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ADA-Gen: Iterative and Incremental Generation of Full-Stack Apps for Learning Agile/DevOps Software Development Practices 17

ADA-Gen: Iterative and Incremental Generation of Full-Stack ...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： Duong, Ta Nguyen Binh School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9789897587467

To learn Agile/DevOps practices effectively, students need to apply them in an actual software development project. This is challenging if students are mostly from non-computing backgrounds and they do not have time in the curriculum to learn programming and related tools. Therefore, it is important to help students who do not possess programming foundations to develop fully functional software during the process of learning Agile/DevOps concepts. We noted that existing low-code/no-code app development platforms have not been designed to teach Agile/DevOps practices. On the other hand, recent AI-based tools for code generation such as GitHub Copilot have been built mainly for programmers. In this work, we designed and implemented ADA-Gen (Agile/DevOps App Generator), a teaching tool leveraging large language models (LLMs) to generate full-stack web apps following an iterative and incremental development methodology widely practiced in Agile/DevOps circles. ADA-Gen is integrated with Jira, one of the most popular platforms for software project management, so students can use it right away without much further setup. This approach allows non-computing students to experience the complete full-stack software development life cycle. We have conducted extensive evaluations of ADA-Gen using various realistic project scenarios. The evaluations demonstrated the capabilities of ADA-Gen in full-stack web app generation, and in providing plenty of learning opportunities for students to appreciate key Agile/DevOps practices. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Software design

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Automated Cardiomegaly Detection from Chest X-rays Using InceptionV3: A Deep Learning Approach 4

Automated Cardiomegaly Detection from Chest X-rays Using Inc...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Jain, Eshika Bhardwaj, Rishabh Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798331530402

In this work, InceptionV3 is applied to identify cardiomegaly from chest X-ray images through a deep learning technique. The dataset, sourced from Kaggle, contains 5, 552 balanced images across two classes: The presence of cardiomegaly, coded as "True" and the absence of cardiomegaly coded as "False". Some pre-processing steps which include image resizing, normalization and augmentation improved the performance of the proposed model. The model was trained in 10 epochs with a batch size of 64 and produced an overall accuracy of 86%. The evaluation criteria such as precision, recall, F1-score, confusion matrix and classification report proved high and fair results on both classes. The results show a slight overfitting problem, which needs to be address in the future work by using approaches such as regularization and dropout. Future work includes using real and bigger datasets, combining different types of data, and using explainable AI for increased model interpretability. This work establishes the possibility of using deep learning for automating the identification of cardiomegaly, which can be an important direction for the development of efficient and accurate diagnostic tools in medical imaging. © 2025 IEEE.

关键词： Image coding

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Permissioned Blockchain Security Pqfabric from Quantum and Classical Attacks 1

Permissioned Blockchain Security Pqfabric from Quantum and C...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Tamilarasi, C. Bhavadrarani, K. Kittappa, Thiagarajan Subashini, N. Anitha, V. Vimalraj, E. B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India Rajalakshmi Institute of Technology Department of Mathematics Chennai600124 India Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Panimalar Engineering College Department of CSE Poonamallee Chennai600123 India School of Computing Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798331518578

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding Byzantine Fault Tolerance (BFT) with quantum-safe encryption. The system gives protection against calculation based attacks by Shor and Grover, tending to huge imperfections in existing blockchain structures. Besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks. © 2025 IEEE.

关键词： Quantum computers

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Big Data-Driven Practices of Educational information technology in Personalized Instruction 14th

Big Data-Driven Practices of Educational Information Technol...

引用

14th international conference on Frontier computing, FC 2024

作者： Chen, Jinhuan School of Marxism Guangdong Polytechnic of Environmental Protection Engineering Guangdong Foshan528216 China

ISBN: (纸本)9789819623907

The advent of the big data era has brought the biggest impact on education work is the use of all kinds of information technology in education work. The use of information technology can significantly improve the quality of teaching, promote the training of talents and promote the development of information technology. The article briefly introduces the necessity and superiority of the application of information technology in teaching, and analyzes the practical use of information technology in education, hoping to provide some reference for China’s information technology teaching. This paper discusses a personalized teaching path optimization model based on the ant colony algorithm, setting variables related to students’ knowledge level, learning preference, difficulty of the target learning task, and matching degree of the learning task to the students, and then seeking the optimal teaching path through the ant colony algorithm, with the aim of helping the students to complete the learning task efficiently. Finally, the superiority of the personalized teaching path optimization model constructed in this paper is verified through experiments (the running time of the collaborative filtering algorithm and the ant colony algorithm varies less, the running time of the collaborative filtering algorithm is maintained in the range of 1.0–1.5, and that of the ant colony algorithm is in the range of 0.1–0.9). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

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Transforming Hospital Visitor Screening with Cloud-Powered Deep Learning for Safety and Security

Transforming Hospital Visitor Screening with Cloud-Powered D...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Joseph Rajan, D. Antony Sivanesan, T.M. Mohankumar, N. Srinivasan, M. Sivaraman, Audithan Velmurugan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Pune India P. S. v College of Engineering and Technology Department of Information Technology Tamilnadu Krishnagiri India Department of Information Technology Jafferpur New Delhi110073 India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331529833

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing the Security of Healthcare Data in Cloud computing Environments 2

Enhancing the Security of Healthcare Data in Cloud Computing...

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2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sajwan, Himani Kumari, Anjali Chauhan, Garima Kumar, Raj School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

Cloud computing has completely evolved the way health care data is managed, but it also introduces critical security vulnerabilities that can compromise the privacy of patient information and threaten regulatory compliance. Herein, we present one of the most severe challenges: securing EHRs in a cloud environment with access to data while maintaining HIPAA compliance. We introduce a novel hybrid framework combining homomorphic encryption with blockchain-based access control and dynamic audit logging. The framework was used with a three-phase experimental approach and was deployed over five major healthcare institutions while being analyzed for 2.3 million patient records over 18 months. Results showed a high success rate of 99.97% against denial-of-service attacks while showing reductions in latency time about data retrieval up to 3 4 % compared to traditional methods of encryption. Security-incident-based variation was reduced up to 78%, and 99.99% data integrity was achieved during concurrent access scenarios. Cost analysis shows that overheads in security management amount to a reduction of 4 5 % against conventional systems. Our results show that the proposed framework is far better for healthcare data security, along with improved operational efficiency and regulatory compliance. This work contributes to the literature by providing a scalable and cost-efficient solution addressing the most complex security requirements of modern health care systems in cloud environments. The fact that the framework is implemented successfully in real life seems to reflect its effectiveness for mass-scale implementation in health organizations. © 2025 IEEE.

关键词： Cloud security

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Low Resolution Deepfake Face Detection using Multi-Scale Discrete Cosine Transform and Vision Transformer 3

Low Resolution Deepfake Face Detection using Multi-Scale Dis...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Uddin, Main Fu, Zhangjie Zhang, Xiang Arnob, Abu Bakor Hayat Nanjing University of Information Science and Technology School of Computer Science Nanjing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (纸本)9798331523893

Deepfake technology uses generative adversarial networks to produce highly authentic counterfeit images and videos, which raises a significant global security concern by disseminating fake information through online platforms, especially on social media. Researchers have developed sophisticated models for deepfake detection, demonstrating high performance on high-resolution images. However, their effectiveness significantly diminishes when applied to low-resolution images. We propose a simple framework that utilizes multi-scale discrete cosine transform and vision transformer for low-resolution deepfake face detection. This framework introduces a multi-scale frequency filter fusion approach to extract subtle frequency features in the frequency domain. Our proposed network has been tested on the FaceForensics++ and Celeb-DF datasets, and it outperforms existing models, achieving superior AUC and F1 scores. © 2025 IEEE.

关键词： Discrete cosine transforms

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Design of Farmland Meteorological Visualization Management System Based on Digital Twins

Design of Farmland Meteorological Visualization Management S...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Xu, Yong Lv, Zhenqiang Chen, Zhiling Chan, Choon Kit Luo, Kun School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia Inti International University Faculty of Education and Liberal Arts Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

To precisely monitor farmland conditions, we need to mitigate the risk of crop yield reduction due to natural disasters and other factors, and enhance the accuracy of agricultural production decisions, an agricultural meteorological environment monitoring system based on digital twin technology has been developed. Unlike traditional meteorological monitoring systems, this design employs digital twin technology to construct a twin model that virtually replicates the physical farmland environment. Meteorological monitoring equipment collects farmland data, which is then uploaded to a virtual visualization interface where real-time remote monitoring, anomaly alerts, and weather forecasts are provided. Experimental results indicate that this system effectively collects and uploads farmland data, enables real-time remote monitoring, and issues timely alerts for abnormal meteorological conditions, thereby offering theoretical guidance and technical support for the advancement of smart agriculture. © 2025 IEEE.

关键词： data collection Digital twin visualization weather forecast weather monitoring

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Comparative Analysis of Execution Time for Sequential and Data-Decomposed Parallel Program

Comparative Analysis of Execution Time for Sequential and Da...

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international Scientific-Professional conference on information technology (IT)

作者： Radovan Čikić Miloš Milić Siniša Vlajić Faculty of Organizational Sciences University of Belgrade Jove Ilića 154 Belgrade Serbia

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

When complex computing in programming is mandatory, it’s important to choose a proper way to minimize execution time. Sequential and parallel approaches to source code execution each have their own pros and cons, and programmers must make valid decisions based on these factors. In this paper, we will conduct the comparative analysis of the performances of both approaching ways, and consider their application in different business and mathematics areas. Based on the experimental results, it was concluded that as the volume of the data processed by the program increases, parallel execution becomes more favorable, whereas sequential execution is more efficient for smaller data sizes. Additionally, the point at which sequential execution ceases to be more efficient was identified.

关键词： Source coding Programming Mathematics information technology Business

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VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering 31

VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answer...

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31st international conference on Computational Linguistics, COLING 2025

作者： Wu, Zhixuan Cheng, Bo Han, Jiale Ma, Jiabao Zhang, Shuhao Chen, Yuli Li, Changbao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Hong Kong University of Science and Technology Hong Kong North China Institute of Computing Technology China

ISBN: (纸本)9798891761964

Video question answering (VideoQA) has recently gained considerable attention in the field of computer vision, aiming to generate answers rely on both linguistic and visual reasoning. However, existing methods often align visual or textual features directly with large language models, which limits the deep semantic association between modalities and hinders a comprehensive understanding of the interactions within spatial and temporal contexts, ultimately leading to sub-optimal reasoning performance. To address this issue, we propose a novel temporal-aware framework for multi-modal video question answering, dubbed VideoQA-TA, which enhances reasoning ability and accuracy of VideoQA by aligning videos and questions at fine-grained levels. Specifically, an effective Spatial-Temporal Attention mechanism (STA) is designed for video aggregation, transforming video features into spatial and temporal representations while attending to information at different levels. Furthermore, a Temporal Object Injection strategy (TOI) is proposed to align object-level and frame-level information within videos, which further improves the accuracy by injecting explicit temporal information. Experimental results on MSVD-QA, MSRVTT-QA, and ActivityNet-QA datasets demonstrate the superior performance of our proposed method compared with the current SOTAs, meanwhile, visualization analysis further verifies the effectiveness of incorporating temporal information to videos. © 2025 Association for Computational Linguistics.

关键词： Question answering

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