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Chinese Automation Congress (CAC)

作者： Yongming Han Zilan Du Zhiqiang Geng Yajie Wang Feng Xie Kai Chen Guizhou Provincial Key Laboratory of Public Big Data Guiyang China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Raw materials consumption and many physical and chemical reactions in the industrial process are not conducive to analyze the production process. Therefore, the ethylene production prediction model based on gated recurrent unit (GRU) is brought forth in this paper, which can be used to accurately predict ethylene production capacity in complex industrial processes. Using the total consumption of raw materials, fuel, water, steam and electricity as the input of the network architecture and the ethylene production as the output, GRU network is set as the kernel to build the production prediction model. Form experiment results comparing with the back propagation (BP) neural network and the long short-term memory (LSTM), GRU can better predict ethylene production, thus reducing production energy consumption and improving production efficiency.

关键词： Production Logic gates Neural networks Prediction algorithms Predictive models big data Training

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Improving Code Summarization Through Automated Quality Assurance

Improving Code Summarization Through Automated Quality Assur...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Yuxing Hu Meng Yan Zhongxin Liu Qiuyuan Chen Bei Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China Pengcheng Laboratory Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665425889

The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may generate low-quality (BLEU4<40) summaries, which will mislead developers. Previous work has shown that it is possible to conduct quality assurance for document generation (QA4DG) and improve the practicability of document generation approaches. Code summarization can also be regarded as a document generation task. This work aims to investigate whether QA4DG approaches can be leveraged to improve code summarization. Specifically, we first investigate whether existing QA4DG approaches can be plugged in code summarization approaches. We find that an automated quality assurance framework for commit message generation named QACom performs best. In-spired by the idea behind QAcom, we propose an ensemble code summarization approach called Ensum. Precisely, given a code snippet, Ensum first uses current code summarization approaches to generate candidate summaries. Then, Ensum predicts the quality of each candidate summary using a collaborative filtering-based component and a retrieval-based component and selects the best candidate summary as the output. Experimental results on two public datasets show that Ensum outperforms three state-of-the-art single approaches and one ensemble approach for code summarization in terms of BLEU-4, METEOR, and ROUGE-L.

关键词： Codes Quality assurance Filtering Collaboration Software reliability Task analysis

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A secure and efficient face template protection scheme based on chaos

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Journal of Information Security and Applications 2025年 93卷

作者： Liu, Jinyuan Wang, Yong Wang, Kun Liu, Zhuo School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing401331 China School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China School of Mathematics and Big Data Guizhou Education University Guiyang550018 China Dazhou Key Laboratory of Government Data Security Dazhou635000 China

Face recognition system has been widely used, and its privacy and security have become increasingly important. In this paper, a secure face template generation method is proposed. Firstly, the extracted feature vector is perturbed to eliminate the correlation, and then the state values of the vector are changed by 3D coordinate transformation. After the feature vector is disturbed again, the irreversible matrix is used to generate the template. The generated template has the characteristics of revocability and irreversibility. Taking advantage of the properties of the chaotic system, such as being sensitive to initial values, and its behavior is uncertain, unrepeatable and unpredictable, the security of generated template is further improved. The experimental results conducted on different datasets show that the scheme has good performance and high efficiency. Privacy analysis and security analysis demonstrate that the scheme has the characteristics of revocability, irreversibility and unlinkability, and can effectively protect the security of the template. © 2025

关键词： Sensitive data

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PanoFlow: Learning 360◦ Optical Flow for Surrounding Temporal Understanding

arXiv

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arXiv 2022年

作者： Shi, Hao Zhou, Yifan Yang, Kailun Yin, Xiaoting Wang, Ze Ye, Yaozu Yin, Zhe Meng, Shi Li, Peng Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou310027 China Multimedia Laboratory Nanyang Technological University 61 Nanyang Dr Singapore637335 Singapore Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany College of Computer Science and Technology Zhejiang University Hangzhou310027 China Data Intelligence Lab Luokung Technology Corp. Beijing100020 China

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ultra-wide Field of View (FoV) offered by 360◦ panoramic sensors. However, due to the unique imaging process of panoramic cameras, models designed for pinhole images do not directly generalize satisfactorily to 360◦ panoramic images. In this paper, we put forward a novel network framework-PANOFLOW, to learn optical flow for panoramic images. To overcome the distortions introduced by equirectangular projection in panoramic transformation, we design a Flow Distortion Augmentation (FDA) method, which contains radial flow distortion (FDA-R) or equirectangular flow distortion (FDA-E). We further look into the definition and properties of cyclic optical flow for panoramic videos, and hereby propose a Cyclic Flow Estimation (CFE) method by leveraging the cyclicity of spherical images to infer 360◦ optical flow and converting large displacement to relatively small displacement. PanoFlow is applicable to any existing flow estimation method and benefits from the progress of narrow-FoV flow estimation. In addition, we create and release a synthetic panoramic dataset FlowScape based on CARLA to facilitate training and quantitative analysis. PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the fresh established FlowScape benchmarks. Our proposed approach reduces the End-Point-Error (EPE) on FlowScape by 27.3%. On OmniFlowNet, PanoFlow achieves an EPE of 3.17 pixels, a 55.5% error reduction from the best published result (7.12 pixels). We also qualitatively validate our method via an outdoor collection vehicle and a public real-world OmniPhotos dataset, indicating strong potential and robustness for real-world navigation applications. Code and dataset are publicly available at PanoFlow. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot2API: Recommending Graphic API from Plot via Semantic Pa...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Zeyu Wang Sheng Huang Zhongxin Liu Meng Yan Xin Xia Bei Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely *** the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API *** collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

关键词： Deep learning Visualization Semantics Neural networks data visualization Software Task analysis

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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A Causal Adversarial Graph Neural Network for Multi-Center Autism Spectrum Disorder Identification

SSRN

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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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dataSESec: Security monitoring for data share and exchange platform 3rd

DataSESec: Security monitoring for data share and exchange p...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Shen, Guowei Liu, Lu Wei, Qin Lei, Jicheng Guo, Chun College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China CETC Big Data Research Institute Co. Ltd. Chengdu China

ISBN: (纸本)9783030260743

data share and exchange platform is an infrastructure of data open and share. How to ensure the security of government data in the exchange and sharing platform is a key problem. To solve this problem, we developed a security monitoring system for data share and exchange platform - dataSESec. A multi-layer graph model is provided to realize multi-source heterogeneous security monitoring metadata organization, data tracking and forensics, and multi-dimensional security monitoring data analysis. The system extracts network traffic data without authorization, which can achieve early security warning. The deployment of the security monitoring system is very flexible, the interface that interacts with the existing platform is very flexible, and the impact on the existing data share and exchange platform is very small. © 2019, Springer Nature Switzerland AG.

关键词： Monitoring

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production

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Chemosphere 2023年 342卷

作者： Hai, Tao El-Shafay, A.S. Goyal, Vishal Alshahri, Abdullah H. Almujibah, Hamad R. School of Electronics and Information Engineering Ankang University China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Department of Mechanical Engineering College of Engineering in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Mechanical Power Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering Taif University P.O. Box 11099 Taif City21974 Saudi Arabia

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system. © 2023

关键词： Gas turbines

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