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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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A REVIEW OF THE APPLICATION OF TECHNOLOGY ACCEPTANCE MODELS IN THE HEALTHCARE 18

A REVIEW OF THE APPLICATION OF TECHNOLOGY ACCEPTANCE MODELS ...

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18th International Conference on Interfaces and Human Computer Interaction 2024, IHCI 2024, the 17th International Conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi Conference on Computer Science and Information systems 2024, MCCSIS 2024

作者： Eskandari, Sadjad Valente, Juan P. Department of Languages Information Systems and Software Engineering Universidad Politécnica de Madrid Madrid28660 Spain

ISBN: (纸本)9789898704580

In the present paper, a comprehensive review was performed on the application of technology acceptance models (TAM) in the healthcare sector. The important post-2019 published papers were searched and extracted by the Google Scholar search engine and important papers were taken into consideration for further analysis related to the use of recently utilized models on the final results of TAM based on different case studies. Although TAM has shown to have an outstanding role in predicting clinician acceptance and health information technology (IT) usage, there is still a need for improvement in cases of standardization, further relationship testing, better data reporting, and the exploration of new variables. In this regard, one can prioritize identifying clinicians' specific beliefs about health IT, enhancing the applicability of TAM in healthcare by evaluating contextual factors, barriers, facilitators, and social norms impacting IT adoption. In addition, based on the classification of recent studies into general variables and results, usefulness, ease of use, trust, and privacy concerns were determined as important factors in patients' acceptance of technology in healthcare services, and related recommendations were provided in this work. TAM-based models offer valuable insights into the adoption and acceptance of healthcare technologies and researchers and healthcare organizations should be mindful of their limitations and consider future research directions to enhance their applicability in an ever-evolving healthcare landscape. © 2024 Interfaces and Human Computer Interaction. All Rights Reserved.

关键词： Electronic health record

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Automatic Multi-Class Non-Functional software Requirements Classification Using Machine Learning Algorithms

Automatic Multi-Class Non-Functional Software Requirements C...

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2024 International Research Conference on Smart computing and systems engineering, SCSE 2024

作者： Saroth, M.A.F. Wijerathne, P.M.A.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350375688

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification. © 2024 IEEE.

关键词： Long short-term memory

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WIP: An Engaging Undergraduate Intro to Model Checking in software engineering Using TLA+ 54

WIP: An Engaging Undergraduate Intro to Model Checking in So...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Loyola University Software and Systems Laboratory Department of Computer Science Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

ISBN: (纸本)9798350351507

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive 'failuresy' through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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Sinhala Sign Language Detection Approach for Deaf People Using Human Pose Estimation

Sinhala Sign Language Detection Approach for Deaf People Usi...

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2024 International Research Conference on Smart computing and systems engineering, SCSE 2024

作者： Nadeesha, S.J.M.J. Wasalthilaka, W.V.S.K. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350375688

People with disabilities have physical, sensory, cognitive, or behavioral health problems that limit their participation in activities and interaction with the environment. Deafness is one of the most common types of disability in society. Since verbal communication is impossible for the deaf, sign language is one of the most often used forms of communication among those disable people. However, a third party who is fluent in sign language is necessary for efficient communication. This research study proposed a sign language detector for Sinhala using Logistic Regression, Ridge Classifier, Random Forest, and Gradient Boosting algorithms with human pose estimation. The method used upper-body stance landmarks with 100 human pose videos and 40 widely used signals to extract features for body language classification. With an overall 86.75% success rate, this study is a major step toward improving deaf people's communication in Sri Lanka. © 2024 IEEE.

关键词： Logistic regression

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing 4

Performance Evaluation of Clustering Algorithms for Enhancin...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Puviskar, S. Prabath Wasalthilaka, W.V.S.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Department of Computing & Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350384864

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes. © 2024 IEEE.

关键词： software testing

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Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Babangida-Sabo, Sumayya Avemaria Utulu, Samuel C. Ajibesin, Abel American University of Nigeria Department of Informaton Systems School of Informtaon Technology and Computing Yola Nigeria Amerucan University of Nigeria Department of Software Engineering School of Information Technology and Computing Yola Nigeria

ISBN: (纸本)9798350358155

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria's attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR. © 2024 IEEE.

关键词： Sustainable development goals

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Framework for Effective Utilization of Distributed Scrum in software Projects

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Computer systems Science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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From User Stories to Domain Models: Recommending Relationships between Entities

From User Stories to Domain Models: Recommending Relationshi...

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Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Bragilovski, Maxim Dalpiaz, Fabiano Sturm, Arnon Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Department of Information and Computing Sciences Utrecht University Netherlands

User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic view of the domain. As such, they can be complemented with domain models that not only help gain this comprehensive view, but also serve as a basis for model-driven development. We focus on the task of recommending relationships between entities in a domain model, assuming that these entities were previously extracted from a user story collection either manually or through an automated tool. We investigate whether an approach based on supervised machine learning can recommend essential relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a machine learning model by using a random forest classifier. The results indicate that our approach has higher precision and F1-score than the baseline rule-based methods. Our findings provide preliminary evidence of the suitability of using machine learning to support the development of domain models, especially in recommending relationships between related entities. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Supervised learning

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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