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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Sailer, Patrizia Department of Information Technology University of Applied Sciences Burgenland Campus 1 Eisenstadt7000 Austria Doctoral Sch Vienna Austria

ISBN: (纸本)9789897587467

Teaching web development in non-technical university programs presents distinct challenges, including varying levels of prior knowledge and engagement among students. This study compares the effectiveness of traditional teaching methods with a flipped classroom approach in two courses. The flipped classroom included asynchronous learning pathways with interactive videos, quizzes and in-class hands-on activities, while the traditional approach relied on lectures followed by guided exercises. The evaluation was conducted using grades, a motivation questionnaire (FAM) and open-ended surveys. Results show that the flipped classroom model improved student engagement, confidence, and collaborative skills, while the traditional approach maintained more structured guidance. Challenges such as time constraints and group dynamics were observed in both formats, though students in the flipped classroom reported higher satisfaction with active learning opportunities. These findings underscore the potential of the flipped classroom to enhance learning outcomes in technical subjects, provided sufficient resources and support are available. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Teaching

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Evaluation of commercial space accessibility in tourist town: A case research of Maotai Town in China

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Frontiers of Architectural Research 2025年第4期14卷 965-982页

作者： Xu, Lai Liu, Yuwei Shen, Jingmi Tan, Jie Hutter, Akos Marcel Breuer Doctoral School Faculty of Engineering and Information Technology University of Pecs Pecs Hungary Department of Production Design Faculty of Art and Design Hankou University Wuhan430212 China Department of Arts and Design Faculty of Fine Arts Chiang Mai University Chiang Mai Thailand Department of Architecture and Urban Planning Institute of Architecture Faculty of Engineering and Information Technology University of Pecs Pecs Hungary

Accessibility is a crucial indicator for evaluating the spatial rationalization of commercial facilities in an area, impacting both commercial development and service efficiency. The objective of this research is to analyze the commercial space layout of Maotai Town in Guizhou Province, China, and to design an evaluation system for commercial space accessibility to enhance the efficiency of commercial space use. Firstly, POI (Point of Interest) Data Visualization Analysis will be employed to identify major commercial hotspots and areas not covered by their service reach. Secondly, the spatial accessibility of commercial spaces will be evaluated using Space Syntax and the Cost-Weighted Model in the topological grid dimension and geometric grid dimension. Finally, we will establish a Minimum Cumulative Resistance Model (MCR Model) based on the analysis results obtained to identify the resistance paths that tourists navigate within the research area. This method will provide a precise framework for evaluating the accessibility of commercial spaces throughout the area. © 2025 The Author(s)

关键词： Accessibility Commercial space Space syntax Cost-weighted distance model MCR model

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DigiEMine: Towards Leveraging Decision Mining and Context Data for Quality Control 28th

DigiEMine: Towards Leveraging Decision Mining and Context D...

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28th International Conference on Enterprise Design, Operations, and Computing, EDOC 2024

作者： Wais, Beate Rinderle-Ma, Stefanie Faculty of Computer Science Research Group Workflow Systems and Technology University of Vienna Vienna Austria UniVie Doctoral School Computer Science DoCS University of Vienna Vienna Austria TUM School of Computation Information and Technology Technical University of Munich Garching Germany

ISBN: (纸本)9783031783371

Quality control processes in manufacturing often still rely on manual tasks. Applying decision mining can support users by providing valuable insight into the process. This paper discusses the potential of integrating contextual information into decision mining to achieve accurate and meaningful decision rules in the context of a case study stemming from the manufacturing domain. To explore this, a new approach, DigiEMine, is presented, which addresses the gap between information extraction and practical decision mining applications by integrating information extracted from engineering drawings with time sequence data in the form of diameter measurements of workpieces. The discovery of relational decision rules is enabled, allowing for contextualization of the decision rules. The output of this approach is presented in both textual decision rules and visually on engineering drawings, empowering users to make informed quality control decisions. The case study includes three datasets originating from cylindrical workpiece production. Results demonstrate the feasibility of the approach and the ability to generate meaningful decision rules across the tested datasets. Its potential applicability extends beyond the presented case study, with conceivable scenarios in multiple domains, such as healthcare or logistics, where integrating context information, such as regulatory data, with time sequence data is required to provide additional context for decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data quality

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AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study

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IEEE Access 2025年 13卷 80258-80271页

作者： Hnatiuc, Mihaela Alpetri, Domnica Sintea, Sorin-Robertino Hnatiuc, Bogdan Margarit Raicu, Gabriel Paun, Mirel Dina, Ionica Electronics and Telecommunications Department Constanţa Maritime University Constanta900663 Romania National University of Science and Technology POLITEHNICA Bucharest Doctoral School of Electronics Telecommunications and Information Technology Bucharest060042 Romania Electrical Engineering Department Constanţa Maritime University Constanta900663 Romania Department of Navigation Constanţa Maritime University Constanta900663 Romania Research Station for Viticulture and Oenology Murfatlar Murfatlar905100 Romania

This study explores the application of the Artificial Intelligence of Things (AIoT) in viticulture for the early detection of grapevine diseases. By integrating Internet of Things (IoT) sensors with machine learning algorithms, the system is designed to detect potential grapevine pathogens in real time. Deployed at the Murfatlar vineyard in Romania, which grows Cabernet Sauvignon and Sauvignon Blanc, the system allows for proactive disease management, thus improving grapevine health and reducing crop losses. IoT sensors are installed in the field to collect real-time data on grapevine health, which is then transmitted to the cloud for storage and analysis. Machine learning (ML) algorithms, running on a server with an NVIDIA R3900 card, process this data to predict potential infections caused by pathogens such as Plasmopara viticola, Uncinula necator, and Botrytis. Cloud computing facilitates data processing and real-time visualization, allowing farmers to make timely, data-driven decisions for disease control. The paper outlines the hardware and software components that constitute the diagnostic system. © 2013 IEEE.

关键词： Cluster computing

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Zero trust in cybersecurity: managing critical challenges for effective implementation

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Journal of Systems and information technology 2025年

作者： Pigola, Angelica Meirelles, Fernando de Souza Doctoral Program in Information Technology Management Fundação Getulio Vargas São Paulo Campos Sao Paulo Brazil

Purpose: This study investigates the challenges of adopting Zero Trust (ZT) as a security strategy in contemporary organizations, where traditional security measures are insufficient. This paper aims to provide a robust and objective framework for managing ZT implementation through an integrated approach. Design/methodology/approach: An expert panel of 29 professionals contributed to identifying and weighting key management criteria for ZT adoption. This study used fuzzy Delphi to achieve consensus and the CRITIC (Criteria Importance through Intercriteria Correlation) method to ensure objectivity in determining criteria importance. Findings: The results reveal four critical dimensions – culture, operations and processes, compliance, and investments – along with 33 specific criteria essential for successful ZT implementation. This study highlights the potential for reversal of rank under dynamic decision-making conditions, emphasizing the necessity for continuous refinement and adaptation. Practical implications: The insights derived from this research offer valuable guidance for security leaders, professionals and consultants in navigating the complexities of ZT adoption. By addressing managerial challenges and providing a structured approach, this study contributes to a smoother transition from traditional cybersecurity models to ZT frameworks. Originality/value: This research offers a novel contribution to ZT management by applying an innovative, management-centered methodology to systematically identify and prioritize the critical challenges involved. It provides a structured framework that enables security managers to prioritize critical areas – cultural, financial, operational and compliance – essential for the successful implementation of ZT. © 2025, Emerald Publishing Limited.

关键词： Cyber attacks

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Scrutinizing the Determinants of Travel Intentions for Heritage Tourism: An Empirical Study

Scrutinizing the Determinants of Travel Intentions for Herit...

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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Eko Sediyono Eka Purnama Harahap Faculty of Information Technology Doctoral Program in Computer Science Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

This study investigates the variables affecting travelers' decision to visit heritage destinations. Heritage tourism can draw visitors because of its historical, cultural, and aesthetic significance. More studies are still required to further understand the elements influencing travelers' decisions to engage in historical tourism. The object of this research is tourists who intend to visit heritage tourism in Indonesia. This study used a quantitative approach by distributing questionnaires online to 100 respondents consisting of domestic and foreign tourists. They were then processed using SmartPLS. The study results show that five variables affect tourists' intentions to visit heritage tourism: Heritage Value (HV), Travel Intention (TI), Visitor Engagement (VE), Quality of Experience (QE), and Social Media Preference (SMP). All these variables positively influence tourist intentions to visit cultural heritage sites. This study concludes that the government and related parties significantly promote heritage sites by prioritizing historical, cultural, and aesthetic values. In addition, associated parties must also pay attention to other factors, such as service quality and travel costs which can affect tourists' intentions to visit heritage sites. In this case, social media can also be an effective means of promoting heritage sites to tourists.

关键词： Surveys Costs Social networking (online) Switched mode power supplies Government Manufacturing Cultural differences Quality of experience information technology Testing

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Evaluating Deep Learning Algorithms for Freeway Mainstream Traffic Control 3rd

Evaluating Deep Learning Algorithms for Freeway Mainstream T...

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3rd International Conference on Cognitive Mobility, CogMob 2024

作者： Amini, Mehran Karadeniz, Ahmet Mehmet Kóczy, László T. Department of Information Technology Széchenyi István University Győr Hungary Doctoral School of Multidisciplinary Engineering Sciences Széchenyi István University Győr Hungary Robert Bosch Kft Budapest Hungary

ISBN: (纸本)9783031817984

Traffic congestion is a universal problem that significantly impacts urban mobility and economic productivity. Accurate traffic flow prediction is crucial for efficient traffic management and congestion mitigation. Traditional methods often struggle to capture the complex temporal dependencies in traffic data. This study explores the effectiveness of Temporal Convolutional Network (TCN) models compared to Long Short-Term Memory (LSTM) models for predicting traffic volumes on freeway networks. Previous research has largely focused on LSTM models, leaving a gap in understanding the potential advantages of TCN models in this context. We address this gap by conducting a comprehensive comparison of LSTM and TCN models, training them on a dataset representing approximate traffic flow, and evaluating their performance using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Our findings indicate that the TCN model outperforms the LSTM model, achieving lower MSE and MAE values and a higher R2 score. These results suggest that TCN models can more accurately predict traffic volumes under conditions with the least captured traffic data, offering a promising tool for real-time approximate traffic management and congestion prevention with reasonable prediction performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traffic congestion

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Adaptive resource allocation and routing for integrated sensing and communications for wireless technologies

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Subramaniyan, Manochandar Venkatasamy, Thiruppathy Kesavan Thotakuruchi Mathiyalagan, Nithya Jakir Hossen, Md. Faculty of Digital Business & Analytics Bharathidasan Institute of Management Tamil Nadu Tiruchirappalli 620014 India Faculty of Information Technology Dhanalakshmi Srinivasan Engineering College Tamil Nadu Perambalur 621212 India Center for Advanced Analytics (CAA) COE for Artificial Intelligence Faculty of Engineering & Technology (FET) Multimedia University Melaka 75450 Malaysia Faculty of Computer Science and Engineering K. Ramakrishnan College of Engineering Tamil Nadu Samayapuram 621112 India Post Doctoral Fellow Department of Engineering & Technology Multimedia University Melaka 75450 Malaysia

The integrated sensing and communications (ISC) application completely changes the game regarding collecting, analyzing, and dispensing facts in real time via combining sensing skills with communication structure. The present research delves into how ISC can enhance decision-making in healthcare, smart towns, and environmental monitoring. Issues with interoperability, electricity intake, ecological resilience, and the worrying latency requirements of task-critical packages are many obstacles to easy integration. Our proposed mechanism, adaptive resource allocation and routing for integrated sensing (ARAR-IS), which uses new wireless technologies, attempts to clear up these problems by allocating and routing assets for incorporated sensing. ARAR-IS dynamically allocates sources and routes information considering the real global and network dynamics. Optimizing resource utilization, mitigating interference, and enhancing dependability in ISC systems are achieved using ARAR-IS, which leverages cognitive routing algorithms and software program-defined networking ideas. With ARAR-IS, various computer-aided engineering regions can benefit from customized remedy plans, far-off patient tracking, and real-time attempts to lessen pollution. These domains include healthcare, proper infrastructure, industrial automation, and environmental monitoring. Thorough simulation-based assessments show that ARAR-IS is a pleasant alternative for ISC systems in many one-of-a-kind situations because of its high reliability, low-strength intake, excessive throughput, and low latency. © The Author(s) 2025.

关键词： Adaptive control Data communication Resource allocation Routing protocols Wireless channels

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Enhancing Learning with Gamification: Empowering Students in Web Development Education 17

Enhancing Learning with Gamification: Empowering Students in...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Sailer, Patrizia Kremsner, Thomas Knipp, Franz Kayali, Fares Department of Information Technology University of Applied Sciences Burgenland Campus 1 Eisenstadt Austria Doctoral School Computer Sciences University of Vienna Waehringerstrasse 29 Vienna Austria Forschung Burgenland Campus 1 Eisenstadt Austria Centre for Teacher Education University of Vienna Porzellangasse 4 Vienna Austria

ISBN: (纸本)9789897587467

Digitalization has transformed educational practices, extending its impact to non-technical fields by incorporating technical subjects such as web development. Students from diverse backgrounds frequently encounter difficulties when attempting to learn web development, often experiencing feelings of being overwhelmed and frustrated. This study aimed to address student challenges by identifying their needs and developing a supportive tool to enhance their learning experience in web development. Therefore, human-centered design was used to conduct preliminary interviews with the students in order gain deeper insights into their perceptions on how to enhance their learning experience. In response to this feedback, a gamified learning platform (GLP) was designed and developed. Subsequently, the same group of students evaluated the GLP through interviews, questionnaires and eye-tracking analyses. The evaluation indicated that students perceived the GLP to be a valuable supplementary tool, although not a comprehensive substitute for conventional methods. The findings suggest that refining the GLP, e.g. modifying the color scheme, and integrating it with approaches like flipped classroom could lead to enhanced learning outcomes in subsequent semesters. This study underscores the importance of student-centered design in developing effective educational tools for technical subjects. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Students

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Review on information Fusion-Based Data Mining for Improving Complex Anomaly Detection

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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2025年第2期15卷

作者： Moldovan, Sorin-Claudiu Iantovics, Laszlo Barna Department of Electrical Engineering and Information Technology Faculty of Engineering and Information Technology George Emil Palade University of Medicine Pharmacy Science and Technology of Târgu Mureş Mureş Târgu Mureş Romania Doctoral School of Letters Humanities and Applied Sciences George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Targu Mures Romania

Anomaly predicated upon multiple distributed hybrid sensors frequently uses hybrid approaches, integrating techniques derived from statistical analysis, probability, data mining, machine learning, deep learning, and signal denoising. Many of these methods are based on the analysis of irregularities, data continuity, correlation, and data consistency, aiming to discern anomalous patterns from normal behavior. By leveraging these techniques information fusion aims to enhance situational awareness, detect potential threats or abnormalities, and improve decision-making processes in complex environments. It addresses uncertainties by integrating data from diverse sources, thereby enhancing performance, and reducing dependency on individual sensors. This study examines applications based on single and multiple sensor data, revealing common strategies, identifying strengths and weaknesses, and potential solutions for detecting and diagnosing anomalies by analyzing low, large, and complex data derived from the context of homogeneous or heterogeneous systems. information fusion techniques are evaluated for their performance on various levels of algorithm complexity. This in-depth bibliographic study involved searching top indexing databases such as Web of Science and Scopus. The inclusion criteria were articles published between 2012 and 2024. The search capitalized on specific keywords as follows: "sensor malfunction," "sensor anomaly," "sensor failure," "sensor fusion," and "anomaly data mining." Publications that did not strictly focus on analytical processing for anomaly detection, diagnosis, and prognosis in sensor data were excluded. In conclusion, the practice of information fusion promotes transparency by elucidating the process of combining information, thereby enabling the inclusion of multitude of perspectives, and aligning with established best practices in the field. Data deviation remains the primary criterion for detecting anomalies using mostly deep learning

关键词： Sensor data fusion

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