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检索条件"任意字段=25th Conference on Innovation and Technology in Computer Science Education"

共 7393 条记录，以下是91-100 订阅

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Feature-Adaptive Meets Domain-Specific Networks for Multi-domain Recommendation 25th

Feature-Adaptive Meets Domain-Specific Networks for Multi-d...

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25th International conference on Web Information Systems Engineering, WISE 2024

作者： Lin, Shengfeng Yuan, Huanhuan Liu, Guanfeng Xian, Xuefeng Cui, Zhiming Zhao, Pengpeng Soochow University Suzhou China Macquarie University Sydney Australia Soochow Vocational University Suzhou China SuZhou University of Science and Technology Suzhou China

ISBN: (纸本)9789819605699

In recent years, multi-domain recommendation systems have rapidly evolved, using a unified model framework to transfer knowledge across domains. However, these methods assign the same task model parameters to all samples from a specific domain. this makes them difficult to capture differences between different sample groups within the same domain and commonalities between the same sample groups across different domains. To address this challenge, we propose a novel model with Feature-Adaptive dynamic network and Domain-Specific networks for multi-domain recommendation (FADS). In our proposed model, the feature-adaptive dynamic parameter network is applied to explore the commonalities among the samples with same certain features values across all specific domains and differences between the groups with different certain features values, while domain-specific networks are used to mine the uniqueness of each domain. To validate our approach, we conduct extensive experiments on two public datasets, and the results confirm the effectiveness of our proposed model. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Domain Knowledge

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An International Examination of Non-Technical Skills and Professional Dispositions in Computing - Identifying the Present Day Academia-Industry Gap 2024

An International Examination of Non-Technical Skills and Pro...

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29th Working Group Reports on innovation and technology in computer science education, ITiCSE WGR 2024

作者： Garcia, Rita Csizmadia, Andrew Pearce, Janice L. Alshaigy, Bedour Glebova, Olga Harrington, Brian Liaskos, Konstantinos Lunn, Stephanie J. Mackellar, Bonnie Nasir, Usman Pettit, Raymond Schulz, Sandra Stewart, Craig Zavaleta Bernuy, Angela School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Newman University Birmingham United Kingdom Berea College BereaKY United States Department of Information Technology Uppsala University Uppsala Sweden University of Connecticut StorrsCT United States Computer and Mathematical Sciences University of Toronto Scarborough Toronto Canada University of Strathclyde United Kingdom SUCCEED Florida International University MiamiFL United States Computer Science Mathematics and Sciency St John's University Queens United States Blekinge Institute of Technology Karlskrona Sweden University of Virginia CharlottesvilleVA United States Faculty of Education University of Hamburg Hamburg Germany Durham University Durham United Kingdom University of Toronto Toronto Canada

ISBN: (纸本)9798400712081

Computing graduates are frequently reported by members of industry to lack in professional dispositions and/or non-technical skills (often referred to as "soft skills"). In this work, we conduct a gap analysis of the alignment between academic preparation and industry expectations through a three-pronged study. First, a literature review explored the academic perspective of how fostering professional dispositions and non-technical skills occurs in tertiary computing education. Second, a literature review identifying industry's expectations of those dispositions and skills for entry-level computing professionals. Finally, a mixed-methods approach, combining a survey and structured interviews of computing industry professionals to identify their opinions on the relative importance of those skills and dispositions. In each of these prongs, we additionally consider whether and how Diversity, Equity, Inclusion, and Accessibility (DEIA) may have been approached and/or incorporated. Our work uncovers a number of gaps. Several skills and dispositions, such as leadership, ethics, and inventiveness, are over-represented in the academic literature compared to industry's expectations, while others such as lifelong learning and professionalism are under-emphasised. Furthermore, some terms such as 'ethics' and 'professionalism' are defined differently by various stakeholder groups, leading to a gap between academic training and industry expectations. Finally, several skills and dispositions, such as collaboration, teamwork, communication, and leadership show evidence of exposure in academia, but require more scaffolded instruction to meet industry expectations. We also found a dearth of coverage in the literature and a lack of focus in industry for DEIA considerations. © 2025 ACM.

关键词： DEIA

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DisFact: Fact-Checking Disaster Claims 25th

DisFact: Fact-Checking Disaster Claims

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25th International conference on Web Information Systems Engineering, WISE 2024

作者： Adesokan, Ademola Hu, Haiwei Madria, Sanjay Missouri University of Science and Technology RollaMO65401 United States University of Pittsburgh PittsburghPA15260 United States

ISBN: (纸本)9789819605750

the rapid proliferation of false information on the internet poses a significant challenge before, during, and after disasters, emphasizing the critical need for domain-specific automatic fact-checking systems. In this study, we introduce DisFact, a new fact-checking pipeline, and a dataset of disaster-related claims generated from the Federal Emergency Management Agency (FEMA) press releases and disaster declarations. Our retrieval method involves no model training, making it more efficient and less resource-intensive. It starts by breaking a lengthy document into sentences;we further apply embeddings to calculate the relevancy score between a claim and document pairs and then compute the similarity score between claims and sentences to rank the retrieved evidence(s). For claim verification, we utilize a deep learning approach that comprises a transformer-based embedding with a feedforward neural network. the experimental findings demonstrate that our fact-checking models achieve top performance on our custom disaster dataset. Furthermore, our models outperform other state-of-the-art models on FEVER and SciFact shared tasks, underscoring the effectiveness of our approach and its adaptability in handling longer documents and generalizing across diverse fact-checking datasets. DisFact signifies a pivotal advancement in automated fact-checking, emphasizing simplicity, accuracy, and computational efficiency. DisFact dataset and code are available on Github (DisFact Dataset and Code - https://***/abdul0366/DisFact). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Quantitative Study on the Moral Tendency and Moral Types of Chinese Adverbs 25th

Quantitative Study on the Moral Tendency and Moral Types of ...

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25th Workshop on Chinese Lexical Semantics, CLSW 2024

作者： Huang, Shaqi Yu, Dong Wang, Hongrui Liu, Pengyuan Liu, Jiaqi College of Information Science Beijing Language and Culture University Beijing China College of International Education Minzu University of China Beijing China National Language Resources Monitoring and Research Center for Print Media Beijing China

ISBN: (纸本)9789819635085

Current research on the semantics of adverbs primarily focuses on emotional and stylistic meaning. However, adverbs also have distinct moral semantics, reflecting their moral tendencies and correlation with moral type. To explore this, we developed a framework for moral semantics and compiled a corpus of 10,313 adverbial phrases from the BCC (Beijing Language and Culture University Corpus Center). Our quantitative analysis revealed that 73 adverbs exhibit clear moral inclinations, with 12 showing particularly strong tendencies. Additionally, we identified 37 adverbs closely linked to specific moral types. these findings deepen our understanding of adverb semantics and highlight the significance of moral dimensions in their usage. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Meeting Professional Service Needs: Integration of Specialized Mathematics and the Application of Mathematical Algorithms in Urban Traffic Management 24

Meeting Professional Service Needs: Integration of Specializ...

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5th International conference on computer science and Management technology, ICCSMT 2024

作者： Yuan, Zhanjiang Lin, Zexin Huang, Yingjie Li, Yuchen Gui, Gaihua Guangdong Polytechnic of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798400709999

In response to the traffic congestion around tourist areas during holidays, this paper conducts an in-depth analysis of vehicle traffic data and proposes several models and solutions. First, the K-means clustering algorithm is used to divide the day into three main time periods based on traffic flow differences, providing scientific insights for traffic management. Second, a genetic algorithm is employed to optimize the signal light configuration of main roads, improving traffic efficiency. third, by identifying cruising vehicles during the May Day holiday, the required number of temporary parking spaces in the tourist area is estimated, alleviating parking pressure. Finally, the T-test model is applied to evaluate the effectiveness of temporary traffic control measures, and the results show that these measures effectively reduced traffic flow and improved operational efficiency. this study is based on the 2024 National College Student Mathematical Modeling Contest, E Problem - Traffic Flow Control, and the results are highly consistent with the findings from actual reference materials. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Efficiency

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Deep Reinforcement Learning-Based Dependent Task Offloading for QoS Optimization in Satellite Edge Computing 25th

Deep Reinforcement Learning-Based Dependent Task Offloading ...

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25th International conference on Web Information Systems Engineering, WISE 2024

作者： Tian, Di Chen, Juan Chen, Peng Zhong, Jie Li, Xi Xiong, Ling School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9789819605699

Satellite edge computing (SatEC) is an emerging computing paradigm for remote and disaster-affected areas with scarce computing resources. It is crucial to make the appropriate offloading strategy to maximize quality of service (QoS) due to the scarcity of satellite computing resources. However, existing researches mostly focus on the offloading of independent tasks without considering tasks with dependencies. To address this problem, we first design a satellite-aerial task offloading (SATO) framework. In the SATO, unmanned aerial vehicles (UAVs) collect ground tasks and upload them to high-altitude platform (HAP), while HAP provides local computing, and low earth orbit (LEO) satellites provide edge computing. Furthermore, we propose SatEC directed acyclic graph (DAG) task offloading (SECDTO) algorithm to investigate the task offloading problem. Specifically, we model tasks with dependencies as a DAG, then we propose an encoder-decoder model with attention based on deep reinforcement learning (DRL) to translate graph vector information into offloading strategies. In order to maximize QoS, we use proximal policy optimization (PPO) to train the model. Simulation results show that our method converges quickly and stably during the training process and outperforms several baseline algorithms in terms of QoS@. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Morphological Transfer Learning for Low-Resource Languages 25th

Morphological Transfer Learning for Low-Resource Languages

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25th Workshop on Chinese Lexical Semantics, CLSW 2024

作者： Wang, Lin Liu, Wuying Xianda College of Economics and Humanities Shanghai International Studies University Shanghai200083 China School of Mathematics and Information Science Nanchang Normal University Nanchang330032 China

ISBN: (纸本)9789819635115

the scarcity of well-structured language resources defeats some natural language processing algorithms to achieve higher performance in low-resource languages. this paper addresses the problem of morphological transfer learning between two languages, proposes an evaluation metric of morphological transfer ratio to estimate the transfer effect, and carries out the transfer experiments of multiword expression (MWE) extraction and neural machine translation (NMT) between any two languages in the three Austronesian languages of Indonesian, Malay and Filipino. In the experiment of MWE extraction, we use a corpus of Language1 to extract MWEs of Language2. In the NMT experiment, we train a Language2-Chinese NMT model on the Language1-resource-enhanced Language2-Chinese parallel sentence corpus. the experimental results show that morphological transfer learning is effective in the tasks of language resource construction and semantic paraphrasing application for low-resource languages, and prove that, due to the linguistic homology and morphological similarity within the same language family, the cross-language morphological reuse of the same language family has stronger transferability and computability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural machine translation

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the Dilemma of Decision-Making in the Real World: When Robots Struggle to Make Choices Due to Situational Constraints 25th

The Dilemma of Decision-Making in the Real World: When Ro...

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25th Annual conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Benali, Khairidine Caleb-Solly, Praminda School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9783031720611

In order to demonstrate the limitations of assistive robotic capabilities in noisy real-world environments, we propose a Decision-Making Scenario analysis approach that examines the challenges due to user and environmental uncertainty, and incorporates these into user studies. the scenarios highlight how personalization can be achieved through more human-robot collaboration, particularly in relation to individuals with visual, physical, cognitive, auditory impairments, clinical needs, environmental factors (noise, light levels, clutter), and daily living activities. Our goal is for this contribution to prompt reflection and aid in the design of improved robots (embodiment, sensors, actuation, cognition) and their behavior, and we aim to introduces a groundbreaking strategy to enhance human-robot collaboration, addressing the complexities of decision-making under uncertainty through a Scenario analysis approach. By emphasizing user-centered design principles and offering actionable solutions to real-world challenges, this work aims to identify key decision-making challenges and propose potential solutions. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based Learning 25th

Cross-Domain Sequential Recommendation with Temporal Encodi...

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25th International conference on Web Information Systems Engineering, WISE 2024

作者： Chen, Lvying Zhang, Ji Zhang, Yuxi Yu, Sujie Li, Bohan Nanjing University of Aeronautics and Astronautics Nanjing China University of Southern Queensland QLD Australia Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing China Key Laboratory of Intelligent Decision and Digital Operations Ministry of Industrial and Information Technology Beijing China

ISBN: (纸本)9789819605699

Cross-domain sequential recommendation (CDSR) aims to predict user-item interactions from historical sequences across domains. Current CDSR approaches mainly focus on leveraging intrinsic connections among items to capture the dependencies across domains for representation learning. However, these approaches still exist major limitations, including: (1) Extensive CDSR methods overlook temporal dynamics, failing to utilize evolving sequential patterns of user-item interactions. (2) Irrelevant features from source domain to target domain lead to negative transfer of user preferences. To overcome these challenges, we propose an innovative Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-based Learning (TP-CDSR). It features a temporal encoding module that captures the evolving sequences of user interactions by considering the temporal effects as kernels. the projection mechanism learns domain-specific matrices to map the user and item representations across domains, which can reduce migration of redundant features. Comprehensive experiments on two real datasets confirm that TP-CDSR achieves superior results compared to various state-of-the-art recommendation algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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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