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IEEE Journal of Selected Areas in Sensors 2024年 1卷 177-189页

作者： Zhao, Yuting Wang, Xulong Peng, Xiyang Li, Ziheng Nan, Fengtao Zhou, Menghui Qi, Jun Yang, Yun Zhao, Zhong Xu, Lida Yang, Po Yunnan University National Pilot School of Software Kunming650106 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Northwest A&F University College of Information Engineering Xianyang712100 China Xi'an JiaoTong-Liverpool University Department of Computing Suzhou215000 China Yunnan First People's Hospital Yunnan650021 China Old Dominion University NorfolkVA23529 United States

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices, such as mobile phones and smartwatches have been widely employed to analyze the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this article, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multiactivities combination scheme based on MDS-UPDRS. Utilizing this scheme, symptom-related and complementary activities are captured. We collected 85 PD subjects of different severity grades using a single wrist sensor. Our best results demonstrate F1 scores of 95.75% for PD diagnosis and the fine-grained classification accuracy of PD disease grade is 82.41% when combing four activities which improved by 11.02% over a single activity. The experiments and theoretical analyses can serve as a useful foundation for future investigations into the effect of proposed solutions for PD diagnosis in uncontrolled environment setup, ultimately leading to self-PD assessment in the home environment. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Creating an Inclusive, Vibrant Learning Environment within a Large, software engineering Program - Experiential Learning Experiences Created for Students, Faculty, and Senior Design Coaches & Sponsors

Creating an Inclusive, Vibrant Learning Environment within a...

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CoNECD 2021

作者： Bailey, Margaret B. Sharma, Naveen Verschage, Lana Conlon, Timothy T. Department of Mechanical Engineering Kate Gleason College of Engineering Rochester Institute of Technology Department of Software Engineering Golisano College of Computing and Information Sciences Rochester Institute of Technology Xerox Corporation Women in Computing Program Golisano College of Computing and Information Sciences Rochester Institute of Technology

Creating an Inclusive, Vibrant Learning Environment within a Large, software engineering Program - Experiential Learning Experiences Created for Students, Faculty, and Senior Design Coaches & Sponsors This presentation describes several initiatives currently underway within a large software engineering (SE) department at a private university within the northeast US to positively affect levels of inclusiveness within the department's learning environment. Information gathered from faculty, staff, student interviews and focus group discussions, as well as a department goal to improve female freshmen retention in the SE major motivated the SE department chair, departmental academic advisors and faculty, and the college's women in computing director to launch this set of aligned activities in collaboration with the NSF ADVANCE funded program (NSF #1209115) at the university level. The resulting activities ideally enhance diversity and inclusion for students from all underrepresented groups in the program with a focus on groups based on gender. Four experiential learning experiences have been created and concurrently implemented within the SE department to promote an inclusive academic environment. These include: (1) faculty targeted discussions and summer readings, (2) an interactive workshop designed for all first year SE students, (3) a workshop created for the coaches and sponsors, most of who are not regular RIT employees, who directly mentor student teams for the two‐semester senior project course, and (4) the development of a resource flowchart which supports students, faculty, and staff in maintaining an inclusive learning environment within the department. The approach used in creating each is adaptive and the four resulting products are multi‐faceted in regards to target audience, modality of learning experience, and composition of creation team. Furthermore, we will discuss key metrics aligned with the department's goal to measure impacts resulting from these

关键词： Students

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A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

arXiv

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arXiv 2023年

作者： Boroujeni, Sayed Pedram Haeri Pashaei, Elnaz School of Computing Clemson University ClemsonSC United States Department of Software Engineering Istanbul Aydin University Istanbul Turkey

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful metaheuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. In this study, eight benchmark datasets including five datasets of the UCI machine learning repository and three challenging shape datasets are used to investigate the general performance of the proposed method. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distan

关键词： Benchmarking

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Design Concept of a Mental Health Monitoring Application with Explainable Assessments 34

Design Concept of a Mental Health Monitoring Application wit...

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34th Australasian Conference on Information Systems, ACIS 2023

作者： De Silva, Upeka Madanian, Samaneh Templeton, John Michael Poellabauer, Christian Schneider, Sandra L. Narayanan, Ajit Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science and Engineering University of South Florida TampaFL33620 United States School of Computing and Information Sciences Florida International University MiamiFL33199 United States Department of Communicative Sciences and Disorders Saint Mary's College Notre DameIN46556 United States

Mental health has become a global issue with growing numbers of cases. Digital phenotyping in mental healthcare provides a highly effective, scaled, cost-effective approach to handling global mental health problems. Monitoring and diagnosis of mental health through mobile technologies are at an early stage. This paper proposes a novel architecture for a mobile application design based on speech and behavioural analysis that can lead to evidence-based monitoring outside of clinical settings. The application is designed to monitor the overall mental health status of individuals based on mood, stress, behaviour, and personality. It proposes to integrate objective mental health assessment from smartphone data with subjective assessments via periodic, short, self-reported, standardized questionnaires. The solution proposes explainable assessments for individual users and clinicians to build user trust and system credibility. This research provides insight into how digitally assisted mental health monitoring can be implemented in future mobile digital technologies using passive sensing. Copyright © 2023 De Silva, Madanian, Templeton, Poellabauer, Schneider and Narayanan.

关键词： Smartphones

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Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

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Journal of Proteins and Proteomics 2025年第2期16卷 141-159页

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Hasan, Md. Rakibul Uddin, Muhammad Shahin Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU) Tangail Bangladesh Department of Computing and Information System (CIS) Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Uttara University Dhaka Bangladesh Department of Software Engineering (SWE) Daffodil International University (DIU) Dhaka Bangladesh

The field of computational bioinformatics and systems biology analysis is rapidly expanding due to advancements in bioinformatics tools. Bipolar disorder (BD) is a severe psychiatric condition affecting both adults and adolescents. In recent years, the risk of stroke has increased among individuals with BD. Background studies indicate that BD and stroke share numerous biochemical and genetic characteristics. Due to traditional endocrinological approaches, no collaborative research exists, and no treatment options have been established for BD and stroke patients. The aim of this study is to identify common molecular pathways and potential therapeutic targets in BD and stroke that could be utilized to predict disease progression. To achieve this, shared genes were identified to determine common pathways. Based on the biochemical, molecular, and genetic interactions among these shared genes, this study identifies the most significant hub genes. To further explore these interactions, analysis such as the protein–protein interaction (PPI) network, topological properties, enrichment analysis, co-expression network, gene regulatory network (GRN), and physical interaction network are conducted. The GO annotation indicates that most of the genes are linked to MAP kinase activity and tumor necrosis factor receptor superfamily binding. The KEGG pathway analysis revealed that the predominant genes are associated with apoptosis and the IL-18 signaling pathway. This comparison helps understand the biochemical and genetic characteristics shared between BD and stroke. Predictive drug analysis has identified consistent potential biomarkers associated with both conditions, providing a scientific foundation for investigating their diagnosis, treatment, and prognosis. Finally, chemical experiments could be conducted to further validate the efficacy of these drugs.

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Towards Edge computing for 6G Internet of Everything: Challenges and Opportunities

Towards Edge Computing for 6G Internet of Everything: Challe...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Abdulwadood Alawadhi Abdullah Almogahed Etidal Azrag School of Computing Universiti Utara Malaysia Sintok-Kedah Malaysia Department of Software Engineering Faculty of Engineering and Information Technology Taiz University Taiz Yemen

The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE.

关键词： 6G mobile communication Cloud computing Data centers Technological innovation Smart cities Wireless networks Smart transportation

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Explicitly Diverse Visual Question Generation

SSRN

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SSRN 2024年

作者： Xie, Jiayuan Zheng, Jiasheng Fang, Wenhao Cai, Yi Li, Qing School of Software Engineering South China University of Technology Guangzhou China Ministry of Education China Department of Computing Hong Kong Polytechnic University Hong Kong Institute of Software Chinese Academy of Sciences Beijing China

Visual question generation involves the generation of meaningful questions about an image. Although we have made significant progress in automatically generating a single high-quality question related to an image, existing methods often ignore the diversity and interpretability of generated questions, which are important for various daily tasks that require clear question sources. In this paper, we propose an explicitly diverse visual question generation model that aims to generate diverse questions based on interpretable question sources. To explicitly perform question generation, our model first extracts the scene graph from the image using the unbiased scene graph generation method, where questions generated based on the scene graphs have interpretable question sources. To ensure the diversity of generated questions, our model selects different subgraphs from the scene graph as question sources. Specifically, we employ a subgraph selector to learn how humans select multiple subgraphs that are suitable for question generation. Finally, our model generates diverse questions based on different selected subgraphs. Extensive experiments on the VQA v2.0 and COCO-QA datasets show that the proposed model outperforms the baselines and is able to interpretably generate diverse questions. © 2024, The Authors. All rights reserved.

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Assessing Cybersecurity Awareness among the Hashemite University Students in Terms of Computer Usage 16

Assessing Cybersecurity Awareness among the Hashemite Univer...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of Computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation

Empirical Study on Mispronunciation Detection for Tajweed Ru...

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computing and Informatics (ICCI), International Conference on

作者： Yousef S Alsahafi Muhammad Asad Department of Information technology Computing and Information technology at Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics.

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Organizational governance: Resolving insufficient practice and quality expectation in Small software Companies. 22

Organizational governance: Resolving insufficient practice a...

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Proceedings of the 2022 European Symposium on software engineering

作者： Micheal Tuape Petrus Iiyambo Jussi Kasurinen Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Dept of Computing Maths and Statistical Sciences University of Namibia Namibia

ISBN: (纸本)9781450397308

The quality of software products is among the most prevalent challenges threatening the software development primarily in small software companies (SSCs). These challenges are associated with insufficient practices affecting the production of software and the development processes. This paper explores the role of governance in streamlining software processes and practices to produce better quality software products. In a cross-sectional survey (n = 127), we reached out to software practitioners working in SSCs from four countries. We examined how SSCs engage in oversight and accountability and how SSCs perform management roles and activities, such as controlling, directing, and guiding in the process of developing software. Our findings indicate that although the SSCs minimally embrace governance practices, the smaller companies have a more challenging task embracing governance practices from the complexities arising out of these companies' structures. This study highlights the aspects of governance that need attention in the smaller category of SSCs. It proposes an organizational governance model to facilitate the SSCs in developing governance strategies to take advantage of the benefits of governance during software development.

关键词： software Organizational governance Small software Companies software processes

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