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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Qianwen Zhu Jiaxing Shang Xinjun Cai Linli Jiang Feiyi Liu Baohua Qiang College of Computer Science Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

In human resource management, employee turnover prediction is very important for company operation since the leave of key employees can bring great loss to companies. However, most existing researches focused on employee-centered turnover prediction, while ignored the historical events of turnover behaviors as well as the longitudinal data of each work. Therefore, in this paper we propose a turnover prediction algorithm named CoxRF from an event-centered perspective, which combines the statistical results of survival analysis with ensemble learning and therefore simplifies the problem to a traditional supervised binary classification problem. In addition, we come up with the concepts of "event-person" and "time-event" to help construct survival data with censored data. We compare CoxRF with several baseline methods on a real dataset crawled from the biggest professional social platform of China. The results demonstrate its effectiveness for predicting turnovers. Besides, we have the following findings: i) gender plays a key role in employee turnover behavior and female employees have a higher turnover rate than male employees; ii) the external environmental factor such as GDP growth also contributes greatly to employee turnovers, which is hardly considered in previous data-driven studies; iii) The employee turnover behavior varies across different industries, where the IT group has significantly higher turnover rate than the government group; iv) Employees with good education backgrounds have a higher turnover rate than those with ordinary backgrounds after working for 3 to 5 years.

关键词： Hazards Companies Prediction algorithms Computer science Predictive models Data models Industries

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BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

arXiv

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

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Deep Convolutional Neural Networks with Random Subspace Learning for Short-term Traffic Flow Prediction with Incomplete Data

Deep Convolutional Neural Networks with Random Subspace Lear...

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International Joint Conference on Neural Networks

作者： Shijie Liao Jing Chen Jiaxin Hou Qingyu Xiong Junhao Wen Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China

Traffic flow prediction is a fundamental component in intelligent transportation systems. However, many existing prediction models endure several shortages. Most of the methods are constructed as a shallow model, which is difficult to reveal the intrinsic spatio-temporal relations embedded in traffic raw data. Moreover, the separation of feature learning and predictor learning brings a sacrifice of model performance. Then the hand designed features are difficult to be tuned appropriately. Finally, few existing methods consider the incomplete data problem which is in fact very severe for practical application. In this work, we develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that integrates random subspace learning and ensemble learning on deep convolutional neural networks. The proposed model takes the traffic flow data as an image, and considers both exploring spatio-temporal correlations in the unified architecture and the incomplete data problem. The experimental results, using traffic data originated from the California Freeway Performance Measurement System (PeMS), corroborate the effectiveness of the proposed approach compared with the state of the art.

关键词： Correlation Predictive models Data models Autoregressive processes Computational modeling Convolutional neural networks Task analysis

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Bus penetration rate estimation of road segment using bus GPS data 30

Bus penetration rate estimation of road segment using bus GP...

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30th Chinese Control and Decision Conference, CCDC 2018

作者： Sun, Dihua Chen, Qiuqu Zhao, Min Zheng, Linjiang Liu, Weining Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing China College of Automation Chongqing University Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China

ISBN: (纸本)9781538612439

To realize the estimation of bus penetration rate in road segment using bus GPS data, a method is proposed in this paper to estimate bus penetration rate in single signal cycle, according to the bus position at the end of red phase at a signalized intersection. Sum of squares for factor A (SSA) of bus penetration rate and time function in different signal cycle length are selected to characterize the accuracy and real-time quality of the estimation, on the basis, objective function is constructed to find appropriate signal cycle length as the time window. Aiming at the possible abnormal points in single cycle bus penetration rate, quartile method was used to eliminate them. In the end, simulation tests were carried out under different traffic flow and bus penetration rates, the results show that the proposed method in this paper has better performance when traffic flow is higher than 600veh/h, and the bus penetration rate is higher than 5%. On the same time, a field test was conducted in an arterial road of Chongqing, the results show that the estimation method proposed in this paper can estimate the bus penetration rate of road segment effectively. © 2018 IEEE.

关键词： Estimation Roads Global Positioning System Real-time systems Linear programming Queueing analysis Data models

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Improving Security Data Access Control for Multi-Authority Cloud Storage

Improving Security Data Access Control for Multi-Authority C...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Jian Wang Kehua Wu Chunxiao Ye Xiaofeng Xia Fei Ouyang Colleage of Computer Science Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China Ecological Environment Big Data Application Center Chongqing Ecological Environment Bureau Chongqing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Cloud computing have brought a lot of advantages, such as reducing the hardware cost and providing a convenient cloud storage service. More and more people choose to put their private data in the cloud. To improve data security and privacy, Wu et al. proposed a new extensive data access control scheme for multi-authority cloud storage systems (NEDAC-MACS) on the basis of the scheme of Yang et al. In this paper, we propose a new scheme that can resist the collusion of the cloud server and user to improve the security of NEDAC-MACS. The cryptographic analysis show that our scheme is still secure in the random oracle.

关键词： Cloud computing Public key Resists Encryption Access control

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Speed guidance model considering driver's operating behavior 30

Speed guidance model considering driver's operating behavior

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30th Chinese Control and Decision Conference, CCDC 2018

作者： Sun, Dihua Gu, Lianqiang Zhao, Min Cheng, Senlin Xie, Fei Yang, Liangyi Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing China College of Automation Chongqing University Chongqing400044 China China Automotive Engineering Research Institute Co. Ltd Chongqing401144 China

ISBN: (纸本)9781538612439

The phenomenon of vehicles' go-stop and acceleration / deceleration change occur in the vicinity of the intersection due to the cyclical change of traffic at lights intersection, which will cause a lot of problems such as fuel consumption and traffic efficiency. The existing research indicated that the speed-guided algorithm can effectively reduce the vehicle idling at intersections and improve the fuel economy of vehicles. However, the existing speed guidance approach on eco-driving ignored the driver's operating characteristics. If there is a big difference between the actual speed and the guide speed, it is difficult for the driver to control the vehicle completely according to the guide speed. Based on this, combined with driver's operating characteristics, the driver behavior model is established, the speed tracking deviation is taken into account and the actual operation state of the vehicle are used as feedback, the guide speed is updated in real-time to improve the adaptability of speed-guided algorithm. The provided approach is simulated by Matlab. The simulation results show that the approach can adapt the driver's operating characteristics to effectively improve the fuel economy and the traffic efficiency. © 2018 IEEE.

关键词： Vehicles Acceleration Planning Mathematical model Fuel economy Adaptation models

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CurveCluster: Automated Recognition of Hard Landing Patterns Based on QAR Curve Clustering

CurveCluster: Automated Recognition of Hard Landing Patterns...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Xu Li Jiaxing Shang Linjiang Zheng Dajiang Liu Lin Qi Liu Liu College of Computer Science Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University China College of Computer Science Chongqing University China Data Analysis Department Sichuan Hantai Technology Co. Ltd. Chengdu China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Flight safety is of vital importance to the aviation industry. As one of the most typical security events, hard landing is extremely concerned by airlines and related studies have received extensive attention in recent years. However, existing regression or risk based models either suffers from low prediction accuracy, or cannot provide good interpretability, making themselves impractical in real applications. To solve these problems, in this paper we propose CurveCluster: a curve clustering-based approach which is able to automatically recognize hard landing patterns from quick access recorder (QAR) data. We first provide a two-level hierarchical classification of hard landing events based on different hard landing patterns. Then we extract curve features from several key QAR parameters through interpolation and resampling. Finally, we apply K-means clustering algorithm on the curve features to automatically recognize the hard landing patterns. We test our approach on a dataset of 9,203 A320 flight QAR data samples and the overall recognition accuracy reaches up to 93.1%. Moreover, our results directly reflect the reasons of different types of hard landing events, which show strong interpretability.

关键词： Feature extraction Interpolation Atmospheric modeling Pattern recognition Aircraft Machine learning Safety

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Fast Tunnel Monitoring Video Stabilization Method Based on Improved ORB Feature

Fast Tunnel Monitoring Video Stabilization Method Based on I...

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第30届中国控制与决策会议

作者： Zhao Min Sun Jian Sun Dihua Tang Yi College of Automation Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education

The stable video stream is the precondition and foundation for the detection of abnormal events. Because of the disturbance of vehicle light and local light mutation in the tunnel scene, the existing video stabilization methods have the problems of real-time and robustness, which are still difficult to meet the application needs. This paper proposes a fast video stabilization method based on improved ORB feature. The proposed method distinguishes the light interference and non-interference regions based on the gradient direction invariance of tunnel lamp regions. Then the modified KSW entropy is applied to obtain dynamic ORB thresholds of different regions. Next the method utilizes the priori distribution of salient feature points to narrow the feature extraction region. More, it establishes affine transformation model with nearest neighbor matching and least-squares to realize global compensation of the tunnel jitter image. Finally, the experimental results indicate that the proposed method improves the performance of real-time and the fluency of video stream, which can meet the application requirements of tunnel monitoring well.

关键词： ORB Feature KSW Entropy Light Interference Fast Stabilization Priori Distribution

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A Fast Image Enhancement Algorithm for Highway Tunnel Pedestrian Detection

A Fast Image Enhancement Algorithm for Highway Tunnel Pedest...

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第30届中国控制与决策会议

作者： Li Yongxue Zhao Min Sun Dihua College of Automation Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education

Pedestrian detection is a necessary means of support in modern traffic management. The error and miss detection rate of traditional pedestrian detection are high due to uneven illumination, dim environment in the tunnel, and the blurred monitored image, which makes it difficult for the subsequent identification. Therefore, in this paper, a fast image enhancement algorithm based on imaging model constraint is proposed and narrowed to the pedestrian ROI in the pavement near the street under the scene of highway tunnel. First, the method uses the combination of global atmospheric light and partitioned atmospheric light to estimate the local atmospheric light. Second, transmission is estimated based on the formula derived from the imaging model constraints. Third, the method uses constant instead of illumination to balance tunnel image illumination. Last, the tunnel image is enhanced according to the imaging model. Furthermore, because of the algorithm's real-time requirement, we propose a narrowing region method to thoroughly improve the overall computing efficiency. Considering about the characteristics of high way tunnel, which is a blurred scene and has difficulty recognizing the foreground from the background, we adopt a method of multi-feature integration to detect the enhanced image. Experimental and comparative analysis results show that the proposed method can rapidly and effectively enhance the tunnel image, and improve the effect of pedestrian detection in high way.

关键词： Constraint of Imaging Model Transmission Atmospheric Light Pedestrian Detection

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A Real-Time Vessel-Buoy Collision Risk Estimation Model for Inland Waterway Navigation

A Real-Time Vessel-Buoy Collision Risk Estimation Model for ...

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第37届中国控制会议

作者： Tingxuan Li Shan Liang Tong Liu Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Automation Chongqing University

The inland waterway is the navigation passage in inland waters including rivers,lakes,reservoirs and channels,in which the buoy plays an important role in the pilotage for guiding vessels（especially in large size） to avoid dangerous water ***,the inland waterway buoys can easily be drifted or even damaged by a vessel crash,which may lead to a serious waterway traffic *** order to increase the security of buoys,the collision risk estimation is seriously required to detect potential Vessel-Buoy collision events ahead of *** by this demand,we propose a real-time Vessel-Buoy collision risk estimation model to reduce the collision *** this study the Kalman Filtering（KF） is firstly introduced to predict *** an improved KUNZI model is built to estimate collision *** experiment results show a real-time dynamic Vessel-Buoy collision risk estimation and reveal the advantages compared to a traditional estimation model.

关键词： Collision Risk Trajectory Prediction Kalman Filtering KUNZI model

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