bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of attack capability of UAV intelligent swarm based on AHP Fuzzy Evaluation
Authors: Han Yueming , Fang Dan , Zhang Hongyan , Li Yuan 
Published: November 2020

Abstract
In order to evaluate the attack ability of UAV intelligent swarm, the evaluation index system of UAV intelligent swarm attack capability is established, the factors influencing the attack capability of UAV intelligent swarm are analyzed, the capability evaluation model is established, and the weight of each factor is determined by AHP, and finally the effectiveness index is evaluated by fuzzy evaluation matrix [1]. The results show that the evaluation results are objective and reasonable, which can provide theoretical reference for the optimal design and use of UAV intelligent swarm system.
Social insurance management depends on the development of computer information technology
Authors: Zhou Yu 
Published: November 2020

Abstract
With the rapid development of economy and science and technology, the application of computer information technology in all walks of life has become more and more extensive. As an important security system, social insurance is an important part of residents’ security in China. With the rapid development of economy and science and technology, the application of computer information technology in all walks of life has become more and more extensive, This paper briefly introduces the necessity and principle of the construction of social insurance management information system, and discusses the measures of effective application of computer information technology in social insurance management, so as to give full play to the role of computer information technology, optimize the social insurance management system, and realize the information management of social insurance, so as to meet the needs of modern social insurance management. It is of great significance for students and social development and progress.
Fine grained sentiment analysis based on Bert
Authors: Chen Chao , Hu Xiaoli , Zhang Huibing , Shou Zhaoyu 
Published: November 2020

Abstract
The fine-grained sentiment mining based on online review data is helpful to analyze user pain points and improve user experience and business marketing. A fine-grained sentiment analysis model based on Bert is proposed. Commodity feature words and emotion words are extracted by dependency parsing; the polarity of emotion was analyzed by the combination of Bert and emotional words; through the corresponding emotional polarity of feature words, we can get the emotional tendency of commodity features. The experimental results in movie review data set and film review data set show that, the accuracy and F1 value of Bert with feature words and emotional words were 94.67% and 94.55%, which were better than other models.
Multi-Agent Reinforcement Learning based on Value Distribution
Authors: Li Ruiqun , Wang Ruobing , Tian Tao , Jia Fukai , Zheng Zhong 
Published: November 2020

AbstractReinforcement learning in a multi-agent setting is very important for real-world applications, but it brings more challenges than those in a single-agent environment. In the multi-agent setting, the agent generally has a bias of overestimation on the value function. In our work, we pay attention to the issue of overestimation bias with continuous actions in the multi-agent learning environment. We propose a method to reduce this bias by adopting the distributional perspective on reinforcement learning. We combine it within the framework of off-policy learning Actor-Critic and propose a novel approach Multi-Agent Deep Distributional Deterministic Policy Gradient (MAD3PG). We empirically evaluate it in three competitive and cooperative multi-agent settings. Our results show that in a series of difficult motor tasks the agents trained by MAD3PG significantly outperforms existing benchmark.
Deep matrix factorization based on self-attention mechanism for student grade prediction
Authors: Guo Zongxin , Liu Tieyuan , Chang Liang , Gu Tianlong 
Published: November 2020

Abstract
Deep learning is gradually emerging in the field of educational data mining. Aiming at student grade prediction in education data mining, a prediction model combining self-attention mechanism and deep matrix factorization (ADMFN) is proposed. It can quickly extract important features of sparse data and process complex nonlinear data. Firstly, the student and course vector corresponding to the score matrix are input into the model and projected to obtain the potential feature vector of the student (course). Then, the potential feature vectors of students (courses) are added into the self-attention mechanism to construct a multi-layer perceptron network. Finally, by constructing a bilinear pooling layer, the output vectors of rows and columns are fused to obtain the predicted value of the score. We compare the existing recommendation system baselines to evaluate our model. The experimental results show that the proposed model is effective in real dataset.
Short-term load forecasting based on LSTM-RF-SVM combined model
Authors: Wei Wei , Jing Huang 
Published: November 2020

Abstract
Aiming at the problem of low accuracy of single model forecasting power load, a combined LSTM-RF-SVM prediction model based on Long Short-Term Memory network (LSTM), Random Forest (RF) and Support Vector Machine (SVM) is designed. Firstly, the preprocessed load data, meteorological data and date data are input into the three models according to the sliding time window. The prediction results of each model are obtained through training. Then the optimal weighted combination method is uesd to calculate the weight coefficients of the three models in the prediction results. Finally, the final prediction results are obtained by integrating the weight coefficients. Experimental results show that compared with other methods, the established forecasting model improves the accuracy of short-term load forecasting.
Forecasting the railway freight volume in China based on combined PSO-LSTM model
Authors: Qiu Yue-Ying , Zhang Qiong , Lei Ming 
Published: November 2020

Abstract
Considering the shortcomings of the current railway freight volume prediction model, this paper proposes a railway freight volume forecasting model based on PSO-LSTM. LSTM can learn long-span nonlinear time series effectively, and it is highly efficient in training forecasting of railway freight volume data. In order to improve the accuracy of parameter selection, PSO is used to optimize neural network model. The simulation results show that the PSO-LSTM model proposed has lower prediction error and higher prediction accuracy than the traditional LSTM model GA-LSTM model. This indicates that this model is an effective prediction model for railway freight volume.
Decision Boundary Extraction of Classifiers
Authors: Gong Ningyuan , Wang Zixiong , Chen Shuangmin , Liu Guozhu , Xin Shiqing 
Published: November 2020

Abstract
In the field of machine learning, explicitly extracting the decision boundary of a classifier not only helps to visualize the differences between different classifiers, but also provides a more convenient way to determine the class of a sample. We proposed a boundary extraction method that relies on the output result of the classifier but not on the classifier mechanism. We find the initial decision boundary based on Voronoi diagram, and then take the smoothness and simplicity as the driving goal, adaptively adjust the shape of the boundary until convergence. In order to verify the effectiveness and usefulness of the algorithm, the decision boundaries generated by four different classifiers, ANN, SVM, Random Forest, and ELM, were visualized on the four data sets, the classification accuracy is also analyzed based on the extracted decision boundaries. The test results show that the visualization results of decision boundaries and the classification accuracy based on explicit decision boundaries are highly consistent with the classification accuracy of the classifier, which can simulate the decision mechanism of the classifier well.
Automatic detection for calcium leaching of dam concrete based on DeepLabv3+
Authors: Zhang Jun , Luo Mang , Li Changsen , Mei Hao 
Published: November 2020

Abstract
Long-termly resistance to upstream water, dam concrete is prone to calcium leaching. It will lead to lesions of the concrete and gradual attenuation of strength, impermeability and frost resistance, thus reducing the durability and service life of the structure and even threatening the safe operation of the dam. At present, the concrete leached calcium is mainly checked by the workforce, but it is time-consuming, inefficient, and hard to quantitatively evaluate, such as the area of leached calcium. A semantic segmentation method based on the DeepLabv3+ with ResNet50v2 backbone is proposed to identify the calcium leaching of dam concrete automatically. The calcium-leached concrete data set is established, including 94 high-resolution images in dam corridors to verify the method. The results indicated that the DeepLabv3+ model finally reaches 0.72 mIoU on the test set, which is a practical way to detect calcium leaching of dam concrete.
Design and implementation of Wisdom classroom management system based on voice control
Authors: Gao Xia , Xiong HongZhou , Xu Fangqin 
Published: November 2020

Abstract
This paper implements a wisdom classroom management system. In response to the difficulties of traditional campus equipment management, this system can automatically collect the status of electrical appliances in the classroom and analyze the data to provide equipment status diagnosis and early warning functions. At the same time, the Raspberry Pi provides a visual 3D management interface for the terminal. In addition, the system also introduces the voice recognition function in deep learning to control the equipment in the classroom more conveniently.
Study on the Teaching Mode Based on OBE-BOPPPS – Taking Computer Application Fundamentals as an Example
Authors: Wang Bei 
Published: November 2020

Abstract
The implementation of flipped classroom can help students achieve the goal of deep learning, and the BOPPPS model which is rising in recent years provides a basic framework for the implementation of flipped classroom. Through the analysis of the concept of flipped classroom and the feasibility of constructing the flipped classroom teaching mode of computer application foundation, this paper summarizes the methods of constructing the flipped classroom teaching of Computer Application Fundamentals, hoping to provide reference for the flipped classroom teaching in higher vocational colleges. Teaching practice shows that the OBE-BOPPPS teaching mode is helpful to solve the problems in the teaching process, improve the teaching quality of university teachers, improve the learning effect of students, and promote the teaching reform of basic computer courses.
Review on the principle, Progress and Application of Block chain Technology
Authors: Xu Yipeng , Guan Kexin , Lei Lei 
Published: November 2020

Abstract
In recent years, electronic currencies such as bitcoin have been favored by people. As the core technology of Bitcoin, blockchain has attracted the attention of more and more experts and scholars at home and abroad. The changes of its classification mode, service mode and application requirements lead to the diversified development of core technology forms. Blockchain is a distributed ledger technology, which evolves into a complete storage system based on logic control functions such as intelligent contracts. Block chain technology is based on decentralized point-to-point transmission network, using cryptography principles and consensus mechanism to ensure the coherence of each node participating in the distributed system, and to ensure the accuracy, consistency, non-tampering or deletion of the recorded information. All these ensure the establishment of a sharing system with high privacy, high security and high efficiency. In this paper, the basic principles and characteristics of blockchain technology are analyzed, and the research status and application of this technology in the financial system are summarized in detail.
A data security exchange method based on automatic arrangement
Authors: Qi Zhenfei , Zhang Fuhua , Hang Feilu , Xie Linjiang , He Yue 
Published: November 2020

Abstract
With the increasingly fierce attack and defense of network security, the security system needs to adopt the coordination of multiple functional modules to improve the security of the system. In practical application, web service protocol transmission and restful specification transmission can be used to ensure the security of data exchange. However, when the two protocols are used together (two security modules), it will cause great problems and workload to the security operation and maintenance. This paper proposes that the security function based on choreography which can distinguish the security operation sequence or script corresponding to the next step according to the protocol of the data package, and arranges the web service protocol and restful protocol together through script and engine to automatically coordinate work, so as to reduce the pressure of security operation and maintenance.
A Small amount of Labeled Data Chinese Online Course Review Target Extraction via ALBERT-IDCNN-CRF Model
Authors: Min Liang , Miao Xianglin , Bi Peng , He Feijuan 
Published: November 2020

Abstract
Aspect sentiment analysis of online course reviews is of great significance in helping users choose courses and improve course quality. Review target extraction is particularly important as the basis of aspect sentiment analysis. Because the current models mostly rely on a large amount of annotation data, there is fewer relevant research on the extraction of online course review targets with higher annotation costs. This paper proposes an ALBERT-IDCNN-CRF review target extraction model for a small amount of labeled data. First, using ALBERT pre-trained sentences obtained dynamic model Chinese word vector coding; Simultaneously, using ALBERT pre-trained model of Transformer obtain sentence abstract features. Then, abstract features are input into the dilated convolutional neural network (IDCNN) to reduce the number of neuron layers and parameters. Finally, conditional random field (CRF) is used to decode and annotate the review sentences to extract the appropriate review objectives. The experimental results on the school online real Chinese online course review data set show that our model has achieved better results than existing models.
HWOBC-A Handwriting Oracle Bone Character Recognition Database
Authors: Li Bang , Dai Qianwen , Gao Feng , Zhu Weiye , Li Qiang , Liu Yongge 
Published: November 2020

Abstract
The oracle bone character (OBC) from ancient China is the most famous ancient writing systems around the world. Identifying and deciphering OBCs is one of the most important topics in oracle bone study. In research, one of the challenges is that the literature review usually leads to a huge cost of time and manpower. Therefore, the digitazation of OBC literature through the automatic recognition is the inevitable trend of future development. However, the OBCs in the literature are usually writing characters while the database of handwriting OBC has not yet been presented. In this paper, we establish a handwriting oracle bone character database called HWOBC, containing 83,245 character-level samples which are grouped into 3881-character categories. We also present the performance of several baseline DCNN-based methods, in which Melnyk-Net exhibits the best accuracy of 97.64%. It is anticipated that the publication of this database will facilitate the development of OBC research.
CDS: Contextual Spoken Language Understanding Model Based on Dual Semantics
Authors: Ma Kun , Hu Minghao , Li Fangzhao , Luo Li 
Published: November 2020

Abstract
Spoken language understanding (SLU) in multi-turn task-oriented dialogue systems usually needs to take historical contextual information into account. To achieve this, current approaches encode dialogue utterances with contextual information to improve the accuracy of slot filling and intent detection. However, this kind of approach ignores the different influences of contextual information on dialogue utterances. Some utterances with independent context will be misunderstood due to the addition of noisy contextual information. In this paper, we propose a Dual Semantics model(CSD) that is based on dual semantics. Each dialogue utterance is encoded based on independent semantics and contextual semantics, where the contributions of these two semantic information are dynamically decided. A large number of experiments on two datasets verify the effectiveness of our approach. Specifically, the accuracy of slot filling at the KVRET dataset increases by 3.3% compared to the baseline model. A dataset of psychological service appointments(PSA), is further collected for the verification of our approach. Compared with existing methods, the accuracy is improved by 4.9% on this dataset.
Research on Single Antenna Co-frequency Mixed Signal Separation Based on Improved EFICA Algorithm
Authors: Ren Hongli , Zheng Yufu , Zhong Na , Cao Xiaofang 
Published: November 2020

Abstract
Signal separation under the condition of single antenna reception is a process of estimating the source signal component by using one-dimensional observation signal vector. Under the condition of single antenna receiving the same-frequency mixed signal, in view of the problem that the stability of the EFICA algorithm will be affected by the selection of a random initial iterative matrix, it is proposed to use the steepest descent method to select a suitable iterative initial matrix to improve the EFICA algorithm. An experimental comparison between the EFICA algorithm and the EFICA improved algorithm is presented. The simulation results show that the improved EFICA algorithm can achieve a better separation effect for the separation of mixed signals at the same frequency.
Study on Condition Monitoring of Lathe Cutting State Under Various Cutting Conditions Based on Motor Current Signature Analysis
Authors: Huang Shiqing , Zou Zhexiang , Lin Yubin , Gu Fengshou 
Published: November 2020

Abstract
Lathe machining occupies a large proportion in modern production and processing, which affects the machining accuracy of the workpiece and results in vibration and noise of the machine tool. To improve the efficiency of lathe cutting state monitoring, this paper presents a new method on studying the monitoring of lathe tool, with Motor Current Signal Analysis (MCSA) as the main research method, the characteristics of stator’s working current are selected as the analysis object. Three cutting depths, 1mm, 2mm, and 3mm are respectively performed on the same workpiece material, and the cutting conditions are blunt tool and sharp tool. The frequency characteristic of the current is analyzed the under various cutting conditions mentioned above and the internal relation of the current variation and the cutting condition is presented as the conclusion of this paper, by which provides a new idea for the research of conditions monitoring and fault diagnosis in field operation on lathe tool.
A dynamic weapon target assignment based on receding horizon strategy by heuristic algorithm
Authors: Zhang Kai , Zhou Deyun , Yang Zhen , Li Xiaoyang , Zhao Yiyang , Kong Weiren 
Published: November 2020

Abstract
Weapon-target assignment problem is the crucial decision support in Command & Control system. As a typical operational scenario, asset defense has less research on the issue of asset-based dynamic weapon target assignment, and the major models of A-DWTA are challenging in practical application. In this paper, an A-DWTA model is first established by receding horizon decomposition strategy, which closely reflects the operational requirement. Then a heuristic algorithm based on statistical marginal return is proposed to solve the A-DWTA model. Experimental results show that the proposed algorithm for solving the A-DWTA model has advantages of real-time and robustness. The obtained decision plan can complete the operational mission in fewer stages and be adjusted adaptively by model and algorithm parameters.
Ordinal Logistic Regression Analysis on Influencing Factors of Space Tourism Expectation Model
Authors: Duan Wenjing 
Published: November 2020

Abstract
Aiming at the problem of data validity in big data and information mining technology, this paper uses the method of ordinal logistic regression analysis based on statistics to evaluate the statistical validity of data, and analyses a case of space tourism expectation mode including 2025 valid samples. By analysing and calculating the significance level of the whole equation and the significance level of the equation goodness of fit in the case, the validity of the statistical data is evaluated, and the data regression equation is formed to support the statistical based artificial intelligence learning method.
Improving the Cold Start Problem in Music Recommender Systems
Authors: Cao Ke Yin , Liu Yu , Zhang Hua Xin 
Published: November 2020

Abstract
The music recommender system can help us extract valuable music from a huge amount of raw information, due to the increasingly severe data sparsity and cold start problems in music recommendation system, the recommendation results will be inaccurate. But the traditional algorithm cannot effectively solve these problems; the existing improved algorithm still requires specific parameters in advance due to its poor stability. In this paper, a proposition for a cold start based on community detection algorithm is proposed. By projecting the bipartite network, calculating the similarity between the User and the Item, the Louvain algorithm is used to perform community detection on the projected One-mode network, so that the new record can be updated to the original community, and then the user group is recommended for music. With higher accuracy, solid stability, and shorter running time (when compared with other ease cold start algorithms), the algorithm in the larger community can be safely applied to the music recommendation system or prediction field.
Data Privacy Protection Method of Internet of Things Based on Blockchain Technology
Authors: Wang Yajie , Huang Wei , Yu Jie , Chen Kai , Wang Fugui 
Published: November 2020

Abstract
In the process of the continuous reform of modern social economic system and the continuous development of science and technology, the distribution of the Internet of things reflects good application value. However, the Internet of things has many distributed nodes, data center, high computing cost and poor supervision. In the process of privacy protection, there are still some objective problems. Therefore, the use of blockchain technology in the privacy protection of the Internet of things privacy data block, structure decentralization, information hierarchy and other aspects, which can establish a more perfect Internet of things privacy protection model and more rich management mechanism, making the prevention of privacy leakage, data tampering and other aspects of privacy protection work has been further developed. Therefore, this paper takes the implementation mode of blockchain smart contract in smart home privacy protection as an application example to analyze the application value of blockchain in the privacy protection of Internet of things.