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19th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2024

作者： Pingos, Michalis Mina, Athos Andreou, Andreas S. Department of Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9789897586965

In the continuously evolving and growing landscape of Big Data, a key challenge lies in the transformation of a Data Lake into a Data Mesh structure. Unveiling a transformative approach through semantic data blueprints enables organizations to align with changing business needs swiftly and effortlessly. This paper delves into the intricacies of detecting and shaping Data Domains and Data Products within Data Lakes and proposes a standardized methodology that combines the principles of Data Blueprints with Data Meshes. Essentially, this work introduces an innovative standardization framework dedicated to generating Data Products through a mechanism of semantic enrichment of data residing in Data Lakes. This mechanism not only enables the creation readiness and business alignment of Data Domains, but also facilitates the extraction of actionable insights from software products and processes. The proposed approach is qualitatively assessed using a set of functional attributes and is compared against established data structures within storage architectures yielding very promising results. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Metadata

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Assessing the Impact of Integrating ChatGPT as an Advice Generator in Educational Software 9

Assessing the Impact of Integrating ChatGPT as an Advice Gen...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Troussas, Christos Krouska, Akrivi Papakostas, Christos Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798350342482

This paper reports on the study of the integration of ChatGPT as an advice generator in custom educational software developed for Java programming. The software, in cooperation with ChatGPT API, pursues providing real-time, context-specific advice to students for better learning. This work adopted a two-fold evaluation approach to evaluating this integration. First, this study examines the effectiveness of this integration with the help of the Interrupted Time Series Analysis methodology to measure possible improvement in the performance metrics of the students in terms of error rates and task completion times. Second, this work presents a custom-designed questionnaire used to get student perceptions regarding the clarity, usefulness, and impact of ChatGPT's advice, and the level of student satisfaction with the user interface. The key takeaways from this research study are the substantial improvements in performance metrics that were noted quantitatively, with students achieving lower error rates and faster completion times after the intervention of ChatGPT. Qualitatively, learners express their satisfaction with the clarity of advice, which gives them an understanding that works on their learning and confidence in Java programming. These findings point toward the promise of integrating such advanced AI solutions in educational software toward a significant improvement in learning outcomes and the necessity of human-aided continuous user feedback for system refinement. © 2024 IEEE.

关键词： Contrastive Learning

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Unsafe-Net: YOLO v4 and ConvLSTM based computer vision system for real-time detection of unsafe behaviours in workplace

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Multimedia Tools and Applications 2024年 1-27页

作者： Önal, Oğuzhan Dandıl, Emre Department of Electronic and Automation Vocational School Bilecik Seyh Edebali University Bilecik Turkey Department of Computer Engineering Faculty of Engineering Bilecik Seyh Edebali University Bilecik Turkey

Unsafe behaviour is a leading cause of death or injury in the workplace, including many accidents. Despite regular safety inspections in workplaces, many accidents occur as a result of breaches of occupational health and safety protocols. In these environments, despite efforts to prevent accidents and losses in hazardous environments, human error cannot be completely eliminated. In particular, in computer-based solutions, automated behaviour detection has low accuracy, is very costly, not real-time and requires a lot of time. In this study, we propose Unsafe-Net, a hybrid computer vision approach using deep learning models for real-time classification of unsafe behaviours in workplace. For the Unsafe-Net, a dataset is first specifically created by capturing 39 days of video footage from a factory. Using this dataset, YOLO v4 and ConvLSTM methods are combined for object detection and video understanding to achieve fast and accurate results. In the experimental studies, the classification accuracy of unsafe behaviours using the proposed Unsafe-Net method is 95.81% and the average time for action recognition from videos is 0.14 s. In addition, the Unsafe-Net has increased the real-time detection speed by reducing the average video duration to 1.87 s. In addition, the system is installed in a real-time working environment in the factory and employees are immediately alerted by the system, both audibly and visually, when unsafe behaviour occurs. As a result of the installation of the system in the factory environment, it has been determined that the recurrence rate of unsafe behaviour has been reduced by approximately 75%. © The Author(s) 2024.

关键词： Object detection

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Virtual Reality in Classrooms: Addressing Ethical Challenges in Educational Technology 4th

Virtual Reality in Classrooms: Addressing Ethical Challenges...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Marougkas, Andreas Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031733437

As Virtual Reality (VR) is on the rise in popularity in learning contexts, the corresponding ethical challenges of its use increase in complexity and importance. This theoretical paper discusses the ethical consequences posed by VR use in education, with a special focus on the perspectives of privacy, data security, and psychological consequences on students. The paper will identify resulting risks and suggest necessary guidelines for educators and developers. The paper discusses how to balance innovative educational benefits with the need to protect student welfare, to promote an ethical approach to the use of VR technology in the classroom setting. This paper contributes to the ongoing debate about the responsible use of new technologies in educational settings and presents policy priorities that seek to protect student rights and welfare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Virtual environments

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Personalized Hint Delivery in Multimedia Learning Environments Using Learning Styles: An Integrated Approach with Artificial Neural Networks and Weighted Sum Model 19

Personalized Hint Delivery in Multimedia Learning Environmen...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Troussas, Christos Krouska, Akrivi Papakostas, Christos Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798331504502

This paper introduces a novel multimedia learning environment designed to support holistic learning of web development. ANNs and the WSM model have been adopted to implement the framework of the VARK learning style model in order to dynamically provide personalized educational hints that assist learners to overcome difficulties and deepen their understanding, thus acquiring a higher level of knowledge in a more general and effective way. In our approach, hints are not an adjunct but a core feature of the learning process, dynamically tailored to each learner in their time of need. An ANN combined with a WSM collaborates to achieve optimal delivery of these hints, ensuring they are timely and context-aware according to the learning styles of individual students defined by the VARK model. Students receive the best possible guidance to make learning much more effective. A structured questionnaire-based evaluation followed by statistical hypothesis testing showed promising results for the efficacy of this multimedia learning environment. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Collaborative Learning through Virtual Escape Rooms: Impact on Participants' Perceptions 19

Enhancing Collaborative Learning through Virtual Escape Room...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Krouska, Akrivi Troussas, Christos Karkazis, Panagiotis Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798331504502

Digital revolution has transformed education, allowing students to interact, communicate, and collaborate beyond traditional classroom settings. computer-Supported Collaborative Learning (CSCL) provides opportunities for teamwork and knowledge sharing regardless of geographic location. Recently, Virtual Reality (VR) has emerged as a promising tool to address these challenges, offering immersive, three-dimensional environments that enhance collaborative learning experiences. Collaborative Virtual Environments (CVEs) extend VR's potential by enabling users to interact and work together in shared virtual spaces. Moreover, virtual escape rooms have gained traction as an innovative approach to game-based learning, combining the benefits of collaboration, problem-solving, and engagement to create an immersive and interactive educational experience. In view of the above, this paper explores the innovative use of virtual reality (VR) and educational digital escape room games as tools to promote collaborative learning in synchronous distance education and lifelong learning. By designing a cooperative game-based learning environment, the study seeks to leverage the problem-solving and teamwork skills inherent in such games to enhance the learning experience. The impact of these technologies on participants' perceptions was assessed comparing the information emerged from two surveys conducted before and after the intervention. The findings show that the incorporation of virtual escape room into collaborative learning environments effects positively participants' perceptions. © 2024 IEEE.

关键词： Virtual environments

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FASTER-AI: A Comprehensive Framework for Enhancing the Trustworthiness of Artificial Intelligence in Web Information Systems 20

FASTER-AI: A Comprehensive Framework for Enhancing the Trust...

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20th International Conference on Web Information Systems and Technologies, WEBIST 2024

作者： Troussas, Christos Papakostas, Christos Krouska, Akrivi Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9789897587184

With increasing embedding of artificial intelligence (AI) in web information systems (WIS), the maximum assurance on the reliability of such AI systems is solicited. Although this aspect is gaining importance, no comprehensive framework has yet been developed to ensure AI reliability. This paper aims to bridge that gap by proposing the AI FASTER framework to enhance the reliability of AI in WIS. The key dimensions of concern within the framework are FASTER-AI: Fairness/bias mitigation, explainability/transparency, security/privacy, robustness, and ethical considerations/accountability. Each one guides in precisely the area where trust shall be accomplished: a decrease in bias, model interpretability, protection of data, resilience of models, and ethics in governance. The implementation methodology for these dimensions involves preliminary assessment, planning, integration, testing, and continuous improvement. Validation of proof for FASTER-AI was created based on in-depth case studies across different verticals: e-commerce, finance, health, and fraud detection. This work has demonstrated how FASTER-AI is applied through illustrative case studies showing promising performance. From the initial results of high improvement in terms of fairness, transparency, security, and robustness, it may be effectively inferred that FASTER-AI can be successfully applied. Copyright © 2024 by SCITEPRESS.

关键词： Differential privacy

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Struggling to Adapt: Exploring the Factors that Hamper the Integration of Innovative Digital Tools in Higher Education Courses 4th

Struggling to Adapt: Exploring the Factors that Hamper the I...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Georgopoulou, Maria Sofia Troussas, Christos Sgouropoulou, Cleo Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

The pervasive impact of digital technologies has permeated all facets of our society. As technology continues to evolve rapidly, it is imperative for educators to adapt and integrate these advancements into their teaching methods. Nevertheless, educators face challenges in keeping pace with technological advancements for educational purposes. This study revolved around three key objectives: mapping the intricate landscape of digital inclusion in higher education, particularly within a department of informatics;exploring teaching practices that enrich and facilitate the overall educational experience;and defining the challenges that faculty members encounter. This distinctive focus represented an innovative approach, shedding light on the underlying concerns within academia regarding obstacles to incorporating digital tools into university courses. The participants in the study were faculty members and undergraduate students from the department of informatics at a Greek university. Data was collected through two distinct questionnaires. A major conclusion of this study is that the types of digital tools faculty prefer are closely related to (a) challenges faced by higher education institutions, such as infrastructure and funding, and (b) their own perceptions and interpretations of their involvement with technology-driven strategies in the classroom. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Educational technology

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Remaining Cycle Time Prediction in Business Processes with TPOT Regression for Automated Machine Learning 4th

Remaining Cycle Time Prediction in Business Processes with T...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Bousdekis, Alexandros Kaftantzis, Savvas Theodoropoulou, Georgia Miaoulis, Georgios Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Process mining allows the extraction of useful information from the event logs and historical data of business processes. Predictive business process monitoring is a sub-field of process mining and deals with runtime methods aimed at generating predictive models about various objectives related to a process instance by taking advantage of Machine Learning (ML) and deep learning algorithms. However, despite the large amount of research works dealing with predictions about next activity prediction and remaining time of running process instances, the objective of cycle time prediction has been overseen although it can provide higher level insights targeted the strategic planning. At the same time, Automated Machine Learning (AutoML) has not been investigated in the predictive business process monitoring domain in order to automate the time-consuming process of designing and optimizing ML pipelines. Therefore, motivated by its promising results in other domains, we propose an approach for remaining cycle time prediction in business processes based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method. The results demonstrate that the proposed approach achieves high accuracy, while it minimizes human intervention in the configuration of ML pipelines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Customer Segmentation in Steel Sales : Optimizing Profitability with RFM Model and K-Means Algorithm

Customer Segmentation in Steel Sales : Optimizing Profitabil...

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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Delimayanti, Mera Kartika Hidayati, Anita Kurniawati, Dewi Rahmadani, Nadya Zahra Faydhurrahman, Mas Brillianesa Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350368451

This paper is on customer segmentation in steel sales, whose objective is to improve profitability and customer relationship management. The current research categorizes customers based on purchasing behaviors by using the RFM model and the K-Means clustering algorithm. Due to this approach, the company will be able to formulate stratified marketing plans for the requirements and Preferences of various customer segments identified and hence ensure high retention and satisfaction. The RFM model assesses the three main customer behaviors of purchase recency, purchase frequency, and monetary value. This gives a set of metrics that provide the overall depth of customer engagement, which is important in segmentation. Then, K-Means is applied to cluster these similar patterns of customer purchasing, to precisely target market customers. The goodness of the segmentation strategy is measured through metrics such as the Elbow Method, Davies-Bouldin Index, and Silhouette Coefficient, which provide proper cluster selection and meaningful segmentation. This eventual outcome has proved that this combined approach will maximize the revenue potential and enhance customer satisfaction tremendously through personalized marketing efforts. Eventually, the embedding of the RFM model within K-Means clustering offers a robust framework for strategic decision-making and realizing competitive advantage within the steel industry. © 2024 IEEE.

关键词： Profitability

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