bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Trust Assessment Model Based on Recommendation and Dynamic Self-adaptive in Cloud Service
Authors: Lu Yiqin , Fang Yahui , Qin Jiancheng 
Published: November 2019

Abstract
Cloud computing is a completely distributed sharing platform, through which users can request personalized cloud services. With the continually expansion of cloud services scale, more and more service providers are offering services with similar functions but different qualities. Users are faced with the problem of selecting the most suitable cloud services from numerous cloud services. In this paper, a trust assessment model based on recommendation and dynamic self-adaptive in cloud service is proposed. In the trust assessment model, trust is divided into direct trust and indirect trust, which can make the evaluation of trust value more accurate. In addition, a recommendation mechanism is proposed in this paper, the third-party nodes make recommendations according to the actual situation, and accept reward or punishment automatically after interacting. Experiment shows that the model can accurately evaluate the trust between nodes, and effectively resist the attack of malicious nodes.
Research on Zero-Sequence Insulator Detection Technology Based on Deep Learning
Authors: Tao Guo , Lianggang Xu , Hongyun Shi , Fengxiang Chen , Shichun Wang , Xiaowei Liu 
Published: November 2019

Abstract
With the expansion of power grid scale, the method of manual inspection is more and more difficult to implement. Nowadays, it is possible to use infrared imaging equipment and target detection method based on deep learning technology to intelligently detect zero-sequence insulator. In this paper, zero-sequence insulator detection technology based on in-depth learning is proposed to detect zero-sequence insulators with different contamination, air humidity and different locations. This technology has the characteristics of low investment cost, high accuracy and strong adaptability in complex environment, which can reduce the labor intensity and workload of power grid patrol personnel. The high accuracy of the system can effectively reduce the outage accidents caused by the deterioration of insulators in the power grid, thus ensuring the safe and stable operation of the power grid.
An Improved Algorithm of UKF-SLAM Based on RBF Neural Network Adaptive Robot
Authors: Lee X Y , Gao G W 
Published: November 2019

Abstract
For Extend Kalman Filtering (EKF) algorithm in the robot simultaneous localization and mapping (SLAM), the linearization of the nonlinear system leads to the error of the system state equation, the large amount of computation caused by the Jacob matrix calculation, and the noise model uncertainty. Filtering instability and other issues. A modified UKF-SLAM algorithm based on RBF network adaptive is proposed. In the absence of prior noise information, the algorithm identifies the process noise and observation noise through RBF network adaptive identification unit, and iteratively corrects the noise covariance and the mean filter new covariance, so that the real-time positioning accuracy of the robot is improved. The experimental results show that this method has higher positioning precision and adaptive ability compared with the EKF algorithm and UKF algorithm.
Research on Training System Optimization of College Teachers Based on Internet+ and Big Data
Authors: Wang Lei 
Published: November 2019

Abstract
With the advent of the era of Internet+ and cloud computing, more and more mature key technologies of big data, including date acquisition, data processing, data storage and visualization analysis, have provided new thoughts and opportunities for the reform of college teachers training system. In this context, this study analyzed the shortcomings of the traditional training mode and conducted SWOT analysis on application of big data technologies in training of teachers. Under the guidance of the idea of Internet + and big data, this study optimized the training steps including training preparation, training practice, training evaluation as well as training feedback. It also proposed some important issues worth attention from the training departments in universities – to enrich the repository of online open courses, to ensure security of the training data, to strengthen cooperation with the big data management department on campus and to improve the training management system.
Dynamic load identification of stochastic structures based on unscented transformation
Authors: Wang Chongwen , Du Chengbin 
Published: November 2019

Abstract
In view of some unknown parameters in structures, which are described by the Gaussian distribution model in this paper, a new dynamic load identification method for stochastic structures is proposed based on unscented transformation (UT). Firstly, the convolution equation for solving structural response is discretized, and the sampling points, namely, sigma points of stochastic parameters are calculated according to the unscented transformation. Then, when the stochastic parameters take each sigma point, the dynamic load is calculated by the direct inversion method combined with the regularization method. Finally, the mean value and standard deviation of the identified load are obtained, and the coefficient of variation and the upper and low bounds of the identified load are defined. The result of an example shows that compared with the Monte Carlo simulation (MCS) and perturbation method (PM), the proposed method has higher computational efficiency and accuracy.
Visualization of Casing Stress Characteristics under Non-Uniform In-situ Stress and Non-uniform Cement Sheath
Authors: Tao Chen , Wei Zhu , Feng Chen , Qinfeng Di , Guangxu Qin , Qing Li 
Published: November 2019

Abstract
Casing damage is a common problem in oil and gas fields due to the complicated stress state of casing. Especially in the horizontal well section, the casing is difficult to be centered, and the eccentric casing is prone to failure under non-uniform in-situ stress. In order to study the influence of non-uniform in-situ stress and non-uniform cement sheath on the stress state of casing, a numerical model of the coupling of casing, cement sheath and formation is established. The stress distribution characteristics of casing under four conditions of uniform in-situ stress uniform cement sheath, uniform in-situ stress non-uniform cement sheath, non-uniform in-situ stress uniform cement sheath and non-uniform in-situ stress non-uniform cement sheath are compared. The results show that the non-uniform cement sheath will cause the stress distribution of casing to be uneven, and the maximum stress on the casing is proportional to the amount of eccentricity. The non-uniform in-situ stress will cause the casing stress to be uneven and affect the extreme value of the stress on the casing inner wall. The results will provide the theoretical basis for the casing damage prediction effectively.
An Analysis of tourist satisfaction of Scenic spots in Pingliang based on Fuzzy Comprehensive Evaluation method
Authors: Guo Weiwei , Li Chunfang 
Published: November 2019

Abstract
There are some basic steps of the fuzzy comprehensive evaluation method of constructing the satisfaction index system, establishing the weight set, determining the evaluation set, establishing the evaluation matrix,the paper comprehensively evaluates the tourists satisfaction in Pingliang scenic spots.
The Application of Support Vector Machine with Elastic Net Regularizationin in Classification of Wheat Seeds
Authors: Jiaoyang Zhao , Xingshi He 
Published: November 2019

Abstract
Machine Learning is playing a big part in data analysis. Aiming at the problem of wheat seed classification, Seed data from UCI Machine Learning Repository is used as experimental data in this paper, firstly Support Vector Machine (SVM) algorithm is used to classify and predict the test set samples of wheat seed data sets. Secondly, Support Vector Machine (SVM) algorithm with Lasso and elastic net regularization is used to classify and predict wheat seed. The experimental results show that the SVM-Elastic Net model has high classification accuracy.
Chinese Reviews Generation Based on HM-BiLSTM Model
Authors: Yuan Jianglin , Guo Zhigang , Chen Gang , Sun Yihe , Yang RuiPeng 
Published: November 2019

Abstract
HM-BiLSTM model is proposed to solve the problem of inaccurate expression of Chinese comment with specific topic, which is generated by existing deep learning models. Firstly, HM-BiLSTM model was created to generate comments with subject attention mechanism algorithm whose purpose was to assist to constrain the theme. Then, imitation writing sentence was retrieved to extract syntactic structure information from Chinese reviews corpus. The subject as well as structure information was both encoded. Lastly, Chinese comments were generated recurrently by HM-BiLSTM model under the constraints of the subject attention mechanism algorithm and syntactic structure information. The experiment results showed that the model generated comments with better quality and accurate theme.
A Mega-Trend-Diffusion and Monte Carlo based virtual sample generation method for small sample size problem
Authors: Yu Xiaoru , He Yanlin , Xu Yuan , Zhu Qunxiong 
Published: November 2019

Abstract
Data-driven modeling has attracted wide attention in academia because of its effectiveness. However, Due to the lack of data, some traditional modeling methods, such as extreme learning machine (ELM), can’t achieve high learning accuracy. A novel approach based on Mega-Trend-Diffusion (MTD) and Monte Carlo is presented in this paper to deal with the problem, named Monte Carlo Mega-Trend-Diffusion (MCMTD). The proposed approach utilizes MTD to estimate the acceptable range of the attributions and Latin hypercube sampling method to sample. ELM is employed to establish the prediction model. In this paper, two real data sets, the multi-layer ceramic capacitors (MLCC) and the purified terephthalic acid (PTA), are used to verify the effectiveness and reasonability of MCMTD. The experimental results show that MCMTD can significantly enhance the accuracy and ability of the forecasting model.
Dynamic regularization discriminant local preserving projection method for fault diagnosis
Authors: He Yanlin , Yan Xiaona , Zhu Qunxiong , Xu Yuan 
Published: November 2019

Abstract
Due to the high dimensionality, serial correlation, and nonlinearity of industrial process data, the primary task for diagnosing fault is to extract key fault features from fault datasets. In this paper, to obtain much more inherent fault information, a dynamic regularization discriminant local preserving projection approach (DRDLPP) based on feature reduction is put forward to diagnose fault, which addresses the small sample size problem of discriminant local preserving projection (DLPP) by incorporating the regularization term into the objective function of DLPP. The enhanced performance of DRDLPP for fault diagnosis over conventional diagnostic approaches mostly benefits from two aspects: One aspect is that DRDLPP can discover local manifold fault information hidden in original sample space by preserving the local neigborhood structure of data; The other aspect is that DRDLPP has the remarkable capacity to capture dynamic information by extending the observation vector with previous observation vectors. What is more, the information criterion function is utilized to capture the optimal dimensionality reduction order and time lag of DRDLPP method. The experimental results of the Tennessee Eastman process demonstrate that the proposed DRDLPP approach provides a better visual performance and achieve lower misclassification rates in fault diagnosis.
Excavating effective information in different stage of backbone to improve semantic segmentation results
Authors: Han Huihui , Fan Lei 
Published: November 2019

Abstract
To overcome the two difficulties in semantic segmentation task:1) the presence of multi-scale objects, 2) the loss of spatial resolution, a new semantic segmentation model is designed in this paper, which can explore effective information from different stage of backbone. Extensive experiments conducted on two public benchmark datasets have proved the effectiveness of each module, and the new model achieves 73.62% mIoU on Cityscapes test set and 79.88% mIoU on PASCAL VOC 2012 test set.
Research on Extraction of Address from Criminal Case Records via Lattice LSTM
Authors: Ding Xiangwu , Yang Ziqi 
Published: November 2019

Abstract
As an important carrier for recording the details of case in the field of criminal investigation, case records contain address of the crime scene which is crucial to the case analysis. This paper uses Lattice LSTM to extract address from case records which combines the advantages of the character-based and word-based models. It can exploit word and word sequence information without performing Chinese word segmentation. For the purpose of optimizing the model, this paper proposes a word vector template which is generated by address repository of Shanghai to pretrain more accurate and more controllable word embeddings. The experiment shows that the model achieves the performance with the precision rate of 96.49%, recall rate of 94.83% and F1 score of 95.65%.
Air Traffic Controller’s Behavior Recognition from Still Images by Transfer Learning
Authors: Xiang Zheng , He Deyang , Deng Weinan 
Published: November 2019

Abstract
The fatigue problem of air traffic controllers is one of the important reasons that en-danger the safety of civil aviation. In this paper, we introduce behavior recognition into the field of fatigue monitoring of civil air traffic controllers. We investigate the method of automatic recognition of controller’s behaviors by using transfer learning and model fusion. The task of behavior recognition for controllers can be regarded as a multi-classification problem (Such as putting heads on desks for a nap, lay with face upward, nod to sleep, talking with others, directing planes normally, Hold up chin on left hand, writing notes on strips radio check, Resting hand on forehead ). Our study solve this problem from three aspects: (1) Set up a data set of controller behavior, including 18247 pictures of 10 actions; (2) Employ two-stream CNN(Resnet50,Inception V3) to extract different features; (3) We investigate different fusion strategy to improve the accuracy of action recognition of controllers. Experiments demonstrate that our method achieves a recognition accuracy of 94.2% and 93.7% on the CAFUC-Controller and Kaggle-Driving datasets.
Privacy Protection for Recommendation System: A Survey
Authors: Huang Weiming , Liu Baisong , Tang Hao 
Published: November 2019

Abstract
Recommendation system have become one of the most well-liked and accepted way to solve overload of information or merchandise. By collecting user’s personal data for processing, suitable lists of information or merchandise are provided to the potential consumers. For online business, recommendation systems have become an extremely effective revenue driver and developed rapidly. Although recommendation systems are great beneficial, directly exposing privacy data to the recommender may lead to leakage of privacy and cause risks. Therefore, quality of recommendation and privacy protection are both important metrics in recommendation. In this paper we present a review investigating development in recommendation systems with privacy protection, including the definition of privacy, classification of privacy leakage, taxonomy of privacy, measuring of privacy risk, policies for privacy protection, approaches of privacy protection and models of privacy protection. We also speculate on the future direction.
Decoding of Visual Information Based on EEG Data
Authors: Zhou Qing , Liu Yadong , Zhong Saisai , Tang Jingsheng , Liu Yaru 
Published: November 2019

Abstract
We perhaps fantasized about reading the mind of the human brain directly and use it in other ways. In this paper, we tried to analyze human brain activity to identify the feature space employed by humans for visual classification. The brain signals driven by visual information were classified, and the visual information classifier were trained, and the visual information were decoded by the brain signals. The 64 channels EEG data induced by 40 types of images were collected, and recurrent neural network(RNN) was used for extracting features of electroencephalogram(EEG) signals and predicting the types of EEG signals. The accuracy of decoding could reach 80.90%. The technology of decoding of visual information based on brain EEG data could be applied to the brain-computer interface(BCI).
Deep Fusion and Ensemble Neural Networks for Point Sets Data
Authors: Kang Yi , Na Zhang , Yuting Zhao , Yiyan Lei 
Published: November 2019

Abstract
The typical character of point cloud is that its format is irregular, so most researchers transform such data into regular format. However, this conversion may induce some noise. In this paper, we design the dedicated neural network unit for point sets data processing. Firstly, to guarantee the permutation invariance of point sets, we infer the neural unit design principles. Secondly, we propose the deep fusion and ensemble neural networks for point sets data, in which we can not only combine different scales of feature representation, but also input the point data into the network directly. Finally, we evaluate the proposed networks on object classification and object part segmentation, which can verify the efficiency of our network.
Fuzzy Control for Off-center Steering System of a Novel Electric Chassis Based on PWM Technology
Authors: Qu Jiwei , Li Yining , Song Shujie , Guo Kangquan 
Published: November 2019

Abstract
A flexible chassis (FC) for an electric vehicle with four in-wheel drive motors is described in this paper. It has a unique structure with an off-center steering shaft. The driving system and steering system are combined, which has considerable advantages in terms of actuation flexibility for the confined spaces. However, there is no rigid constraint for steering, and it is difficult to coordinate the motions of electromagnetic steering lock (ESL) and the wheel. To solve this problem, a pulse-width modulation (PWM) technology is introduced to control the ESL. With the control of PWM signal, the chassis can achieve stepwise driving and steering. Then, fuzzy logic is introduced to control PWM signal of the ESL so that it can match the steering motion of the wheels. To validate the system, steering signal step input tests and sinusoidal input tests were conducted on a self-made test bench. The results demonstrate that, the off-center steering system is with good steering performance, and the linkage and tracking errors of the left and right wheel are all in an acceptable range. The proposed control strategies are feasible and effective, and can ensure smooth turning for the chassis.
A novel hierarchical clustering approach based on data gravitation model
Authors: Zhang Peng , Xiang Xiwang , She Kun 
Published: November 2019

Abstract
The purpose of clustering is to group the data points into several clusters according to their similarity. Inspired by law of gravitation, each data point considered as an object is exposed in data space and influenced by the data gravitation of the other data points. In this paper, a novel hierarchical clustering approach (NGHC) is proposed based on local data gravitation. Firstly, the dataset is partitioned into some clusters as intermediate result by a simple gravity-based clustering approach. Then the intermediate clusters are merged by a new linkage measure which combines the data gravitations between two clusters until the satisfactory result is obtained. Two real-world datasets are used to validate the clustering performance of NGHC compared with five representative clustering algorithms. The results exhibit that the NGHC algorithm obtains the best performance on low- and high-dimensional datasets.
Multi-Variant Execution Research of Software Diversity
Authors: Liu Zhenwu , Zhang Zheng , Zhang Jiexin , Liu Hao 
Published: November 2019

Abstract
As more and more software products are threatened by malicious reverse analysis, along with software products are pirated, tampered with and so on, it is of great significance to study software security protection technology in depth. As a software security protection technology, software diversification introduces uncertainty into the target program and provides probabilistic protection for the target program. Multi-Variant Execution (MVE) are fine-grained implementation of software diversification that produces functionally identical variants at the system call level. This article first introduces the related concepts of multi-variant execution. Secondly, it expounds the key technologies of multi-variant execution implementation-variant generation, variant monitor, input/output and synchronization, monitor-variant communication. Security of monitors and different variant communication technologies are analyzed and compared, their advantages and defects are pointed out respectively. Several implementation methods of multi-variant execution design are summarized. Finally, the reasons for the false negatives and false positives of attack events are analyzed, and the prospects and challenges of using multi-variant execution techniques to implement security systems are summarized.
An improved algorithm for radar adaptive beamforming based on machine learning
Authors: Bai Moyu , Liu Hao , Chen Haochuan , Gu Shengming , Zhang Zhenhua 
Published: November 2019

Abstract
In the field of radar digital signal processing, adaptive beamforming is a widely used technique for suppressing interference and noise. The Least Mean Square Algorithm (LMS) is a simple and easy algorithm for adaptive digital beamforming. However, it has the disadvantage of not achieving a balance between convergence speed and stability. In order to improve the performance of adaptive beamforming, this paper firstly reviews the classical LMS algorithm and then the machine learning optimization algorithm. Improvement effects of the three machine learning methods on the LMS algorithm are analyzed. The results show that the improved LMS algorithm based on AdaGrad exhibits the best performance. The algorithm can independently adjust the adaptive learning rate of different parameter components, making the iterative process of the adaptive beamforming more stable, efficient, and suitable for both theoretical research and engineering practice.
A Precautionary Loss-based Bayesian Algorithm for Zero Failure Analysis of Exponential Model
Authors: Zhang Fan , Liu Yongjing , Luo Laijun 
Published: November 2019

Abstract
The aim of this paper is to study the Bayesian reliability analysis of the exponential distribution under a precautionary loss function based on zero failure data. In the Bayesian algorithm, the prior distribution of the parameter is assumed to be Gamma distribution. Bayesian and E-Bayesian estimators of the failure rate and reliability of exponential model are obtained under the precautionary loss function. Finally, a practical example is utilized to validate the effectiveness and robustness of the proposed estimators.