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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries 61

SConE: Simplified Cone Embeddings with Symbolic Operators fo...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： Computer circuits

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Empirical Insights into Product Security Challenges and Practices in software Development

Empirical Insights into Product Security Challenges and Prac...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Rafi, Saima Akbar, Muhammad Azeem Khan, Arif Ali School of Computing Engineering & the Built Environment Edinburgh Napier University EH10 5DT United Kingdom Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90570 Finland

ISBN: (纸本)9798331510503

Ensuring product security in software development is crucial, encompassing hardware, firmware, and holistic protection measures. With rising cyber threats, organizations face challenges in safeguarding software products, often viewing security as a bottleneck. To address these issues, we conducted an empirical study with 22 practitioners across various industries, exploring challenges and practices in product security. Our findings highlight both the applicability of security practices and the new challenges in their implementation. This research contributes valuable insights, enabling organizations to proactively address security concerns and develop robust strategies for protecting their software products. © 2024 IEEE.

关键词： Challenges empirical study Practices Product security

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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A Performance Evaluation of the Clustering-based Labelling Scheme

A Performance Evaluation of the Clustering-based Labelling S...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Klaib, Alhadi A. Venters, Colin C. Elmergib University Department of Software Engineering Khoms Libya Huddersfield University School of Computing and Engineering Huddersfield United Kingdom

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

XML is a common technique for formatting and exchanging data across the Internet world. Updating and retrieving XML data is an active research area. In addition, indexing XML data is a significant task to improve the efficiency of XML queries. Labelling nodes is the used technique for indexing XML data efficiently. The Clustering-based Labelling Scheme (CLS) was proposed to address some limitations and challenges of indexing XML data. This paper aims to evaluate the performance of the clustering-based labelling scheme. Thus, a set of experiments were identified and designed for the evaluation purpose. Moreover, an appropriate dataset for testing the CLS scheme was identified. Subsequently, the experiments were carried out, and then the results were analysed and assessed to gain the findings of the experiments. Consequently, the results of these experiments suggest that the CLS achieved the target results with few expectations. Therefore, this evaluation showed an improvement in the performance and oefficiency of labelling XML documents. © 2022 IEEE.

关键词： XML

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

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Computers, Materials & Continua 2022年第6期71卷 5603-5618页

作者： Shahid Mehmood Imran Ahmad Muhammad Adnan Khan Faheem Khan T.Whangbo Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Education sector has witnessed several changes in the recent *** changes have forced private universities into fierce competition with each other to get more students *** competition has resulted in the adoption of marketing practices by private universities similar to commercial *** get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their *** study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the *** techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore *** results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local *** on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

关键词： Social media higher education sentiment analysis content analysis competitive analysis text mining

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Oxygen: A Distributed Health Care Framework for Patient Health Record Management and Pharmaceutical Diagnosis 4

Oxygen: A Distributed Health Care Framework for Patient Heal...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Wickramarathna, Maleesha De Silva, Kithmini Lekamalage, Vihanga Senanayake, Janith Perera, Jeewaka Ruggahakotuwa, Laneesha Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the COVID-19 pandemic, the world is confronting various healthcare issues, and healthcare automation is more crucial than ever. The pandemic has revealed the limitations of existing digital healthcare systems to manage public health emergencies. There is no registered population for many healthcare institutions in Sri Lanka, as a result, there is a communication gap. Electronic Health Record systems (EHRs) are becoming popular to share patient details but accessing scattered data across several EHRs while safeguarding patient privacy remains a challenge. Most of these medical records are in printed format and manually entering those into EHR systems is time-consuming and error prone. Not only that pharmaceutical error is a critical healthcare problem, but it is even riskier to visit doctors for pharmaceutical diagnosis during a pandemic. This research introduces a Blockchain-based patient health record system, an Optical Character Recognition (OCR) and Natural Language Processing (NLP) based Medical Document Scanner, a Drug Identifier based on Image Processing and a Medical Chatbot powered by NLP as four novel approaches to address these issues. Altogether with the results, this research aims at introducing a solution for the limitations in healthcare while providing a distributed healthcare framework for the healthcare community worldwide. © 2022 IEEE.

关键词： Health care

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An Experimental Comparison Between Tree-Based and Machine Learning Approaches for Predicting an Oil Temperature in an Electricity Transformer Dataset 2nd

An Experimental Comparison Between Tree-Based and Machine Le...

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2nd International Conference on From Smart City to Smart Factory for a Sustainable Future, SCFF 2024

作者： Do, Anh Kim Thi Nguyen, Vinh Dinh School of Computing and Information Technology Eastern International University Thủ Dầu Một Viet Nam Department of Software Engineering FPT University Can Tho City Viet Nam

ISBN: (纸本)9783031656552

Time series data forecasting remains a challenging problem within research communities. A plethora of machine learning and deep learning architectures have been devised to tackle the wide array of time series datasets across different domains. Recent advancements in machine learning algorithms have demonstrated impressive performance. However, there remains a limitation regarding the time-consuming nature of both training and testing phases. Hence, this study endeavors to address whether any methods can achieve comparable performance to recent machine learning algorithms without requiring extensive training time. Through our research, we have identified XGBoost as the optimal solution for addressing this issue. Through comprehensive experiments across three study cases encompassing multivariate to multivariate prediction, multivariate to univariate prediction, and univariate to univariate prediction scenarios, we discovered that XGBoost outperformed other state-of-the-art machine learning techniques in the multivariate-to-multivariate prediction case under ETD dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning time series forecasting tree-based method

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Securing the Silver Surfers: Cybersecurity Awareness Among Older Adults 19

Securing the Silver Surfers: Cybersecurity Awareness Among O...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ahmad Nawi, Haslinda Sutan Omar, Siti Fatimah Basaruddin, Suzana Info. Science & Computing Department Management and Science University Selangor Malaysia Software Engineering Section UniKL MIIT Kuala Lumpur Malaysia Informatics & Analytics Section UniKL MIIT Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

This research investigates the perceptions of cybersecurity among older adults in Malaysia, aged 60 years and above, who are reliant on technology for basic activities. The data from 331 participants showed that 30.8% were online for 4-8 hours daily, while 3.3% were completely disconnected. Interestingly, more than half of the respondents were unaware of cybersecurity threats, especially malware and ransomware, which put their data in jeopardy. While there is some awareness related to unauthorized access and computer fraud, knowledge gaps remain significant, and effective educational interventions are thus needed. Informal, self-directed learning appears to be a widespread way of acquiring skills in this age group, often supplemented by community workshops and peer networks. Given that almost half of the older adults spend considerable time online, improving their digital skills in tandem with their support systems is an important goal for enhancing their online experiences. It indicates the need for a twin approach: enhancing cybersecurity awareness with specialized educational programs, alongside developing flexible opportunities for informal learning. Educational institutions and relevant policies are encouraged to prioritize building hands-on, specific cybersecurity education for seniors. Also, user interface design considering cybersecurity-related issues can make using the technological environment as user-friendly and secure as possible. These strategies will contribute to creating an inclusive digital society, where older adults are confident and safe in using digital technologies while continuously contributing to their personal and professional development. © 2025 IEEE.

关键词： Economic and social effects

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