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7th International conference on educational technology Management, ICETM 2024

作者： Zhang, Dan Zhao, Dong Ma, Xiujuan Wang, Jiawei School of Computer Science Qinghai Normal University Qinghai Xining China State Key Lab of Tibetan Intelligent Informntion Processing and Application Co-Established by Province and Ministry Qinghai Normal University Qinghai Xining China Talent and Personnel Department Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400717468

This paper explores the core position of Data Structure in information-related education and the teaching challenges it faces. By adopting the hybrid teaching education concept of"gradual progress, truth-seeking and innovation" and combining it with the modern educational thinking of"Internet +", this paper is committed to building a hybrid teaching model to improve students’ learning efficiency, understanding and practical skills. By analyzing and improving existing teaching methods, this report proposes a series of innovative strategies to cultivate high-quality talents who can adapt to the needs of future technological development. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Feature analysis and selection for BGP anomaly detection 28

Feature analysis and selection for BGP anomaly detection

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28th conference on innovation in Clouds, Internet and Networks, ICIN 2025

作者： Hakimi, Rifqy Reed, Martin J. School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331542399

Automated incidence reporting requires accurate and timely anomaly detection. This paper considers this in the context of the Border Gateway Protocol (BGP). BGP is crucial for Internet routing but is vulnerable to attacks due to a lack of widespread authentication. While rare, BGP disruptions can be highly detrimental to Internet performance and security. Detecting BGP anomalies helps network operators protect their networks and improve Internet reliability. In this work, we investigate and develop anomaly detection for BGP using Machine-Learning. We extract relevant features of BGP control plane messages from RIPE RIS and RouteViews public datasets. After extracting features, we employ various feature selection algorithms to extract the most relevant features and explore balancing the datasets. Finally, we explore detection latency, an important operating parameter for automated anomaly detection. The results show that BGP anomalies can be detected in the order of seven minutes when using real attack data and that the observation point for the BGP data has a significant effect on anomaly detection. © 2025 IEEE.

关键词： Adversarial machine learning

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The Odyssey Journey: Top-Tier Medical Resource Seeking for Specialized Disorder in China 25

The Odyssey Journey: Top-Tier Medical Resource Seeking for S...

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proceedings of the 2025 CHI conference on Human Factors in Computing Systems

作者： Ka I Chan Siying Hu Yuntao Wang Xuhai Xu Zhicong Lu Yuanchun Shi Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Global Innovation Exchange (GIX) Institute Tsinghua University Beijing China Department of Computer Science City University of Hong Kong Hong Kong China Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China and School of Computer Science and Technology Qinghai University Xining China Google New York City New York USA Department of Computer Science George Mason University Fairfax Virginia USA Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China and Qinghai University Xining China

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CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction 31

CACA: Context-Aware Cross-Attention Network for Extractive A...

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31st International conference on Computational Linguistics, COLING 2025

作者： Chen, Bingfeng Xu, Haoran Luo, Yongqi Xu, Boyan Cai, Ruichu Hao, Zhifeng School of Computer Science Guangdong University of Technology China China College of Science Shantou University China

ISBN: (纸本)9798891761964

Aspect Sentiment Quad Prediction(ASQP) enhances the scope of aspect-based sentiment analysis by introducing the necessity to predict both explicit and implicit aspect and opinion terms. Existing leading generative ASQP approaches do not modeling the contextual relationship of the review sentence to predict implicit terms. However, introducing the contextual information into the pre-trained language models framework is non-trivial due to the inflexibility of the generative encoder-decoder architecture. To well utilize the contextual information, we propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network. When implicit terms are present, the Context-Aware Cross-Attention Network enhances the alignment of aspects and opinions, through alternating updates of explicit and implicit representations. Additionally, contrastive learning is introduced in the implicit representation learning process. Experimental results on three benchmarks demonstrate the effectiveness of CACA. Our implementation will be open-sourced at https://***/DMIRLAB-Group/CACA. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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innovation and Practice of Blended Teaching Based on BOPPPS Teaching Model-Taking University computer Fundamentals Course as an Example 24

Innovation and Practice of Blended Teaching Based on BOPPPS ...

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7th International conference on educational technology Management, ICETM 2024

作者： Wang, Min Zhao, Yang Chen, Yuxi Information Institute Yunnan Normal University Yunnan Kunming China

ISBN: (纸本)9798400717468

With the rapid development of science and technology, the importance of information technology competence is increasingly highlighted, making the cultivation of students’ basic information technology skills an essential focus of school education. However, due to the singular nature of traditional teaching models and the emergence of monotonous teaching methods, the effectiveness of information technology course instruction falls short of expectations. BOPPPS teaching model and blended learning are highly favored for emphasizing student participatory learning, personalized learning, as well as the cultivation of collaborative communication and problem-solving abilities. Taking university computer fundamentals courses as an example, this paper constructs a blended learning model based on the BOPPPS teaching model and designs a teaching process including pre-class knowledge construction, in-class knowledge transfer, and post-class knowledge reflection. Subsequently, the teaching model is put into practice, and the results demonstrate a significant improvement in the final exam average scores of experimental class students compared to those in the control group, indicating a superior level of deep understanding of the subject matter. Additionally, a questionnaire survey on students’ learning ability shows that the blended teaching model based on BOPPPS significantly improve students’ deep learning ability. Finally, the questionnaire survey on the learning effects of the students in the experimental class reveals that the BOPPPS-based hybrid teaching model enhances classroom participation and interaction, fosters students’ initiative and interest in learning, and emphasizes their active role in the learning process. Consequently, students show a greater inclination toward utilizing BOPPPS-based hybrid teaching models in future learning endeavors. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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5G technology and Impact on Modern Engineering 4

5G Technology and Impact on Modern Engineering

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kate, Priya Pacharney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Department of Computer Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

5G technology represents a progressive leap in Wi-Fi conversation, providing unheard-of pace, connectivity, and capacity. Its deployment has profound implications for modern engineering, impacting industries such as telecommunications, healthcare, transportation, and clever towns. With speeds up to one hundred instances quicker than 4G and latency as little as 1 millisecond, 5G permits real-time facts trade, fostering improvements like independent vehicles, far-flung surgeries, and the Internet of Things (IoT). Currently, Engineers are challenged to design infrastructure that will assist the dense community of gadgets related to 5G, necessitating advancements in antenna design, power performance, and statistics security. The era also quickens the adoption of part computing, reducing reliance on centralised facts centres and ensuring quicker processing near the date supply. The transformative impact of 5G extends beyond connectivity. It reshapes the layout and capability of present-day engineering structures, emphasising adaptability, resilience, and scalability. As 5G continues to conform, its function in driving innovation and redefining engineering paradigms becomes increasingly glaring. © 2025 IEEE.

关键词： 5G mobile communication systems

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ITiCSE06 - proceedings of the 11th Annual SIGCSE conference on innovation and technology in computer science education: Foreword

ITiCSE06 - Proceedings of the 11th Annual SIGCSE Conference ...

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ITiCSE06 - proceedings of the 11th Annual SIGCSE conference on innovation and technology in computer science education 2006年 2006卷 iii页

作者： Davoli, Renzo Goldweber, Michael Salomoni, Paola Universit di Bologna Italy Xavier University United States

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NESTOOLS: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models 31

NESTOOLS: A Dataset for Evaluating Nested Tool Learning Abil...

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31st International conference on Computational Linguistics, COLING 2025

作者： Han, Han Zhu, Tong Zhang, Xiang Wu, Mengsong Xiong, Hao Chen, Wenliang Institute of Artificial Intelligence School of Computer Science and Technology Soochow University China

ISBN: (纸本)9798891761964

Large language models (LLMs) combined with tool learning have gained impressive results in real-world applications. During tool learning, LLMs may call multiple tools in nested orders, where the latter tool call may take the former response as its input parameters. However, current research on the nested tool learning capabilities is still under-explored, since the existing benchmarks lack relevant data instances. To address this problem, we introduce NESTOOLS to bridge the current gap in comprehensive nested tool learning evaluations. NESTOOLS comprises a novel automatic data generation method to construct large-scale nested tool calls with different nesting structures. With manual review and refinement, the dataset is in high quality and closely aligned with real-world scenarios. Therefore, NESTOOLS can serve as a new benchmark to evaluate the nested tool learning abilities of LLMs. We conduct extensive experiments on 22 LLMs, and provide in-depth analyses with NESTOOLS, which shows that current LLMs still suffer from the complex nested tool learning task. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Pseudo-label Data Construction Method and Syntax-enhanced Model for Chinese Semantic Error Recognition 31

Pseudo-label Data Construction Method and Syntax-enhanced Mo...

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31st International conference on Computational Linguistics, COLING 2025

作者： Wu, Hongyan Lin, Nankai Jiang, Shengyi Wang, Lianxi Yang, Aimin College of Computer National University of Defense Technology China School of Information Science and Technology Guangdong University of Foreign Studies China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Technology Guangdong University of Technology China School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (纸本)9798891761964

Chinese Semantic Error Recognition (CSER) has always been a weak link in Chinese language processing due to the complexity and obscureness of Chinese semantics. Existing research has gradually focused on leveraging pre-trained models to perform CSER. Although some researchers have attempted to integrate syntax information into the pre-trained language model, it requires training the models from scratch, which is time-consuming and laborious. Furthermore, despite the existence of datasets for CSER, the constrained size of these datasets impairs the performance of the models. Thus, in order to address the difficulty posed by a limited sample set and the need of annotating samples with semantic-level errors, we propose a Pseudo-label Data Construction method for CSER (PDC-CSER), generating pseudo-labels for augmented samples based on perplexity and model respectively, which overcomes the difficulty of constructing pseudo-label data containing semantic-level errors and ensures the quality of pseudo-labels. Moreover, we propose a CSER method with the Dependency Syntactic Attention mechanism (CSER-DSA) to explicitly infuse dependency syntactic information only in the fine-tuning stage, achieving robust performance, and simultaneously reducing substantial computing power and time cost. Results demonstrate that the pseudo-label technology PDC-CSER and the semantic error recognition method CSER-DSA surpass the existing models. © 2025 Association for Computational Linguistics.

关键词： Syntactics

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Explainable and Layout-Aware Timing Prediction 24

Explainable and Layout-Aware Timing Prediction

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43rd International conference on computer-Aided Design, ICCAD 2024

作者： Lyu, Zhengyang Li, Xiaqing Du, Zidong Guo, Qi University of Science and Technology of China Hefei China State Key Lab of Processors Institute of Computing Techology CAS Beijing China Shanghai Innovation Center for Processor Technologies Shanghai China Ministry of Education Key Lab for Big Data and AI in Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400710773

Accurate and fast timing prediction at early design stages is crucial for achieving timing closure in very-large-scale integration (VLSI) design. Machine learning (ML) based approaches have been widely adopted for timing prediction to pursue both of accuracy and speed. Unfortunately, these approaches fail to fully exploit the layout features which can significantly impact the timing metrics, thus leading to great accuracy degradation. In this paper, we propose LE-Timing, an end-to-end explainable and layout-aware timing prediction approach that can achieve better accuracy by leveraging important layout information. The key insight is that crucial information about coupling capacitance between routing wires can significantly impact timing metrics, and fully exploiting this layout information in the timing prediction model can substantially improve its accuracy. However, efficiently leveraging layout features while maintaining accuracy and interpretability poses three key challenges: node-level layout-graph integration, interpretability and training efficiency. To address these, we further propose three key technologies specially designed for LE-Timing correspondingly, including a layout-encoding net model, an explainable cell model, and an explicit-message-passing scheme. Experimental results on 14 open-source designs with advanced 7-nm technology node demonstrate that LE-Timing achieves R2 score 0.92 for arrival time prediction and represents strong interpretability. © 2024 Copyright is held by the owner/author(s).

关键词： Convolutional neural networks

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