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Modeling source code in bimodal for program comprehension

Neural Computing and Applications 2024年第22期36卷 13815-13832页

作者： Wen, Dongzhen Zhang, Xiaokun Diao, Yufeng Zhao, Ziyun Jiang, He Lin, Hongfei School of Computer Science and Technology Dalian University of Technology 2rd Ling Gong Road Liaoning Dalian116024 China Department of Software Engineering School of Software Technology Dalian University of Technology No. 321 Tuqiang Street Dalian Economic and Technological Development Zone Liaoning Dalian116024 China School of Computer Science and Technology Inner Mongolia University for Nationalities 536 West Huolinhe Street Inner Mongolia Tongliao028000 China

Source code is an intermediary through which humans communicate with computer systems. It contains a large amount of domain knowledge which can be learned by statistical models. Furthermore, this knowledge can be used to build software engineering tools. We find that the functionality of the source code depends on the programming language-specific token which build the base structure, while identifiers provide natural language information. On this basis, we found that the knowledge in the source code can be sufficiently learned more when modeling the source code in bimodal. This paper presents the bimodal composition language model (BCLM) for source code modeling and representation. We analyze the effectiveness of bimodal modeling, and the results show that the bimodal approach has great potential for source code modeling and program comprehension. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Domain Knowledge

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A Deep Reinforcement Learning based VM Scheduling Strategy Decreasing Data Center Communication costs 24

A Deep Reinforcement Learning based VM Scheduling Strategy D...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xiao, Zhijiao Liu, Xiao Ming, Zhong Software Engineering Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798350319934

With the rapid development of cloud computing, more and more complicated services are deployed on the cloud. The frequent communication between services places a great burden on the network of the data center and leads to a decline in the quality of service. Researches have shown that the costs of communication between services vary with the placements of virtual machines on which services are running. Therefore, the virtual machine strategy should be optimized by migrating virtual machines with high communication frequency closer. Based on fat-tree network topology, a Deep Q network based online virtual machines scheduling strategy(DQN-VMS) is proposed. The objective is to obtain an optimized virtual machine scheduling strategy that brings down communication costs while guaranteeing the quality of service. The calculation model for communication costs is built for the fat-tree network topology. A reward function that takes into account the communication costs and Service Level Agreements violation(SLAV) rate is designed. The experiment shows that the proposed strategy performs about 26.81% and 40.55% better in comparison with two virtual machine scheduling strategies based on graph partition algorithm and greedy algorithm, which are commonly used in existing data centers. © 2022 IEEE.

关键词： Virtual machine

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RMGCN: Masked Graph Convolutional Networks for Relation-Aware Entity Alignment with Dangling Cases 12th

RMGCN: Masked Graph Convolutional Networks for Relation-Awa...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Liu, Xinyu Zhou, Feng Li, X.Y. Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031446924

Entity alignment (EA) is to match entities referring to identical real-world facts among different knowledge graphs (KGs). For simplicity, most previous work ignores the existence of dangling entities in the source KG. However, entity alignment task with dangling entities has emerged as a novel demand in real scenarios. Some work explores new dangling-aware loss functions and transfers existing methods on dangling settings, whose performance is still not satisfactory. Thus, in this work, we propose Relation-aware Masked Graph Convolutional Networks (RMGCN). In the learning stage, it can not only take advantage of the masking mechanism to alleviate the negative impact from nearby dangling neighbors, but also take both graph structure and relations of KGs into consideration. In the inference stage, it performs a two-step alignment which firstly filters out dangling entities, and then align the remaining entities. We adopt a novel distribution-based dangling entity detection method in first step to decrease the error propagation from dangling detection to the following EA task. The experimental results show that our model outperforms all state-of-the-art models with at least 7.5% on F1 score. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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Weak Signal Detection based on Beta Divergence 24

Weak Signal Detection based on Beta Divergence

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Liu, Jiahao Liu, Haoxuan Pu, Wenqiang Zhou, Rui You, Ming-Yi Shi, Qingjiang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The Chinese University of HongKong Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9781665496261

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. Information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals. © 2023 IEEE.

关键词： Cooperative communication

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Fast Rail Surface Inspection Method and System Based on 3D Line Laser Imaging 30

Fast Rail Surface Inspection Method and System Based on 3D L...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Liu, Lingshu Kang, Yihua Liu, Fuqiang Liu, Shiyuan Huazhong University of Science and Technology School of Mechanical Science and Engineering Wuhan China Software Algorithm Department Beijing Metallurgical Technology Development Co. LTD. Beijing China

ISBN: (纸本)9798350391916

Due to the significance and complexity of rail surface quality inspections on production lines, manual completion is inefficient. Existing 3D vision techniques, such as stereo vision, are too complex and cumbersome for industrial use. This paper introduces a straightforward and fast line laser calibration method, along with a rail surface defect detection approach using line laser 3D reconstruction. By projecting line lasers onto the sides of a calibration board of specific thickness, the position of the laser centerlines in the side areas is extracted to ascertain the world coordinates of the corresponding point clouds. Four sets of line laser measurement devices are then employed to conduct a full coverage scan of the rail. A streamlined multi-camera joint calibration method is presented, which stitches together 3D point clouds from various perspectives to recreate the complete cross-sectional profile of the rail. After transforming into a surface depth variation map, defects are identified based on depth thresholds. The method not only precisely locates and categorizes defects but also reduces reconstruction errors by 0.1mm and simplifies the calibration process by eliminating the need for multiple rotations of calibration targets, thereby speeding up and enhancing the feasibility of industrial application. The approach overcomes the complexities and cumbersome processes associated with traditional methods not suited for industrial settings. The system can detect at a speed of 1.5m/s, identifying surface defects deeper than 0.3mm with a verified accuracy of less than 0.02mm, thus fulfilling the requirements for rail surface defect inspection. © 2024 IEEE.

关键词： Inspection

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Many-to-Few Decomposition: Linking R2-Based and Decomposition-Based Multiobjective Efficient Global Optimization Algorithms

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhao, Liang Huang, Xiaobin Qian, Chao Zhang, Qingfu Department of Computer Science City University of Hong Kong Hong Kong National Key Laboratory for Novel Software Technology Nanjing University China

In multiobjective optimization, the R2 indicator is widely used for designing indicator-based algorithms, and the Tchebycheff approach is commonly employed in decomposition-based algorithms. Despite their wide use, the connection between these two different paradigms is still not well understood, particularly in the field of multiobjective efficient global optimization (MOEGO). Considering that expected improvement (EI) is a cornerstone in efficient global optimization, this paper first studies the relationship between R2-based EI and Tchebycheff-based EI. Then, we introduce a Many-to-Few (M2F) decomposition framework, offering a new perspective for linking the R2-based method and the Tchebycheff decomposition approach. By incorporating M2F decomposition into MOEGO, a new algorithm called R2/D-EGO is proposed. At each iteration, R2/D-EGO utilizes the Tchebycheff decomposition paradigm to generate a set of candidate solutions, each one corresponding to a different weight vector. Subsequently, a subset of query points is selected from the candidates based on the lower bound of R2-based EI. Empirical results indicate that the proposed R2/D-EGO is highly competitive in comparison with both R2-based and decomposition-based MOEGO algorithms in the parallel (or batch) setting. IEEE

关键词： Monte Carlo methods

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Breaking Semantic Artifacts for Generalized AI-generated Image Detection 38

Breaking Semantic Artifacts for Generalized AI-generated Ima...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zheng, Chende Lin, Chenhao Zhao, Zhengyu Wang, Hang Guo, Xu Liu, Shuai Shen, Chao School of Cyber Science and Engineering Xi'an Jiaotong University China School of Automation Science and Engineering Xi'an Jiaotong University China School of Software Engineering Xi'an Jiaotong University China

With the continuous evolution of AI-generated images, the generalized detection of them has become a crucial aspect of AI security. Existing detectors have focused on cross-generator generalization, while it remains unexplored whether these detectors can generalize across different image scenes, e.g., images from different datasets with different semantics. In this paper, we reveal that existing detectors suffer from substantial accuracy drops in such cross-scene generalization. In particular, we attribute their failures to "semantic artifacts" in both real and generated images, to which detectors may overfit. To break such "semantic artifacts", we propose a simple yet effective approach based on conducting an image patch shuffle and then training an end-to-end patch-based classifier. We conduct a comprehensive open-world evaluation on 31 test sets, covering 7 Generative Adversarial Networks, 18 (variants of) Diffusion Models, and another 6 CNN-based generative models. The results demonstrate that our approach outperforms previous approaches by 2.08% (absolute) on average regarding cross-scene detection accuracy. We also notice the superiority of our approach in open-world generalization, with an average accuracy improvement of 10.59% (absolute) across all test sets. Our code is available at https://***/Zig-HS/FakeImageDetection. © 2024 Neural information processing systems foundation. All rights reserved.

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ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (Version 2.0) Utilizing Deep Learning Recognition Models 15

ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (V...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Ahmed, Ahmed Abdelhadi Gochoo, Munkhjargal Taghizadeh, Mohamad Otgonbold, Munkh-Erdene Batnasan, Ganzorig Department of Computer Science and Software Engineering Al Ain United Arab Emirates Iran University of Science and Technology Department of Electrical Engineering Iran

ISBN: (纸本)9798350382396

As stated by the United Arab Emirates's (UAE) Community Development Authority (CDA), there are around 3,065 individuals with hearing disabilities in the country. These individuals often struggle to communicate with broader society and rely on scarce sign language (SL) interpreters. Moreover, Arabic's dialects diversity compounds the issue by causing dialects in the Arabic Sign Language (ArSL). Hence, the call for a standardized reference for ArSL in the region is a priority. To address these challenges, we've developed an Emirate Sign Language (ESL) electronic dictionary (e-dictionary) with a dataset of 127 signs and 50 sentences, recorded by hearing-impaired individuals in the UAE with various degrees of deafness. Supervised by certified interpreters and validated by ESL's department head at CDA in Dubai, the recordings were made using Azure Kinect DK, resulting in 708 recordings. The dataset is then processed to 10fps. The e-dictionary offers features such as webcam-based sign recognition using YOLOv8 technology, voice-based signing via Arabic Automatic Speech Recognition, text-based signing, and words spelling in ArSL. © 2023 IEEE.

关键词： Character recognition

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Image Computation for Quantum Transition Systems

Image Computation for Quantum Transition Systems

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Hong, Xin Gao, Dingchao Li, Sanjiang Ying, Shenggang Ying, Mingsheng State Key Laboratory of Computer Science Beijing China University of Technology Sydney Centre for Quantum Software and Information Sydney Australia

ISBN: (纸本)9783982674100

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems. © 2025 EDAA.

关键词： Model checking

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Laws of Quantum Programming

arXiv

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arXiv 2024年

作者： Ying, Mingsheng Zhou, Li Barthe, Gilles Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Max Plamck Institute for Security and Privacy Bochum Germany IMDEA Software Institute Madrid Spain

In this paper, we investigate the fundamental laws of quantum programming. We extend a comprehensive set of Hoare et al.’s basic laws of classical programming to the quantum setting. These laws characterise the algebraic properties of quantum programs, such as the distributivity of sequential composition over (quantum) if-statements and the unfolding of nested (quantum) if-statements. At the same time, we clarify some subtle differences between certain laws of classical programming and their quantum counterparts. Additionally, we derive a fixpoint characterization of quantum while-loops and a loop-based realisation of tail recursion in quantum programming. Furthermore, we establish two normal form theorems: one for quantum circuits and one for finite quantum programs. The theory in which these laws are established is formalised in the Coq proof assistant, and all of these laws are mechanically verified. As an application case of our laws, we present a formal derivation of the principle of deferred measurements in dynamic quantum circuits. We expect that these laws can be utilized in correctness-preserving transformation, compilation, and automatic code optimization in quantum programming. In particular, because these laws are formally verified in Coq, they can be confidently applied in quantum program development. © 2024, CC BY.

关键词： Algebra

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