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检索条件"任意字段=Lecture Notes in Computer Science"

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Do Comments Matter? Investigating Students' Source Code Comment Behaviour and Its Relation to Academic Success in a CS1 Course 27th

Do Comments Matter? Investigating Students' Source Code Comm...

引用

27th International Conference on Interactive Collaborative Learning-ICL

作者： Kerschbaumer, David Schatz, Christoph Ruprechter, Thorsten Guetl, Christian Steinmaurer, Alexander Graz Univ Technol Graz Austria ITU Inst Digital Sci Austria Linz Austria

ISBN: (纸本)9783031856488;9783031856495

Comments in source code have been perceived to enhance understandability, readability, and knowledge retention by professional programmers and educators alike. Programming novices in introductory programming courses are therefore taught to use comments to highlight specific code sections, clarify complex algorithms, and organize source code structure. Despite the perceived importance of comments, little research exists regarding the relation between commenting behavior and student performance in such courses, which is inherently linked to code correctness. To expand on this gap, this study analyzes over 40 000 comments across 2 800 submissions from 1 085 students over two semesters enrolled in a first-semester computer science 1 (CS1) course at a Western European university. Our analysis reveals a notable thematic difference in the use of comments between high-performing and failing students: high-performing students utilize comments primarily for task-related explanations, whereas failing students tend to more frequently use them to describe the program's syntax. However, there is no significant correlation between the actual number of comments and students' course performance. Finally, all students exhibit a considerable shift in commenting behavior over time, transitioning from full sentences to conveying essential information through keywords instead. Altogether, this large-scale study, which draws upon data from a realistic educational context, sheds light on the role of code comments in student performance, offering insights beneficial to both educators and researchers.

关键词： source code comments educational data mining computer science education

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LLMSecCode: Evaluating Large Language Models for Secure Coding 8th

LLMSecCode: Evaluating Large Language Models for Secure Cod...

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8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

作者： Rydén, Anton Näslund, Erik Schiller, Elad Michael Almgren, Magnus Computer Science and Engineering Chalmers University of Technology Gothenburg41296 Sweden

ISBN: (纸本)9783031769337

The rapid deployment of Large Language Models (LLMs) requires careful consideration of their effect on cybersecurity. Our work aims to improve the selection process of LLMs that are suitable for facilitating secure coding (SC). This raises challenging research questions, such as (RQ1) Which functionality can streamline the LLM evaluation? (RQ2) What should the evaluation measure? (RQ3) How to attest that the evaluation process is impartial? To address these questions, we introduce LLMSecCode, an open-source evaluation framework designed to assess LLM SC capabilities objectively. We validate the LLMSecCode implementation through experiments. We find a 10% and 9% difference in performance when varying parameters and prompts, respectively. We also compare some results to reliable external actors, where our results show a 5% difference. We strive to ensure the ease of use of our open-source framework and encourage further development by external actors. With LLMSecCode, we hope to encourage the standardization and benchmarking of LLMs’ capabilities in security-oriented code and tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Open systems

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Smartphone Indoor Fusion Localization with Trust Region-Based Magnetic Matching 18th

Smartphone Indoor Fusion Localization with Trust Region-Bas...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhu, Yu Jia, Yan Zou, Kaiyi Niu, Xiaoguang School of Computer Wuhan University Whuhan430072 China Cloud Network Operations Department China Telecom Group Co. Ltd. Beijing China

ISBN: (纸本)9783031714634

Magnetic indoor localization has attracted great attention in recent researches because of its advantage of not requiring additional equipment. However, existing magnetic matching methods have limited stability and accuracy due to insufficient consideration of gradient non-convergence and local optimal solution problem during the gradient descent iteration process. Moreover, in areas with inadequate magnetic features, the magnetic localization can be unreliable, leading to significant errors in certain regions. Therefore, we design a magnetic matching method using the trust region to adjust the utilization of gradient information during the matching process, dynamically balancing convergence efficiency and accuracy. By the way, we propose a fusion localization method FTRM, which enhances the robustness and accuracy of localization by determining the fusion weights of magnetic matching and pedestrian dead reckoning in the localization system through uncertainty, leading to more stable and accurate localization. The experiments show that the fusion localization system attain average accuracy of 0.545 m in real-world scenarios, achieve a 17.3% improvement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gradient methods

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TaylorShift: Shifting the Complexity of Self-attention from Squared to Linear (and Back) Using Taylor-Softmax 27th

TaylorShift: Shifting the Complexity of Self-attention fro...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Nauen, Tobias Christian Palacio, Sebastian Dengel, Andreas Department of Computer Science RPTU Kaiserslautern-Landau Gottlieb-Daimler-Straße Kaiserslautern Germany Trippstadter Str. 122 Kaiserslautern Germany

ISBN: (纸本)9783031781711

The quadratic complexity of the attention mechanism represents one of the biggest hurdles for processing long sequences using Transformers. Current methods, relying on sparse representations or stateful recurrence, sacrifice token-to-token interactions, which ultimately leads to compromises in performance. This paper introduces TaylorShift, a novel reformulation of the Taylor softmax that enables computing full token-to-token interactions in linear time and space. We analytically determine the crossover points where employing TaylorShift becomes more efficient than traditional attention, aligning closely with empirical measurements. Specifically, our findings demonstrate that TaylorShift enhances memory efficiency for sequences as short as 800 tokens and accelerates inference for inputs of approximately 1700 tokens and beyond. For shorter sequences, TaylorShift scales comparably with the vanilla attention. Furthermore, a classification benchmark across five tasks involving long sequences reveals no degradation in accuracy when employing Transformers equipped with TaylorShift. For reproducibility, we provide access to our code under https://***/tobna/TaylorShift. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Efficient Attention Machine Learning Transformer

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ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation—Methods and Results 27th

ICPR 2024 Competition on Multiple Sclerosis Lesion Segmenta...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rondinella, Alessia Guarnera, Francesco Crispino, Elena Russo, Giulia Di Lorenzo, Clara Maimone, Davide Pappalardo, Francesco Battiato, Sebastiano Department of Mathematics and Computer Science University of Catania Catania Italy Department of Biomedical and Biotechnological Sciences University of Catania Catania Italy Department of Drug and Health Sciences University of Catania Catania Italy UOC Radiologia ARNAS Garibaldi Catania Italy Centro Sclerosi Multipla UOC Neurologia con Stroke Unit Azienda Ospedaliera per l’Emergenza Cannizzaro Catania Italy

ISBN: (纸本)9783031801389

This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatically segmenting multiple sclerosis lesions in MRI scans. Participants were provided with a novel annotated dataset comprising a heterogeneous cohort of MS patients, featuring both baseline and follow-up MRI scans acquired at different hospitals. MSLesSeg focuses on developing algorithms that can independently segment multiple sclerosis lesions of an unexamined cohort of patients. This segmentation approach aims to overcome current benchmarks by eliminating user interaction and ensuring robust lesion detection at different timepoints, encouraging innovation and promoting methodological advances. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Long-Tailed Hashing with Wasserstein Quantization 27th

Long-Tailed Hashing with Wasserstein Quantization

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Fu, Zujun Lai, Hanjiang Pan, Yan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9783031783043

Long-tailed hashing is to learn hash functions in unbalanced distribution datasets to represent images as binary hash codes for fast and accurate image retrieval. In contrast to balanced distribution datasets, unbalanced distributions are more common in the real world. However, Existing long-tailed hashing methods only focus on how to better learn from unbalanced datasets to improve performance, without giving good consideration to quantization error, which is very crucial in hash learning. In this paper, we propose a simple but efficient quantization method for long-tailed hashing. Specifically, to address the lack of samples in the tail classes, we take a uniform discrete distribution as the optimal target distribution. We use the Sliced Wasserstein distance as a measure of distribution distance. It makes good use of the discrete nature of hash functions and has low computational complexity. Then we formulate the optimization objective of the quantization error as minimizing the distance between the output of the learned hash function and this objective distribution, which can be added as an additional term of the loss function to existing long-tailed hashing methods. We conduct experiments on two long-tailed datasets, and the results show that our proposed method greatly improves the performance of existing long-tailed hashing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hash functions

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Mobile Crowdsourcing Task Assignment Algorithm Based on ConvNeXt and GRU 18th

Mobile Crowdsourcing Task Assignment Algorithm Based on Con...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Fan, Zequn Pan, Qingxian Gao, Zhaolong Luan, Peng Wei, Kai Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China Yantai Institute of Technology Yantai264005 China Yantaishan Hospital Shandong Province Yantai264003 China

ISBN: (纸本)9783031714634

Task assignment is a crucial aspect of mobile crowdsourcing research. The focus is on allocating appropriate perceptual tasks based on task and worker characteristics to optimize perceptual quality. Tasks arrive dynamically in a crowdsourcing system, and the platform cannot assign all tasks at the beginning. This can lead to the task allocation process falling into a local optimal solution. To address the problem, this paper proposes a two-stage prediction algorithm that utilises historical spatio-temporal data of crowdsourcing services. Firstly, the temporal data is converted into image data through Markov Transition Field. Then, the task availability is transformed into a categorisation problem. In the first stage, a ConvNeXt-based network is used to predict the task availability. In the second stage, a GRU-based network is used to predict the task duration. This paper presents the results of several experiments that confirm the effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

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A Flexible Framework For Using NLP In XR

A Flexible Framework For Using NLP In XR

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International Conference on Extended Reality (XR Salento)

作者： Miller, Andrew Kalafatis, Stavros Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9783031717062;9783031717079

The increasing use of Extended Reality (XR) brings a need for more advanced Human-computer Interaction (HCI) technologies to allow for intuitive and robust collaboration. However, challenges arise when also ensuring interactions are both natural and effective, particularly when incorporating flexible verbal communication. Prior research has explored many multi-modal interaction technologies, yet there exists a need for a framework to allow for human-computer communication in specifically XR applications. This work proposes a novel framework that can incorporate advanced means of Natural Language Processing (NLP) to handle flexible verbal communication while ensuring computer interpretation is robust. Drawing from prior research that identified limitations of information given in XR visuals and challenges in using widely available NLP tools, the proposed framework uses a rule-enforced command extraction pipeline to ensure consistent human-like processing, while preserving the flexibility of more open-domain verbal dialogue. The design employs a sequence of NLP techniques for spoken utterance analysis, language specific rules to extract usable game commands, and verification on the structured command with a user-reinforced hypergraph method of data storage. The framework and its processing pipeline was evaluated using a wide-range of human-like phrasings of commands deemed representative of verbalized instructions for human-computer collaboration in an XR context. Framework performance was then compared against a Large Language Model (LLM) that was customized to extract commands using the same rules. These results serve to showcase the potential of the proposed framework through demonstrated examples and early results while also noting areas of needed performance enhancements.

关键词： eXtended Reality (XR) Human-computer Interaction (HCI) Natural Language Processing (NLP)

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A Lightweight Method to Survey with Protecting Privacy yet Maintaining Accuracy 18th

A Lightweight Method to Survey with Protecting Privacy yet...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yuxuan Di, Xinyu Liu, Ningbo Zhang, Xianchao Ren, Wei School of Computer Science China University of Geosciences Wuhan China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada International College Chongqing University of Technology Chongqing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China Ministry of Education Chengdu China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China

ISBN: (纸本)9783031714665

In a survey, a lot of questions and answers are listed and participators will select one of the answers for each question. Sometimes, participators may not be willing to select the preferred one item in the answers because the item may damage the privacy of themselves. If this question is not answered, then it reveals that the question or the answers are sensitive, which also damages the privacy. Besides, each participator may have distinct privacy concerns even for the same questions and answers, thus the privacy-aware questions and answers cannot be determined and be removed in advance. Thus, the dilemma is how to select the answers for privacy-aware questions yet maintain the utility of the answering results in the survey. In this paper, we propose a general method to protect the various privacy in the investigation without the knowledge of privacy to be protected in advance. The main idea is that participators can randomly select answers if the privacy is the concern for themselves in the answers for this question. Our method is lightweight so as to be convenient in practices. We extensively analyze the accuracy of survey results to justify that our method can protect the privacy yet maintain the utility of the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking in Unconstrained Videos 17th

BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghyeon Ju, Chan-Yang Kim, Gun-Woo Lee, D.-H. Department of Applied Artificial Intelligence Hanyang University Ansan Korea Republic of Department of Computer Science and Engineering Gyeongsang National University Jinju Korea Republic of

ISBN: (纸本)9789819609000

Multi-face tracking (MFT) is a subtask of multi-object tracking (MOT) that focuses on detecting and tracking multiple faces across video frames. Modern MOT trackers adopt the Kalman filter (KF), a linear model that estimates current motions based on previous observations. However, these KF-based trackers struggle to predict motions in unconstrained videos with frequent shot changes, occlusions, and appearance variations. To address these limitations, we propose BoT-FaceSORT, a novel MFT framework that integrates shot change detection, shared feature memory, and an adaptive cascade matching strategy for robust tracking. It detects shot changes by comparing the color histograms of adjacent frames and resets KF states to handle discontinuities. Additionally, we introduce MovieShot, a new benchmark of challenging movie clips to evaluate MFT performance in unconstrained scenarios. We also demonstrate the superior performance of our method compared to existing methods on three benchmarks, while an ablation study validates the effectiveness of each component in handling unconstrained videos. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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