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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Optimizing Collective Building Management through a Machine Learning-based Decision Support System 4

Optimizing Collective Building Management through a Machine ...

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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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Leveraging Light Intensity Structural Breaks for IoT Context-Awareness 10

Leveraging Light Intensity Structural Breaks for IoT Context...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Violos, John Stavrianos, Konstantinos Voutsas, Fotios Leivadeas, Aris École de technologie supérieure Department of Software and IT Engineering Montreal Canada Harokopio University of Athens Dept. Informatics and Telematics Athens Greece Netdata Inc San Francisco United States

ISBN: (纸本)9798350373011

Sensor data serves as a valuable information source that facilitates context-awareness within an Internet of Things (IoT) environment, thereby augmenting its capacity to adapt intelligently to evolving conditions and user requirements. This paper explores the utilization of structural breaks within time series to enhance context-awareness in the realm of a smart IoT environment, particularly within smart homes. By identifying shifts or disruptions in the underlying sequential measurements of light intensity, this research aims to develop a methodology capable of understanding the action of an entity that interacts with a smart environment. Specifically, by leveraging machine learning and statistical techniques for sensor time series, we delve into the detection of structural breaks, elucidating their significance in capturing relevant context information crucial for providing situational awareness. Through empirical validation and a case study with an IoT device, the paper demonstrates the efficacy of incorporating structural break analysis as a pivotal component in enhancing the sensing and awareness of ambient systems deployed in residential settings. © 2024 IEEE.

关键词： Smart homes

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Adopted Acceptance Test-Driven Development (ATDD) to produce Robotic Process Automation (RPA) for reducing teaching workload

Adopted Acceptance Test-Driven Development (ATDD) to produce...

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Asia-Pacific Conference on Communications

作者： Nuttaporn Phakdee Wantana Sisomboon Jirayus Arbking Software Engineering Department Faculty of Informatic Chonburi Thailand Software Engineering Department Faculty of Informatics Chonburi Thailand Faculty of Informatics Burapha University Thailand

Robotic Process Automation (RPA) has emerged as a significant technology across various industries, including the field of education. Educators face a substantial workload that hinders their ability to effectively develop students. To address this issue, the implementation of RPA can be considered to alleviate the burden on teachers. Acceptance Test-Driven Development (ATDD) is a collaborative methodology involving stakeholders, developers, and testers, ensuring software adherence to desired requirements. By integrating ATDD with RPA, teaching processes can be streamlined, repetitive tasks automated, and overall workload reduced. Consequently, instructors can dedicate more time to critical aspects of their roles. The RPA development process can be guided by the six phases of the RPA Life Cycle. During the discovery phase, an evaluation report, specifically the Forrester Wave TM , identified UiPath as the optimal tool. Its features aligned well with the requirements of the teaching support process. Furthermore, a combination of the RPA position strategy, group interviews, and online questionnaires involving lecturers from the Faculty of informatics at Burapha University guided the word validation process for the RPA application. The design phase followed a structured flow to produce the automated application. The results demonstrated a 71.42-fold reduction in time spent on checking word errors in student project reports and communication between teachers and students. This successful integration of RPA in the education sector showcases its potential for enhancing instructional efficiency and student development.

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5026-5039页

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen518132 China Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This article therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. © 2020 IEEE.

关键词： Benchmarking

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CRAFTER: A Persona Generation Tool for Requirements engineering 19

CRAFTER: A Persona Generation Tool for Requirements Engineer...

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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Karolita, Devi Grundy, John Kanij, Tanjila Obie, Humphrey McIntosh, Jennifer Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne Australia Department Informatics Engineering Faculty of Engineering Palangka Raya University Palangka Raya Indonesia Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Melbourne Australia

ISBN: (纸本)9789897586965

Personas, a user characterisation, have been widely used in requirements engineering (RE) to enhance the understanding of end-users and their needs. However, the persona generation process is time-consuming and demands familiarity with a user-centered approach. The central issue lies in existing tools for automatically generating personas, which are restricted to generating persona templates and provide limited user control to tailor personas according to their specific needs. This paper introduces CRAFTER, a persona generation tool that uses Large Language Models (GPT-3.5 model). This tool not only automates persona creation but also offers recommendations to users for generating personas tailored to their requirements. The study involved an online questionnaire with 19 respondents who utilised the tool, providing feedback that indicated the tool’s sufficiency for persona generation while identifying areas for improvement. Beyond its primary function, CRAFTER stands out by providing guidance to requirements engineers throughout the persona creation process. The tool grants users the flexibility to customise personas based on their specific requirements, acknowledging the crucial human subjectivity in persona development. Additionally, CRAFTER promotes persona reusability, allowing users to save and reuse generated personas for future projects. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Requirements engineering

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Usability Issues With Mobile Applications: Insights From Practitioners and Future Research Directions

arXiv

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arXiv 2025年

作者： Weichbroth, Pawel Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gdansk Poland

This study is motivated by two key considerations: the significant benefits mobile applications offer individuals and businesses, and the limited empirical research on usability challenges. To address this gap, we conducted structured interviews with twelve experts to identify common usability issues. Our findings highlight the top five concerns related to: information architecture, user interface design, performance, interaction patterns, and aesthetics. In addition, we identify five key directions for future research: usability in AI-powered mobile applications, augmented reality (AR) and virtual reality (VR), multimodal interactions, personalized mobile ecosystems, and accessibility. Our study provides insights into emerging usability challenges and trends, contributing to both the theory and practice of mobile human-computer interaction. © 2025, CC BY.

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Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Slovakia

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead.

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Iterative Design of a Decision Support System for Fall Risk Detection in residential care facilities

Iterative Design of a Decision Support System for Fall Risk ...

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作者： Tschorn, Niklas Keuchel, Maren Müller, Inga Milkov, Sarah Potthoff, Christian Windrath, Dioselina Weber, Yvonne Meister, Sven Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Dortmund / Health Care Informatics Faculty of Health School of Medicine Witten/Herdecke University Speicherstraße 6 Witten Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany HS Gesundheit Bochum Bochum Germany Diakonie Michaelshoven Cologne Germany Connext Communication GmbH Paderborn Germany Health Care Informatics Faculty of Health School of Medicine Witten/Herdecke University Witten Germany

Nursing care jobs are predominantly described as burdensome. In particular, the workload and the time available to deal with it are in a state of imbalance. Nurses are very often under time pressure and have to make decisions under psychological, emotional and physical stresses. Machine learning methods and in particular, Decision Support Systems (DSS) can be used to support nurses with improved information (e.g. visualization of individual health risks) for decisions that are not easy to make. As falls are one of the most common health problems in care facilities, we will present a concept of a DSS for the prevention of falls that was developed in the PFLIP research project. We explain our user-centered design process based on ISO 9241-210-2019. User feedback is obtained at each phase so that nurses can identify problems at an early stage. The result is a conceptual design, visualized as a click dummy, to identify individual fall risks and predict preventive measures to reduce the risk of falling. © 2024 The Author(s), published by De Gruyter.

关键词： Electronic health record

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AI and the Law: Evaluating ChatGPT’s Performance in Legal Classification

arXiv

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arXiv 2025年

作者： Weichbroth, Pawel Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gdansk Poland

The use of ChatGPT to analyze and classify evidence in criminal proceedings has been a topic of ongoing discussion. However, to the best of our knowledge, this issue has not been studied in the context of the Polish language. This study addresses this research gap by evaluating the effectiveness of ChatGPT in classifying legal cases under the Polish Penal Code. The results show excellent binary classification accuracy, with all positive and negative cases correctly categorized. In addition, a qualitative evaluation confirms that the legal basis provided for each case, along with the relevant legal content, was appropriate. The results obtained suggest that ChatGPT can effectively analyze and classify evidence while applying the appropriate legal rules. In conclusion, ChatGPT has the potential to assist interested parties in the analysis of evidence and serve as a valuable legal resource for individuals with less experience or knowledge in this area. © 2025, CC BY.

关键词： Laws and legislation

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