bluuebunny/crossref_metadata_2025_split huggingface dataset:
Case Study for Regression Testing using Genetic Algorithm
Authors: Saraswat Birendra Kumar , Parashar Binayak , Takuli Arun Kumar , Singh Samender 
Published: September 2021

AbstractFor quality-assured information engineering software assessment has become critical. Software quality is defined as all the features and proprieties of a software system that can meet certain criteria to test a software product to know how good it is. Software testing is a necessary process for software development, and automating data production helps reduce time and expense. The design of software tests in today’s industry is mostly focused on the experience of the experts, while the tools for test automation can be used only for pre-planned tests. The new investigation focuses on the safe, accurate and effective discriminatory regression testing. A selective algorithm for regression testing is considered secure and sound if the disclosing of test cases is not ignored. Precision that tests the sum of the algorithm moving through the test cases without alteration. The successful selection of these cases is one of the key issues in regression testing. Due to the utility of selective regression testing, an efficient algorithm is important due to the speed of test case selection. We use a formal language approach in this article, to explain the problems and to give tentative solutions. For the test case selection problem, we find the genetic algorithm. We demonstrate that this genetic algorithm that satisfies both secure and reliable standards take time and time in general exponentially. In addition, a secure algorithm requires the most severe case of O (n2) time and Ont) (location, in which n is as many statements as possible, and t is as many test cases as possible.
A review on impact of distributed generation penetration in deregulated electricity market having renewable energy sources in India
Authors: Maurya Bajrangi 
Published: September 2021

Abstract
Nowadays it is seen that, distributed generation (DG) system based on conventional energy sources and renewable energy sources (RESs), specially using solar energy, is playing an important role to match load demand across India. Energy policies are promoting renewable energy resources and in the coming time to increase the energy efficiency, DGs installation and RESs scheme will play a vital role. It can be observed that India’s load rate is increasing rapidly day by day. Unplanned management made without planning can adversely affect the grid and put a serious load demand on the power grid. Distributed generation systems can be very useful to avoid such problems, especially if we use renewable energy. In this paper, we have mentioned about how we can manage the load demand and how to meet the power supply of the people using the distributed generation. In this review, strength, stability and reliability of distributed generation (DG) system is discussed with different aspects including different scenarios. This analysis shows a major action on the power system planning and working of the grid system. Recent works are included in this review. Different steps have also been taken for the integration of DGs with various scenarios in electric power systems. The main objective of this review is, how to achieve better integration of flexible demand and demand response, so that demand side Management (DSM) with the help of distributed generation can be fulfilled and create grid proficiency. Different challenge in DG system has also been mention in this research.
Effect of Water Temperature on the Compressive Strength of Silica Fumes based Porous Concrete
Authors: Sharma Aniket Kumar , Pandey Shobhit , Jain Ayush , Shekhar Shreya 
Published: September 2021

Abstract
Pervious concrete is a zero droop solid, which comprises of coarse aggregates, water and different cementitious materials. As there is no fine utilized in the concrete so it’s at some point referred as no-Fine concrete. Also pertaining to its water draining capacity it is also known as called permeable concrete. Pervious concrete is special kind of high porosity solid which permits water from precipitation to either infiltrate into the ground water or to the other storage facility. Since it provides better friction to vehicle tyres and hence also prevents skidding. It utilized for asphalt since it gives a genuine presentation against sliding for vehicles in stormy days and a far superior sound retention property. In this examination the concrete supplanted with silica fumes with different mixing water temperature were tested, so as to arrive at an optimum level of workability and strength. Concrete Blocks of Standard Sizes were prepared and relieved at a standard time period as per IS recommendations. Distinctive solid blend extents, for example, OPC and SPC are set up to the check the compressive quality of pervious concrete. The outcomes show the Pervious Concrete containing fifteen percentage of silica smoke can accomplish maximum compressive at an age of twenty eight days of relieving individually and at low mixing water temperature 100C the workability and slum value enhances. With addition of more silica fume in mixer the value of permeability was decreasing.
Research Algorithm on the Wheat Phenotypic Feature Extraction based on Image Processing
Authors: Guo Xiaojuan , Cheng Jifu , Gao Guohong , An Jingliang 
Published: September 2021

Abstract
In order to solve the problems of manpower and long cycle in traditional manual measurement, a wheat phenotypic feature extraction and analysis system was designed and implemented based on the image processing technology. The system can analyze and estimate the phenotypic parameters of wheat leaf, plant and wheat ear in different growth stages. The paper introduces the estimation algorithm of wheat leaf and wheat ear characteristics in detail, including the following contents: image acquisition, image preprocessing, feature extraction, parameter data estimation, etc. the estimated data provide data basis and reference for wheat breeding experts.
Research on Mobile Robot Vision Navigation Algorithm
Authors: Guo Ling , Sun Chao 
Published: September 2021

Abstract
Robot navigation is one of the key technologies of indoor robots and it is the primary solution to the problem of intelligent robot. In this paper, the visual navigation problem of mobile robot was analyzed and the robot vision navigation algorithm based on monocular vision was proposed. The algorithm obtained three-dimensional information of obstacle through data fusion, builder a three-dimensional model of obstacle, and optimized the navigation path by using the heuristic best search algorithm, and realized the drawing of the robot navigation path.
Research on Algorithm of DRC Catalog Generation Based on Machine Learning
Authors: Jiang Yuan , Yang Wenhui , Miao Fang , Ni Yating , Huang Anqi 
Published: September 2021

Abstract
Aiming at the current logic and expansion issues in data governance caused by data scheduling in two directions: business retrieval and data processing. Data Oriented Architecture proposes a Registration Intermediate Library(RIL) and a Catalog Intermediate Library(CIL) to achieve separation of management and application. Among them, the business-oriented catalog intermediate library automatically classifies and labels data registration information through machine learning, and realizes the automatic generation of catalogs.
There are many text classification methods based on machine learning, but the model obtained through unbalanced data set training often has performance degradation. In order to solve this problem, category weights are introduced in the feature vector to reduce the influence of most samples on the model parameters. Experiments on THUCNews text classification data set show that the proposed method can effectively improve the performance of baseline system, solve the problem of unbalanced training data categories and solve the problem of automatic generation of DRC directory.
Research on air quality prediction method in Hangzhou based on machine learning
Authors: Fu Zhongjie , Lin Haiping , Huang Bingqiang , Yao Jiana 
Published: September 2021

AbstractAir pollution has become the subject of many current environmental studies, and the quality of air is directly related to the quality of life and health of human beings. In this paper, the Bayesian network model is used to predict air quality in Hangzhou. Six air pollutants SO2, NO2, O3, CO, PM2.5 and PM10 are used as the evaluation factors of the model, and AQI value is the output of the model, and then the Bayesian network model is established. Finally, the model is used to predict air quality and compare with the actual value. The results show that the accuracy of air quality prediction is over 80%, and the predicted value is close to the actual value in most cases, and this shows that Bayesian network model has a certain practical value as a means of air quality prediction.
Dynamic scheduling mechanism for software-defined security resources based on multi-mode load balancing
Authors: Zhang Tao , Shao Zhipeng 
Published: September 2021

Abstract
Rather than the traditional rigid and decentralized security mechanism, software-defined security provides a flexible and centralized solution by extracting security mechanisms from the security hardware layer to the software layer, but the existing software-defined security mechanism is prone to the problem of high load on physical devices performing security functions leading to performance limitations. To address these problems, this paper proposes a dynamic scheduling mechanism for security resources based on load balancing, while designing the southbound interface of software-defined networks to be scheduled by the control plane of software-defined security for flows according to established policies and logical topologies. The mechanism can match the load resources required by the security components, thus avoiding the problem of overall performance degradation due to excessive load and long queuing time of individual devices.
Study on Dynamic Surface Visualization Simulation Based on GPU
Authors: Zhang Shinan , Pan Jun 
Published: September 2021

Abstract
In the visual simulation of battlefield geographic environment, the realistic water surface effect can increase the reality of battlefield and enhance the immersion of training personnel, but the fluctuation of water body and the optical characteristics of water surface will also lead to the increase of algorithm complexity and the increase of data quantity, which will affect the fluency of the system. With the improvement of computer graphics hardware performance, in order to reduce the pressure of CPU, many complicated graphics computing has been transferred from CPU to GPU gradually. This chapter’s dynamic surface real-time rendering algorithm will make full use of the programmable characteristics of GPU and powerful computing power, the surface reflection refraction texture sampling, bump mapping, texture movement and other operations are put into the GPU, in order to ensure the surface fidelity, improve the rendering speed.
Research on optimal utilization of demand side resources based on quantitative analysis
Authors: Zhu Qing , Wei Si-Ya , Li Xue-Ming , Meng Xian-Hai 
Published: September 2021

Abstract
Through quantitative analysis, the power grid, environment and user behavior are analyzed, and the influencing factors of resource operation on demand side are found, and the high related parameters of resource operation on demand side are formulated based on this. Based on the principle of relative gain matrix, the interaction model including grid, environment and user behavior on demand side resources is established to realize the optimal utilization of demand side resources.
Improved cat swarm optimization for permutation flow shop scheduling problem
Authors: Pei Xiaobing , Tang Yan 
Published: September 2021

Abstract
To minimize the maximum make span of permutation flow shop scheduling problem, this paper proposed a cat swarm optimization algorithm based on estimation of distribution algorithms. This article Take the cat swarm optimization as the framework, embedded in the Estimation Distribution Algorithm, in the search mode, mining the excellent gene link in the solution sequence by using the probability matrix to combination blocks, speed and position tracking mode using characteristics of cat swarm optimization updates in the cat in order to update the excellent solution sequence generator group. Finally, through the simulation test and result comparison of Carlier and Reeves standard example set, the good robustness and global searching ability of the algorithm are verified.
Design of TR module aging control system based on adaptive filtering algorithm
Authors: Jiang Lei , He Jinsong 
Published: September 2021

Abstract
As an important part of active phased array radar, the working stability of TR module has a direct impact on the detection performance of radar system. This paper designs a set of TR electrical aging test system, for batch component aging test at the same time. Based on the principle of spontaneous heating under full power operation, the test environment was designed. The design of wave control board circuit met the excitation requirements in the work of the components. Meanwhile, the adaptive filtering algorithm and classical control theory were combined to control the TR working temperature, which realized the requirements of stable aging test of batch TR module. This system can accurately monitor whether the TR component products fail in the process of electrical aging, and at the same time can more accurately control the working temperature of the tested products within the required temperature range. It also provides a technical basis for further analysis and improvement of TR component product performance parameters. This design method can also be applied to the aging screening of other electronic board cards or components with high reliability requirements.
Complex Document Layout Segmentation Based on An Encoder-Decoder Architecture
Authors: Yao Jia , Huang Linlin 
Published: September 2021

Abstract
In our work, we propose an end-to-end encoder-decoder network for complex document layout segmentation. The proposed multi-scale feature extraction network with two parallel branches is applied to further process the feature maps, where one branch enriches the multi-scale information of the feature maps by building feature pyramids, another branch is introduced to capture the dependencies between different locations and integrate long-range context information. Moreover, we merge the outputs of the two branches to enhance the feature representation so as to further improve segmentation accuracy. Experimental results on datasets of PubLayNet and DSSE-200 demonstrate the effectiveness of our proposed method, which yields pixel-wise accuracy of above 99%.
A Just-in-Time Mechanism for Computer System
Authors: Chen Qijian , Zhong Yuanchang , Liang Taohua 
Published: September 2021

Abstract
As the installed programs will consume increasing space of hard disk, the computer becomes more and more bulky and costly. However, it’s harder and harder to make a larger yet still light and cheap hard disk. Therefore, computers used in most occasions are suggested to cancel their hard disks, and have their operating system programs and the application programs ‘installed’ on internet, instead of being installed on local hard disk. The data is accessed via supper high speed internet when the program booting. The operating system additionally serves as an open platform for registering the application software as well as the traditional role. This mechanism will bring us many gains. First, the user will no longer be restricted by the limited storage space, and will literarily have a never discarded and unlimited storage space that is shared by his or her all intelligent terminals. Second, the computers can become lighter and therefore easier to carry. Third, the user can get the latest version program once he or she launches the program. Last, the program provider can take the initiative to push the newest message to its users easily if they have subscribed.
Research on synergic Control Algorithm and Collision Avoidance of unmanned aerial vehicle Formation
Authors: Dai Feiyang , Zhou Shaolei , Yan Shi , Liu Xuanbing , Hou Pengsen 
Published: September 2021

Abstract
The current study mainly researches the problem of leader-follower based formation cooperative maintenance of fixed-wing UAV. First, the fixed-wing UAV formation with the leader-follower structure is used as the research object, and the flight dynamics model is established; the formation synergic control law is designed for the follower and the leader in the formation, especially the follower’s control law is improved to realize the state tracking control of the follower by controlling the speed and roll Angle of UAV; as stated by the master-slave formation, the distributed topology is introduced to realize the information interaction between adjacent UAVs, and on this basis, a collision avoidance strategy has been added to prevent drones from colliding with each other in the formation. Finally, the formation control algorithm and collision avoidance strategy are simulated through data simulation, and the results prove that the UAV formation can effectively complete the formation task and avoid collisions within the formation.
Feature Purity: A Quantitative Evaluation Metric for Feature Extraction in Convolutional Neural Networks
Authors: Chen Wenjun , Cong Chao , Huang Liwen 
Published: September 2021

Abstract
The quantitative analysis of specific convolutional layer feature information in Convolutional Neural Networks (CNN) has received considerable critical attention in the field of deep learning. In this paper, we proposed an innovative measuring method based on the principle of information entropy to quantitatively analyze the performance of feature extraction in CNN. In the method, the feature purity was defined as the normalized entropy of activation histogram, which was calculated from all kernels in feature layer of CNN. Feature purities were evaluated respectively in different layers of CNN as the inspection for internal structure in specific classification models. At the same time, general degrees of purities among different models were compared with classification performances. The experiments were performed in models of AlexNet, VGG, ResNet, and SENet with different epochs trained by cifar10 and ImageNet1000 datasets. Additionally, a visualization evaluation was performed by grad-CAM method in the manners of the intra-model and inter-model purities. Experimental results showed a strong relationship between feature purity and the classification accuracy in each model. Additionally, the visualized feature map also demonstrated that salient region extracted in specified feature layer had good consistency with purity value. As a result, the proposed feature purity can serve as a quantitative metric representing the degree of feature extraction in CNN without the dependency of label or specific network structure; and has significant interpretability and versatility across models.
Improving Distant Supervised Relation Extraction using shortest dependency path
Authors: Luo Yang , Zhang Lijun , Yin Dawei , Peng Dezhong , Yang Ke 
Published: September 2021

AbstractDistant supervised relation extraction is the problem of classifying the semantic relation between a pair of entities in a given sentence, where data is created by aligning unlabelled text with knowledge base automatically. Prior researches indicated that most of the sentences in the distant supervised relation extraction setting are very long and benefit from the attention over words. We note that the shortest dependency path between the entity pair in the syntax analysis tree of a sentence can help word based attention. In this paper, we propose a noval distant supervised neural relation extraction method, which makes use of the shortest dependency path to supervise the learning of word attention. Through extensive experiments on benchmark datasets, we demonstrate effectiveness of our model.
Deep learning based handwritten digit recognition in Android
Authors: Li Yiqing 
Published: September 2021

Abstract
Handwritten digit recognition refers to the recognition of handwritten digits written and printed on paper, and the recognition results are stored in the computing machine in the form of text. According to the characteristics of a handwritten digital image, based on the multi-layer perceptron method, this paper introduces the source and principle of handwritten digital image recognition in detail, and analyzes the steps, related functions and theories of handwritten digital image recognition in sequence. The proposed algorithm was validated on the handwritten dataset MNIST, and the model was evaluated and adjusted. Eventually, the trained model was migrated to Android.
An intelligent routing optimization strategy based on deep reinforcement learning
Authors: Zhou Xiong , Guo Hongyu 
Published: September 2021

Abstract
Finding the optimal strategy in network routing has always been a NP hard problem. Due to the complexity and dynamics of network traffic, the existing intelligent routing algorithms have poor generalization ability. Therefore, this paper proposes an intelligent routing strategy based on deep reinforcement learning, and with the help of SDN control can dynamically collect network traffic distribution information, can dynamically adjust the routing strategy. Compared with traffic engineering algorithms such as TCMP and DRL-TE, the end-to-end delay is optimized under different throughput.
CWTs: A Public Large-Scale Chinese Web Table Data Set
Authors: Wang Changlong , Ren Zhilong , Bi Siyun , Zhang Rong , Fu Qibin , Zhang Zhichang 
Published: September 2021

Abstract
As the world’s largest information sharing platform, web pages contain a large amount of table data. However, these table data emphasize the presentation of data to users in a more intuitive way, which cannot be understood by computers. How to obtain large-scale web table data and carry out in-depth applications has become the focus of data science. This paper designs an extraction framework for Chinese Web table data. This framework extracts Web table data containing Chinese from the Web and constructs an open Chinese Web table data set CWTs (Chines Web Tables). The data set is quantitatively analyzed in multiple dimensions such as scale and data distribution. The results show that the data set obtained in this paper can provide a preliminary data basis for table data query, question answering system, and knowledge base construction, at the same time provide an optional channel for building a large-scale machine-understandable Chinese knowledge base.
Research on Data Calibration of Air Quality
Authors: Feng Qiming 
Published: September 2021

Abstract
In recent years, the urban air pollution in our country has become more and more serious, which has aroused widespread concern of the general public and the scientific community. The micro air quality detector not only costs little, but also can real-time monitor the air quality of a certain area in a grid way, so it can be used as the supplement of national survey point data. Based on the canonical correlation analysis of the data, it is found that the concentration deviation of “two dust and four gas” is significantly related to the meteorological parameters, among which the concentration deviation of PM2.5, PM10, NO2 and O3 is greatly related to the factors of pressure and humidity, and it is also known that the correlation between concentration deviation and humidity is the largest. And the concentration deviation between self-built point and national survey point is modeled. The results of this study can provide a method for the completion of urban air quality data, and the research method can provide a reference for data mining.