bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Design of Site-based Interactive Installation Concerning Cyber Speech Violence Grounded on Arduino
Authors: Qi Wang 
Published: April 2021

Abstract
Cyber speech violence is committed in the non-physical form on online social platforms. In some online incidents that have already drawn much attention, what the victim said and did is often infinitely exaggerated and misinterpreted. Meanwhile, a large number of netizens usually criticize these incidents with their own limited perceptions and understandings, standing on the commanding heights of morality. In consequence, with the on-going development and spread of the incidents, more netizens will participate in such violence. Despite the helplessness of the victims, more criticisms emerge as follows, thus fueling the violence in virtual world to become real-life violence. In order to alert people to deeply understand the harm of online speech violence, the research applies Arduino, the open-source platform, in the design of site-based interactive installations. Such application aims to present intricate virtual perceiving behaviors in a structured and storyboard manner, thereby surpassing the design of traditional experiential interaction. Furthermore, it enables the operator to enter the scene from the first perspective, which is more in line with human cognitive habits. By means of constructing scenes, shifting plots and designing interactive process, the prevailing virtual speech violence in the online world will unfold before the eyes of the viewers. Taking the “site-based interactive installation of internet speech violence”, supported by Arduino as an instance, this research has evidenced the feasibility of employing Arduino in site-based interactive installations, as well as has provided reference for the reform and development of constructing experiential interactive installations.
Mo-BoNet: A TIME SERIES CLASSIFICATION MODEL BASED ON COMPUTER VISION
Authors: Mingcheng Li , Yubo Dong , Hongli Wang , Pengchao Li 
Published: April 2021

Abstract
Time series are widely distributed in many fields. Classical statistical methods are difficult to model the deep meaning of time series, and the deep learning methods based on recurrent neural network has great limitations when it is applied to indefinite long time series. In order to solve the above problems, a time series classification model based on computer vision is proposed, which transforms the time series classification problem into image classification problem. Firstly, three kinds of images with different linewidth corresponding to the time series are used as input to reduce the information loss in the conversion process. Secondly, the transfer learning model based on MobileNetV3-Large is used to encode the image data, and XGBoost is used for classification. The experimental results show that the classification effect of this model is better than that of the classical image classification model, and its XGBoost is also better than other ensemble methods, which proves the feasibility of computer vision method in time series classification task.
Research on Key Techniques for Super-resolution Reconstruction of Satellite Remote Sensing Images of Transmission Lines
Authors: Si Weiguo , Han Jiajia , Yang Zhi , Ma Xiao , Deng Yuanjing , Luo Meng , Li Chuang , Zhao Binbin , Wang Jian , Zhao Bin 
Published: April 2021

Abstract
The data point target on the satellite remote sensing image of the transmission line is restricted by satellite remote sensing imaging equipment and transmission conditions, which makes it difficult to guarantee the clarity of the transmission line. Image super-resolution technology aims to recover high-resolution images from low-resolution images to improve the detailed information of the transmission line itself, which is of great significance for the intelligent inspection of transmission line satellite remote sensing and hidden danger monitoring. Aiming at the problems of traditional methods relying on multi-frame image sequences and the reconstruction results are too smooth, this paper proposes a single-frame remote sensing image super-resolution method based on the boundary balance generation confrontation network. Experimental results show that this method can provide more high-frequency information, and the reconstruction result is closest to the real image. Compared with neighbor interpolation, bicubic interpolation and other methods based on deep convolutional neural networks, the PSNR of the results in this paper is significantly improved, and effectively enhances the detailed information of the transmission line and the surrounding environment.
A Car-Following Model Using Online Sequential Extreme Learning Machine
Authors: Yan X H , Zhou Z W 
Published: April 2021

Abstract
To prevent the sedimentation and loss of real-time data reflecting the car-following behavior and use it for modeling services, a car-following model using online sequential extreme learning machine is proposed. This model based on car-following behavior is affected by the driver’s memory effect, and a forgetting factor is introduced according to the timeliness of incremental data in different traffic conditions. It is trained, validated, and tested by NGSIM project data. Compared with other data-driven car-following models, the model has higher predictive accuracy and better real-time performance. With the help of experimental simulation, it verifies that the average time of the model’s online update is about 0.004s, which can fully meet the real-time requirements of the online traffic simulation system. The research results show that the car-following model proposed in this article is reasonable and feasible.
A BERT based Chinese Named Entity Recognition method on ASEAN News
Authors: Zhuang Haoyu , Wang Fu , Bo Songlin , Huang Yongzhong 
Published: April 2021

Abstract
As the first step of building a knowledge graph to record the ASEAN counties’ information, we aim to conduct Named-entity Recognition (NER) on the Chinese news about ASEAN counties. We employ a Bi-directional gated recurrent unit to replace the LSTM architecture to improve both models’ effectiveness and capability in understanding polysemous words. The state-of-the-art word embedding model, BERT, has also been included to generate qualified word vectors for the NER task. Besides, we also propose a similarity-based dataset partition method to help model learning the polysemy within the Chinese news. Experiments have been done to demonstrate that the combination of such improvements can benefit the models’ performance in identifying different types of named entities.
Cutting parameter optimization method in multi-pass milling based on improved adaptive PSO and SA
Authors: Fang Yilin , Zhao Lili , Lou Ping , Yan Junwei 
Published: April 2021

Abstract
In the production process, cutting parameters greatly affect the production cost and energy consumption, so it is very important for manufacturers to optimize cutting parameters. In this paper, an improved particle swarm optimization (PSO) is presented to optimize cutting parameters for minimizing carbon emissions, production cost and processing time in multi-pass milling. First, a multi-objective optimization model of cutting parameters is established with number of milling passes as one of decision variables. Then, an improved adaptive simulated annealing particle swarm optimization (IAPSOSA) is proposed to obtain the optimal solution of cutting parameters. At last, a case study is given to illustrate that the proposed method is effective to optimize cutting parameters for economic and environmental benefits.
Research on the Teaching Mode of College English Based on Artificial Intelligence
Authors: Liu Aihua , Kong Dehua 
Published: April 2021

Abstract
In recent years, the breakthroughs of AI have recently occurred in the fields of natural language processing, image recognition and so on. How to introduce AI technology into college English teaching and efficiently integrate with college English “teaching” and “learning” has not yet been fully studied. The AI technologies, especially in NLP, provide the possibilities of enhancing college English teaching qualities and build the new college English teaching mode. Studies on the efficient connection of teaching and learning concerning the applying of AI are scarcely found. This thesis analyses the integrate on of AI and college English teaching, especially the feedback of self-regulated learning evaluation and try to probe new teaching modes against the AI backdrop. Related researches have demonstrated that it will provide new ideas and entry points for the reform of the current college English teaching mode, and is of great significance for achieving the goals of college English teaching.
The application of eye tracking technology in the evaluation of shoulder fit of male suit
Authors: Yang Lin Mei , Li Yue , Wang Xiao Gang 
Published: April 2021

Abstract
With the development of science and technology, intelligent production of clothing has become a major trend, and male suit, as a kind of clothing with stable style and subtle changes, is undoubtedly one of the goals of intelligent production. In fact, in the intelligent production of men’s suits, fitness evaluation has certain research significance for both the early pattern design and the later quality evaluation. However, up to now, the evaluation system for the fitness of men’s suits is not perfect, and most of them lack a certain objectivity. Therefore, this paper will analyze and solve this problem by using eye tracker experiment to evaluate the fitness of men’s suits’ shoulders. By dividing the interest area of men’s suit style and selecting the eye movement index of the tester, the eye movement data of four views and five interest areas of men’s suit were analyzed and compared, and finally the evaluation standard of the fit of men’s suit shoulder was obtained.
Analysis of Software Security level based on system clustering and correlation
Authors: Yin Shizhuang , Shi Quan , Wang Yadong , Wang Shuai , Wu Weiyi 
Published: April 2021

Abstract
With the wide use of electronic devices, passwords are used increasingly frequently. How to effectively evaluate password security needs our further research. In this paper, the known leaked passwords obtained from the network are analyzed by cluster analysis, frequency analysis, and correlation analysis by using system clustering and correlation, the strength grade model of the password is established, and the relationship among the factors of the password is analyzed. It shows that most people’s personal information plays an important role in building passwords.
Research and Design of Rice Wine Aging Device Based on Fluent
Authors: Ni Hongjun , Wang Kaixuan , Lv Shuaishuai , Wang Xingxing , Zhang Fubao , Liu Peng 
Published: April 2021

Abstract
Rice wine has a spicy and poor taste, which becomes soft and long aftertaste after natural aging for more than one year. According to the problems of long time, large area and low efficiency in natural aging process, rice wine aging device was designed in this paper. Firstly, the requirements of rice wine aging device were analyzed to establish its three-dimensional model. Then, heat transfer performance of this device was analyzed with Fluent software and the effect of temperature field distribution on the aging process of flower dew was studied, which determines the best aging temperature. Finally, a prototype of rice wine aging device was made, and the samples of rice wine were aged in it. The results show that the aging device can keep 55? for 12 days, which can achieve the effect of natural aging for one year, and the aging effect is significant, which is of great significance for the realization of mechanized production in wine industry.
Research on Data Quality Control Method of Eucalyptus Plantation Sensor Network
Authors: Guang Deng , Xinwen Yu , Dongpu Fan , Chen Zhu , xu Zhang 
Published: April 2021

Abstract
The Internet of Things technology can provide data support for precise management measures and reflect the effect of management activities in time through long-term monitoring of plantation forest stands and environmental factors. However, the networking and deployment of the Internet of Things based on sensor network in the plantation forest has just started, and the stability of the sensor network itself is not very reliable, so the data quality will be greatly affected. In this paper, based on the experimental data and the experience of continuous data post-processing such as meteorology, the data quality of eucalyptus plantation forest sensor network is preliminarily analyzed. Then, on this basis, some algorithms of data interpolation and data integration are given according to different observation indexes, which lays a foundation for the further application of sensor network data of plantation forest in the future.
Track slab crack detection based on full convolutional neural network
Authors: Li Wenju , Chen Huiling , Zhang Yaoxing , Shi Yanjiao 
Published: April 2021

Abstract
In recent years, with the rapid development of railway industry, railway safety has become an issue of increasing concern to people. The crack is the biggest threat to the safety of the railway. Because the pictures can only be collected at night, the pictures collected have many problems such as noise and uneven illumination. In order to solve these problems, this paper proposed a method of track slab crack image segmentation based on full convolutional neural network. In this method,VGG-16 is used as the backbone network, and the dilated convolution is added into the backbone network. Then the spatial pyramid pooling method is used to extract more global and local feature information. In addition, the HED network is used to obtain multiple output results. Finally, in order to make the model more discriminative to the characteristics of each channel, SE module is added. Experimental results show that the proposed method reaches 81.84%,67.68% and 84.55% in precision, IoU and F1-score respectively in the self-made data set, which is superior to the traditional segmentation method and U-Net method, and can meet the needs of practical engineering.
Research on BP neural network in enterprise credit rating management based on artificial intelligence era
Authors: Ye Jingyi , Han Xiao 
Published: April 2021

Abstract
BP neural network has been gradually applied in the field of economic management. It has many advantages that traditional methods can’t achieve, but there are also disputes and problems. In this paper, the BP neural network model is established to analyze the credit risk indicators of listed companies, and the learning system of enterprise credit rating management is established to predict the future credit risk, so as to achieve the purpose of enterprise risk management, and the corresponding conclusions and suggestions are given to promote the development and innovation of enterprise management and artificial neural network.
RETRACTED: Analysis of Application of Tennis Electronic Referee Based on Artificial Intelligence in Tennis Matches
Authors: Shi Xiaoyu , Zhang Jianxin 
Published: April 2021

Abstract
In today’s era of rapid development of artificial intelligence, with the continuous development of technology, electronic referees have become an important part of many sports events, and artificial intelligence technology has become the brain of competitive sports and integrated into major events. this article uses artificial intelligence algorithms, graph theory and ID3 algorithms to establish an analysis of whether electronic referees violate rules in tennis matches. And use post-pruning technology to realize the pruning of the classification decision tree, generate classification rules, and complete the construction of the classification decision tree model used by the tennis electronic referee in the tennis match. The experimental results show that the application analysis of the artificial intelligence-based tennis electronic referees in tennis matches explored in this paper is more recognized and liked by athletes and spectators, and the athletes’ perception of the fairness of the game has increased by about 55%.
Flipped Classroom Teaching System Design under the Background of Subject Reform Based on Information Technology
Authors: Wang Yang , Tong Wuning , Lv Qingli , Luo Yu , Wei Nian 
Published: April 2021

Abstract
With the rapid development of modern information technology, some traditional teaching models in colleges and universities will not be able to adapt to the pace of change. The new teaching model flipped classroom not only meets the requirements of our country’s new curriculum reform, but also allows students to reduce the burden of learning after class, and achieve the best of both worlds. The purpose of this article is to study the design of the flipped classroom teaching system under the background of subject reform based on information technology. This article adopts literature research method, survey research method and action research method to carry out research. This article summarizes the flipped classroom teaching mode and practical cases through a literature review of relevant domestic and foreign literature. This article proposes how schools and teachers should respond to the development of information technology and the development trend of “flipped classroom” under the current situation, and provide certain guiding suggestions for future classroom teaching reforms. This article selects two classes of teachers and students in a university in this city as the research objects, investigates the current teaching status of the curriculum, summarizes and analyzes the characteristics and problems, and draws on the results of existing practical research to initially build a flipped classroom suitable for this teaching practice Model, and based on this model, select the content, carry out the teaching design and practical experiment of the flipped classroom, and finally this article carefully summarizes and reflects on the practice. The survey results show that. 85.45% of students find it helpful to increase the communication activities between teachers and students and between students in the classroom, and students can gain from the exchange and cooperation.
Design and Implementation of The Computer’S Main Structure with Waterproof and Moisture-Proof Function
Authors: Gong Jianhu 
Published: April 2021

Abstract
In order to prevent the harm caused by high air humidity to the computer, a series of structures, such as exhaust parts, lower shell, support legs, water baffle, filter parts, air inlet parts and waterproof shell, are set in this paper to solve the waterproof and moisture-proof problem of the main structure in the computer hardware system.
Analysis of the Design and Performance of Microcomputer Fault Alarm System
Authors: Gong Jianhu 
Published: April 2021

Abstract
The fault detection and alarm system designed in this paper, which can be used in home, including a series of structures such as fault detector, cooling plate, support plate, controller, voice chip, voice player, alarm lamp, cooling fin, etc, is convenient for users to obtain fault alarm information effectively and carry out maintenance.
Research on Real Scene 3D Modelling Based on Multi-view
Authors: Liu Rui , Yao Liwu , Yan Lei , Li Heping , Liu Xiaolong , Yang Yongwen 
Published: April 2021

Abstract
3D modelling based on multi-view images is a method of taking scene images with digital itemsets, extracting and matching feature points in the images, and solving camera parameters to establish a 3D model. This method has the characteristics of high work efficiency, low cost and high degree of scene restoration, this article uses this method to research and experiment on the 3D modelling process of indoor scenes.
Adaptive Modification of Digital Twin Model of CNC Machine Tools Coordinately Driven by Mechanism Model and Data Model
Authors: Zhao Peng , Sun Beibei 
Published: April 2021

Abstract
The digital twin of CNC machine tools has become one of the important technical means to realize intelligent manufacturing. To reflect the real conditions of the whole life cycle of the machine tools, an adaptive modification method of the digital twin model of CNC machine tools coordinately driven by mechanism model and data model is proposed. Taking the dynamic stiffness of the CNC machine tool as the combined object, firstly the finite element method is used to establish the initial digital twin mechanism model, then the simulation data of the mechanism model is used to generate enough samples to establish a surrogate model to realize the construction of the initial data model. At different stages of the life cycle of CNC machine tools, the minimum deviation between the dynamic response obtained by the simulation of the digital twin model and the measured response is set as the objective function, and intelligent optimization algorithm is adopted to adaptively modify the digital twin model to ensure the consistency between the digital twin model and the physical object. The established digital twin model is used to predict the dynamic performance of CNC machine tools, which lays the foundation for the whole life cycle management decision of CNC machine tools.
Generation of brown gas from a dry cell HHO generator using chemical decomposition reaction
Authors: Sivakumar B , Navakrishnan S , Cibi MR , Senthil R 
Published: April 2021

Abstract
Pollution is a factor that makes humans lives worse; because of this pollution, so much of health hazards are uprising. This article mainly contributes to constructing a dry hydroxy cell generator that generates brown gas, which is usually known as hydroxy gas. The primary catalyst used is lye (NaOH) and caustic potash (KOH), which is to be incorporated with stainless steel and followed by supplying a unidirectional current. The reaction is accelerated since the catalyst offers a proxy pathway to expedite the response. The law of electrolysis of faraday governs this process. To lower the consumption of fossil fuel, which is the primary root cause of pollution. This dissertation provides detailed scrutiny on brown gas generators and its best harvest, which is known to be the highest production volume of HHO gas. This journal deals with how the change in the catalyst, power rating and concentration ratio affects the brown gas production and how NaOH differs from KOH in HHO gas production. The average time taken to produce 1 litre of HHO gas using NaOH is about 53 seconds, whereas it is about 80 seconds for KOH.
Comparative Seismic Analysis of Conical and Pyramidal Frustum Shaped Commercial Building (G+6) at Janakpuri, Delhi
Authors: Sindhu Nachiar S , Anandh S , Kumar Ajit , Puskar Abhinav , Mohammed Fardeen 
Published: April 2021

Abstract
In the modern building construction, the trend of constructing structures that are economical as well as aesthetically appealing is increasing. These structures often employ ideas and architectural features that are diverse from the emblematic conventional structures. The unconventionally shaped building has irregularities in the distribution of mass, stiffness and strength along the height of the building. Conical and pyramidal frustum shaped building are those structures that have remarkable shape and utilizes both inclined and vertical columns. In these structures, the floor area of the plan changes at each specific storey. With the intensification in complexity and innovation of the building construction, earthquake forces have become an imperative factor to be considered while construction of these avantgarde structures. The Indian Standard code IS-1893: 2002 (Part-I) describes the approach for the structures with irregularities considering the varying mass, floor-area ratio and stiffness of the storeys. The objective of the paper is systematic seismic analysis of a conical and pyramidal frustum shaped commercial building(G+6) at Janakpuri, Delhi using STAAD. Pro V8i. The buildings are located in seismic zone IV. The result of the analysis aids to understand and compare the storey drift, storey displacement and stiffness of the structures subjected to earthquake forces.