bluuebunny/crossref_metadata_2025_split huggingface dataset:
X-ray activity from different types of stars
Authors: He Lin , Wang Song , Xu Xiao-Jie , Soria Roberto , Liu Ji-Feng , Li Xiang-Dong , Bai Yu , Bai Zhong-Rui , Guo Jin-Cheng , Qiu Yan-Li , Zhang Yong , Xu Ruo-Chuan , Qian Ke-Cheng 
Published: July 2019

Abstract
X-ray emission is an important indicator of stellar activity. In this paper, we study stellar X-ray activity using the XMM-Newton and LAMOST data for different types of stars. We provide a sample including 1259 X-ray-emitting stars, of which 1090 have accurate stellar parameter estimations. Our sample size is much larger than those used in previous works. We find a bimodal distribution of the X-ray to optical flux ratio (log(f
X/fV
)) for G and K stars. We interpret that this bimodality is due to two subpopulations with different coronal heating rates. Furthermore, using the full widths at half maxima calculated from Hα and Hβ lines, we show that these stars in the inactive peaks have smaller rotational velocities. This is consistent with the magnetic dynamo theory that presumes stars with low rotational velocities have low levels of stellar activity. We also examine the correlation between log(f
X/fV
) and luminosity of the excess emission in the Hα line, and find a tight relation between the coronal and chromospheric activity indicators.
KLF7 overexpression in bone marrow stromal stem cells graft transplantation promotes sciatic nerve regeneration
Authors: Li Wen-Yuan , Zhu Guan-Yu , Yue Wen-Jiang , Sun Guang-Da , Zhu Xiao-Feng , Wang Ying 
Published: July 2019

Abstract

Objective. Our previous study demonstrated that the transcription factor, Krüppel-like Factor 7 (KLF7), stimulates axon regeneration following peripheral nerve injury. In the present study, we used a gene therapy approach to overexpress KLF7 in bone marrow-derived stem/stromal cells (BMSCs) as support cells, combined with acellular nerve allografts (ANAs) and determined the potential therapeutic efficacy of a KLF7-transfected BMSC nerve graft transplantation in a rodent model for sciatic nerve injury and repair. Approach. We efficiently transfected BMSCs with adeno-associated virus (AAV)-KLF7, which were then seeded in ANAs for bridging sciatic nerve defects. Main results. KLF7 overexpression promotes proliferation, survival, and Schwann-like cell differentiation of BMSCs in vitro. In vivo, KLF7 overexpression promotes transplanted BMSCs survival and myelinated fiber regeneration in regenerating ANAs; however, KLF7 did not improve Schwann-like cell differentiation of BMSCs within in the nerve grafts. KLF7-BMSCs significantly upregulated expression and secretion of neurotrophic factors by BMSCs, including nerve growth factor, ciliary neurotrophic factor, brain-derived neurotrophic factor, and glial cell line-derived neurotrophic factor in regenerating ANA. KLF7-BMSCs also improved motor axon regeneration, and subsequent neuromuscular innervation and prevention of muscle atrophy. These benefits were associated with increased motor functional recovery of regenerating ANAs. Significance. Our findings suggest that KLF7-BMSCs promoted peripheral nerve axon regeneration and myelination, and ultimately, motor functional recovery. The mechanism of KLF7 action may be related to its ability to enhance transplanted BMSCs survival and secrete neurotrophic factors rather than Schwann-like cell differentiation. This study provides novel foundational data connecting the benefits of KLF7 in neural injury and repair to BMSC biology and function, and demonstrates a potential combination approach for the treatment of injured peripheral nerve via nerve graft transplant.
Research on the Development Path of Competitive Intelligence System of Commercial Banks Based on SCP
Authors: Xu Rongzhen , Han Jiashan , Wang Meiqi 
Published: July 2019

Abstract
Competition is the essence of market economy, and how commercial Banks reform and develop and occupy a dominant position in competition is a common concern in the fields of finance and information science. By sorting out relevant results, it can provide reference for the development and research of commercial Banks. Starting from financial intelligence, this paper discusses the development evolution of commercial Banks from the perspective of competitive intelligence. Based on SCP analysis of the development dilemma of commercial Banks, this paper proposes the development path of competitive intelligence system of commercial Banks. Putting forward new ideas for the development of commercial Banks, combined with the needs of the industry, in-depth competition intelligence collection and processing work for the innovation and development of commercial Banks is of great significance.
A New Variable Structure Multi-Model Target Tracking Algorithm
Authors: Wang Linxi , Hu Xiaoxi , Han Xun , Kuang Yin , Yang Xinquan 
Published: July 2019

Abstract
Maneuvering targets tracking technology has important research value in the fields of space target detection, civil aviation, ground traffic control and so on. To solve this problem, a new variable structure multi-model tracking algorithm based on fuzzy logic and strong tracking filter is proposed. The basic ideas of fuzzy logic inference and strong tracking filter are analyzed. An adaptive grid multi-model algorithm based on strong tracking filter and fuzzy interaction is established. The algorithm is also compared with generalized pseudo-Bayesian algorithm, interactive multi-model algorithm and traditional variable structure multi-model algorithm. The simulation results further prove the correctness and effectiveness of the proposed algorithm. Under the same simulation conditions, tracking performance of the proposed algorithm is significantly improved, and it has a higher cost-effectiveness ratio. Therefore, the proposed algorithm has a good application prospect in maneuvering target tracking.
Dual Channel Audio Watermarking Algorithm Based on Embedded Strength Optimization
Authors: Luo Yifan , Peng Dezhong 
Published: July 2019

Abstract
This work proposes a new dual-channel audio watermarking algorithm to improve the imperceptibility of watermark while maintaining the robustness. First, we use the correlation method to detect the pitch of the origin audio, by which the audio will be segmented adaptively. Then, we implement the discrete wavelet transform (DWT) on the audio signal to get the approximate decomposition coefficients. On this basis, the coefficients are divided into odd-order and even-order channels by the coefficient serial number. According to the high similarity between the two channels, the coefficients of the even-order channel are embedded into the odd-order channel in the form of an echo. To improve the imperceptibility of the watermark, we apply the cross-correlation method to optimize the embedding strength to minimize the signal-to-noise ratio (SNR). By this way, the robustness of the watermark will be improved without significantly affecting the original audio quality. In the watermark extraction stage, the coefficients cross-correlation between the embedding channel and the embedded channel is used to detect the signal peaks. According to the peak positions, the watermark can be extracted, so the blind detection is realized. We test the proposed method by simulation experiments, and the experimental data show that the robustness of the proposed method is higher than the method proposed in [1].
Enhanced Noncontact Vital Signs Detection Method for Slightly Moving Subjects Using Tracks Catching and Parameters Estimation (TCPE)
Authors: Chen Deken , Hong Weijun , Dong Yaru 
Published: July 2019

Abstract
A novel signal processing method based on track catching and Parameters estimation(TCPE) is proposed to detect the vital signs of slightly moving subjects, which can extract the vital signs of subjects with the influence of Gaussian noise or harmonic interference when the subject is stationary or slightly moving without much body shake. The experiment results show that the proposed method can detect the vital signs of slightly moving subjects with higher accuracy comparing with traditional methods.
A New Privacy Protection Security Scheme for Blockchain
Authors: Qiao Kang , You Wei , Tang Hongbo 
Published: July 2019

Abstract
The strong security and strong privacy protection features of the blockchain are important aspects of the development of the blockchain, but the security and privacy protection features of the blockchain are not perfect. With the continuous development and wide application of blockchain technology, the problem of privacy leakage is becoming more and more prominent and must be fully taken seriously. This paper proposes a new blockchain signature scheme based on the combination of aggregate signature and ring signature for the privacy protection of transaction addresses in blockchain. This scheme uses a ring signature as the basis for the signature algorithm. On the basis of this, it combines the aggregate signature, which hides the address information of the signer on the one hand and fixes the signature length on the other hand. This enhances the privacy protection capability of the transaction address in the blockchain, and also effectively reduces the signature space of the blockchain system, and solves the problem of capacity expansion to some extent.
Effect of Corporate Tax Avoidance on the Investment-cash Flow Sensitivity
Authors: Rui Ji 
Published: July 2019

Abstract
This paper aims to disclose the comprehensive effect of corporate tax avoidance on investment cash flow sensitivity. for this purpose, a number of enterprises listed on shanghai and shenzen stock exchanges (a-share enterprises) during 2009∼2015 were selected to explore the effect of corporate tax avoidance on the free cash flow of enterprises. in light of the preivous studies, an investment cash flow sensitivity model was created based on book-tax difference (btd), and the descriptive statistics were subjected to regression analysis. the research reveals that enterprises with a high level of tax avoidance have a high investment cash flow sensitivity; tax avoidance of an enterprise can directly increase the cash flow, and suppress the cash flow by raising the deferred financing cost. the research findings shed new light on the decision-making of corporate tax avoidance.
Flower identification based on Deep Learning
Authors: Tian Mengxiao , Chen Hong , Wang Qing 
Published: July 2019

Abstract
In the field of plant scientific research, agroforestry investigation and production and management, plant identification is crucial basic work, and flower identification is an important part of plant identification. Given the present artificial defects of labor cost, low efficiency and low accuracy in present artificial flower information query and traditional computer vision method, the study built a modified tiny darknet in flowers classification method. Seventeen types of flower datasets published by Oxford University are taken as the research objects and the input of the neural network model. The deep network classification model is trained to automatically extract the characteristics of flower images. Combined with softmax classifier, the flower test images are classified and identified. The experimental results show that the classification accuracy is 92% which is higher than the classification algorithm results of the original model and some current mainstream models. This model has a simple structure, few training parameters, and has achieved a good recognition effect. It is suitable for automatic classification and recognition in the field of flower planting and is convenient for the retrieval of agricultural plant information database.
Efficient and Dynamic Byzantine Fault-tolerant Protocol
Authors: Zhang Chengyun , Lin Pei 
Published: July 2019

Abstract
With the development of blockchain, distributed computing and other technologies, the problem of Byzantine fault tolerance has been paid more and more attention. After Lamport raised this problem and gave the corresponding solution, more and more people optimized the solution to the problem in terms of algorithm efficiency and security. After studying the previous algorithm, we used the idea of “predictive execution” and “resource minimization” to convert the original three-phase protocol into two phases and under normal circumstances only 2f+1 nodes can be used to complete the work. Greatly improved the efficiency of the algorithm. At the same time, we consider the total order problem and propose a system to execute the request made by the client in chronological order to prevent the correct client request from being stolen by the enemy. Finally, we consider that the previous BFT protocol did not fully consider the problem of external nodes dynamically joining the system, and optimized the previous solution to enable external nodes to join the BFT system safely and quickly.
Fast robust matching algorithm based on BRISK and GMS
Authors: Cen Chen , Li Runqi , Xu Xi 
Published: July 2019

Abstract
In the image stitching technology, traditional feature extraction algorithms have uneven feature points distribution, many redundant features, time-consuming feature points precision matching and low image registration accuracy. In view of these problems, this paper proposes an improved image registration algorithm based on BRISK and GMS. In this method, the image is first divided into meshes and BRISK algorithm is used for extracting image features, then the Brute Force matching algorithm is used for rough image matching. Finally, the mesh motion estimation method is used for feature quantity statistics, and error matching is removed to obtain a set of fine matching feature points for image registration. This paper verified the robustness of the improved algorithm by comparing it with other methods in the Mikolajczyk data set. The experimental results show that this algorithm achieves higher matching accuracy on the basis of maintaining speed compared with the original algorithm, and the average accuracy is improved by 8.02%. It has better performance than the traditional algorithm, which can be used for occasions with higher requirements on registration accuracy and real-time performance.
Research and Analysis of Blockchain Data
Authors: Yang Xiaojing , Liu Jinshan , Li Xiaohe 
Published: July 2019

Abstract
Blockchain technology is characterized by anti-counterfeiting, non-tampering and easy to implement smart contracts, and is known as a new technology that will lead to social change. Therefore, the study of data in blockchain has important theoretical and practical significance. The author proposes a three-layer model from the perspective of data analysis; on the basis of this model, the data structure and data type of blockchain, as well as the data structure and operation principle of smart contract are studied. Finally, the seven problems and correlations of blockchain data analysis are summarized.
Research on Recommendation Algorithms Based on Collaborative Filtering
Authors: Zhao Yongxia , Zhang Bo 
Published: July 2019

Abstract
With the rapid development of Internet industry in the era of Web 2.0, creation and sharing of content becomes easier and easier, and people’s accessing methods to information have changed. For information consumers, the exponential growth of information makes users use information inefficiently. Users are eager to get more valuable information for themselves. For information producers, they also need a way to make their information be concerned by more potential users. Collaborative filtering algorithm is the most widely used and successful recommendation algorithm. A good recommendation algorithm can help users to find their favorite videos in massive videos, and even relatively unpopular videos can be pushed to users who may be interested in it.
Research on Method of Improving Thermal Efficiency of Oilfield Horizontal Water Jacket Heating Furnace Based on Smith Algorithms
Authors: Xu Xiang-qian , Zhou Hao-bin 
Published: July 2019

Abstract
In view of the low thermal efficiency of oil field horizontal water jacket heating furnace, the single neuron SVM-Smith predictive control method was proposed to improve the heating efficiency according to the structure of the reformed heating furnace. The Support Vector Machine (SVM) was used to predict the flow rate of heated medium and improved the accuracy of Smith prediction and compensation. The single neuron PID control and Smith predictive compensation were combined to tune the parameters of the control system. The experiments showed that the thermal efficiency had been significantly improved, and the control algorithm had achieved good control effect.
STRNN: End-to-end deep learning framework for video partial copy detection
Authors: Hu Yanzhu , Mu Zhongkai , Ai Xinbo 
Published: July 2019

Abstract
The task of partial copy detection in videos aims at determine if one or more segments of the query video are already present in the data-set, while giving the information of similar portion time period. At present, most effective algorithms of partial copy detection in videos are designed as three steps: feature extraction, feature matching and time alignment. The separation of feature matching and time alignment module ignores the spatio-temporal information of partial copy to some extent. Therefore, satisfactory performance is not obtained. In order to reduce this loss, this article does not decompose it into two separate tasks, but using a single convolution neural network to solve these two aspects. First, we sample video frames and extract CNN features, calculate the spatio-temporal relationship matrix of the source video and the query video, and then graphically map the matrix and train the convolution neural network based on the object detection task of the RefineDet model. Finally, in the query phase, the time period of the partial copy is deduced based on the detection result. In this paper, we evaluate the performance of the algorithm on the real complex video copy detection data-set VCDB which is significantly improved compared with the state-of-the-art partial copy detection framework.
Dynamic Adaptive Streaming based on Deep Reinforcement Learning
Authors: Chen Jian , Wu JianYing 
Published: July 2019

Abstract
Dynamic adaptive streaming over HTTP (DASH) is the dominant technology of multimedia delivery over the Internet. In DASH system, adaptive bitrate (ABR) algorithms running on client-side video player are the key to improve user quality of experience (QoE). However, most existing ABR algorithms employ fixed control rules to make bitrate decisions based on throughput, playback buffer size, or a combination of the two. As a result, their performance in the complicated and fluctuant network environment is incompetent. In this paper, we propose QRL, a bitrate adaptation approach based on deep reinforcement learning. QRL uses double Q-Learning, an enhanced Q-Learning method. After training the neural network model, the algorithm can select proper bitrates for future video segments based on all the information collected by client during the video playback process. Simulation results show that QRL achieves better performance than other algorithms.
Research on geographic information servicing construction plan and application of urban and rural water conservancy
Authors: Xitao Huang , Huaien Li , Yu Zhang , Shaobing Zhao 
Published: July 2019

Abstract
With the vigorous development of digital and smart cities in China, the digitalization and informationization level of various industries have improved. However, the level of informatization in the township area and below is relatively low, and resources are relatively scarce. Remote sensing and geographic information technology can provide diversified products, services and interconnections for urban construction, planning, management, environment, water conservancy, electricity, pipelines, and transportation. In view of the application requirements of the water conservancy department, this paper discusses the construction framework design of the grid-based fine management service platform and the urban and rural water conservancy management system, in order to provide scientific reference for the water resources information management of relevant departments.
An Analysis of the Application of Flipped Class Teaching Mode in Photoshop Courses in Higher Vocational Colleges
Authors: Cong Han , Jun Chen , Haimei Gong 
Published: July 2019

Abstract
Flipped class is a new teaching method, which changes the traditional teacher-centered teaching mode, and improves the class participation of higher vocational students, helps students to think actively and understand the relevant knowledge deeply. In the process of teaching Photoshop course in higher vocational colleges, improve students’ interest in learning and promote the quality of teaching.
Application of Automatic Speech Recognition (ASR) Algorithm in Smart Home
Authors: Chen Hong , Zhang Bo 
Published: July 2019

Abstract
Study on practical ASR system for smart home had significant meanings for the development of the smart home. Through analysis of embedded ASR technology and the control technology of smart home, people could utilize VS1003 to record the speech for audio decoder chip by taking the NL6621 template as the platform. And, the ASR system on smart home could be realized by utilizing the Hidden Markov Model (HMM) algorithm to conduct speech model training and speech matching. Test showed that, the ASR system has relatively high recognition rate and real-time property.
A novel SVM parameter tuning method based on advanced whale optimization algorithm
Authors: Yin Xuehao , Hou YanDong , Yin Jiabao , Li Chao 
Published: July 2019

Abstract
The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE).
Artificial Intelligence and Its Application in Computer Network Technology
Authors: Sun Wenhui , Li Cuimin , Ren Ripeng 
Published: July 2019

Abstract
This paper briefly introduces the concept, origin, characteristics and application of artificial intelligence, expounds the application advantages of artificial intelligence in computer network technology, analyzes the problems existing in computer network technology, and launches the application of artificial intelligence in computer network technology. In-depth study, this article can play a certain reference and help for the application of artificial intelligence in computer network technology, gives full play to the value and role of artificial intelligence, promotes the development of computer network technology, and better meets the current social and economic development process.