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Software Defect Prediction via Deep Belief Network

Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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A review on vision-based analysis for automatic dietary assessment

arXiv

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arXiv 2021年

作者： Wang, Wei Min, Weiqing Li, Tianhao Dong, Xiaoxiao Li, Haisheng Jiang, Shuqiang School of Computer and Engineering Beijing Technology and Business University Beijing100048 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China University of Chinese Academy of Sciences Beijing100049 China

Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are not only burdensome but timeconsuming, and contain substantial biases and errors. Recent advances in Artificial Intelligence (AI), especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake. Scope and approach: This review presents Vision-Based Dietary Assessment (VBDA) architectures, including multi-stage architecture and end-to-end one. The multi-stage dietary assessment generally consists of three stages: food image analysis, volume estimation and nutrient derivation. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and finally highlight critical challenges and future trends for VBDA. key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., fine-grained food analysis and accurate volume estimation. This review aims to encourage researchers to propose more practical solutions for VBDA. Copyright © 2021, The Authors. All rights reserved.

关键词： Nutrition

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

A hybridized parallel bats algorithm for combinatorial probl...

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作者： Nguyen, Trong-The Qiao, Yu Pan, Jeng-Shyang Chu, Shu-Chuan Chang, Kuo-Chi Xue, Xingsi Dao, Thi-Kien Intelligent Information Processing Research Center Fujian University of Technology Fujian Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Traveling salesman problem

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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Review Spam Detection Based on Multi-dimensional Features 9th

Review Spam Detection Based on Multi-dimensional Features

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Deng, Liming Wei, Jingjing Liang, Shaobin Wen, Yuhan Liao, Xiangwen College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Digital Fujian Institute of Financial Big Data Fuzhou350116 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou350108 China

ISBN: (纸本)9783030596040

Review spam detection aims to detect the reviews with false information posted by the spammers on social media. The existing methods of review spam detection ignore the importance of the information hidden in the user interactive behaviors and fail to extract the indistinct contextual features caused by irregular writing style of reviews. In this paper, a new review spam detection method based on multi-dimensional features is proposed. The method utilizes the principal component analysis to get low-dimensional features to characterize the user-product relationship. Then, a neural network constructed with nested LSTM and capsule network is trained to extract textual context features and spatial structure features. Finally, the model combines the text and user behavioral features as the overall features, which are used as the input to the classification module to detect spam reviews. Experimental results show that the F1 value of our proposed method is 1.6%~3.5% higher than the existing methods, indicating the efficiency and effectiveness of our model, especially on the natural distribution datasets. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction

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Joint design of public transit and autonomous vehicle mobility systems for tourist cities considering private tour trips

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Transportation Research Part C: Emerging Technologies 2025年 178卷

作者： Shi, Ziyi Luo, Sida Niu, Luyao Shao, Chunfu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University #3 Shangyuancun Haidian District Beijing100044 China Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure Xinjiang University Urumgi830046 China

Promoting shared autonomous vehicles (SAVs) in tourist cities is promising, and probably induces so-called private tour trips ("PT trips") — Travelers go through multiple intermediate stops with their preferred dwell time before reaching destinations in SAVs for entertainment and privacy (e.g., sightseeing and photography). For mobility service providers to serve different types of demand throughout the day including PT trips, this study proposes the Shared Autonomous Vehicle and Transit (SAV-T) integrated system, which strikes a balance between efficiency and accessibility through feeding fixed-route transit (FRT) by SAV. Service characteristics of SAVs are devised, prohibiting carpooling for PT trips to guarantee privacy and permitting it for normal trips. To overcome the difficulty to determine SAV fleet size and FRT network layout required to accommodate different types of demand, we adopt a parsimonious continuum approach and derive flow equilibrium equations using a network queuing theory. Accordingly, we compute the system cost consisting of agency and user costs for both peak and off-peak periods. By minimizing the system cost, SAV-T design problem is formulated as a mixed integer program and solved numerically. We demonstrate the advantage of integrating various SAV types with transit despite serving those "costly" PT trips in tourist cities — SAV-T saves system costs (up to 15%) compared to related transit systems that need not serve a significant number of PT trips. The impact of PT trips on the integrated system design is examined, including the fleet size of SAVs and layout of FRT in different scenarios, by varying the proportion of PT trips, total demand levels, etc. © 2025 Elsevier Ltd

关键词： Autonomous vehicles

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Research on Accurate Model of Lithium Battery

Research on Accurate Model of Lithium Battery

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第32届中国控制与决策会议

作者： Yongxue Ye Hongyan Ma Jingjian Yang Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data College of Electrical and Information EngineeringBeijing University of Civil Engineering and Architecture

In order to build accurately the external characteristic curve of lithium-ion batteries,this paper improve existing lithium-ion battery models,and verify the improved lithium-ion battery model in *** this paper,for reflecting the accuracy of the improved external characteristics of the lithium-ion battery model,the external characteristic curves of lithium-ion battery model simulation before and after improvement are compared under different temperature and discharge current *** comparison results show that under different operating conditions,the improved model can more accurately reflect the experimental conditions of lithium battery discharge in the concave function discharge area than before the improvement.

关键词： lithium battery mathematical model research Simulink modeling data comparison

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Binary Image Steganalysis Based on Histogram of Structuring Elements

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第9期30卷 3081-3094页

作者： Lu, Wei Li, Ruipeng Zeng, Lingwen Chen, Junjia Huang, Jiwu Shi, Yun-Qing Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518055 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Utilizing statistical models of binary images is a common and effective means to steganalyze binary images, and the design of the statistical model is essential to the performance of steganalysis. In this paper, we propose a new model based on a histogram of pixel structuring elements (SEs), which is a suitable representation of a binary image for the task of steganalysis. The texture property and the dependency among pixels are considered inside the SEs. The SEs with different patterns will be evaluated comprehensively according to a statistical criterion, and some of them will be selected to construct the feature set for training the steganalyzer. The distributions of these selected SEs, which contain many highly flippable pixels, will be emphasized by the criterion, and they can reflect the difference between cover images and stego-images. Finally, a series of experiments are conducted on two datasets, and the results show that the proposed scheme significantly outperforms state-of-the-art schemes.

关键词： Markov processes Histograms Shape Image edge detection Distortion Gray-scale Visualization Binary images steganalysis structure element histogram

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Self-Unwrapping Phase-Shifting for Fast and Accurate 3-D Shape Measurement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Zeng, Jinghui Ma, Wenxin Jia, Wei Li, Yucheng Li, Haowen Liu, Xuewen Tan, Mingkui South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 511442 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Guangzhou Shiyuan Elect Technol Co Ltd CVTE Res Guangzhou 510530 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Key Labo Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China

Phase unwrapping is one of the most important procedures in 3-D phase measurement profilometry (PMP) and has attracted great research interest in the past few decades. However, the existing phase unwrapping methods require additional patterns or preassumptions to determine the fringe orders for further absolute phase retrieval, which dramatically affect the measurement efficiency. To address this problem, we propose a generic self-unwrapping phase-shifting (SUPS) algorithm that retrieves the absolute phase without external information or priors. To this end, we first embed a novel space-varying phase shift (SPS) that uniquely determines the fringe order information into sinusoidal patterns, and then extract it to retrieve the absolute phase by pixelwise calculation. Our method achieves higher efficiency over previous methods while preserving the measurement precision, and the minimum number of patterns required is only four. All the theoretical, simulation, and extensive experimental results demonstrate its superiority in fast and accurate 3-D shape measurement.

关键词： Phase measurement Shape measurement Three-dimensional displays Software engineering Mathematical models Indexes Encoding 3-D shape measurement fringe projection profilometry phase measurement profilometry (PMP) phase unwrapping structured light

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