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Parallelize Accelerated Triangle Counting Using Bit-Wise on GPU 1

Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Lin, Li Ouyang, Dian He, Zhipeng Li, Chengqian School of Computer Science and Network Engineering Guangzhou University Guangzhou China

ISBN: (数字)9789819962228

ISBN: (纸本)9789819962211

Triangle counting is a graph algorithm that calculates the number of triangles in a graph, the number of triangles is a key metric for a large number of graph algorithms. Traditional triangle counting algorithms are divided into vertex-iterator and edge-iterator when traversing the graph. As the scale of graph data grows, the use of CPU with other architectural platforms for triangle counting has become mainstream. Our accelerating method proposes an algorithm for triangle counting on a single machine GPU, and performs a two-dimensional partition algorithm for large-scale graph data, in order to ensure that large graph data can be correctly loaded into GPU memory and the independence of each partition to obtain the right result. According to the high concurrency of GPU, a bit-wise operation intersection algorithm is proposed. We experiment with our algorithm to verify that our method effectively speeds up triangle counting algorithm on a single-machine GPU. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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A Computability Perspective on (Verified) Machine Learning 26th

A Computability Perspective on (Verified) Machine Learning

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26th IFIP WG 1.3 International Workshop on Algebraic Development Techniques, WADT 2022

作者： Crook, Tonicha Morgan, Jay Pauly, Arno Roggenbach, Markus Department of Computer Science Swansea University Wales Swansea United Kingdom Université de Toulon Aix Marseille Univ CNRS LIS Marseille France

ISBN: (纸本)9783031433443

In computer science there is a strong consensus that it is highly desirable to combine the versatility of Machine Learning (ML) with the assurances formal verification can provide. However, it is unclear what such ‘verified ML’ should look like. This paper is the first to formalise the concepts of classifiers and learners in ML in terms of computable analysis. It provides results about which properties of classifiers and learners are computable. By doing this we establish a bridge between the continuous mathematics underpinning ML and the discrete setting of most of computer science. We define the computational tasks underlying the newly suggested verified ML in a model-agnostic way, i.e., they work for all machine learning approaches including, e.g., random forests, support vector machines, and Neural Networks. We show that they are in principle computable. © 2023, Springer Nature Switzerland AG.

关键词： Formal verification

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Unmixing Detrital Zircon U-Pb Age Distribution Based on Multi-objective Optimization 21st

Unmixing Detrital Zircon U-Pb Age Distribution Based on Mult...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Deng, Zhihao Zhou, Guangyan Chen, Guanyu Wang, Zuheng Hu, Jun Wang, Quanyu School of Computer Science China University of Geosciences Wuhan430074 China School of Earth Science China University of Geosciences Wuhan430074 China Informatization Office China University of Geosciences Wuhan430074 China China Mobile Group Design Institute Co. Ltd Beijing100089 China

ISBN: (纸本)9789819601271

The unmixing method for detrital zircon U-Pb age distribution based on simulation statistics has issues with high computational complexity, inconsistent evaluation criteria, and poor algorithm convergence. This paper proposes a novel multi-objective optimization method for the unmixing of detrital zircon U-Pb age distribution. It comprehensively considers multiple quantitative similarity metrics for U-Pb age distribution and designs a multi-objective optimization unmixing model. An improved NSGA-II-based algorithm is implemented to achieve the unmixing of detrital zircon U-Pb age distribution. Compared to the commonly used Inverse Monte Carlo method, this approach significantly reduces computational complexity and efficiently achieves sampling and mixing simulations of complex source-to-sink systems, yielding better provenance analysis results. Model tests and practical applications have demonstrated the effectiveness and significant application potential of the multi-objective optimization method for detrital zircon U-Pb age distribution unmixing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multiobjective optimization

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The Application of computer Database System in Educational Information Management

Lecture Notes on Data Engineering and Communications Technol...

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lecture notes on Data Engineering and Communications Technologies 2022年 129卷 141-148页

作者： Hu, Bin Sichuan University Jinjiang College Sichuan Meishan China

The development and progress of mankind promote continuous innovation in science and technology. It is the age of information and the age of science and technology at 21st century. The appearance of computers is an important milestone in the development of science and technology. It has greatly improved the efficiency of people's work and study and has also promoted the globalization of information. Among computer science and technology, database system is very important, and its development speed is very fast, today, the application of database system is also more and more common. A good application of computer database system in education information management can effectively improve the management level and work efficiency. This article analyzes and discusses computer database system application in education information management and explains it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

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Differentiable Discrete Optimization Using Dataless Neural Networks 16th

Differentiable Discrete Optimization Using Dataless Neural N...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Jena, Sangram K. Subramani, K. Velasquez, Alvaro LDCSEE West Virginia University MorgantownWV United States Department of Computer Science University of Colorado Boulder BoulderCO United States

ISBN: (纸本)9783031496134

The area of combinatorial optimization is characterized by the search for optimal combinations of discrete variables that satisfy some set of constraints. Famous problems in this space include maximum satisfiability and maximum independent set. Due to their discrete dynamics, these problems are not differentiable in their natural formulations. In this paper, we explore the counter-intuitive direction of differentiable discrete optimization by leveraging the recently discovered dataless neural networks, which have been used to yield a single differentiable function that is equivalent to the maximum independent set problem. In particular, we leverage the dataless neural networks framework to derive differentiable forms for a variety of NP-hard discrete problems and prove the correctness of our derivations. The proposed differentiable forms open up the avenue for continuous differentiable optimization to be brought to bear on classical discrete optimization problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Combinatorial optimization

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CoDPoC IP: A Configurable Data Protection Circuit to Support Multiple Key Agreement Scheme 26th

CoDPoC IP: A Configurable Data Protection Circuit to Suppor...

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Peng, Yijing Wang, Junhui Wang, Zhenyu Guo, Zhenbin Wang, Yaohua Deng, Ding Li, Shaoqing Guo, Yang Key Laboratory of Advanced Microprocessor Chips and Systems National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China College of Electronic Science and Technology National University of Defense Technology Changsha China College of Semiconductors Hunan University Changsha China

ISBN: (纸本)9789819788002

Physical Unclonable Function(PUF)-based security protocols are effective in mitigating various security threats, including eavesdropping, interception, and cloning. Nevertheless, most existing protocols are implemented in software, with the primary drawback being their inability to integrate with actual PUF and their inefficiency. Although some strategies incorporate PUF into their design, these systems do not allow for configuration changes and are deficient in flexibility. In this paper, we propose the CoDPoC IP, a configurable data protection circuit that supports PUF-based various key agreement authentication schemes and allows for flexible scheme design adjustments through configurable registers. The CoDPoC IP offers two advantages: 1) hardware-level implementation incorporates PUF, leading to higher resource utilization, efficiency, and security;and 2) the IP supports flexible configuration of schemes. Experimental results indicate that the hardware execution time is 0.03504 milliseconds. The proposed scheme is successfully implemented and verified with low-overhead hardware. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data privacy

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Generate-then-Revise: An Effective Synthetic Training Data Generation Framework for Event Detection 23rd

Generate-then-Revise: An Effective Synthetic Training Data G...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Du, Huidong Sun, Hao Liu, Pengyuan Yu, Dong School of Information Science Beijing Language and Culture University Beijing China

ISBN: (纸本)9789819783663

Large language models (LLMs) struggle with event detection (ED) due to the structured and variable number of events in the output. Existing supervised approaches rely on a large amount of manually annotated corpora, facing challenges in practice when event types are diverse and the annotated data is scarce. We propose Generate-then-Revise (GtR), a framework that leverages LLMs in the opposite direction to address these challenges in ED. GtR utilizes an LLM to generate high-quality training data in three stages, including a novel data revision step to minimize noise in the synthetic data. The generated data is then used to train a smaller model for evaluation. Our approach demonstrates significant improvements on the low-resource ED. We further analyze the generated data, highlighting the potential of synthetic data generation for enhancing ED performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data assimilation

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Deducing Matching Strings for Real-World Regular Expressions 9th

Deducing Matching Strings for Real-World Regular Expression...

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9th International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Yan, Yixuan Su, Weihao Zheng, Lixiao Wang, Mengxi Chen, Haiming Peng, Chengyao Li, Rongchen Chen, Zixuan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101400 China College of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9789819986637

Real-world regular expressions (regexes for short) have a wide range of applications in software. However, the support for regexes in test generation is insufficient. For example, existing works lack support for some important features such as lookbehind, are not resilient to subtle semantic differences (such as partial/full matching), fall short of Unicode support, leading to loss of test coverage or missed bugs. To address these challenges, in this paper, we propose a novel semantic model for comprehensively modeling the extended features in regexes, with an awareness of different matching semantics (i.e. partial/full matching) and matching precedence (i.e. greedy/lazy matching). To the best of our knowledge, this is the first attempt to consider partial/full matching semantics in modeling and to support lookbehind. Leveraging this model we then develop PowerGen, a tool for deducing matching strings for regexes, which randomly generates matching strings from the input regex effectively. We evaluate PowerGen against nine related state-of-the-art tools. The evaluation results show the high effectiveness and efficiency of PowerGen. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： generation matching string modeling regex semantics

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Numerical Analysis of Pareto Set Modeling 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Shu, Tianye Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Recently, neural network-based inverse models have been used for multi-objective optimization. The basic idea is to approximate the mapping from the Pareto front to the Pareto set. In general, inverse modeling from a low-dimensional objective space to a high-dimensional decision space is difficult. However, good results have been reported in the literature. In this paper, we numerically examine the performance of inverse modeling. For visual examination, we use 2- and 3-objective distance minimization problems with 2 to 1,000 decision variables. Our experimental results show that inverse modeling improves the quality of the final population, which is used as the training data. One interesting observation is that improving fitting accuracy by increasing the neural network complexity does not improve the quality of the model outputs. We also demonstrate that inverse modeling works well even for large-scale multi-objective problems with 1,000 decision variables when high-quality training data are available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural network models

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Jointly Learning Structure-Augmented Semantic Representation and Logical Rules for Knowledge Graph Completion 1

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Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Jiang, Jinxing Xu, Lizhen School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China

ISBN: (数字)9789819962228

ISBN: (纸本)9789819962211

Knowledge Graph Complementation (KGC) aims to predict the missing triples in incomplete knowledge graphs (KGs). However, existing approaches rely either on structural features, semantic features or logical rules. There is not yet a unified way to exploit all three features mentioned above. To address this problem, this paper proposes a new KGC framework, SSL, which jointly embeds structure-augmented semantics representation in the natural language description of triples with their logical rules. First, SSL fine-tunes a pre-trained language model to capture the semantic information in the natural language description corresponding to the triple. Then, for logical rules corresponding to different relation types, we augment them to embeddings of entities and relations to activate stronger representation capabilities. Finally, by jointly training, we can obtain more expressive embeddings for the downstream KGC task. Our extensive experiments on a variety of KGC benchmarks have demonstrated the effectivity of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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