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From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education 19th

From Sparse to Smart: Leveraging AI for Effective Online Jud...

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19th European Conference on Technology Enhanced Learning (EC-TEL)

作者： Pereira, Filipe Dwan Moraes, Maely Henklain, Marcelo Hellas, Arto Oliveira, Elaine Gasevic, Dragan Barreto, Raimundo Mello, Rafael Univ Fed Roraima Boa Vista Parana Brazil Cesar Sch Recife PE Brazil Aalto Univ Espoo Finland Univ Fed Amazonas Manaus Amazonas Brazil Monash Univ Melbourne Vic Australia

ISBN: (纸本)9783031723148;9783031723155

Online Judges (OJs) have gained substantial traction in programming education due to their ability to simultaneously present problem-solving challenges to students while offering instant feedback and correction. Such technologies are also essential to allow students in remote areas to access quality and equitable education. Nonetheless, OJ systems often lack sufficient amounts of annotated data (i.e., labelled data) about the topics of the problems that they aim to support, which makes choosing appropriate problems hard. Topic annotations hold significant value for instructors when selecting problems for assignments and for novice students seeking independent use of OJ systems. In this work, we propose and evaluate a pre-trained deep learning architecture and an active learning methodology to automatically annotate OJ problems in the context of introductory programming. Our results show that, when using a smaller amount of data, the methodology demonstrates performance comparable to those of the existing state-of-the-art methods for the identical task.

关键词： Educational Text Mining computer Education Online Judges Resource-limited

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Gameplay Performance as a Predictor of Adaptive Expertise in Primary School Students 13th

Gameplay Performance as a Predictor of Adaptive Expertise in...

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13th International Conference on Games and Learning Alliance, GALA 2024

作者： Rocha, Mariana Dondio, Pierpaolo School of Computer Science Technological University Dublin Dublin Ireland

ISBN: (纸本)9783031782688

The present study explores the potential of gameplay performance to predict gains in adaptive expertise among primary school students. More specifically, this research evaluates adaptive number knowledge (ANK), an ability to understand whole-number relations and apply this knowledge flexibly to solve new problems. Our study involved 130 primary school students playing the digital game "Seven Spells", which focuses on arithmetic operations and numeric tables. Students’ ANK skills were assessed through a pen-and-paper timed task before and after playing the game for six weeks. Three gameplay performance metrics were collected: the highest level achieved in a multi-player mode, the highest score in single-player mode, and the leaderboard position in single-player mode. Linear regression analysis revealed that the progress against virtual opponents significantly predicted various improvements in ANK skills. Conversely, higher leaderboard positions in the single-player mode were unexpectedly correlated with some reduced ANK gains, likely due to a lack of engagement in this game mode. Student feedback indicated a preference for multiplayer mode, which may have fostered higher engagement and skill development. Our contribution suggests games can support adaptive expertise development and highlights the importance of identifying specific game design features for learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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A Virtual Reality Based Therapeutic Approach for Stuttering Intervention 16th

A Virtual Reality Based Therapeutic Approach for Stuttering ...

引用

16th International Conference on Virtual, Augmented and Mixed Reality (VAMR)

作者： Cecil, J. Tetnowski, John A. Tentu, Sriram Kumar Oklahoma State Univ Ctr Cyber Phys Syst Dept Comp Sci Stillwater OK USA Oklahoma State Univ Commun Sci & Disorders Stillwater OK USA

ISBN: (纸本)9783031610462;9783031610479

Stuttering, a speech disorder with far-reaching effects on communication and emotional well-being, has long posed challenges for those affected. This study introduces a novel approach to address this issue by harnessing the potential of Virtual Reality (VR)-based therapy, specifically designed for teenagers who stutter. In this paper, the overall approach underlying this innovative therapy is discussed along with the outcomes of this pilot project. By immersing participants in simulated scenarios closely resembling real-life speaking situations, we aimed to provide an innovative and engaging platform for therapy. The VR environments were designed using advanced 3D engines and VR software tools, offering a realistic and interactive experience. Participants engaged in therapy sessions equipped with VR headsets and controllers, allowing them to navigate and interact within these immersive environments. The outcomes of this pioneering study revealed a positive trend, with a significant majority of participants exhibiting a notable reduction in stuttering frequency. Beyond the quantitative improvements, participants reported that the VR-based therapy was not only effective but also intriguing and beneficial. This research showcases the potential of VR technology to transform stuttering therapy by replicating authentic speaking scenarios, thus offering a more engaging and impactful avenue for enhancing communication skills and emotional well-being among individuals who stutter. Through this innovative approach, we open new doors for addressing the multifaceted challenges of stuttering, ultimately fostering improved communication and overall quality of life for those affected.

关键词： Virtual Reality (VR) stuttering therapy speech disorders immersive technology communication skills enhancement Human-computer Interaction (HCI) Mixed Reality (MR) simulation-based learning

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Automating Pathway Extraction from Clinical Guidelines: A Conceptual Model, Datasets and Initial Experiments 30th

Automating Pathway Extraction from Clinical Guidelines: A C...

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30th International Conference on Cooperative Information Systems, CoopIS 2024

作者： Grathwol, Daniel van der Aa, Han López, Hugo A. Technical University of Denmark DTU Compute Lyngby Denmark Faculty of Computer Science University of Vienna Vienna Austria

ISBN: (纸本)9783031813740

Clinical pathways are structured, multidisciplinary care plans utilized by healthcare providers to standardize the management of specific clinical problems. Designed to bridge the gap between evidence and practice, clinical pathways aim to enhance clinical outcomes and improve efficiency, often reducing hospital stays and lowering healthcare costs. However, maintaining pathways with up-to-date, evidence-based recommendations is complex and time-consuming. It requires the integration of clinical guidelines, algorithmic procedures, and tacit knowledge from various institutions. A critical aspect of updating clinical pathways involves extracting procedural information from clinical guidelines, which are textual documents that detail medical procedures. This paper explores how Large Language Models (LLMs) can facilitate this extraction to support clinical pathway development and maintenance. Concretely, we present a conceptual model for using LLMs in this extraction task, provide a dataset comprising thousands of clinical guidelines for academic research, and share the results of initial experiments demonstrating the efficacy of LLMs in extracting relevant pathway information from these guidelines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clinical guidelines Clinical pathways Conceptual model Large language models Process extraction

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Efficient Wildcard Searchable Symmetric Encryption with Forward and Backward Security 1st

Efficient Wildcard Searchable Symmetric Encryption with For...

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Zhang, Xi Su, Ye Wei, Zhongkai Shen, Wenting Qin, Jing College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Mathematics Shandong University Jinan250100 China College of Computer Science and Technology Qingdao University Qingdao266071 China

ISBN: (纸本)9789819997848

Wildcard Searchable Symmetric Encryption can achieve flexibility and pattern matching while protecting data privacy. There is a promising future for combining secure wildcard search with emerging technologies such as AI. However, there are challenges in reducing the communication cost and improving security. In this paper, we propose an efficient wildcard searchable symmetric encryption with forward and backward security. The complexity of communication costs in the search protocol is O(1), independent of the number of characters of wildcard keywords, or files in the search result. It is achieved by a double-compressed index in which the character set and file identifiers are encoded simultaneously. Then, the double-compressed index provides a possibility to achieve oblivious keyword query and update, so that the proposed scheme only reveals the query type (search or update) without anything else. It achieves forward and backward security using the distributed multi-point function and an additively homomorphic symmetric encryption scheme. Detailed security proof and the theoretical comparison show the improvement in security and efficiency. The acceptable overheads of the proposed scheme are presented by the extensive performance evaluation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pattern matching

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Joint Entity and Relation Extraction Based on Bidirectional Update and Long-Term Memory Gate Mechanism 23rd

Joint Entity and Relation Extraction Based on Bidirectiona...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Qian, Yili Ren, Enlong Xu, Haonan School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China

ISBN: (纸本)9789819783663

Joint entity recognition and relation extraction are important tasks in natural language processing. While some previous work has recognized the importance of relation information in joint extraction, excessively focusing on relation information without utilizing entity information may lead to information loss and affect the identification of relation tuples. Additionally, ignoring the utilization of original information may result in the loss of hierarchical and semantic information, further reducing the richness of information. To address these issues, we propose a bidirectional information updating mechanism that integrates entity and relation information, iteratively fusing fine-grained information about entities and relations. We introduce a long-term memory gate mechanism to update and utilize original information using feature information, thereby enhancing the model’s ability for entity recognition and relation extraction. We evaluated our approach on two Chinese datasets and achieved state-of-the-art results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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HDNeXt: Hybrid Dynamic MedNeXt with Level Set Regularization for Medical Image Segmentation 17th

HDNeXt: Hybrid Dynamic MedNeXt with Level Set Regularizatio...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Cao, Haoyu Han, Tianyi Yang, Yunyun School of Science Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9789819609628

Deep learning has been extensively employed in the field of medical image segmentation, demonstrating its robustness and efficacy. However, the pursuit of consistent segmentation performance across diverse instrumental conditions and the challenge of achieving precise boundary delineation in segmented images remain significant hurdles. In this paper, we aim to develop a model capable of achieving consistent, high-quality segmentation of identical regions of interest across varying instrumental conditions, with precise boundary delineation. Toward this end, we introduce our Hybrid Dynamic MedNeXt (HDNeXt) model, an advanced framework capable of dynamically generating weights across diverse medical images to maintain consistently high segmentation performance. HDNeXt builds on the robust segmentation framework of MedNeXt by incorporating dynamic convolution techniques, which endow the model with the capability for dynamic weight adjustment, significantly enhancing its segmentation performance. To tackle the second challenge, we devised a novel loss function, LCR, formulated on the Curvature of the segmentation boundary and Region-Fitting energy derived from level set methods, which significantly enhances boundary precision during training and optimizes overall segmentation performance. Experiments were conducted on the abdominal CT datasets Synapse and the cardiac MRI datasets ACDC to demonstrate the efficiency and effectiveness of our method. Our method achieved an average Dice coefficient of 84.38 on the Synapse datasets and 93.59 on the ACDC datasets, surpassing other 2D state-of-the-art segmentation models and achieving optimal performance for 2D medical image segmentation. Codes are available at https://***/HaoyuCao/HDNeXt. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Extending 6D Object Pose Estimators for Stereo Vision 4th

Extending 6D Object Pose Estimators for Stereo Vision

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Pöllabauer, Thomas Emrich, Jan Knauthe, Volker Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Fraunhoferstraße 5 Darmstadt Germany Technical University Darmstadt Karolinenplatz 5 Darmstadt Germany

ISBN: (纸本)9789819787043

Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo can directly infer the distance of an object, while mono-vision requires internalized knowledge of the object’s size. To extend the state-of-the-art in 6D object pose estimation to stereo, we created a BOP compatible stereo version of the YCB-V dataset called YCB-V DS and extended multiple state-of-the-art pose estimator algorithms to be applicable to stereo. Our method outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo vision and can easily be adopted for other dense feature-based algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo vision

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A Blockchain-Based Approach to Improve Data Integrity in Federated Cloud Environment 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Jana, Sagnik Majumder, Koushik Dasgupta, Anurag Shaw, Rabindra Nath Ghosh, Ankush Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology WB Haringhata India Computer Science and Engineering Technology Valdosta State University ValdostaGA31698 United States Chandigarh University Punjab Mohali India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

This work focuses on the critical problem of enhancing data integrity within federated cloud environments. It explores a novel approach that leverages the principles of blockchain technology to provide a robust and decentralized solution to these challenges. By integrating blockchain's immutable and consensus-driven ledger with the complexities of federated cloud ecosystems, this research aims to establish a new framework for data integrity assurance that nullifies the limitations of traditional centralized approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Efficient Low-Polygon Building Model Generation with Opening Detection 1

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41st computer Graphics International Conference, CGI 2024

作者： Wang, Yixian Lin, Boheng Chen, Weiya Wan, Lin School of Software and Engineering Huazhong University of Science and Technology Wuhan China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

With the rapid increase of complexity and volume of 3D building models, industries such as digital games and computer-aided design face considerable challenges. One feasible solution is to generate low-polygon models for complex building meshes in a relatively short time to apply in a level of detail (LOD) algorithm. We propose an efficient building model simplification algorithm with opening detection, aiming to realize an extremely low polygon count while preserving their visual features. Our algorithm has three steps. We detect and construct the opening structures presented in the building model to incorporate in the subsequent carving process;improve the approach to generate a visual hull that represents the fundamental boundary of the building model and carve out details with high efficiency;and apply planarization and simplification algorithms to obtain the simplified model. Our algorithm can generate highly simplified building models while preserving important opening details to serve as the roughest level in the LOD hierarchy. Experimental results demonstrate that our algorithm efficiently handles building models with complex opening structures and outperforms other recent simplification algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mesh generation

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