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8th International conference on computer science and Artificial Intelligence, CSAI 2024

作者： Cai, Weishan Xu, Jun School of Computer Science Guangdong University of Education Guangdong Guangzhou China

ISBN: (纸本)9798400718182

Investigating integration techniques for heterogeneous Knowledge Graphs (KGs) is crucial for constructing large-scale KGs that enhance knowledge coverage. A core task within this domain is Entity Alignment (EA), which seeks to identify entities with equivalent meanings across different KGs. In recent years, the number of EA methods has proliferated. However, most existing investigations primarily focus on model technology classification, benchmark evaluations, experimental datasets, and industry assessments. there remains a paucity of research addressing a unified framework for EA methods. Consequently, this paper presents a systematic and comprehensive study of current EA methodologies. We propose two generic frameworks that clarify the connections and distinctions between supervised and unsupervised EA approaches. Furthermore, we analyze the key technologies associated with each module in these frameworks and evaluate the principal contributions of various mainstream EA methods. Finally, we conduct extensive experiments utilizing the cross-lingual dataset DBP15K, to analyze and summarize the experimental results from 24 classical and advanced EA methods. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models 31

Rethinking Vocabulary Augmentation: Addressing the Challenge...

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31st International conference on Computational Linguistics, COLING 2025

作者： Lin, Nankai Zeng, Peijian Zheng, Weixiong Jiang, Shengyi Zhou, Dong Yang, Aimin School of Information Science and Technology Guangdong University of Foreign Studies China School of Computer Science and Technology Guangdong University of Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (纸本)9798891761964

the performance of multilingual language models (MLLMs) is notably inferior for low-resource languages (LRL) compared to high-resource ones, primarily due to the limited available corpus during the pre-training phase. this inadequacy stems from the under-representation of low-resource language words in the subword vocabularies of MLLMs, leading to their misidentification as unknown or incorrectly concatenated subwords. Previous approaches are based on frequency sorting to select words for augmenting vocabularies. However, these methods overlook the fundamental disparities between model representation distributions and frequency distributions. To address this gap, we introduce a novel Entropy-Consistency Word Selection (ECWS) method, which integrates semantic and frequency metrics for vocabulary augmentation. Our results indicate an improvement in performance, supporting our approach as a viable means to enrich vocabularies inadequately represented in current MLLMs. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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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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the optimal service selection and procurement strategy for cost-sensitive small and medium-sized cloud application providers 24

The optimal service selection and procurement strategy for c...

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

作者： Bai, Jing Zhang, Longchang School of Management Science and Engineering Dongbei University of Finance and Economics Liaoning Dalian China School of Information Engineering Suqian University Jiangsu Suqian China

ISBN: (纸本)9798400709999

With the rapid development of the digital economy and the continuous dynamic changes in demand for cloud services, a single cloud resource service provider is no longer capable of meeting the diverse requirements of the evolving cloud *** study primarily focuses on cost-sensitive small and medium-sized cloud application providers and explores how they can optimize their service procurement strategies to maximize the profits and cloud resource *** combining stochastic programming with newsboy model, we delve into the optimal selection strategy when faced with heterogeneous cloud resource *** to this analysis result, we design a service provider selection and composition algorithm based on break-even point rate to determine the allocation of resources from each cloud resource *** research findings indicate that startup or smaller-scale enterprises with limited costs are more inclined towards selecting only a mature and stable cloud service providers to mitigate operational risks and reduce expenses;whereas businesses experiencing stable growth but higher costs tend to prefer multiple high-quality cloud service providers for composition in order to achieve maximum returns, thereby enhancing competitiveness while improving service levels and customer *** study contributes towards promoting scientific and effective procurement decision-making by small and medium-sized cloud application providers in hybrid-cloud environments, supporting steady development and long-term competition. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Stochastic programming

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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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Machine Learning-Based GPU Energy Prediction for Workload Management in Datacenters 15

Machine Learning-Based GPU Energy Prediction for Workload Ma...

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15th IEEE Annual Computing and Communication Workshop and conference, CCWC 2025

作者： Ismalej, Brandon Smith, Matthew Jiang, Xunfei California State University Northridge Department of Computer Science NorthridgeCA91330 United States

ISBN: (纸本)9798331507695

Data centers (DCs) have become a vital component of a digital economy, accounting for approximately 1% of worldwide energy consumption, as the reliance on cloud services and GPU hardware increases for high-performance computing (HPC) tasks. Despite various strategies for improving energy efficiency, there is a lack of research integrating real-world workload traces into the prediction of GPU power consumption, mainly due to the lack of publicly available data. this research addresses this gap by utilizing synthetic data generated to mimic the GPU intensity of real-world workload characteristics and developing GPU power prediction models for integration into the GPUCloudSim Plus simulator. We conducted a statistical analysis of Alibaba and Helios workload traces and ran experiments with diverse GPU-intensive tasks to emulate real-world workloads. Using task-average metrics, we trained four machine learning (ML) models to predict GPU power consumption, with the best performing XGBoost model yielding an RMSE of 1.217. We propose this modeling design to integrate a GPU power prediction model into the GPUCloudSim Plus simulator. Energy-aware simulations for GPU-intensive workloads could be verified on workloads from real data centers, advancing the study of workload management. © 2025 IEEE.

关键词： Prediction models

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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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Project portfolio management in the fintech industry: Practices, challenges, and a proposed framework 16

Project portfolio management in the fintech industry: Practi...

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16th International conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International conference on Project MANagement, ProjMAN 2024 - 14th International conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Olsen, Susanne Steenshard, Julie Alberte Holm Kadenic, Maja Due Department of Business Development and Technology Arhus BSS Aarhus University Birk Centerpark 15 Herning7400 Denmark

this study explores project portfolio management (PPM) practices within the fintech industry, driven by the digital transformation of finance. Fintech firms face the challenge of balancing technological innovation with regulatory compliance to optimize portfolio value. Despite its significance, PPM lacks prioritization in fintech, leading to varied approaches. this study commences with a literature review followed by a mixed-method investigation of Danish fintech PPM practices. theoretical and empirical analyses inform a proposed PPM framework tailored to fintech's dynamics and environment. Our research sheds light on fintech PPM, providing insights for both academics and practitioners to navigate this rapidly evolving sector and enhance portfolio management in fintech organizations. © 2025 the Author(s).

关键词： Project management

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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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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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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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