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22nd RoEduNet Conference: Networking in Education and Research, RoEduNet 2023

作者： Nedianu, Gabriel-Catalin Enescu, Nicolae Mancas, Catalina Computers and Electronics Software Engineering Faculty of Automatics Craiova Romania Faculty of Automatics Computers and Electronics Department of Computers and Information Technology Craiova Romania

ISBN: (纸本)9798350393651

The quality of the air around us is a major concern nowadays, a lot of health issues are associated with air pollution. This article presents an application of mobile and cloud technologies for real-time air quality monitoring. An Android mobile application was developed for monitoring the values of pollutants in the air from an air quality station. The station was designed to continuously acquire values from different sensors and to send them to databases. The mobile application provides real-time updates, notifications and historical values for the air parameters monitored. © 2023 IEEE.

关键词： Air quality

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Evaluating BERT-based language models for detecting misinformation

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Neural Computing and Applications 2025年第16期37卷 9937-9968页

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Information Technology and Data Science Faculty Sebelas Maret University Ir Sutami 36A Central Java Surakarta57126 Indonesia Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further. © The Author(s) 2025.

关键词： BERT-based models Comparative analysis Rumour detection Text embeddings

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions 7

Intelligent-Based Extraction of Business Capabilities from C...

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7th International Conference on information technology, information Systems and Electrical engineering, ICITISEE 2023

作者： Fahmi, Faisal Hamid, Siti Hafizah Ab Yuadi, Imam Airlangga University Faculty of Political and Social Science Department of Library and Information Science Indonesia Universiti Malaya Faculty of Computer Science & Information Technology Department of Software Engineering Malaysia

ISBN: (纸本)9798350382266

In the era of Big Data, information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia. © 2023 IEEE.

关键词： Competition

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Measures of Organizational Process Performance in the Capability Maturity Model Integration 24

Measures of Organizational Process Performance in the Capabi...

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24th International Arab Conference on information technology, ACIT 2023

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kittaneh, Ahmed Ali, Ahmed Alsarhan, Ayoub Alkhalidy, Muhsen The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Artificial Intelligence and Data Science Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Cyber Security Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Information Technology Jordan

ISBN: (纸本)9798350384307

The Capability Maturity Model Integration (CMMI) is widely utilized by organizations to facilitate process improvement. Among its 22 process areas, this paper focuses on the Organizational Process Performance (OPP) area, which assesses the effectiveness of a process. Specifically, this study examines one goal (establishing performance baselines and models) and two practices (establishing quality and process performance objectives and selecting processes within OPP. To measure these practices, the Goal Questions Metrics (GQM) approach is employed. Data was collected using a questionnaire, and the defined measures are validated quantitatively. 250 responses were used for analysis. The results obtained indicate that implementing the defined measures in companies would be beneficial for achieving high-quality processes in OPP and advancing the maturity level of CMMI. © 2023 IEEE.

关键词： Goal Questions Metrics (GQM) software Quality, Organizational Process Performance, Capability Maturity Model Integration, CMMI

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Recent Advances in Edge Computing for 6G

Recent Advances in Edge Computing for 6G

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2022 International Conference on Intelligent technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Alawadhi, Abdulwadood Almogahed, Abdullah University of Science and Technology Faculty of Engineering and Computing Department of Computers and Information Technology Yemen Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Edge computing is a new technology that enables 6G development by bringing cloud capabilities closer to end users to overcome traditional cloud flaws such as high latency and a lack of security. Few studies identify recent advances, challenges, and opportunities in 6G edge computing. As a result, this paper presents a review to identify recent advances in edge computing in 6G. It also discusses the challenges and opportunities of such new trends in the computing landscape. The paper identifies challenges (security and privacy, offloading and task division, bandwidth, service management, advanced communication, general-purpose computing on edge nodes, quantum computing and edge computing, and IoT heterogeneous platforms) as well as opportunities (academic-industry collaborations, algorithms and libraries that are lightweight and virtualization and micro-operating systems). Finally, some conclusions have been drawn after identifying current research directions. © 2022 IEEE.

关键词： Internet of things

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Leaf Guard: Detecting the Diseases in Banana Leaf 5

Leaf Guard: Detecting the Diseases in Banana Leaf

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Yathurshan, K. Barathy, S. Karini, G. Sajitha, P. Abeygunawardhna, Pradeep Rajapakse, Kaushalya Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Electrical & Electronic Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper introduces 'Leaf Guard,' a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, 'Leaf Guard' provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation. © 2023 IEEE.

关键词： Cultivation

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EMARec: a sequential recommendation with exponential moving average

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Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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Towards Edge Computing for 6G Internet of Everything: Challenges and Opportunities 1

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

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

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

ISBN: (纸本)9781665472753

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. © 2023 IEEE.

关键词： Internet of Everything

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

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Computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

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