bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Sparse Group Log Ridge for the Selection of Variable Groups
Authors: Yao Song , Cui Lipeng , Ma Sining 
Published: November 2021

AbstractIn recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.
Optimization Method of Web Fuzzy Test Cases Based on Genetic Algorithm
Authors: Qu Sheng , Zhang Zheng , Ma Bolin , Shao Yuwen 
Published: November 2021

Abstract
In order to solve the problems of low code coverage, few vulnerabilities found, and poor fuzzing effect caused by the small number of test cases and single types in Web fuzzing, on the basis of studying the current Web fuzzing methods, the existing fuzzing Web applications are tested Program research. A genetic algorithm-based method for optimizing fuzzing test cases for Web applications is proposed. It analyzes and counts the traffic of public network website business with Web service attack characteristics, and uses genetic algorithms to generate a large number of test cases with various types to explore the Web service vulnerability that exists. Based on the creation of a Web attack signature database with weights, this method uses genetic algorithms to randomly pre-generate the test cases of the fuzzing test, and uses the response of the Web service to repeatedly iterate the weights of different attack signatures in the Web attack signature database. So as to generate the best test cases. Experimental analysis shows that this method effectively finds security vulnerabilities in Web applications.
Expedited Tensor Program Compilation Based on LightGBM
Authors: Liu Gonghan , Li Yue , Wang Xiaoling 
Published: November 2021

Abstract
If the traditional deep learning framework needs to support a new operator, it usually needs to be highly optimized by experts or hardware vendors to be usable in practice, which is inefficient. The deep learning compiler has proved to be an effective solution to this problem, but it still suffers from unbearably long overall optimization time. In this paper, aiming at the XGBoost cost model in Ansor, we train a cost model based on LightGBM algorithm, which accelerates the optimization time without compromising the accuracy. Experimentation with real hardware shows that our algorithm provides 1.8× speed up in optimization over XGBoost, while also improving inference time of the deep networks by 6.1 %.
An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources
Authors: Zhao Hongyang , Xie Qiang 
Published: November 2021

Abstract
In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.
Implementation of Tactical Decision Aids Based on Event Knowledge Graph
Authors: Jia Zhen , Chu Yang , Liu Zhi 
Published: November 2021

Abstract
This paper proposes a new tactical decision aids method based on event knowledge graph (EventKG). In the warfare domain, EventKG can be constructed through event types design, event network construction and transition probability computation between events. Initially, four event classes are introduced in accordance with the OODA loop, and eighteen subclasses are further decomposed. With the aids of a common event template, all the events taking place in the battle field can be described. Event networks are built by adopting the hierarchical task network (HTN) and described through Bayesian network, to exhibit various relations between battle events. Transition probability, namely the occurrence probability of next possible event, is computed by using the prior probability and conditional probability of event occurring. On the basis of structured EventKG, entity knowledge graph (EKG) and entity relation knowledge graph (ERKG), tactical decision aid instructions can be generated by combining with the battlefield situation information.
Emotion recognition based on multi-channel EEG signals
Authors: Shi Huiping , Xie Hong , Wu Mengran 
Published: November 2021

Abstract
Emotion recognition is a key technology of human-computer emotional interaction, which plays an important role in various fields and has attracted the attention of many researchers. However, the issue of interactivity and correlation between multi-channel EEG signals has not attracted much attention. For this reason, an EEG signal emotion recognition method based on 2DCNN-BiGRU and attention mechanism is tentatively proposed. This method firstly forms a two-dimensional matrix according to the electrode position, and then takes the pre-processed two-dimensional feature matrix as input, in the two-dimensional convolutional neural network (2DCNN) and the bidirectional gated recurrent unit (BiGRU) with the attention mechanism layer Extract spatial features and time domain features in, and finally classify by softmax function. The experimental results show that the average classification accuracy of this model are 93.66% and 94.32% in the valence and arousal, respectively.
Lightweight target detection model for embedded platform
Authors: Li Yuhuan , Wang Jie , Shi Baodai 
Published: November 2021

Abstract
The detection speed of target detection algorithm depends on the performance of computer equipment. Aiming at the problems of slow detection speed and difficult trade-off between detection accuracy and detection speed when the target detection model is used in embedded devices, a lightweight target detection model based on the improved Tiny YOLO-V3 is proposed. Firstly, we analyze the time consumption of each layer structure in the convolutional neural network, and do a lot of experiments and tests. Then, we compress the time-consuming structure substantially. Secondly, we propose the segmentation and fusion module to improve the detection accuracy. Finally, we use the remote sensing data set of Wuhan University for experiments, and the experimental results show that compared with Tiny YOLO-V3, the detection speed is improved by 4 times, and the accuracy is improved by 2 percentage points.
Application of Convolutional Neural Network Method in Brain Computer Interface
Authors: Chen Lingzhi , Deng Wei , Ji Chunjin 
Published: November 2021

Abstract
Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.
Research on Face Recognition Algorithm Based on Multi-Class Support Vector Machine
Authors: Xue Liuxun , Li Hui , Wang Ping , Lin Zhiyang , Li Huanyu , Xu Jie , Shi Chongyue 
Published: November 2021

Abstract
Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.
Robustness Testing Framework For Neural Network Image Classifier
Authors: Li Duo , Dong Chaoqun , Liu Qianchao 
Published: November 2021

Abstract
Neural network has made remarkable achievements in the field of image classification, but they are threatened by adversarial examples in the process of application, making the robustness of neural network classifiers face danger. Programs or software based on neural network image classifiers need to undergo rigorous robustness testing before use and promotion, in order to effectively reduce losses and security risks. To comprehensively test the robustness of neural network image classifiers and standardize the test process, starting from the two aspects of generated content and interference intensity, a variety of robustness test sets are constructed, and a robustness testing framework suitable for neural network classifiers is proposed. And the feasibility and effectiveness of the test framework and method are verified by testing LENET-5 and the model reinforced by the adversavial training.
A L1 Regularized Logistic Regression Model for Highdimensional Questionnaire Data Analysis
Authors: Wang Jiasheng 
Published: November 2021

AbstractThe LI regularization method, or Lasso, is a technique for feature selection in high-dimensional statistical analysis. This method compresses the coefficients of the model by using the absolute value of the coefficient function as a penalty term. By adding L1 regularization to log-likelihood function of Logistic model, variable screening method based on the logistic regression model can be realized. The process of variable selection via Lasso is illustrated in Figure 1. The purpose of the experiment is to figure out the important factors that influence interviewees' subjective well-being using L1 regularized logistic regression. Experiments have been performed on CGSS 2017 data. Important features have been successfully selected by using the L1 regularization method.
Research on the Garbage Classification Problem Based on Convolutional Neural Network
Authors: Wu Shuang , Li Zeyu , Chen Xinqiong , Zhong Peiwen , Mei Liangcai , Cai Xing 
Published: November 2021

Abstract
In order to better promote garbage classification, machine learning models are used to discover and solve garbage classification problems. First, the factor analysis is used to conduct field investigation and data analysis on residents' perception of waste classification. Second, convolutional neural network (CNN) is used to classify and recognize garbage images, which is used to assist the judgment of garbage classification. We should put forward some reasonable classification suggestions to better promote the problem of garbage classification.
RESEARCH ON THE PREDICTION OF DEFORMATION PROPERTIES BASED ON NEURAL NETWORK
Authors: Jin Lu , Zhang Chunli , Meng Xiangyuan 
Published: November 2021

Abstract
The direct analysis method based on deformation properties needs to predict the lateral deformation of the steel frame-support structure in advance, but it takes time and labor to use both finite element numerical simulation and experimental research. In this paper, the BP neural network receives the parameters of the steel frame-support structure, such as the aspect ratio, the second-order effect parameters, the slenderness ratio of frame columns, the rigidity ratio of beam-column bus and the deformation limit, with the lateral displacement of the column as the output layer, the second-order lateral deformation properties of the steel frame-support structure can be quickly obtained. Comparing the prediction results of BP neural network with those of the existing fitting formulas, the absolute difference and the relative error ratio between them are very small, which shows that the accuracy of the second-order column roof migration using the BP neural network prediction is high. The BP neural network can be used to judge the applicability of the direct analysis method based on the deformation properties of the steel frame-support structure under various parameters.
IEPet: A Lightweight Multiscale Infrared Environmental Perception Network
Authors: Jiang Xinhao , Cai Wei , Yang Zhiyong , Xu Peiwei , Dong Qingjiang 
Published: November 2021

Abstract
In recent years, the development of unmanned driving technology requires continuous progress in environment perception technology. Aiming at the key research direction of infrared environment perception in unmanned driving technology, a lightweight real-time detection network model for infrared environment perception, IEPet, is proposed. The model backbone adds the BottleneckCSP module and the proposed DCAP attention module, which can significantly improve the detection ability and spatial position perception ability while maintaining light weight. At the same time, the model improves the detection accuracy by using a 3-scales detection head. Comparative experiments on the unmanned driving data set show that compared with the lightweight model YOLOv4-tiny, the model proposed in this paper has an increase in F1 Score by 1.48% and an average detection accuracy by 6.37% to reach 84.31%. And the model is lighter. It shows that the proposed IEPet model can better meet the excellent performance required for infrared environment perception.
A novel method for discriminating multi-sensor fusion based on coherent feature
Authors: Han Jun , Hu Yang , Zhou Ming , Tang Yuwen 
Published: November 2021

Abstract
In order to solve the problem of multi-sensor fusion discrimination of the same radar emitter, a multi-sensor fusion discrimination method based on coherent feature is proposed in this paper. First, the simultaneous interpreting of the pulse sequences received by different sensors is carried out. Then the peak value of the autocorrelation function between pulses is used to determine whether the same source is used. The simulation results show the effectiveness of the proposed method.
Research on Real-time Sensing Technology of Aerospace Interactive Behavior Based on Infrared Array Force Tactile
Authors: Ni Bo , Cao Jiaxing , Mao Zhiyong , Cheng Lei , Shen Huifeng , Chen Jigang 
Published: November 2021

Abstract
Aerospace robots can replace astronauts to complete dangerous work in the expanded space. How to efficiently and reliably interact with robots in complex and harsh space environment has become an urgent problem and technical challenge. Based on the infrared array force tactile technology, an infrared point force touch detection system is developed to realize the intention sensing of aerospace human-robot interaction. Based on multipoint positioning technology combined with image method and global combined optimal tracking method, the control software is designed, and the effective human-robot interaction in the process of cooperation is realized. Finally, the touch panel test effect of the system is given. This research can provide a theoretical basis for the practical application of aerospace human-robot interaction.
Linking and link complexity of geometrically constrained pairs of rings
Authors: Orlandini E , Tesi M C , Whittington S G 
Published: November 2021

Abstract
We investigate and compare the effects of two different constraints on the geometrical properties and linking of pairs of polygons on the simple cubic lattice, using Monte Carlo methods. One constraint is to insist that the centres of mass of the two polygons are less than distance d apart, and the other is to insist that the radius of gyration of the pair of polygons is less than R. The second constraint results in links that are quite spherically symmetric, especially at small values of R, while the first constraint gives much less spherically symmetric pairs, prolate at large d and becoming more oblate at smaller d. These effects have an influence on the observed values of the linking probability and link spectrum.
Evaluation of regional simulation of surface ozone over Southeast Asia using ground-based observation at two existing Global Atmospheric Watch stations
Authors: Permadi Didin Agustian , Oanh Nguyen Thi Kim 
Published: November 2021

Abstract
High level of ground level ozone concentrations was found in most of Southeast Asian (SEA) large cities and often exceeded the national ambient air quality standard. Ozone and PM10 are among of the critical air quality parameters that cause the unhealthy air quality index. Effort to mitigate ozone pollution is greatly complicated due to the photochemistry processes therefore photochemical smog modelling has been widely used. Surface ozone simulation in SEA was done using CHIMERE and weather research forecast (WRF) model. Emission inventory of ozone precursors was done for three countries in the domain, i.e. Indonesia, Thailand and Cambodia. Modelling performance evaluation for meteorological parameters and ozone at the SEA big cities was done in another study. This paper focused on the model evaluation conducted at the two remote sites represented by 2 (two) global atmospheric watch (GAW) remote stations of Bukit Kototabang (BKT) and Danum Valley (DNV). Evaluation result showed an overestimation of observed ozone in BKT while a contradictive result was seen in DNV station which was due to the ozone chemistry and inaccurate estimation of emissions (both anthropogenic and biogenic emission). The evaluation conducted at the remote sites was not even better than that conducted previously at the urban areas. Statistically, only mean normalized gross error and unpaired peak accuracy values that satisfy the criteria for surface ozone modelling suggesting major improvement required for ozone precursors emission inventory data.
Numerical simulation of hydraulic performance with free overfall flow
Authors: Yao Li , Ye Xiao-hui , Huang Xiao-chong , Zheng Ke-hong , Fitri Arniza , Lestari Fera 
Published: November 2021

Abstract
Recently, free overfall flows have attracted attention of many researchers in the world. It is because the free overfall can be used as a tool in measuring the discharges and flows in any open channel systems or water collection systems for various flow regime at different shape of channels. In this study, the hydraulic performances of free overfall flow were explored using numerical simulation for three shapes of open channels including uniform rectangular, rectangular circumscribed, and rectangular inscribed steps. The hydraulic performances discussed in this study are including the flow regime, the maximum pressure, drop length and the energy dissipation rate. Numerical simulations were performed under different discharges Q (between 0.0005 m3/s and 0.0015 m3/s) and excise repair densities (y/H) for 1:2, 1:3 and 1:4. Visual observations identified various flow regimes with largely identical flow patterns in different discharges and configurations. Comparisons of properties indicated that there is a better depressurization and energy dissipation performances at the open channel with the shape of rectangular inscribed steps compare to the uniform rectangular in the small charge. Comparative study also revealed that the large excise repair densities of rectangular circumscribed steps might be optimal in terms of energy dissipation performances.
A study on carbon cap and trade effect to cost of electricity in accordance with the Merit Order of 300-400 MW coal power plants
Authors: Putra A S , Garniwa I , Arilanggaaji E S , Pradana S S 
Published: November 2021

Abstract
Electricity is the basis of national development in a country. Power plants in Indonesia produces up to 283.8 TWh and are dominated by coal power plants which increase the amount of the greenhouse gases (GHG). In order to prevent more environmental problems, Indonesia ratified Paris Agreement by publishing the roadmap of Nationally Determined Contribution (NDC) that committed in reducing 29% of GHG emissions in 2030, which 11% of them are from the energy sector contributions. This research focuses on the implementation of the carbon cap and trade (CAT) between coal power plants having 300-400 MW capacity, which can affect their cost of electricity (Rp/kWh). It is well known that cap and trade (CAT) is a method used for reducing the mitigation cost of emission reduction in an effective way. From this research, it is found that the highest rise of incremental cost belongs to the 300 MW power plant in scenario 9 and the increase is from Rp.431.00/kWh to Rp.462.77/kWh, or approximately 7.37%. This research also shows that the most optimal carbon price is in the range of Rp. 130,165 to Rp.130,183 because the rank of the 330 MW and 400 MW power plant in merit order changes over in this condition. In the future, this research can be used as a comparison with the higher coal power plant capacity, so that an alternative way is obtained to determine the more optimal merit order.