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3rd International Conference on big data, Information and Computer Network, BDICN 2024

作者： Zhou, Chenchen Wang, Wei Tian, Maohong Liang, Jian Zhang, Dequan Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China

ISBN: (纸本)9798350363821

As an important carrier and component of industrial clusters, industrial districts are an important way for regional economic growth, and the economic effects of industrial districts have attracted more and more attention. Therefore, how to identify high-quality districts with good development trends and obvious competitive advantages from a large amount of district information has become an increasingly important topic. This article takes industrial and intelligent manufacturing districts as an example and proposes an innovative industrial and intelligent manufacturing zone rating method that combines rule-based modeling, Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI) models. Collect structured and unstructured data, including real-Time operational data, historical records, market trends, and expert opinions, from multiple sources through big data technology. Apply rule-based models and Analytic Hierarchy Process (AHP) to prioritize and weight various factors that affect regional performance, and obtain district ratings;And use the Kmeans method for clustering to obtain district level classification. Finally, the paper combines the outputs of the rule-based models, AHP, and AI models to create an overall rating system. This system continuously monitors district performance, providing accurate and comprehensive ratings to support decision-making in industry and government. © 2024 IEEE.

关键词： Smart manufacturing

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Modal-Adaptive Spatial-Aware-Fusion and Propagation Network for Multimodal Vision Crowd Counting

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Kai Zou, Xuetao Zhu, Peng Sang, Jun Chongqing University School of Big Data and Software Engineering Chongqing401331 China

With the rapid development of AI and deep learning technologies, smart consumer electronic devices integrate a variety of sensors, especially thermal imaging or depth imaging technology. Some researchers became keen to explore multimodal vision crowd counting techniques based on RGB-thermal or RGB-depth images. However, existing multimodal counting methods ignore the unique contribution of each modality and the effective propagation of features while achieving multimodal fusion. To address these issues, we propose a Modal-adaptive Spatial-aware-fusion and Propagation Network (MSPNet) for multimodal crowd counting. First, we design a coarse-fine cross-modal dynamic fusion (CCDF) module, which fully captures modality-specific and modality-shared information in different semantic spaces using a spatial activation function-guided fine-grained adaptive weighted fusion mechanism. Then, we adopt adapted different convolutions to channel concatenate the features output by multi-stage CCDF. Finally, considering the visual differences of the same object under different imaging angles, we design a dual-dilation adaptive feed-forward propagation module constructed by the dual dilation transposed attention and the feed-forward neural network improved by the gating mechanism to effectively propagate the enhanced multi-modal fusion features to the counting regression module. Experimental results show that MSPNet outperforms the existing state-of-the-art methods on two multimodal crowd counting datasets ShanghaiTechRGBD and RGBT-CC. © 1975-2011 IEEE.

关键词： Image coding

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One-Handed Gesture Recognition Based on Improved ST-GCN and CA Attention Mechanism

One-Handed Gesture Recognition Based on Improved ST-GCN and ...

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Automation, Robotics and Computer engineering (ICARCE), International Conference on

作者： Guixin Wang Department of Software Engineering School of Big Data and Computer GuangDong BaiYun University Guangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

The article takes one-handed gesture video as the research object, and mainly focuses on the recognition process and method of “isolated words” and static gesture mixtures. Firstly, the algorithm of extracting the key point positions of frame pictures is proposed by grouping rules of frame sequences of different gestures. This algorithm solves the problem of invalid gesture images affecting the recognition due to the redundancy of gesture images when extracting gestures from traditional gesture videos. Finally, a new network (ST-GCN-CA) is proposed, which improves the traditional spatio-temporal graph convolutional network (ST-GCN) and adds the CA attention mechanism. The method and network in this paper are applied to the Chinese isolated word sign language dataset (CSL-500) single-handed video, and the experimental results show that the recognition accuracies in the isolated word gesture video and the hybrid video reach 94.3% and 94.1%, respectively, which improves the recognition accuracies in comparison with other methods in the public domain.

关键词： Sign language Attention mechanisms Accuracy Image recognition Automation Graph convolutional networks Convolution Redundancy Robots

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Score-based causal feature selection for cancer risk prediction

Score-based causal feature selection for cancer risk predict...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shanshan Huang Qingsong Li Lei Wang Yuanhao Wang Li Liu School of Big Data & Software Engineering Chongqing University Chongqing China

The primary goal of cancer risk prediction is to find dominant features, with each being responsible for cancer diagnosis. As a result, selected feature sets often converge to inexplicable and implausible results. Existing score-based causal models adopt a score function to learn a local causal structure to explicitly characterize a unique causal configuration as a variable number of nodes and links, which however suffer from nonconvex optimization and global incompleteness. This leads us to present a score-based approach to construct a causal network by optimizing a score function with a convex solution under the constraint of causal Markov property. It can be analytically shown that the resulting causal network satisfies the causal Markov property, and as a result, all cause-effect dependencies can be retained and are globally consistent. An additional node selector is introduced to choose the most dominant causal features. Empirical evaluations on three benchmarks and one in-house cancer risk datasets suggest our approach significantly outperforms the state-of-the-arts.

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Just-In-Time Method Name Updating With Heuristics and Neural Model

Just-In-Time Method Name Updating With Heuristics and Neural...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Zhenting Guo Meng Yan Hongyan Li Zhezhe Chen Weifeng Sun School of Big Data and Software Engineering Chongqing University Chongqing China

Ensuring the quality and conciseness of method names is pivotal for the readability and maintainability of source code. However, for developers, it often presents challenges, particularly during the course of code evolution. Throughout this process, developers sometimes may neglect to update the method name, resulting in inconsistency which could potentially mislead developers and introduce future bugs. In this paper, we propose the task of “Just-In-Time (JIT) Method Name Updating” which automatically performs method name updates to avoid inconsistent names and fix them before being introduced into code bases. Specifically, we propose an approach that combines heuristic rules and a neural model. The heuristic rule-based component mainly focuses on the single-token changes for our empirical findings that the proportion of single-token modifications is extensive, and often corresponds to code-indicative updates. The neural model-based component is a customized Seq2seq model considering code changes and the new method body’s token type. To evaluate our approach, we conduct extensive experiments on the collected dataset with over 108K method name-body co-change samples from popular Java projects. The results show that our method outperforms the three baselines on all metrics. In particular, our approach achieves a significant improvement in Accuracy and improves method generation baseline by 23.5%.

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Revisiting and Improving Retrieval-Augmented Deep Assertion Generation

arXiv

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arXiv 2023年

作者： Sun, Weifeng Li, Hongyan Yan, Meng Lei, Yan Zhang, Hongyu School of Big Data and Software Engineering Chongqing University Chongqing China

Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particular state, and a test oracle (e.g., assertion), which specifies the behavior in that state. To reduce manual efforts in conducting unit testing, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach, to generate assertions for a unit test. Despite being promising, there is still a knowledge gap as to why or where integration works or does not work. In this paper, we describe an in-depth analysis of the effectiveness of integration. Our analysis shows that: ① The overall performance of integration is mainly due to its success in retrieving assertions. ② integration struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test, resulting in many tokens being incorrectly modified;③ integration is limited to specific types of edit operations (i.e., replacement) and cannot handle token addition or deletion. To improve the effectiveness of assertion generation, this paper proposes a novel retrieve-and-edit approach named EDITAS. Specifically, EDITAS first retrieves a similar focal-test from a pre-defined corpus and treats its assertion as a prototype. Then, EDITAS reuses the information in the prototype and edits the prototype automatically. EDITAS is more generalizable than integration because it can ① comprehensively understand the semantic differences between input and similar focal-tests;② apply appropriate assertion edit patterns with greater flexibility;and ③ generate more diverse edit actions than just replacement operations. We conduct experiments on two large-scale datasets and the experimental results demonstrate that EDITAS outperforms the state-of-the-art approaches,

关键词： Integration

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DSFM: Enhancing Functional Code Clone Detection with Deep Subtree Interactions

DSFM: Enhancing Functional Code Clone Detection with Deep Su...

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International Conference on software engineering (ICSE)

作者： Zhiwei Xu Shaohua Qiang Dinghong Song Min Zhou Hai Wan Xibin Zhao Ping Luo Hongyu Zhang KLISS BNRist School of Software Tsinghua University Beijing China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Functional code clone detection is important for software maintenance. In recent years, deep learning techniques are introduced to improve the performance of functional code clone detectors. By representing each code snippet as a vector containing its program semantics, syntactically dissimilar functional clones are detected. However, existing deep learning-based approaches attach too much importance to code feature learning, hoping to project all recognizable knowledge of a code snippet into a single vector. We argue that these deep learning-based approaches can be enhanced by considering the characteristics of syntactic code clone detection, where we need to compare the contents of the source code (e.g., intersection of tokens, similar flow graphs, and similar subtrees) to obtain code clones. In this paper, we propose a novel deep learning-based approach named DSFM, which incorporates comparisons between code snippets for detecting functional code clones. Specifically, we improve the typical deep clone detectors with deep subtree interactions that compare every two subtrees extracted abstract syntax trees (ASTs) of two code snippets, thereby introducing more fine-grained semantic similarity. By conducting extensive experiments on three widely-used datasets, GCJ, OJClone, and bigCloneBench, we demonstrate the great potential of deep subtree interactions in code clone detection task. The proposed DSFM outperforms the state-of-the-art approaches, including two traditional approaches, two unsupervised and four supervised deep learning-based baselines.

关键词： software maintenance Codes Source coding Semantics Cloning Detectors Syntactics

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Revisiting and Improving Retrieval-Augmented Deep Assertion Generation

Revisiting and Improving Retrieval-Augmented Deep Assertion ...

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IEEE International Conference on Automated software engineering (ASE)

作者： Weifeng Sun Hongyan Li Meng Yan Yan Lei Hongyu Zhang School of Big Data and Software Engineering Chongqing University Chongqing China

Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particular state, and a test oracle (e.g., assertion), which specifies the behavior in that state. To reduce manual efforts in conducting unit testing, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach, to generate assertions for a unit test. Despite being promising, there is still a knowledge gap as to why or where integration works or does not work. In this paper, we describe an in-depth analysis of the effectiveness of integration. Our analysis shows that: ① The overall performance of integration is mainly due to its success in retrieving assertions. ② integration struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test, resulting in many tokens being incorrectly modified; ③ integration is limited to specific types of edit operations (i.e., replacement) and cannot handle token addition or deletion. To improve the effectiveness of assertion generation, this paper proposes a novel retrieve-and-edit approach named EDITAS. Specifically, Editas first retrieves a similar focal-test from a pre-defined corpus and treats its assertion as a prototype. Then, Editas reuses the information in the prototype and edits the prototype automatically. Editas is more generalizable than integration because it can ❶ comprehensively understand the semantic differences between input and similar focal-tests; ❷ apply appropriate assertion edit patterns with greater flexibility; and ❸ generate more diverse edit actions than just replacement operations. We conduct experiments on two large-scale datasets and the experimental results demonstrate that Editas outperforms the state-of-the-art approache

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SeeMe: An intelligent edge server selection method for location-aware business task computing over IIoT

SeeMe: An intelligent edge server selection method for locat...

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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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Makeup Transfer Based On Laplacian Pyramid Network 34

Makeup Transfer Based On Laplacian Pyramid Network

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Pattern Recognition and Tracking XXXIV 2023

作者： Chen, Yan Gong, Wenhui Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University - Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

Makeup transfer aims to extract a specific makeup from a face and transfer it to another face, which can be widely used in portrait beauty, and cosmetics marketing. At present, existing methods can achieve the transfer of the entire facial makeup, but the quality of makeup transfer is not excellent because there may be a mismatch between the two images. In this paper, we propose a facial makeup transfer network based on the Laplacian pyramid, which can better preserve the facial structure from the source image and achieve high-quality transfer results. The model consists of three parts: makeup feature extraction, facial structure feature extraction, and makeup fusion. The makeup extraction part is used to extract the facial makeup from the reference image. And the facial structure feature extraction part is used to extract facial structure from the source image, in order to solve the loss of facial details when extracting facial structural features, we used the method based on Laplacian Pyramid. The makeup fusion part will fuse the facial makeup with facial structure features. Many experiments on the MT dataset have shown that this method can transfer makeup successfully without changing the original facial structure, and achieve advanced performance in various makeup styles. © 2023 SPIE.

关键词： Feature extraction

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