bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short text classification based on bidirectional TCN and attention mechanism
Authors: Zuo Ying , Jiang Lifen , Sun Huazhi , Ma Chunmei , Liang Yan , Nie Shuaibao , Zhou Yongheng 
Published: December 2020

Abstract
The context-related semantic information of the text in the traditional short text classification algorithms are not fully captured, a text classification model based on bidirectional temporal convolutional network and attention mechanism (BTCA) was proposed. Multi-layer dilated convolution was used to increase the receptive field and better capture long distance dependent information. At the same time, attention mechanism was used to increase the attention to the local key feature in the text, and the bidirectional temporal convolution network was used to extract contextual multi-scale semantic information to enrich semantics, the problem of sparse short text features was solved to a certain extent, and text classification effect was improved. The public corpus of the THUCNews was used to conduct a comparative experiment. It is pointed out that effect of short text classification is improved by using BTCA model, with an accuracy rate of 91.47%, which is better than commonly used models.
Resource Consumption Forecasting for Information System using Holt-Winters Method and Linear Regression Method
Authors: He Yunrui , Yiying-yan  , Yijie-dang  , Zhiyu-chen  
Published: December 2020

Abstract
Through the analysis of historical data and model training, the resource consumption forecasting of information system can estimate the utilization of resources in the future and help to make resource planning in advance. The traditional methods are usually based on the experience of operation and maintenance personnel and the preliminary statistics of subsequent plans, which is time-consuming and labor intensive, and cannot adapt to unplanned changes. In order to solve these issues, an automatic resource consumption forecasting method based on Holt-Winters and linear regression algorithm is proposed in this paper. The experimental results show that the method can well predict the periodic and incremental resource consumption changes.
Design of photovoltaic hot spot detection system based on deep learning
Authors: Ren Yifeng , Yu Yongjun , Li Jing , Zhang Wenhua 
Published: December 2020

Abstract
At present, it is difficult to detect the photovoltaic (PV) hot spots and the recognition efficiency is low. In this paper, an improved Single Shot MultiBox Detector (SSD) algorithm was designed for PV hot spot detection. The algorithm used the MobileNet network to replace the VGG16 convolutional neural network structure in the original SSD. This network is a depthwise separable convolution structure. Using it for feature extraction can reduce the number of parameters in the structure and achieve the purpose of speeding up the network. The experimental results show that the improved algorithm can detect the hot spots of PV array with good confidence, low detection rate and good robustness. Compared with the You Only Look Once(YOLO) algorithm and the original SSD algorithm, the detection speed is significantly improved, which verifies the effectiveness of the algorithm.
Research on Alarm Association Mechanism of Information System Based on FP-growth Algorithm
Authors: He Yunrui , Yiying-yan  , Yijie-dang  , Yingwen-ren  , Xinyi-le  
Published: December 2020

Abstract
With the continuous informatization advancement of State Grid Corporation, the scale of information networks has become increasingly complicated, therefore effective alarm filtering and correlation has become the focus of the operation and maintenance of information system. In this paper, FP-growth algorithm is used to mine the association rules of alarm data in information system. Aiming at the problems of short message gateway pressure, core alarm delay and even omission caused by large-scale network alarm, the calculation method of the confidence is optimized, which solves the problem of confidence calculation distortion caused by frequent alarm term. The experimental results show that the optimized FG-growth algorithm combines the alarm information effectively and has some reference significance for the actual operation and maintenance work.
DeepCorNet: A efficient taxi-hailing prediction model
Authors: Mao Feng , Li Zhiheng , Zhang Kai 
Published: December 2020

Abstract
To enhance the taxi service, it is critical to improve the accuracy and efficiency of taxi-hailing prediction. Recently, the deep learning models have become the most advanced methods for taxi-hailing prediction, because they can well characterize the spatiotemporal dependencies of traffic states and achieve high prediction accuracy performances. However, most deep learning models suffer from expensive training cost. In this paper, we selected CNN as base model, the main challenge is how to convert taxi-hailing demand into image for most cases. To address this challenge, we proposed the DeepCorNet model, which generated the image of taxi-hailing demand by mutual information between regions. The experiment results showed that when taking both the model accuracy and training efficiency into consideration, the proposed model is probably more preferable, because it can conduct a relatively high prediction accuracy (close to GNN model) with much less computation efforts.
Research on 6G characteristic attenuation rate
Authors: Xu Guangbin 
Published: December 2020

Abstract
The terahertz band characteristic attenuation rate in 6G mobile communication system is studied. Through the model building and simulation, the results show that 6G radio wave fluctuates greatly with the increase of frequency in dry air, and there are multiple peaks. The characteristic attenuation rate of radio wave in water vapor at low frequency band is smaller than that in dry air, and adverse at high frequency band. The results also show that the characteristic attenuation rate increases with the increasement of rainfall, and is faster than that of frequency. The characteristic attenuation rate has little effect on the visibility. For frequency above 150GHz, the characteristic attenuation rate is greatly affected by snowfall intensity, while the frequency below 150GHz is relatively less affected by snowfall intensity. Under the frequency band of 20GHz, the characteristic attenuation rate increases obviously with the increase of frequency, and the characteristic attenuation rate is relatively gentle with the increase of frequency when it is above 20GHz. Meanwhile, the change is not obvious with the water content in the dust. For a certain water content, frequency from 0 to 350 GHz and temperature from 0 to 60 °C, the characteristic attenuation rate mainly increases with the increase of frequency, almost does not change with temperature.
Predicting Cardiovascular Events by Machine Learning
Authors: Zhang Caiwei , Qu Junhao , Li Weicheng , Zheng Lehan 
Published: December 2020

Abstract
In the field of medical treatments, machine learning has become an essential technology to mine known data. People always build models to assist doctors to judge. In this paper, 14 features of heart disease patients in two cities, Cleveland and Switzerland, are analyzed by multiple types of neuronal networks and several classifiers after cleaning the data. The model based on the featured data from heart disease patients is designed to predict whether the patients have heart disease or not. Our experimental results on the prediction of cardiovascular events in two cities show that the logistic regression classifier outperforms other methods.
Fuzzy Separation And Shrinkage Clustering
Authors: Yu Ning , Zong Yanxiang , Liang Haoran , Zhu Heying , Liu Kunhong , Wu Qingqiang 
Published: December 2020

Abstract
Clustering has many applications in data mining and machine learning. Fuzzy clustering methods have been widely used in clustering. However, fuzzy clustering methods still have a fatal problem: the cluster radius sensitivity problem. The cluster radius sensitivity problem means that clusters with smaller radius will predominate in clustering and obtain more data points. Aiming at this problem, we propose a fuzzy separation and shrinkage clustering algorithm (FSC). FSC uses cluster membership degrees and cluster sizes to construct a new membership distribution, and then moves the data points according to this new membership distribution. The accuracies of our algorithm on wine, iris, balance scale and seeds are as follows: 98.82%, 97.27%, 63.07% and 91.34%. Our contributions are: (1) We propose a fuzzy separation and shrinkage clustering algorithm, which can solve the cluster radius sensitivity problem. (2) The performance of our algorithm on the UCI datasets goes beyond the benchmark algorithms.
Network Traffic Classification Model Based on Multi-Task Learning
Authors: Nie Shuaibao , Jiang Lifen , Sun Huazhi , Ma Chunmei , Liang Yan , Zhou Yongheng , Zuo Ying 
Published: December 2020

Abstract
A multi-task learning network traffic classification model (MTL-NTC) is experimentally studied. The identification task of the MTL-NTC model contains three tasks: identifying the type of traffic, predicting the bandwidth, and the duration of the flow. Identifying the type of traffic is the main task. It includes the classification of five types of traffic. The bandwidth prediction and the duration of the flow are auxiliary tasks, which are used to improve the performance of the main task. In the public network traffic data set QUIC, the MTL-NTC model and the common model are used for comparative experiments. The experimental results show that the recognition accuracy of this model is 97.33%, which is better than the common models.
The negative mayfly optimization algorithm
Authors: Zhao Juan , Gao Zheng-Ming 
Published: December 2020

Abstract
The global best or historical best positions were involved in updating positions of individuals in swarms for almost all of the swarm-based nature-inspired algorithms with exceptions for the averages. However, literal research to the particle swarm optimization (PSO) algorithm had proved that the all of the candidates would also leave the global best and the historical best candidates and such improvement would result in a better performance. Similarly, the negative mayfly optimization (MO) algorithm was proposed based on such conditions. Simulation experiments were carried out and verified that the negative MO algorithm could perform better than the original MO algorithm, especially in optimizing the multimodal benchmark functions.
Parameter optimization of support vector machine based on improved grid algorithm
Authors: Wang Shenghua , Dong Li , Hua Huichun 
Published: December 2020

Abstract
Support vector machines SVM) is a very popular algorithm used widely in the classification problem. The basic idea of SVM is to construct two parallel hyperplanes to separate two classes of instances and maximize the distance between the hyperplanes. In this paper, we propose a new algorithm called SVM-ICC-DBTCPRP one to solve the parameter optimization problem by improving the grid algorithm used by Fayed and Atiya. In the new algorithm there are two main sub-algorithms that are used to preselect the support vectors for reducing the time of training and preselect the parameter range to reduce the number of training respectively. Six typical data sets are selected to verify the effectiveness of our algorithm. The computed results show that our algorithm has the obvious advantage on the aspect of elapsed time than the one of Fayed and Atiya.
Research on the Performance of Machine Learning Algorithms for Intrusion Detection System
Authors: Li Yue , Xu Wusheng , Ruan Qing 
Published: December 2020

Abstract
Intrusion Detection System (IDS) is a critical approach to ensure network system security. Currently, network attacks are complicated and volatile. Moreover, hackers are also more inclined to adopt new attack techniques to obtain users’ privacy. Under this circumstance, the intelligent intrusion detection system has become the primary approach to detect hackers’ attacks. In this study, intelligent intrusion detection models applying the novel UNSW-NB15 data set as well as various machine learning algorithms are investigated. Furthermore, data set is pre-processed through using one-hot encoding and normalization in our experiment. Subsequently, the performance comparison of six different types of machine learning algorithms in intrusion detection tasks was implemented. The experimental results reveal that in the complex and changeable network traffic data, machine learning technology has presented desired performance.
CPU utilization prediction method based on composite model
Authors: Hu Benran , Peng Wuzhuo , Li Yanjun , Ren Jiyuan , Li Yiqun 
Published: December 2020

Abstract
The business system server under the big data environment is always faced with a large number of data services, which makes the performance of its core hardware CPU face a serious test. Therefore, the prediction of server CPU utilization is of great significance in server resource allocation. In response to this situation, a method of server CPU utilization prediction based on ARMA-BiLSTM composite model is proposed. In this method, the original CPU utilization data is decomposed by wavelet decomposition, and the trend item and the detail item of CPU utilization data are obtained. Based on the Bi-LSTM neural network model, the trend item is modeled and predicted, and the detail item is modeled and predicted using the ARMA model. The prediction results of the two are added together to obtain the final prediction result. Through experimental verification, the proposed prediction model has higher accuracy than traditional models.
BERT-based Contextual Semantic analysis for English Preposition Error Correction
Authors: Huang Guimin , Wang Jiahao , Tang Hui , Ye Xuan 
Published: December 2020

Abstract
In the writing of English as a Second Language (ESL) learners, preposition related errors account for nearly 30 percent in the all grammatical errors (Bitchener et al. 2005)[1]. Unlike verbs and nouns, isolated preposition does not mean anything, but in different contexts, prepositions usually have different meanings so that ESL students hardly master how to use all prepositions correctly. In this publication, we present a preposition error correction (PEC) model based BERT (Devlin et al., 2018)[2] which contains rich contextual semantic information, fine-tuned BERT models to intensify sentence semantic in output, and calculate the preposition matching scores according to the formula.
Research on Short-Term Forecast of Power Load Based on Adam-Gru
Authors: Jia Pengtao , Miao Yunfeng 
Published: December 2020

Abstract
To forecast the short-term power load accurately and reliably, a short-term power load model based on gate recurrent unit was proposed. In order to improve the accuracy of the model, the Adam algorithm is used to calculate the GRU hyperparameter Optimization, so as to construct an Adam-GRU short-term power load forecasting model. The performance of the forecasting model was evaluated by the Root Mean Square Error and running time, then the forecast results were compared with LSTM method. The experimental results show that Adam-GRU has a higher accuracy than the LSTM model, an increase of 15.54%, and a time complexity reduction of 15.75%. Compared with the RNN model, the accuracy has improved 10.84%. The results show that the short-term power load forecasting model and parameter optimization method based on Adam-GRU can effectively forecast the power load data.
Research on Approximate Fitting Method of Activation Function Based on Gradient Equalization in Deep Learning
Authors: Wang Zhichen , Li Hongliang 
Published: December 2020

Abstract
Activation function plays an important role in neural network model. Neural networks can express more complex situations by introducing nonlinearity due to activation functions. In this paper, an approximate fitting method of activation function based on gradient bisection is proposed. The algorithm can fit the activation function curve well. You can use less space for storage on your computer. Through experiments, the storage capacity can be reduced by one time compared with the traditional approximate fitting method, but the accuracy of the model is not affected.
Research on Landscape Information Model Construction Based on Visualization Technology
Authors: Zhao Lingnong , Zhou Zhen 
Published: December 2020

Abstract
With the development of information technology, the functions provided by computer digital technology are more and more widely used in the process of landscape architecture design. The application of visualization technology in landscape planning and design provides a powerful help for us to understand the characteristics and behaviors of the objective existence of the world. From the perspective of landscape architecture design, this paper makes an in-depth study of the construction mode of landscape information model, and puts forward the process of landscape information model construction and its corresponding functional characteristics in combination with relevant research status at home and abroad, so as to perfectly combine theory with practice.
Application Analysis of Infrared thermal imaging Technology in Intelligent Manufacturing Field
Authors: Xingyu Chen , Hao Li 
Published: December 2020

Abstract
With the continuous development of science and technology, traditional manufacturing technology has also been developed. It has gradually developed in the direction of intelligent manufacturing technology through continuous integration with the advantages of machinery, energy, materials and other technologies. Intelligent manufacturing technology has been widely used by virtue of its own advantages such as high quality, high efficiency and energy saving. At present, in order to better improve the efficiency of intelligent manufacturing technology, relevant personnel have begun to apply infrared thermal imaging technology to the manufacturing field.
A General Framework for Chinese Domain Knowledge Graph Question Answering Based on TransE
Authors: Pei Zhongmin , Zhang Jie , Xiong Wei , Li Li , Luo Zhangkai 
Published: December 2020

Abstract
This paper introduces a general question answering (QA) framework for Chinese domain knowledge graphs to serve various industries. The question is analyzed by shielding invalid characters in this framework, and the TransE calculation of entities and relations is used to search answer, rather than traditional logical queries that rely heavily on predefined rules. In this way, the QA framework can be easily applied to different fields with little manual participation, and reduce the migration cost caused by the lack of scalability of the existing QA systems, which are designed only for specific domains. Then, the proposed framework is operated on the space launch knowledge graph and has achieved HITS@1, HITS@3, HITS@5 scores of 60.60%, 80.87%, 85.61% separately, which is competitive with other restricted domain knowledge graphs. In addition, numerical results on the knowledge graph of the Three Kingdoms and the COVID-19-Character verified the scalability of the QA framework.
Symbol-Based End-to-End Raw Audio Music Generation
Authors: Wu Changxuan , Lan Ting , Yu Chunyan , Wang Xiu 
Published: December 2020

Abstract
In recent years, deep learning has emerged in the audio field with many excellent models and beats non-depth methods in the quality of generated audio. This paper implements a symbol-based end-to-end music generation model. This model generates piano music corresponding to the pitch of the musical score using a two-dimensional “Piano-roll” liked structure as input. The experiments show the generated music obtains good performance and achieves a result similar to the original song in pitch, melody, and timbre. Compared with other generation methods, the input of our model is simple, easy to obtain, and can generate music through an end-to-end method.
DenseNet-ResNet-LSTM model for modulation recognition of communication signal
Authors: Li ZongYu , Zhang YanDong 
Published: December 2020

Abstract
The traditional artificial neural network provides a low recognition rate in the modulation recognition of communication signals, suffering from the difficulty of feature extraction. Also, it requires a high signal-to-noise ratio (SNR). In order to solve these problems, this paper proposes the combined model: DenseNet-ResNet-LSTM. In the proposed model, DenseNet and ResNet extract different spatial features of samples, and then LSTM extracts the sequence of samples. Also, the attention mechanism is employed to improve the learning efficiency and ability to learn important features. Experimental results show that the proposed model achieves higher accuracy and better generalization ability over the CNN-LSTM network.
A Face Recognition Algorithm Based on Dual-Channel Images and VGG-cut Model
Authors: Su Dongchu , Li Yong , Zhao Yunping , Xu Rui , Yuan Bo , Wu Wenjuan 
Published: December 2020

Abstract
In the embedded system environment, both large amount of face image data and the slow recognition process speed are the main problem facing face recognition of end devices. This paper proposes a face recognition algorithm based on dual-channel images and adopts a cropped VGG-like model referred as VGG-cut model for predicting. The training set uses the same single-layer images of the same person combined into dual channels as a positive example, and single-layer images of different people are combined into dual channels as a negative example. After model training, the trained face verification model is used as the basis of face recognition, and the final recognition result can be obtained through loop matching and similarity ranking. The experimental results on a RISC-V embedded FPGA platform show that compared with the ResNet model called in Dlib, the VGG-like model and MobileFaceNet trained by Keras, our algorithm is increased by 240×, 88×, and 19× in recognition speed, respectively without significant accuracy reduction.
Attention-DPU: Dual-path UNet with an attention mechanism for ultrasound image segmentation
Authors: Wei Pengli , Tong Mengjun 
Published: December 2020

Abstract
With the continuous advancement in computer vision, image segmentation has achieved fruitful results in many applications such as medical image processing. In recent years, UNet and Dual Path Network (DPN) have achieved promising results in medical image segmentation. UNet cannot effectively obtain new features and reuse features. Also, DPN cannot effectively transfer the contour information of shallow blocks to the subsequent deep blocks. This paper proposes a dual path U-shaped network with the attention mechanism (Attention-DPU); taking advantage of the two networks. In the proposed network, the ordinary convolutional layer is replaced by the micro block with a dual path. Also, the attention mechanism is adopted to improve the efficiency and accuracy of segmentation.