for Proficient Cryptocurrency M...

引用

28th International conference on knowledge based and Intelligent information and engineering Systems, KES 2024

作者： Avros, Renata Kesselman, Uri Volkovich, Zeev Software Engineering Department Braude College of Engineering in Karmiel Israel

Cryptocurrency is a digital or virtual currency that uses cryptographic techniques for secure transactions and unit creation. Unlike fiat currencies, cryptocurrencies operate on decentralized networks using blockchain technology. Mining, a key process in cryptocurrencies like Bitcoin, ensures network security and integrity, grounding on the application of cryptographic hash functions generating fixed-size outputs from inputs, where any input change results in a different hash. This research explores using compressed sensing methodology to enhance cryptocurrency mining efficiency. By exchanging hash functions and the Merkle tree by a compressed sensing-based procedure, the study proposes a method to improve the system's security, which is yet connected to significant performance impacts. While the performance impact needs careful consideration, the potential security improvements could be particularly advantageous given the specific characteristics of our system with limited transaction volume. © 2024 The Authors.

关键词： Cryptocurrency

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Impact of alternative designs of multimedia elaboration on learning outcomes: Towards personalized learning of software engineering. 13

Impact of alternative designs of multimedia elaboration on l...

引用

13th IEEE Global engineering Education conference (IEEE EDUCON)

作者： Chikasha, Samuel Van Petegem, Wim Valcke, Martin KULeuven Leuven Belgium Univ Ghent Ghent Belgium

ISBN: (纸本)9781665444347

This study takes place in the context in which learning analytics is starting to gain more ground in seeking to understand some of the critical issues on how multimedia learning can support personalized learning design. This paper investigates the critical issues of individual learner differences regarding the impact of alternative designs of multimedia enhanced eLearning communities. In this study, an online course in the domain of software engineering was used. The study involved Bachelor of Science students from an institution of higher education in Zimbabwe taking a course of software engineering. The study centers on the impact of student learning styles, cognitive styles, study approaches and prior knowledge on the efficacy, satisfaction, learning experience and outcomes related to software engineering learning in an eLearning community enhanced with alternative designs of multimedia elaborations. The study will go a long way in informing design issues to support personalized learning for Southern African learners in their context. The text based online eLcarning community enriched with static graphics;animations, audio, and video were used. The results point to clear differential impact based on the cognitive styles on the effect of multimedia enhanced community. In field independent learners, the multimedia design based on text, animation and audio resulted in higher learning outcomes and satisfaction but decreased cognitive load. In field dependent learners, the learning outcomes and satisfaction levels seemed to be lower, however an increase in cognitive load was noticed. This effect was obvious with the addition of audio to the environment. Only prior knowledge and cognitive style were the significant predictors for learning outcome. This was only realized in the learning environment enhanced with text and static graphics.

关键词： individual difference cognitive Style personalized learning software engineering learning eLearning community

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

引用

26th International conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： knowledge graph

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A Study of Cross-Border E-Commerce Supply Chain Research Rrends: based on knowledge Mapping and Literature Analysis 6

A Study of Cross-Border E-Commerce Supply Chain Research Rre...

引用

6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2024

作者： Zhao, Li Wan, Yulin Software Engineering Institute of Guangzhou College of Digital Economy and Management Guangzhou China Universiti Teknologi Malaysia Faculty of Management Johor Bahru Johor Skudai Malaysia

ISBN: (纸本)9798350316520

This research involves a detailed bibliometric analysis and knowledge mapping to investigate the convergence of cross-border e-commerce (CBEC) and supply chain, specifically emphasizing the impacts on computer science. Through a detailed analysis of 134 articles from the Web of Science (WoS) and Scopus databases, covering the years 2012 to 2023, the study reveals significant trends in the integration of advanced computational technologies and digital innovations into business models and marketing strategies within the cross-border e-commerce supply chain sector. It emphasizes the role of blockchain, AI, and IoT in sustainability, operational flexibility, and customer-focused logistics. Identifying key contributors in computer science, the study promotes cross-disciplinary collaboration to enhance CBEC supply chain management. This thorough compilation seeks to spark more research and cooperation, especially in computer science, to address the challenges and opportunities arising from the digital transformation of global trade logistics. © 2024 IEEE.

关键词： Supply chain management

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Active Deduction Heuristic Algorithm of Standard Contradiction Separation Rule based on Reinforcement Learning 18

Active Deduction Heuristic Algorithm of Standard Contradicti...

引用

18th International conference on Intelligent Systems and knowledge engineering, ISKE 2023

作者： Zhong, Jian Liu, Peiyao Chen, Shuwei School of Computer and Software Engineering National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Xihua University Chengdu China

ISBN: (纸本)9798350318401

Machine learning has been a prominent research focus in recent years, and integrating machine learning methods into automated theorem provers has become a current trend in theorem proving research. Standard contradiction separation rule is a newly proposed multi-clause, dynamic, and synergized deductive inference rule. The key aspect of standard contradiction separation rule is how to plan deduction paths. In order to better plan the deduction path, this paper explores the dynamic learning within the process of contradiction separation deduction and introduces a novel active deductive heuristic algorithm based on reinforcement learning. Experimental results demonstrate that this new algorithm enhances the reasoning capability of the theorem prover Prover9. © 2023 IEEE.

关键词： Reinforcement learning

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Predicting software Energy Consumption Using Time Series-based Recurrent Neural Network with Natural Language Processing on Stack Overflow Data 1

Predicting Software Energy Consumption Using Time Series-Bas...

引用

1st Asian conference on Communication and Networks, ASIANComNet 2024

作者： Deepajothi, S. Dasari, Kalyankumar Krishnaveni, N. Juliana, R. Shrivastava, Neeraj Muppavaram, Kireet SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Tamil Nadu Chengalpattu India Chalapathi Institute of Technology Mothadaka Department of CSE-CS Guntur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Loyola ICAM College of Engineering and Technology Loyola Campus Nungambakkam Department of Information Technology Chennai India Medicaps University Indore India Gitam Deemed To be University Department of CSE Hyderabad India

ISBN: (纸本)9798350367003

In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. Especially, the increasing amount of data and IoT devices require more storage space and computational power, which results in higher energy consumption. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This NLP techniques can analyze the text of questions and answers. This involves tokenization, lemmatization, and named entity recognition to identify terms and phrases related to energy consumption. This study examines the concerns of practitioners about energy consumption on Stack Overflow via the utilization of lexicon-based sentiment analysis, a concept in NLP, combined with RNNs. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners' desire to start conversations in the field of energy is closely linked to the utilization of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users. © 2024 IEEE.

关键词： Application programs

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Artificial Intelligence Automation in Advent of Human Resource Management 6

Artificial Intelligence Automation in Advent of Human Resour...

引用

6th Parul International conference on engineering and Technology, PICET 2024

作者： Kathuria, Samta Duggal, Angel Swastik Lande, Jayapal Law College Dehradun Uttaranchal University Dehradun India School of Electronics & Electrical Engineering Lovely Professional University Punjab India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350369748

While academic productivity in automated processes, such as artificial intelligence (AI), has increased fast, we currently need a complete knowledge of the implications of using these techniques in human resource management (HRM) just at organizational (firms) and personal (employee) levels. Despite an increasing curiosity in academics, study on AI-based HRM systems is scarce and scattered. As a result, the purpose of this study is to organize the scholarly inputs on automated processes so far and to define what its strengths approach and problems for HRM and to provide a sophisticated view of what has been recognized, and what needs to be recognized, and future directions of study to shape a global HRM research program. More study on the function of AI-assisted apps in HRM activities and patterns of interaction in big multinational businesses disseminating such technologies is required. © 2024 IEEE.

关键词： Resource allocation

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A Model based Approach for Generating Modular Manufacturing Control Systems software 34

A Model Based Approach for Generating Modular Manufacturing ...

引用

34th International conference on software engineering and knowledge engineering, SEKE 2022

作者： El Hamlaoui, Mahmoud Qamsane, Yassine Laghouaouta, Youness Mishra, Anant ENSIAS Mohammed V University in Rabat Rabat Morocco Siemens Technology Automation Engineering Software CharlotteNC28273 United States National Institute of Posts and Telecommunications Rabat Morocco

ISBN: (纸本)1891706543

Digitalization is transforming manufacturing systems to become more agile and smart thanks to the integration of sensors and connection technologies that help capture data at all phases of a product's life cycle. Digitalization promises to improve manufacturing flexibility, quality, productivity, and reliability. However, there is still a significant need of effective methods to develop models that could enhance the capabilities of manufacturing systems. Formal methods and tools are becoming essential to achieve this objective. Within this context, this paper introduces a formal software solution for the automatic generation of modular manufacturing control systems software. The proposed solution leverages software Model-based Design (MBD) techniques to reduce development effort, time, and human error by automating several manual steps. © 2022 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Industry 4.0

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KRLGI: knowledge Representation Learning based on Global Information for Reasoning 36

KRLGI: Knowledge Representation Learning Based on Global Inf...

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36th IEEE International conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Wu, Bo Mao, Yingchi Chi, Fudong Liu, Yingjie Ding, Silong Qi, Rongzhi Hohai University College of Computer Science and Software Engineering Nanjing China Huaneng Lancang River Hydropower Inc Technology Research and Development Center Kunming China

ISBN: (纸本)9798331527235

knowledge Graph Reasoning based on Representation Learning maps the entities and relations into a vector space, assessing entity-pair similarities to deduce unknown facts. However, existing methods focus solely on the local importance of entities and ignore isolated entities, leading to the issue of missing feature information. To solve this, we propose knowledge Representation Learning based on Global Information (KRLGI). KRLGI adopts an attention-based biased random walk algorithm to obtain global information and determine the importance of global entities. The global entity importance goes through a conversion into attention weights, and these weights are integrated with the local entity importance. Subsequently, the local and global entity importance can be used together to represent entity embeddings. Globally integrated representations can reveal richer semantics and enhance reasoning capabilities. Experimental results indicate that KRLGI outperforms other baselines in reasoning accuracy on four public datasets. Notably, on the FB15k-237 dataset, KRLGI shows significant improvements over the best baseline, with increases of 6.37% in MRR, 2.5% in HITS@1, and 5.5% in HITS@10. © 2024 IEEE.

关键词： knowledge graph

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