bluuebunny/crossref_metadata_2025_split huggingface dataset:
Big Data Analytics of Movie Rating Predictive System
Authors: Choudhry Neeta , Xie Jianyun , Xia Xiaoling 
Published: July 2020

Abstract
In every organization the data is a significant part that can be separated as structured, unstructured and semi-structured. Unstructured data cannot be administered in the real-time by RDBMS or Hadoop. PySpark can be used for realtime data analysis of movie rating data collection. To generate the modified recommendations, method is intended that is Recommender Systems. Some complications that a Recommender System encountered are data sparsity, cold start, popular bias and scalability. In this research paper collaborative technique is applied for m-ovie rating recommender system. By getting benefits of collaborative filtering technique, we conclude that we use perspective metadata that is freely accessible. Concealed suggestions occur in the middle of movie ratings to personalize recommendations. Our recommended methodology demonstrate novelty by providing the modified recommendations irrespective of research field and of customer’s capability. By the usage of MovieLens dataset a significant development above supplementary standard techniques in evaluating global enactment and capability to yield appropriate and top-ratings at the top of the recommendation list.
On Approximate Querying Large-Scale JSON Data
Authors: Lv Teng , Yan Ping , He Weimin , Wang Tao 
Published: July 2020

Abstract
JSON is a kind of data exchange format based on data types of JavaScript. JSON has been widely used in data exchange applications. To process and query large JSON file is time consuming. It requires not only massive storage disks to store large amounts of JSON data and its intermediate results, but also large CPU times to query and process the massive JSON data. so ordinary methods usually face many difficulties in such cases. Therefore, the key problem of query processing large JSON file is how to process queries efficiently. Therefore, approximate processing large-scale JSON query is an effective way to reduce query response time. So research of approximate query of large-scale JSON data can improve user’s query experience and query effect. This paper researches the major questions of approximate query of large JSON file, such as core query features of JSON model, and methods of approximating query large-scale JSON data.
Research on 3D Facial Expression Simulation Technology
Authors: Wang Zhongmin , Liu Li 
Published: July 2020

Abstract
In order to generate realistic facial animation, this paper uses depth learning object detection technology to detect the three wrinkles of human face, and then the adaptive threshold algorithm is used to extract the wrinkle curves, finally applies realistic wrinkle animation to the virtual face model. In the aspect of expression animation simulation, based on MPEG-4 standard, a database of expression parameters covering six major expressions has been established, which divide the human face into four main animation units and other auxiliary animation units. According to the displacement of animation unit between neutral expression and other expressions, the facial bone points of virtual model are driven to move, thus realizing realistic facial expression animation.
The Design and Implementation of Information Management and Control System for Military Products Intelligent Assembly Line Based on JIT
Authors: Zhao Yuenan , Du Jinsong , Li Xiang , Hua Jinhu , Liu Qingjun 
Published: July 2020

Abstract
To enhance the capabilities of advanced military product manufacturing, this paper carries out a new design and implementation of advanced military product assembly line. Through research on the current bottleneck problems and the aims of the production line, the system structure is designed and the function requirements are analysed to complete the software architecture design. Finally, the JIT information management and control system is completed based on information technology and lean manufacturing.
Design and Implementation of 400 MHz Video Real-Time Transmission System Based on Self-Organizing Wireless Mesh Network
Authors: Gao Li , Yang Jiali , Lan Mingying , Ren Xupeng 
Published: July 2020

Abstract
At present, the common wireless multimedia transmission systems are mainly based on mobile networks and wireless local area networks. Most of the devices work in the 2.4 GHz/5 GHz frequency band and have poor signal penetration capability, which is not suitable for complex environment applications. Meanwhile, Wireless mesh networks are characterized by self-organization, self-healing, multi-hop and so on. In this paper, we down-converted the 2.4 GHz Wi-Fi signal, designed and implemented a wireless mesh network architecture broadband wireless multimedia transmission system based on the IEEE 802.11s protocol, which works at 400 MHz, the open band I. The system consists of a mobile central node station and 15 simple mobile stations to form a self-organized network, realizing independent wireless multimedia transmission in complex environment. According to results, its signal can diffract most objects in real scenes, such as 2-storey basement and 3-storey above ground buildings, which has high mobility, high reliability and meets complex wireless environment applications such as complex buildings, basements, jungles, etc.
An Improved Particle Swarm Algorithm with Heuristic Factors and Empirical Operators
Authors: Gao Chao , Jiao Libin , Li Xiaofeng 
Published: July 2020

Abstract
An improved particle swarm algorithm (PSA) is proposed based on the universal framework of the intelligence optimization algorithms (IOA). Regarded as increment to the standard PSA, the improved PSA takes the essence of the standard PSA, and additionally utilizes various heuristic factors and different empirical operators introduced by multiple other IOA. The capability of global and local optimization can be strengthened by the free search, the differential evolution and the quantum rotation comprehensively. The genetic variation is adopted to avoid premature convergence, while the greed strategy is adopted to guarantee monotonous convergence. The numerical simulation indicates that the improved PSA achieves better performance than the standard PSA.
Study on Demonstration of Mulched Drip Irrigation in Maize Field under Surface Water in Mountain Area
Authors: Sun Xiaowei , Liu Yanhong , Wang Shuhong 
Published: July 2020

Abstract
In recent years, the technology of water-saving irrigation in agricultural irrigation is strongly supported and promoted. Plastic film mulching cultivation with Chinese characteristics has been widely promoted and applied in plain areas, but also is exploring researched in mountain area. In this paper, according to conditions of complex mountainous topography and elevation changes, mulched drip irrigation in maize field is designed and studied under surface water as water source. The reasonable optimization of design and obtained economic efficiency are also explained.
Application Research of Process Design Analysis based on Digital Technology
Authors: Fan Hongbin , Luo Yiheng 
Published: July 2020

Abstract
With the improvement of people’s aesthetic concept, simple bamboo-rattan knitting technology has been unable to meet the people’s design requirements for fur fabric, people hope that fur products have a sense of fashion, artistry and personalized characteristics. We study the development of bamboo-rattan knitting technology, adopting survey method, literature analysis method for data collection, mining the design methods of bamboo-rattan knitting technology in the field of packaging design. From the perspective of bamboo-rattan packaging, we analyze several common methods, but the designing efficiency is low, and the designing accuracy is not high. In order to further improve efficiency of designing, we introduce the digital technology, which can provide an interactive platform for designers, control the whole design process, monitor the production of design schemes and the effect of design schemes, and this technology can achieve standardized production, improve design accuracy, and adopt CAD, Photoshop and 3Dmax, which can be taken as digital technology platform to realize the simulation analysis of bamboo-rattan packaging design. We take digitalization technology as the main method, and integrate with the existing bamboo-rattan packaging methods, put forward the design process of bamboo-rattan fur knitting technology packaging based on digitalization technology, design the bamboo-rattan fur knitting packaging, therefore, get the design effect of bamboo-rattan packaging based on digitalization technology, and confirm that this method can improve the design efficiency of bamboo-rattan knitting products, but also meet the needs of people for bamboo-rattan fur knitting products.
Design and Implementation of SQL Injection Vulnerability Scanning Tool
Authors: Zhao Juanjuan , Liu Changhua 
Published: July 2020

Abstract
According to the “Top Ten Security Vulnerabilities List” (OWASPTop 10) released by OWASP in 2017, SQL injection attacks are still at the top of the list, and there are many ways of SQL injection attacks, which cause great harm. Although there are many vulnerability scanning tools, there is still a high rate of false negatives. Aiming at the current problems of SQL injection vulnerability detection, this paper proposes a scanning tool for SQL injection vulnerabilities. First, use the crawler framework scrapy to obtain the URL associated with the form and the a tag, and segment the URL based on the improved simhash algorithm. Deduplicate the link, then analyze the injection point to modify the URL parameter value injection test, and determine whether there is a vulnerability based on the response result of the server. The experimental results show that the detection method achieves a 96.50% URL deduplication rate in the crawler module, which greatly reduces the rate of false negatives. It is more suitable for detecting whether a website has a SQL injection vulnerability.
Design and Research of Omni-directional Moving AGV
Authors: Song Li , Ju Hong , Li Weixin , Sun Chongshu , Yuan Bing 
Published: July 2020

Abstract
Omni-directional mobile AGV can operate flexibly in a narrow space, which can replace heavy and dangerous labour to a greater extent and improve labour productivity. In this paper, the steering wheel integrated with walking and steering functions is taken as the driving unit, and the main research contents include: through digital structure design, the chassis structure and electrical control system of omnidirectional AGV are designed. Experiments show that the design realizes flexible operation of zero turning radius and 360-degree omnidirectional movement in narrow space, and meets the requirements of robot handling operation in complex environment.
On-line Defect Detection and Classification of Latex Gloves
Authors: Wang Xiangming , Zhang Zhongkai 
Published: July 2020

Abstract
At present, the detection of latex glove is mainly done manually. In order to realize the online detection of latex glove, this paper presents a method of defect detection and classification of latex glove based on multi-feature extraction to achieve online detection and classification of latex glove. Firstly, the target pixel and the background pixel were segmented using the OTSU method. Secondly, the image was pre-processed using median filtering, edge extraction and morphological closed arithmetic processing, and then the defect was framed using the minimum circumscribed rectangular frame selection method. Finally, the characteristics of three kinds of defects were proposed by analysis. Feature vectors were extracted using texture features and HOG features. The classification model selects SVM support vector machine to train the classifier to realize the classification of defects. When training the classifier, the Gaussian radial basis function was used as the kernel function. In view of the characteristics of the types of defects in latex glove, first classify the defects in a “one-to-one” manner, and then implement multi-classification. The experimental results show that the online defect detection and classification of latex glove based on multi-feature extraction has achieved good results.
RETRACTED: Research on Financial Management Innovation of Small and Medium Enterprises in the Context of Big Data
Authors: Jiang Lijun 
Published: July 2020

Abstract
The era of big data provides new vitality for the development of enterprises. And the traditional application pattern of financial management cannot come up with the development requirement of the era of big data. Financial management is the core work of enterprise operation and management, and good financial situation can support the sustainable development of enterprises. This paper launches the research on the innovation of enterprise financial management in the era of big data, which includes the analysis of the current situation of financial management in the era of big data and the the accordingly various impacts on enterprise financial management, the discussion of the advantages brought to enterprise financial management in the era of big data, and further discussion of the innovation of enterprise financial management in the era of big data, aiming to enable enterprises to correctly cope with the context of the era of big data, and make financial optimization management play an important role in the innovation and development of enterprises.
Load Balancing Mechanism Based on Linear Regression Partition Prediction in Spark
Authors: Huang Zichun , Wei Wenguo , Xie Guiyuan 
Published: July 2020

Abstract
With the rise of cloud computing, the internet of things, social networks and other technologies, information data is expanding rapidly, and traditional processing and storage systems have been difficult to deal with massive data. Spark is a fast and efficient MapReduce implementation developed after Hadoop. However, the shuffle operation in spark will cause the data set on some working nodes to be too large, while other nodes may be idle, which will affect the performance of spark jobs. This phenomenon is called data skew. Aiming at the problem of data skew in spark platform, this paper proposes a load balancing mechanism based on linear regression partition prediction: SP-LRP (Spark load balancing mechanism based on Linear Regression Partition), SP-LRP predicts the partition size of the reduce tasks at run-time, leverages the skew detection algorithm to identify the skew partition and adjusts the task resource allocation according to the fine-grained resource allocation algorithm. We use the benchmark dataset to evaluate SP-LRP, and compare the average execution time of two algorithms and the load balancing degree of reducers in different situations. The experimental confirmed the efficiency of SP-LRP in their respective usage scenarios.
Blind Super-Resolution for Single Remote Sensing Image via Sparse Representation and Transformed Self-Similarity
Authors: Liu Yuhang , Sun Weidong 
Published: July 2020

Abstract
Super-resolution (SR) reconstruction is one of the effective ways to improve the spatial resolution of remote sensing images, but the blur kernel estimation without any additional prior information is the key to the quality of SR reconstruction. Facing the above problem, a blind super-resolution method via sparse representation and transformed self-similarity is proposed in this paper. In this method, the blur kernel as well as the reconstructed high-resolution image are estimated at the same time using the structural self-similarity from a single image, a down-sampled version of the observed image is used as the training samples for the dictionary learning, and the internal patch search space is expanded using the transformed self-similarity to improve the quality of estimation. Experiment results show that, our method performs well both in quality of kernel estimation and SR reconstruction.
Logistics Support Path Planning Model of Forest Fire Based on Ant Colony Algorithm
Authors: Tian Shuchao , Zhou Ziqing , Wang Tiejun , Xia Qing , Wen Zhehua , Wang Baoxue , Liu Ling 
Published: July 2020

Abstract
The problem of the network node and the complex constrain in the path planning has become a difficulty in forest fire logistics support. The traditional ant colony algorithm is improved. We establish two objective functions for the distance between nodes and the time required to finish task. We optimize the two objective functions. We redesigned the calculation method of heuristic information and the update function of pheromone. We set a time window to limit the time to complete urgent tasks. Finally, an example is given for verification. The results show that the improved algorithm can solve the problem of logistics support path planning model of forest fire. The results are more close to the actual situation.
Research on Target-Driven Navigation of Mobile Robot Based on Deep Reinforcement Learning and Preprocessing Layer
Authors: Yu Gang , Zhang Chang , Xu Jiexiong , Sun Tongda 
Published: July 2020

Abstract
Recently, with the rise of deep reinforcement learning model, robot navigation based on this method has a huge advantage compared with traditional slam method, which has attracted extensive attention. However, when the navigation algorithm trained in the virtual environment is transferred to the real environment, the navigation performance of the robot will decline sharply because of the great difference between the virtual environment and the real environment. In order to improve the navigation ability of mobile robot, this paper implements a mobile robot navigation system based on deep reinforcement learning without environment map and only visual input. At the same time, in order to solve the problem of poor generalization ability of deep reinforcement learning from virtual environment to real environment, this paper proposes a preprocessing layer with knowledge and combines it with deep reinforcement learning module. The combined algorithm model alleviates the performance fault problem caused by the migration algorithm and the performance difference between virtual sensor and real sensor. At the end of this paper, a navigation experiment based on the turtlebot is designed, which proves that the deep reinforcement learning algorithm with the preprocessing layer can alleviate the performance fault problem caused by the migration algorithm, and have a certain ability of obstacle avoidance and avoidance without the environment map.
X-DenseNet: Deep Learning for Garbage Classification Based on Visual Images
Authors: Meng Sha , Zhang Ning , Ren Yunwen 
Published: July 2020

Abstract
In order to effectively solve the problem of garbage classification, this paper designs a garbage classification model based on deep convolutional neural network. Based on Xception network, combined with the idea of dense connections and multi-scale feature fusion in DenseNet, the X-DenseNet is constructed to classify the garbage images obtained by visual sensors. This paper conducts experiments through the process of “obtaining dataset-preprocessing data-building X-DenseNet model-training and testing model” and the accuracy of the model on the testing set is up to 94.1%, which exceeds some classic classification networks. The X-DenseNet automatic garbage classification model based on visual images proposed in this paper can effectively reduce manual investment and improve the garbage recovery rate. It has the vital scientific significance and application value.
Three-branch Semantic Segmentation Network
Authors: Wei Lihua , Ma Yingdong 
Published: July 2020

Abstract
Effective Semantic segmentation requires both spatial details and object-level semantic information. Meanwhile, context information is also important for complex scene understanding. However, it is hard to meet these demands simultaneously in the top-down CNN structure. In this paper, we tackle this problem with a Three-branch Semantic Segmentation Network (TSS Net). The proposed TSS Net consists of three parts, including a spatial network, a semantic network and a context network. The spatial network utilizes convolutional layers with small stride and a spatial pyramid pooling module to extract multi-scale spatial features. In the semantic network, multiple level features are combined to enhance semantic information. The context network integrates different scales contextual information to facilitate objects localization in complex scene. The proposed semantic segmentation framework has been evaluated on the CamVid and the Cityscapes datasets. Experimental results demonstrate that the TSS Net achieves state-of-the-art performance.
Design of Trajectory Planning System for River Crab Farming with Automatic Feeding Boat
Authors: Wang Xiaoyu , Hong Jianqing , Sun Yueping , Zhao Dean 
Published: July 2020

Abstract
The bait feeding of river crab farming needs to be covered evenly on the whole pond. At present, feeding is mainly carried out by manual driving or remote control ship-borne feeding machine on the pond. The feeding accuracy and efficiency are relatively low, and it is difficult to guarantee feeding effect. In view of the above situation, based on the mobile automatic feeding boat equipped with GPS, this paper proposes a complete coverage trajectory planning method with irregular quadrilateral pond, and a trajectory planning system based on SuperMap Objects is designed to generate reciprocating traverse feeding trajectory automatically. The simulation is performed to verify the feasibility of the trajectory planning method. The results show that the trajectory planning system can meet the requirements of automatic and uniform feeding on the crab pond.
An Evaluation Model for Authors’ Academic Influence Based on Multi-source Heterogeneous Database in Bilingual Environment
Authors: Deng Zhijian , Zhang Zhenhua , Xiong Chun , Chen Mingqin , Huang Yanting , Li Junran , Shen Jiawen 
Published: July 2020

Abstract
As the internationalization of academic rise up, it is insufficient to evaluate a scholar’s influence only taking domestic academic contribution as reference. To have a better understanding of scholars’ academic contribution in an international scale, this paper proposes a new evaluation system of academic influence based on the optimal dataset of entity recognition in bilingual environment. The new evaluation system has been experimented on a real literature dataset of DBLP. The research is conducted by the following steps. First, handle bilingual data. Second, optimize the literature data with entity recognition technology. Third, make an academic influence evaluation. Experiment results show that the new evaluation system is feasible and reasonable.
Left and Right Consistent Stereo Image Detection and Classification Based on Deep Learning
Authors: Tang Lijuan , Wang Qing , Chen Guannan 
Published: July 2020

Abstract
With the development of stereo camera technology, the increasing of the data volume of binocular image makes the target detection and classification of binocular image become one of the research directions in the field of computer vision. Compared with monocular image, binocular image contains the depth information of the image. For using the depth information of the image to improve the target detection and classification accuracy of image, this text puts forward a method of detecting and classifying left and right consistent objects in stereo images based on deep learning, which is based on the Mask R-CNN network and adds the binocular matching algorithm to improve the accuracy of image target detection and classification. The experiment compares the evaluation indexes of monocular and binocular.