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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Martingale Theory Based Definition and Analysis of Energy Self-Sustainability in Batteryless Internet of Things

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Fan, Xinyu Hu, Jie Yang, Kun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Nanjing University School of Intelligent Software and Engineering Jiangsu Suzhou China University of Essex School of Computer Science and Electronic Engineering United Kingdom

Radio-frequency (RF) based Wireless Energy Transfer (WET) enables devices to avoid wired power-supply and battery replacements. The ubiquitous availability of RF energy elevates 'energy self-sustainability' as a pivotal goal for wireless sensor networks. However, for RF energy harvesting networks, the term 'energy self-sustainability"lacks a precise mathematical characterization. This paper presents a robust mathematical definition for energy self-sustainability within integrated data and energy networks. By utilizing Martingale theory, we develop a mathematical framework that determines the energy self-sustainability, by acquiring the stochastic properties of energy harvesting and consumption processes. The fundamental paradigm of utilizing this framework to derive energy self-sustainability is demonstrated. In-depth explorations have been conducted on the diverse stochastic characteristics of energy harvesting and consuming processes. Additionally, this study delves into the anticipated uninterrupted operating duration and associated energy expectation. Monte-Carlo simulations confirm the precision of our theoretical evaluations. By analyzing the correlation between harvested and consumed energy in the context of varied energy self-sustainability requirements, this paper provides design guidance for energy transmitters and batteryless wireless devices. © 2017 IEEE.

关键词： Stochastic systems

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Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids

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计算机科学 2022年第6期49卷 44-54页

作者： Ran WANG Jiang-tian NIE Yang ZHANG Kun ZHU College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China School of Computer Science and Engineering Nanyang

As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand ***-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption *** this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the *** approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical *** on the extracted information,the residential appliances can be scheduled effectively and ***,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning *** simulation experiments are conducted with real-world *** results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR *** the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

关键词： Demand response(DR) Customer clustering Deep learning 6G-enabled industrial Internet of things(IIOT) Smart srid(SG)

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Towards Quantifying Requirements Technical Debt for software Requirements concerning Veracity: A Perspective and Research Roadmap

arXiv

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arXiv 2024年

作者： Perera, Judith Tempero, Ewan Tu, Yu-Cheng Blincoe, Kelly Galster, Matthias School of Computer Science The University of Auckland Auckland1010 New Zealand Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand Department of Computer Science and Software Engineering College of Engineering University of Canterbury Christchurch1010 New Zealand

software practitioners can make sub-optimal decisions concerning requirements during gathering, documenting, prioritizing, and implementing requirements as software features or architectural design decisions - this is captured by the metaphor 'Requirements Technical Debt (RTD).' In our prior work, we developed a conceptual model to understand the quantification of RTD and support its management. In this paper, we present our perspective and the vision to apply the lens of RTD to software requirements concerning veracity, i.e., requirements related to truth, trust, authenticity, and demonstrability in software-intensive systems. Our goal is to cultivate awareness of veracity as an important concern and eventually support the management of RTD for software requirements concerning veracity, what we term as 'Veracity Debt,' through its quantification. Copyright © 2024, The Authors. All rights reserved.

关键词： Requirements engineering

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CSL: A Large-scale Chinese Scientific Literature Dataset 29

CSL: A Large-scale Chinese Scientific Literature Dataset

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yudong Zhang, Yuqing Zhao, Zhe Shen, Linlin Liu, Weijie Mao, Weiquan Zhang, Hui School of Information Engineering China Shenzhen University School of Computer Science and Software Engineering China Tencent AI Lab United States Information Technology Center for National Science and Technology Infrastructure Beijing China

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code are available at https://***/ydli-ai/CSL . © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Natural language processing systems

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HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets

arXiv

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arXiv 2025年

作者： Ban, Lulu Zhu, Tao Lu, Xiangqing Qiu, Qi Han, Wenyong Li, Shuangjian Chen, Liming Wang, Kevin I-Kai Nie, Mingxing Wan, Yaping School of Computer Science University of South China 421001 China School of Computer Science and Technology Dalian University of Technology 116024 China Department of Electrical Computer and Software Engineering University of Auckland New Zealand

Cross-dataset Human Activity Recognition (HAR) suffers from limited model generalization, hindering its practical deployment. To address this critical challenge, inspired by the success of DoReMi in Large Language Models (LLMs), we introduce a data mixture optimization strategy for pre-training HAR models, aiming to improve the recognition performance across heterogeneous datasets. However, directly applying DoReMi to the HAR field encounters new challenges due to the continuous, multi-channel and intrinsic heterogeneous characteristics of IMU sensor data. To overcome these limitations, we propose a novel framework HAR-DoReMi, which introduces a masked reconstruction task based on Mean Squared Error (MSE) loss. By raplacing the discrete language sequence prediction task, which relies on the Negative Log-Likelihood (NLL) loss, in the original DoReMi framework, the proposed framework is inherently more appropriate for handling the continuous and multichannel characteristics of IMU data. In addition, HAR-DoReMi integrates the Mahony fusion algorithm into the self-supervised HAR pre-training, aiming to mitigate the heterogeneity of varying sensor orientation. This is achieved by estimating the sensor orientation within each dataset and facilitating alignment with a unified coordinate system, thereby improving the cross-dataset generalization ability of the HAR model. Experimental evaluation on multiple cross-dataset HAR transfer tasks demonstrates that HAR-DoReMi improves the accuracy by an average of 6.51%, compared to the current state-of-the-art method with only approximately 30% to 50% of the data usage. These results confirm the effectiveness of HAR-DoReMi in improving the generalization and data efficiency of pre-training HAR models, underscoring its significant potential to facilitate the practical deployment of HAR technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Data assimilation

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Body Part Segmentation of Anime Characters

Body Part Segmentation of Anime Characters

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作者： Ou, Zhenhua Liu, Xueting Li, Chengze Wen, Zhenkun Li, Ping Gao, Zhijian Wu, Huisi College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Information Sciences Saint Francis University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong College of Electronic and Information Engineering Shenzhen University Shenzhen China

Semantic segmentation is an important approach to present the perceptual semantic understanding of an image, which is of significant usage in various applications. Especially, body part segmentation is designed for segmenting body parts of human characters to assist different editing tasks, such as style editing, pose transfer, and animation production. Since segmentation requires pixel-level precision in semantic labeling, classic heuristics-based methods generally have unstable performance. With the deployment of deep learning, a great step has been taken in segmenting body parts of human characters in natural photographs. However, the existing models are purely trained on natural photographs and generally obtain incorrect segmentation results when applied on anime character images, due to the large visual gap between training data and testing data. In this article, we present a novel approach to achieving body part segmentation of cartoon characters via a pose-based graph-cut formulation. We demonstrate the use of the acquired body part segmentation map in various image editing tasks, including conditional generation, style manipulation, pose transfer, and video-to-anime. © 2024 John Wiley & Sons Ltd.

关键词： Semantic Segmentation

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MSF-Net: Multi-level Semantic Feature Network Extractor for Paraphrase Identification

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第4期50卷 1391页

作者： Xue, Wenrui Zhang, Yujun Wang, Xinyu Ge, Shuting School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Technology Dalian Maritime University Dalian116000 China

To address inaccurate semantic representation and challenges in interpreting rare words within deep learning-based paraphrase identification tasks, this paper introduces a multi-level semantic feature network extractor (MSF-Net). The MSF-Net model represents an end-to-end dual-stage, multi-level semantic information learning architecture. Specifically, a topic-level semantic feature extraction module is incorporated to discern the topic distribution of the text. Initially, this module synergizes the text's hidden state, acquired from the Bi-GRU module, with the topic extractor for joint learning of local-global semantic information in the text. MSF-Net employs a multi-attention module to proficiently capture word relationships and semantic details by modeling the complete text sequence, informed by learned topic and context information, thereby aiding the model in paraphrase identification. Comparative and ablation experiments on the extensive LCQMC text dataset are presented in this paper. The MSF-Net model achieves precision, recall, F1 score, and accuracy rates of 78.69, 94.14, 85.72, and 87.13, respectively. The results substantiate MSF-Net's superiority over baseline models in capturing semantic information and reinforcing paraphrase recognition tasks. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Semantics

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MCGMark: An Encodable and Robust Online Watermark for LLM-Generated Malicious Code

arXiv

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arXiv 2024年

作者： Ning, Kaiwen Chen, Jiachi Zhong, Qingyuan Zhang, Tao Wang, Yanlin Li, Wei Zhang, Yu Zhang, Weizhe Zheng, Zibin The School of Software Engineering Sun Yat-Sen University China Peng Cheng Laboratory China The School of Computer Science and Technology Harbin Institute of Technology China The School of Computer Science and Engineering Macau University of Science and Technology China

With the advent of large language models (LLMs), numerous software service providers (SSPs) are dedicated to developing LLMs customized for code generation tasks, such as CodeLlama and Copilot. However, these LLMs can be leveraged by attackers to create malicious software, which may pose potential threats to the software ecosystem. For example, they can automate the creation of advanced phishing malware. To address this issue, we first conduct an empirical study and design a prompt dataset, MCGTEST, which involves approximately 400 person-hours of work and consists of 406 malicious code generation tasks. Utilizing this dataset, we propose MCGMARK, the first robust, code structure-aware, and encodable watermarking approach to trace LLM-generated code. We embed encodable information by controlling the token selection and ensuring the output quality based on probabilistic outliers. Additionally, we enhance the robustness of the watermark by considering the structural features of malicious code, preventing the embedding of the watermark in easily modified positions, such as comments. We validate the effectiveness and robustness of MCGMARK on the DeepSeek-Coder. MCGMARK achieves an embedding success rate of 88.9% within a maximum output limit of 400 tokens. Furthermore, it also demonstrates strong robustness and has minimal impact on the quality of the output code. Our approach assists SSPs in tracing and holding responsible parties accountable for malicious code generated by LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Watermarking

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Context-based Operation Merging in Real-Time Collaborative Programming Environments 25

Context-based Operation Merging in Real-Time Collaborative P...

引用

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhou, Hongguang Ma, Yifan Xu, Wenhua Wang, Mingjie Du, Bowen Fan, Hongfei School of Software Engineering Tongji University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom Jiangsu China

ISBN: (纸本)9781665405270

Real-time collaborative programming environments support a team of programmers to edit shared source code at the same time, where each local editing operation is captured and immediately transmitted to remote sites in a fine-grained manner. However, under real-world network conditions, collaborators are usually plagued by data congestion and transmitted errors. In this study, we define and analyze the content relationships among operations, and propose a Context-based Operation Merging Algorithm (COMA). Technically, the COMA examines the content relationships among a series of editing operations and merges content-related operations. Powered by the COMA, real-time collaborative programming environments can significantly compress editing operations to cope with complex network situations (such as network fluctuation and interruption) and improve the user experience of real-time collaboration. The proposed COMA has been implemented in a real-time collaborative programming environment prototype, namely CoEclipse. Preliminary user evaluations, correctness analysis and performance evaluations have demonstrated the effectiveness, correctness, and efficiency of the algorithm. © 2022 IEEE.

关键词： Merging

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