bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Lightweight and Fast Image Style Transform Algorithm
Authors: Xue Nan , Duan Jin , Song Xuanmeng , Han Xuehui , Yu Lintao 
Published: July 2019

Abstract
Image style transform technology refers to the use of a convolutional neural network to extract the style of a famous painting, thereby converting the input image into a corresponding style image. The current methods are mainly divided into two types, namely, the style transform method proposed by Gatys and the fast image style transform method (FNST) proposed by Li Feifei. The common problem between the two methods is that the hardware requirements are high, which is not conducive to a wide range of popularization and application. In order to use image style transform on more terminals and even mobile phones, research h on lightweight and fast image style transform is of great significance. This paper draws on the deep-wise split convolution in MobileNets to prun the convolutional layer in the loss network. After the pruning, the parameter content is only 11% of the original model; the residual component in Image Transform Net is trimmed. The parameter content was reduced by 96% compared to the original model. However, the amount of parameters in the process of weight reduction is greatly reduced, which may lead to the degradation of the stylized effect. It is proved by experiments that the style degradation of the method is not obvious, that is to say that the performance loss caused by the reduction of the parameter quantity is acceptable.
Multiconstrained routing based on artificial bee colony algorithm and dynamic fireworks algorithm
Authors: Zhang XianWei , Hu Youbing 
Published: July 2019

Abstract
In this paper, a mathematical model for multiconstrained routing optimization problem is established. The multi-objective optimization problem is transformed into a single-objective optimization problem by adding a penalty function. Then the artificial bee colony algorithm (ABC) is used for route search. Because the ABC algorithm is easy to fall into the local optimal deficiencies, the dynamic fireworks algorithm (dynFWA) is introduced for local search, which can ensure fast global search and fast guarantee. In the process of searching for the optimal solution, the success rate is improved by about 1.05% compared with the PSO_ACO algorithm optimized by the ant colony algorithm, which is 6.18% higher than the standard PSO algorithm and the standard ABC algorithm. The minimum average cost of the search is about 0.53% higher than the PSO_ACO algorithm, which is about 1.87% higher than the other two algorithms. The simulation results show that the algorithm can effectively solve the multiconstrained routing problem under large-scale networks.
An Improved Multidimensional Query Language for Oil and Gas Development Data Warehouse
Authors: Zhao Jidong , Yao Tingzhen 
Published: July 2019

Abstract
Multidimensional data analysis is the most central technique for processing data in data warehouse and On-Line Analysis Processing (OLAP). In order to solve the problem of multi-dimensional analysis expression and query processing in oil and gas development data warehouse, this paper proposes an improved multi-dimensional query language application method to realize effective interaction between query terminal and OLAP server. This formal language has the following advantages, such as simplicity, flexibility and easy to use. It also meets the needs of multi-dimensional query for oil and gas data analysis, and has various display modes. The results can be displayed in two-dimensional space. And it provides an extended function beyond the standard MDX, simple grammar format, concise and clear semantic expression, and is conducive to query processing conversion.
Implementation of system for college students’ career planning based on user interest model
Authors: Li Xiaomin , Zhang Jianrong , Wan Jiabing , Zhang JinKai , Zhu Chenchao , Guo Nianfeng , Xu Hao 
Published: July 2019

Abstract
In order to help users discover their real interests, and recommend positions and tasks that match their interests scientifically and accurately, the system is different from most of the ability enhancement and job-hunting system on the market. This system relies on artificial intelligence technology, and we design the function of long-term companionship and personalized solutions to help users find the career direction they are interested in, and push tasks periodically according to the recommendation algorithm to help users improve their vocational skills, ultimately to help college students to identify what they really like and rapidly improve their ability.
Study for Submerged Arc Welding Tracing Based on Hopfield Neural Network
Authors: Song Nan , Wu Xiaomeng 
Published: July 2019

Abstract
This paper presents a new method based on Hopfield neural network to find out the welding center. The method transforms the problem of image processing into an optimization problem for searching the welding center. The energy function is constructed to meet the properties of welding image such as great noise. The algorithm combined with median filtering and neural network is also put forward. Examples show that the algorithm is practice and effective in detecting the welding center.
RETRACTED: Research on Information Management Model of Petroleum Enterprises under the Big Data Background
Authors: Yang Jinquan 
Published: July 2019

Abstract
Application of big data is analysed via the present information management of petroleum enterprises in this paper. The logical structure and application architecture are proposed based on hodiernal production, administration and management of oil firms in China and mainstream big data technology. Meanwhile, future applications of the information management are looked ahead under big data environment.
A survey of power flow calculations considering distributed generation
Authors: Wu Xiaomeng , Guo Xinyu , Yang Mingyue , Shi Zheng 
Published: July 2019

Abstract
With the construction of China’s smart grid and the gradual implementation of the power market, the traditional centralized power grid mode has been unable to meet the demand of electricity in today’s society. The introduction of distributed generation has become a new trend in the future development of power grids. Due to the introduction of distributed generation, many new node types will appear in the distribution network. It is often difficult to achieve the expected results when using traditional power flow algorithms when dealing with these nodes. Therefore, it is particularly important to study the power flow calculation of distribution networks with distributed generation. This paper discusses in detail the impact of distributed power sources on distribution networks and the current research status of distribution network power flow calculations with distributed generation.
Some Properties of the WJ Distribution and Implication in Information Theory
Authors: Liang Geying , Xue Han , Jia Qiong , Wu Junhua 
Published: July 2019

Abstract
The WJ probability density distribution function describes a general mechanism for various stochastic processes including extreme events and critical phenomena. This work investigates the potential application of the WJ distribution in information theory, by means of exploring the distribution itself, the probability density distribution function of information entropy and an expression for relative information entropy. Changing the multiple parameters of the function, the WJ probability density distribution function as well as the corresponding information entropy function distribution and relative information entropy is systematically analysed and compared. The characteristics of the WJ probability density distribution function and information entropy function are explicitly manifested, showing application prospective of the distribution in information theory.
PM2.5 Prediction based on Multifractal Dimension and Artificial Bee Colony Algorithm
Authors: Zhang Chen , Wang Xiaofeng , Chen Shengbing , Zou Le , Tang Chao 
Published: July 2019

Abstract
PM2.5 pollution is becoming more and more serious in China. A novel feature selection method based on multi-fractal dimension (MFD) and artificial bee colony algorithm (ABC) is proposed to improve the prediction accuracy of PM2.5. In this method, the MFD is used as the evaluation criterion of feature subsets, and an improved ABC is taken as the search strategy. In this paper, we first use the MFD+ABC method to select the optimal feature subset, then use SVR to predict the next day’s concentration of PM2.5 in Shanghai and Guangzhou. The experimental results show that the proposed method has better performance in both the number of selected features and the prediction accuracy.
An Improved Method for Interest Point Detection in Human Activity Video
Authors: Hu Zhe , Zhang Hong , Yang Yang , Yang Chujun 
Published: July 2019

Abstract
An improved method for human activity recognition in RGB-D video using improved color-depth local spatio-temporal features (CoDe4D) detection method is presented in this paper. Firstly, histogram equalization and correction function are employed to suppress noise of rgb frame and depth frame, respectively. Then a saliency map is constructed by the improved CoDe4D method. In this way, feature points can be obtained by the saliency map, which integrates both depth information and RGB information. Next, the depth cuboid similarity feature (DCSF) is utilized to describe feature vectors. Meanwhile, visual words are generated by bag of feature method. To further improved the estimation accuracy, the SVM with generalized histogram intersection kernel is applied to train and predict categories. It shows good performance on MSR Daily Activity 3D datasets.
Quantification of emotional values for attributes from sentiment analysis based on mobile phone reviews
Authors: Tao Wan , Zhang Qiang , Zhang Mengyao , Jiang Yan 
Published: July 2019

Abstract
E-commerce online reviews are commonly short in length, lack of contextual semantics, and are mixed with explicit emotions and implicit emotions. The subjects of implicit emotions are not easy to be extracted and be defined. Now product-oriented attributes are used to define implicit and fine-grained emotions with the help of the domain emotion dictionary that is constructed by extracted explicit attribute-emotion pairs through using a variety of part-of-speech rules. And implicit emotion and attribute pairs for product “mobile phone” are mined with the help of explicit attribute-emotion pairs and probability calculation method. At last, explicit and implicit emotion values are calculated by considering degree and negative modifiers. The experimental results show that proposed method is effective and has certain commercial value. And the process method is easy to extend to other product fields.
Reviews and analyses the privacy-protection system for multi-server
Authors: Hwang Min-Shiang , Fajar Cahyadi Eko , Chiou Shu-Fen , Yang Cheng-Ying 
Published: July 2019

Abstract
Zhu proposed a provable privacy-protection system which could achieve two kinds of privacy protection and switch between them optionally by users. He claimed his scheme could achieve anonymous, privacy-protection, and practical multi-server architecture. His scheme is based on chaotic maps. He thus claimed his scheme can not only own high efficiency and unique functionality but is also robust to various attacks and achieves perfect forward secrecy. However, in this article, we will review Zhu’s scheme, applied in a multi-server environment and adopted in the provable privacy protection system. In addition to the review, we analyzed its protocol and elaborated its weakness of resulting insecurity.
Research on the Relationship between Virtual Economy and Real Economy Development in China
Authors: Shang Yu 
Published: July 2019

Abstract
The development scale and degree of virtual economy should be adapted to the development level of real economy, and maintaining a moderate development proportion relationship between the two is the key to promoting sound and rapid economic development. Based on the perspective of economic development and the logistic model in population ecology, this paper constructs a regional virtual economy and real economy development model on the basis of the relationship between virtual economy and real economy development, studies the equilibrium and stability of the coordinated development of the two, and empirically studies the coordinated development ratio of virtual economy and real economy in China using the relevant data of the development of virtual economy and real economy from 1996 to 2008. The coordinated development relationship between virtual economy and real economy can reflect the evolution trend of economy. The coordinated development ratio can play a certain early warning role for the excessive expansion of virtual economy, and effective monetary and fiscal policies can be formulated according to the relationship between the actual ratio and the reasonable development ratio.
Cultivation of Computational Thinking in College Computer Basic Teaching
Authors: Ru Zhang 
Published: July 2019

Abstract
Cultivating college students’ computational thinking is an important teaching plan in computer basic teaching in university stage. Teachers should integrate the teaching content according to the level of computer basic knowledge and ability of college students, classify the basic teaching contents of college computer scientifically and increase the part of experimental teaching in the college computer teaching classroom. Using scientific computer teaching methods can improve students’ computational thinking effectively, so that the comprehensive ability of college students can be improved.
An Audio-Visual Whisper Database in Chinese
Authors: Zhou Jian , Hu Yuting , Lian Hailun , Pang Cong , Wang Huabin , Tao Liang 
Published: July 2019

Abstract
Converting whisper to normal vocalized speech has been a hot research topic in speech signal processing area. A complete and large scale whisper database is a major basis for this task. In this paper, we propose a multimodal whisper database in Chinese mandarin. A total of 103 syllables and 100 sentences were carefully selected. 5 male and 5 female participants pronounced the syllables and sentences in whisper and normal styles respectively, result in 4096 parallel speech utterances and 263, 849 frames of voicing face and lip image sequences. The beginning and ending sample point of each syllable were labeled both for speech signal and voicing face video. The lip region of interest were also extracted and provided in the proposed database. Experiments in various speech conversion tasks in different speech database show the effectiveness of the proposed multimodal whisper speech database.
A tracking method combines with detection algorithm based on deep separable convolution network
Authors: Shi Yanni , Liang Xun 
Published: July 2019

Abstract
With the emergence of a large number of artificial intelligence technologies, deep learning has become the key technology in computer vision area. Object tracking is one of the most important technology in the field of computer vision. Thus we studied about tracking algorithms and proposed a method mainly hopes to solve the occlusion problem in complex tracking scene. Using object detection algorithms based on deep learning to increase the speed of associations and improve tracking effect. It can return the position of the tracking object unsupervised. Then extract features to store in features library, so that the prediction of trajectory whose features can highly be matched is more accurate and the associations are more reliable. Experiment shows our tracking algorithm combines with detection algorithm based on depthwise separable convolution networks not only has a smaller and faster model but also achieved a robustness and real-time tracking in scene where objects are under occlusions.
Research on Small Target Recognition Technology Based on Deep Learning
Authors: Li Yuewu , Hu Jianwang , Ji Bing 
Published: July 2019

Abstract
All small target recognition has always been a difficult problem in the field of target recognition. Traditional target detection methods have met a bottleneck. With the development of deep learning, the field of target recognition has witnessed a boom based on deep learning. According to the characteristics of the small target recognition, this paper briefly introduces the traditional target recognition method, and studies the methods of target detection based on the deep learning. It mainly summarized the development of research on target detection algorithm based on deep learning. Finally, it summarized the influence of the depth study on target recognition and the trend of development in the future.
Analysis of the design of military building skin under the guidance of camouflage concept
Authors: Zhou Ruochong , Zhu Kaer , Liu Yang 
Published: July 2019

Abstract
It is very important to apply the camouflage concept to military building design. With the increasing attention of the building skin, there are more and more researches on the use of the building camouflage skin to achieve the purpose of militarization. Based on this, this paper summarizes and studies the ideas, means and methods of building skin design, with a certain reference value for the use of the military building skins in China.
RETRACTED: Research on Clustering Algorithm Based on Big Data Background
Authors: Wu Chunqiong 
Published: July 2019

Abstract
In recent years, machine learning has developed rapidly and clustering is an important research direction in the field of machine learning. After continuous and in-depth research, the clustering algorithm for small-scale data sets has made great progress and many effective clustering algorithms have been studied. However, these algorithms are difficult to obtain satisfactory results when dealing with large-scale data sets. The reason is mainly due to the high computational complexity and the weak ability to process high-dimensional data. It is more and more important to process these massive data in time. Therefore, the research on clustering algorithm for large-scale data sets has become one of the important tasks in the field of machine learning. This paper summarizes the meaning of big data and clustering algorithm, sorts out the clustering algorithm suitable for big data and gives suggestions on the research of clustering algorithm under big data. It is hoped that some research achievements can be achieved in the following studies.
Research on Application of Missile Blockchain Based on Nation Secret Algorithm
Authors: Fang Yi , Cong Linhu , Deng Jianqiu , He Weixin , Chen Yuliang 
Published: July 2019

Abstract
Missile data has the characteristics of multiple data items, large data volume and high data value, which puts forward higher requirements for data storage security. The application of block chain technology in missile business data storage can realize the safe, reliable and decentralized storage of data. The SM3 algorithm is a Hash function that supports the data structure and detects whether the data is tampered with in the blockchain. The SM3 algorithm is applied to the missile business blockchain to realize the localization of encryption, and the operation of SM3 algorithm is optimized under the condition that the missile business data contains a large number of documents, tables and pictures. Finally, the Go language was used for testing, and the results showed that the operation speed of SM3 algorithm could be improved by about 30% through optimization, and the optimization effect was obvious.
Aeronautical γ Spectrum Noise Reduction Method Based on LS-SVM Segmentation Regression
Authors: Ci Ying , Liu Jun , Chen Lei , Li Guang , Yang Feng 
Published: July 2019

Abstract
In order to reduce noise of the aaeronautical γ spectrum caused by factors such as less counting, short measuring time and poor measuring environment, this article adopts a segmented noise reduction method combined with the machine learning method, LS-SVM(Least Squares Support Vector Machines) and weighted stacking method according to the energy window distribution of the spectrum. The experimental results show that the segmented noise reduction method based on LS-SVM can significantly reduce the statistical fluctuation. In addition, the method presents good adaptability and generalization ability.
Research and Design of Integrated Information Transmission System for Large Warships Formation
Authors: Wang Nan , Liu Yi 
Published: July 2019

Abstract
The Navy is a comprehensive and highly integrated service consisting of submarines, surface ships, aviation forces, coastal defense forces and marines. The Navy is a systematic and technology-intensive military. Compared with other military services, the naval equipment requires higher system performance. The naval combat unit is mainly a fleet composed of several different fleets of warships and aircraft. Information warfare of naval fleet is divided into information level (network) attacks and defenses. In the future maritime information warfare, it is necessary to strengthen the defense capability of formation information warfare to ensure the security of communication and computer infrastructures. The integrated information transmission system of large naval fleet is designed to improve the information defense capability of naval operations. It has strong comprehensive defense capabilities of computer anti-reconnaissance, anti-jamming, anti-virus and anti-destruction. In the final analysis, it is to improve the security of computers.
Research and Application of License Plate Recognition Technology Based on Deep Learning
Authors: Yao Li , Zhao Yingbin , Fan Jinghua , Liu Min , Jiang Jianpeng , Wan Yan 
Published: July 2019

Abstract
There are many types of vehicle license plates in China, including new energy license plates, large truck license plates, government vehicle license plates, and military license plates. The existing commercial license plate recognition system only targets common license plates and does not completely cover the full range of license plates. Therefore, this paper proposes an SSD-based end-to-end license plate recognition system (LPR-SSD). The LPRSSD network architecture consists of upper and lower classification networks: the upper layer network is used for vehicle license detection and classification, and the lower layer network is used for license plate character detection and classification. In order to enhance the generalization performance of the LPR-SSD network, in addition to the real license plate image captured by the camera, this paper synthesizes 50K simulated license plates for each type of license plate according to the legal document [1]. Experiments show that LPR-SSD achieved a faster convergence speed during training. After the test set verification, the accuracy of license plate location detection and classification reaches 98.3%, and the character recognition accuracy rate reaches 99.1%.
Research on Construction Method of Chinese Domain Ontology Based on Relation Extraction
Authors: Qi Shanshan , Zheng Limin , Yang Lu 
Published: July 2019

Abstract
Domain ontology plays an increasingly important role in the information extraction process and has become a useful tool for knowledge acquisition, knowledge sharing, and modeling. However, the existing domain ontology construction methods are mainly based on manual mode, lacking systematic and engineering methods. This paper proposes a construction method of Chinese domain ontology based on relation extraction, which fully considers the automatic extraction of semantic hierarchical relations, and guarantees the semantic richness and accuracy of relational triples. The construction method of Chinese domain ontology based on relation extraction is applied to the field of food safety events. The constructed ontology has the advantages of reusability and scalability, which proves that the method is logical and operability.