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arXiv 2022年

作者： Akbar, Muhammad Azeem Khan, Arif Ali Mahmood, Sajjad Rafi, Saima Department of Software Engineering LUT University Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90570 Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia University of Murcia Department of Informatics and Systems Murcia Spain

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to the lack of frequent communication and coordination in GSD, the planning and managing of the requirements change process becomes a challenging task. As in DevOps, requirements are not only shaped by development feedback but also by the operations team. This means requirements affect development, development affects operations and operations affect requirements. However, DevOps in GSD still faces many challenges in terms of requirement management. The purpose of this research project is to develop a DevOps requirement change management and implementation maturity model (DevOps-RCMIMM) that could assist the GSD organizations in modifying and improving their requirement management process in the DevOps process. The development of DevOps-RCMIMM will be based on the existing DevOps and RCM literature, industrial empirical study, and understanding of factors that could impact the implementation of the DevOps requirement change management process in the domain of GSD. This vision study presents the initial results of a systematic literature review that will contribute to the development of maturity levels of the proposed DevOps-RCMIMM. © 2022, CC BY.

关键词： Standardization

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Enhancing Smart Tourism With Chatbots: Focus on the Metamodel of Domain-Specific language and Emerging Technologies

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Expert systems 2025年第7期42卷

作者： Lamya Benaddi Adnane Souha Charaf Ouaddi Abdellah Chehri Abdeslam Jakimi Brahim Ouchao Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Errachidia Morocco Department of Computer Sciences and Mathematics RMC Kingston Canada

The tourism sector is adopting smart solutions to offer visitors more personalised and sustainable experiences. By leveraging urban infrastructure and new technologies, tourist destinations aim to enhance the interaction between travellers and their environment. Artificial intelligence (AI) and natural language processing (NLP) play a key role in this transformation, particularly through chatbots. They are AI-driven applications designed to simulate human-like conversations, enabling users to interact with digital services through text or voice interfaces. In the tourism sector, they facilitate real-time access to information and services, improving the visitors' experience. These applications typically rely on intent recognition APIs, which may be proprietary, requiring access fees and potentially leading to high implementation costs. This study explores the use of a domain-specific language (DSL) dedicated to chatbot development for smart tourism. The first contribution comprises various research topics and emerging technologies used to improve smart tourism experiences and their impact on key tourism components such as attractions, accessibility, amenities, activities, available packages, and ancillary services. Second, this work aims to present the key concepts of model-driven engineering involved in constructing a DSL and to introduce our approach to building a DSL, with a focus on presenting the DSL metamodel. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.

关键词： artificial intelligence chatbot DSL metamodel smart tourism

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Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing

Multi-Relational Graph Neural Architecture Search with Fine-...

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IEEE International Conference on Data Mining (ICDM)

作者： Xin Zheng Miao Zhang Chunyang Chen Chaojie Li Chuan Zhou Shirui Pan Department of Software Systems and Cybersecurity Monash University Australia School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China School of Electrical Engineering and Telecommunications University of New South Wales Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Australia

Graph neural architecture search (NAS) has gained great popularity in automatically designing powerful graph neural networks (GNNs) with superior learning abilities, significantly relieving human effort and expertise reliance. Despite the advanced performance of automated learning, existing graph NAS models mainly work on single-relational graphs, while the widespread multi-relational graphs in real-world applications, are not well addressed. Moreover, current search spaces of automated GNNs are generally coarse-grained by simply integrating typical GNN layers and hyper-parameters, resulting in severe limitations on search capacities and scopes for creating innovative GNN architectures. To tackle the limitations of single-relational setting and coarse-grained search space design in existing graph NAS, in this paper, we propose a novel framework of multi-relational graph neural architecture search, dubbed MR-GNAS, to automatically develop innovative and excellent multi-relational GNN architectures. Specifically, to enlarge search capacities and improve search flexibility, MR-GNAS contains a fine-grained search space that embraces the full-pipe multi-relational message passing schema, enabling expressive architecture search scopes. With the well-designed fine-grained search space, MR-GNAS constructs a relation-aware supernet with a tree topology, to jointly learn discriminative node and relation representations. By searching with a gradient-based strategy in the supernet, the proposed MR-GNAS could derive excellent multi-relational GNN architectures in multi-relational graph analysis. Extensive experiments on entity classification and link prediction tasks over multi-relational graphs illustrate the effectiveness and superiority of the proposed method.

关键词： Message passing Graph neural networks Topology Data mining Task analysis

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Emerging case oriented agents for sustaining educational institutions going green towards environmental responsibility

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Journal of systems and Information Technology 2019年第2期21卷 186-214页

作者： Anthony Jnr, Bokolo Abdul Majid, Mazlina Romli, Awanis Department of Computer Science Norwegian University of Science and Technology NTNU Trondheim Norway Universiti Malaysia Pahang Faculty of Computer Systems and Software Engineering Kuantan Malaysia

Purpose: The purpose of this paper is to design a system deployment model that integrates case-based agent technique to develop an eco-responsibility decision support tool for greening educational institutions toward environmental responsibility. Design/methodology/approach: Data were collected through questionnaires distributed among a statistical population that comprised practitioners across educational institutions in Malaysia that implement green practices. The questionnaire measured the feasibility of the developed tool based on factors derived from the literature. Accordingly, descriptive, exploratory and factor analysis approach using statistical package for social sciences (SPSS) was used to test the feasibility of the developed tool. Findings: Results from descriptive analysis confirm the tool is feasible based on mean values that range from 4.1619 to 3.6508 on a five-point scale, indicating that the tool is effective in sustaining educational institutions going green. Besides, results from exploratory analysis verify the reliability of the tool based on the acceptable Cronbach’s alpha reliability coefficient score higher than 0.7 and Kaiser–Meyer–Olkin value being above 0.5. Finally, results from factor analysis reveal that the developed tool is usable, efficient, helpful, flexible and credible and supports educational institutions in going green at 88.44 per cent of the total variance, suggesting that the respondents are satisfied with the tool. Research limitations/implications: The sample population in this study comprises only practitioners from educational institutions in Malaysia. Theoretically, this research provides feasibility factors and associated items that can be used in evaluating developed information systems. Practical implications: Practically, this study develops an eco-responsibility decision support tool to facilitate green strategies and provides information on how practitioners in educational institutions can improve green growth. So

关键词： Sustainable development

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An efficient hybrid similarity measure based on user interests for recommender systems

An efficient hybrid similarity measure based on user interes...

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Expert systems

作者： Hawashin, Bilal Lafi, Mohammad Kanan, Tarek Mansour, Ayman Department of Computer Information Systems Al-Zaytoonah University of Jordan Amman Jordan Department of Software Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Computer Science Al-Zaytoonah University of Jordan Amman Jordan Department of Communication and Computer Engineering Tafila Technical University Tafilah Jordan

Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time. © 2019 John Wiley & Sons, Ltd

关键词： Recommender systems Artificial intelligence Learning systems Problem solving Cold start problems Comprehensive evaluation Content based filtering Item similarity Mean absolute error Similarity measure Similarity measurements User interests

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection

arXiv

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arXiv 2023年

作者： Yu, Jinyang Hamdan, Sami Sasse, Leonard Morrison, Abigail Patil, Kaustubh R. Research Center Jülich Germany Institute of Systems Neuroscience Medical Faculty Heinrich Heine University Düsseldorf Germany Research Center Jülich Germany Department of Computer Science 3 - Software Engineering RWTH Aachen University Aachen Germany Max Planck School of Cognition Stephanstrasse 1a Leipzig Germany

Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirically compared MV and k-fold CV using benchmark and real-world datasets. By employing Bayesian tests, we compared generalization estimates yielding three posterior probabilities: practical equivalence, CV superiority, and MV superiority. We also evaluated the differences in the capacity of the selected models and computational efficiency. We found that both MV and CV select models with practically equivalent generalization performance across various machine learning algorithms and the majority of benchmark datasets. MV exhibited advantages in terms of selecting simpler models and lower computational costs. However, in some cases MV selected overly simplistic models leading to underfitting and showed instability in hyperparameter selection. These limitations of MV became more evident in the evaluation of a real-world neuroscientific task of predicting sex at birth using brain functional connectivity. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

Towards Cluster-Based Split Federated Learning Approach for ...

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Cyber Security in Networking Conference (CSNet)

作者： Mohamad Wazzeh Mohamad Arafeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT Engineering Ecole de Technologie Supérieure (ÉTS) Montreal Canada Department of Mathematics and Computer Science Université du Québec à Trois-Riviéres (UQTR) Trois-Rivières Canada Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

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Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Michael O. Hodge, Julian Jin, Peter L. Protz, Ella Wong, Elizabeth Obe, Ruth Shakeri, Ehsan Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Sharma, Vijay R. Mossahebi, Sina Ren, Lei Peterson, Stephen W. Polf, Jerimy C. Dartmouth College Departments of Mathematics United States U. of Maryland Dept. of Mathematics and Statistics United States James M. Bennett High School SalisburyMD United States Florida Atlantic University Department of Mathematics and Sciences United States Brookdale Community College Department of Mathematics United States U. of Houston Dept. of Computer Science and of Software Engineering Clear Lake United States U. of Maryland Dept. of Information Systems United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa M3D Inc. United States

ISBN: (纸本)9798350362480

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel 'Big-data REU Integrated Development and Experimentation' (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research. © 2024 IEEE.

关键词： Phantoms

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