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17th International Conference on computer Supported Education, CSEDU 2025

作者： Frankford, Eduard Crazzolara, Daniel Vierhauser, Michael Meißner, Niklas Krusche, Stephan Breu, Ruth University of Innsbruck Department of Computer Science Austria University of Stuttgart Institute of Software Engineering Germany Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9789897587467

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Teaching

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Predictive Classification Model of Crop Yield Data Using Artificial Neural Network 5

Predictive Classification Model of Crop Yield Data Using Art...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kanagaraj, R. Elakiya, E. Kumaresan, A. Selvakumar, J. Suganya, J. Shruthi, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Mailam Engineering College Mailam India Bosch Global Software Technologies Coimbatore India

ISBN: (纸本)9798350321425

Agriculture is under tremendous pressure to produce more productivity with fewer resources as a result of the expanding global population. Decision-making along the entire supply chain needs to be improved considering competing economic, environmental, and societal demands. Data from satellites, drones, sensors, and other sources must be processed in order to make informed judgments. However, sophisticated machine learning and data analytics methods must be used due to the complexity and volume of the data. This initiative covers crucial duties in yield prediction and precision agriculture. Management zones are areas of a field that have high inter-regional variability and low intra-regional variability, dividing the field into uniform zones for which agronomic practices should be modified independently. For fertilizer optimization and post-harvest logistics, yield prediction is crucial. In order to make up for the nutrients that have been taken out of the soil, fertilizer type and quantity must be adjusted accordingly. The yield data is also essential for enhancing harvesting, shipping, storage, and sales. So, utilizing web scraping and convolution neural networks, the proposed model has been forecast yields from input satellite photos. The result of investigations is illustrated that the artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data. © 2023 IEEE.

关键词： Neural networks

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Masked Image Self-Learning and Knowledge Distillation for Source-Free Unsupervised Domain Adaptation in Crowd Counting 2

Masked Image Self-Learning and Knowledge Distillation for So...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Zeng, Jia Ma, Chaoqun Shao, Penghui Qing, Anyong Wang, Yang Southwest Jiaotong University School of Electrical Engineering Chengdu China Concordia University Department of Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798350331417

Unsupervised Domain Adaptation (UDA) techniques leverage labeled data from a source domain to adapt to unlabeled data from a target domain, offering a primary solution for addressing cross-domain challenges in crowd counting. However, in real-world scenarios, accessing source data during the model adaptation process within the target domain can be impractical due to privacy and security concerns. This gives rise to a practical yet challenging issue known as Source-Free Unsupervised Domain Adaptation (SFUDA), where model adaptation is limited to the utilization of an existing source model and unlabeled target data. To tackle this challenge, we propose an algorithm that combines mask image self-learning and knowledge distillation to address SFUDA in crowd counting effectively. Specifically, the student model uses masked images to self-learn at both feature and output levels, exploring clues and contextual relationships around masked image blocks to predict occluded areas. Furthermore, we employ knowledge distillation to boost the model's performance within the teacher-student framework, encompassing feature extraction and density map generation stages. To mitigate cumulative errors, we stochastically recovery a subset of parameters from the source model. We conducted extensive experiments to compare our approach with a modified SFUDA algorithm, and the results demonstrate competitive performance across multiple datasets. © 2023 IEEE.

关键词： Crowd counting Domain adaptation Knowledge distillation Self-learning Source-free

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Innovating Project Management: AI Applications for Success Prediction and Resource Optimization 2nd

Innovating Project Management: AI Applications for Success P...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Tarawneh, Monther AbdAlwahed, Huda AlZyoud, Faisal Computer Science Department Isra University Amman Jordan Software Engineering Department Isra University Amman Jordan Information Technology Department Tafilah Technical University Tafila Jordan

ISBN: (纸本)9783031569494

The revolution of Artificial Intelligence (AI) has made transformative changes in all industries. The field of software engineering has great benefit from AI by Automat all process. Applying Artificial intelligence (AI) technologies, particularly Deep Neural Network (DNN) models, to project management will transform traditional methodologies to new ways. This work investigates how AI can be used within project management to better allocate resources, estimate time, and predict costs. Data from many recourses used to improve accuracy in project planning, refine how risks are managed, and develop new methods. Data includes real projects portfolio, open-source data, and simulated data. The integration of AI in project management examines project success and client satisfaction. This work shows 99% accuracy for learning and 78% for testing of predicting project success and highlights the need to constantly update methods to effectively use the big potential changes AI can bring to project management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

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computers, Materials & Continua 2022年第8期72卷 2323-2339页

作者： Sami Ullah Muhammad Ramzan Talib Toqir A.Rana Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College University Faisalabad38000Pakistan Department of Computer Science and IT The University of Lahore54590Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)11800PenangMalaysia Department of Software Engineering Government College University Faisalabad38000Pakistan

In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar *** of such material where more than one person is involved has a spate challenge as compared to other text analysis *** are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been *** Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu ***,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the *** accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue *** that,we have preprocessed the data and selected dialogues with common *** the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of *** have tuned the algorithms according to the Urdu language *** experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and *** believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

关键词： Dialogue analysis conversational opinion mining sentiment analysis sentiment analysis in Urdu language deep learning machine learning

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Approach of Different Classification Algorithms to Compare in N-gram Feature Between Bangla Good and Bad Text Discourses 1

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Bitto, Abu Kowshir Bijoy, Md. Hasan Imam Khan, Saima Mahmud, Imran Biplob, Khalid Been Badruzzaman Department of Software Engineering Daffodil International University Dhaka1207 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1207 Bangladesh

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Bangla Natural Language Processing (BNLP) is a newish challenge in Artificial Intelligence. With the rapid expansion of the Bangla language, it is now adopted on a variety of platforms, including social media, communication platforms, news media, and so on. The classification of text documents becomes an important factor in resolving the challenge of information organization and knowledge management. This study uses five supervised classification methods to explore the categorization of Bangla text discourse using N-gram (unigram, bigram, and trigram) features. Bangla text discourse is collected from different platforms such as social media, personal Bangla blogs, and people's utterances in order to accomplish the research goal. After collecting data, the most difficult part of the Bangla language preprocessing is completed, which includes adding contractions, removing punctuations, encoding, and a variety of other operations. For this study, 1499 text documents were initially used, with 1459 Bangla text discourses being used after preprocessing. To convert the text into a token, N-gram feature methods utilizing TF-IDF-Vectorizer are used. During the experiment phase, unigram, bigram, and trigram feature techniques are used to apply Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest (RF), Multinomial Naive Bayes (MNB), and K-Nearest Neighbors (KNN) models to the dataset. In the unigram and bigram features, Multinomial Naive Bayes (MNB) outperformed all other classifiers, with the highest accuracy of 89.31% and 86.94%, respectively. The trigram feature of K-Nearest Neighbors (KNN) achieves a maximum accuracy of 84.25%, and the proposed model can classify the Bangla text document as Good or Bad Discourse. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge management

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Editorial Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年第4期21卷 516-520页

作者： Zhou, Xiaokang Leung, Carson K. Wang, Kevin I-Kai Fortino, Giancarlo Shiga University Faculty of Data Science Shiga Hikone522-0069 Japan University of Manitoba Department of Computer Science WinnipegR3T 2N2 Canada University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand University of Calabria DIMES Department Rende CS87036 Italy

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics - including affective learning, reinforcement learning, and transfer learning - are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand. © 2004-2012 IEEE.

关键词： Big data

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An Empirical Evaluation of the Effectiveness of Spider and Proxy Modes for Web Security Testing 10

An Empirical Evaluation of the Effectiveness of Spider and P...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Chiu, Kuan-Wei Yang, Shih-Sheng Lee, Shin-Jie Lee, Wen-Tin National Cheng Kung University Department of Computer Science and Information Engineering Taiwan National Kaohsiung Normal University Department of Software Engineering and Management Taiwan

ISBN: (纸本)9798350304770

Spider and proxy modes are two commonly employed methods supported by dynamic application security testing (DAST) software. Despite efforts to enhance the automated spider's efficiency, deep exploration of web applications is still constrained by the need for manual intervention in certain complex UI operations. In this regard, the proxy mode serves a crucial intermediary, intercepting and inspecting request messages exchanged between the browser and the web application during manual or scripted browsing activities. This study aims to assess the efficacy of these two modes in terms of code coverage and the number of requests, utilizing two popular PHP-based open-source web applications. The experimental findings demonstrate that employing a hybrid mode (Spider-Last) yields a significant improvement compared to using the spider or proxy mode independently. © 2023 IEEE.

关键词： software testing

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