bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analog Combining in Intelligent Reflecting Surface Assisted System with Low-Resolution ADCs
Authors: Xu Zhixin , Wang Rui , Liu Erwu 
Published: December 2020

Abstract
Intelligent Reflection Surface (IRS) and low-resolution analog-to-digital converters (ADCs) are proven to be efficient ways to release the burden of power consumption for future wireless systems. In this paper, the effect of IRS and quantization on the performance of multiple-input multiple-output (MDVIO) channels is investigated in terms of channel capacity. The reflection matrix of the IRS and analog combiner matrix are optimized jointly. To this end, the expression of capacity with low SNR is derived, and the optimization problem of the reflection matrix is converted to semidefinite programming (SDP). A gradient-based approach is proposed to optimize the analog combiner matrix and the reflection matrix in a general case. Simulation results are provided to assess the performance of proposed designs, it is shown that the IRS can improve the performance of the system where there is no line-of-sight channel to some extent, and a larger capacity could be obtained in low SNR expression than general expression under same conditions.
Multi-Modal Biometrics based on Data Fusion
Authors: Yang Hongxun , Sun Eason , Cheng Cheng , Ding Anthony H 
Published: December 2020

Abstract
With the development of intelligent application, biometrics recognition technology has been widely concerned and applied in many fields of the real world, such as access control and payment. The traditional biometrics are usually based on single modality data of the subjects, but they are limited by the feature information capacity and the bottleneck in recognition accuracy. In this paper, a multi-modal biometric recognition framework is presented, which utilizes a multi-kernel learning algorithm to fuse heterogeneous information of different modal data. In order to extract complementary information from them, we combine the kernel matrix to form the mixed kernel matrix, and then give the final classification results. The experimental results on multiple biometric datasets show that our method can obtain higher recognition accuracy compared with the existing single mode and multi-mode fusion methods.
Visual Analysis of World Earthquakes based on Data Science and Statistical Methods
Authors: Xiangrong Xing 
Published: December 2020

Abstract
Earthquake is a very serious geological disaster, often accompanied by serious casualties. Therefore, it is necessary to explore the law of earthquake occurrence and take corresponding measures in time to reduce losses and casualties. However, it is difficult to predict the mechanism and principle of earthquakes. In this study, we utilize the methods and tools of data science to explore the regularity of earthquake occurrence. This paper is a practice of enforcing the workflow of data science, which uses computer algorithm to make fast statistics of data and conduct visualization processing to study the law of earthquake occurrence. Compared with the traditional research methods, it has important significance.
Intelligent Steward The Definition of Intelligent Machine Assistant and the Chronological Method of Intelligent Era Machine
Authors: Qi Kailong 
Published: December 2020

Abstract
This paper proposes the definition of intelligent steward, a personal intelligent technology assistant who can improve business management and quality of life by using AI technology for the first time. Intelligent steward mainly serves employers to complete business steward business by using comprehensive tools such as artificial intelligence, intelligent machine, voice recognition, image recognition, machine translation, in-depth learning, human-computer interaction, mobile Internet. From the perspective of practical activities of business steward mode innovation, its main modes are classified into four types: independent professional business steward company, business steward in subdivided industries, generalized business steward crowd sourcing organization and AI intelligent steward with the intelligent machine as the main carrier. Intelligent steward is an intelligent partner in the business work and daily life of users, which will realize the extension and agency of human brain, and provide a new economic development form for the era of economic decision-making and consumer sovereignty. In this paper, we use Fibonacci series of mathematics and computer model program to get the corresponding relationship between the intelligent era and the common era, which to a certain extent promotes the evolution of human civilization. Taking 2020 as the first year of intelligent era era, the intelligent era chronology is the product of the era of machine intelligent civilization. It will be gradually recognized by the machine network world as the computing power and thinking of the machine evolve, completing unified evolution of human civilization, machine civilization, human and machines mixed civilization.
Research and analysis on the development of intelligent toilet
Authors: Gong Ming , Li Kejing , Tian Tao , Mao Xianbian , Wang Chen 
Published: December 2020

Abstract
Intelligent toilet, also known as “electronic toilet” or “smart toilet”, was first invented by the Americans in 1964, and then introduced to Japan by Japanese businessmen. After the product is optimized in design, it began to be popularized in Japan in the 1980s and 1990s.In the early 1990s, Japanese businessmen has introduced intelligent toilet into China, so far, opened the domestic. The development of domestic intelligent toilet can be roughly divided into three stages: the first stage of domestic intelligent toilet development is 1990-1995, also known as the birth period of Chinese intelligent toilet, domestic intelligent toilet experienced the first stage of the cradle period, began to enter its growth period 1995-2015;2015 is a special and unusual year in the history of China’s intelligent toilet. After 2015, domestic intelligent toilet enters its third stage of development.
The Application of LightGBM in Microsoft Malware Detection
Authors: Pan Qiangjian , Tang Weiliang , Yao Siyue 
Published: December 2020

Abstract
The development of new technologies has caused computers one of the most popular electronic products. However, there is always a number of people who intend to take advantages of others through attacking others’ computers. To avoid property damage as much as possible, a precise and efficient detection is essential. This work uses the dataset which was generated by combining heartbeat and threat reports collected by Microsoft’ s endpoint protection solution to find out an effective solution. Since the dataset is large and has many categorical variables, reduction of memory and label encoding are used in data cleaning. Further, to handle the dimension problem and improve training efficiency, Chi-square testing is applied, and the top 42 fields are selected. Then, three algorithms (Logistic Regression, KNN and LightGBM) are chosen to build models and results are got respectively. The results show that LightGBM model achieves the best accuracy that AUC reaches 0.720687, and it is the most time-saving way. To the end, according to the feature importance from LightGBM algorithm, this work pick top-three important variables to analyze the underlying causes in the malware attack. One of the results reveals that the computer which has anti-virus software with bugs or pitfalls will suffer more attacks.
A comparative analysis of multi-machine learning algorithms for data-driven RANS turbulence modelling
Authors: Wu Junjie , Li Jiahua , Qiu Xiang , Liu Yulu 
Published: December 2020

Abstract
To improve the speed or accuracy of Reynolds-averaged Navier-Stokes in numerical simulations of turbulence, five machine learning algorithms including Random Forests, eXtreme Gradient Boosting, K-Nearest Neighbours, Support Vector Machine and Artificial Neural Network are introduced in this paper. We establish the nonlinear mapping relationship between the average flow field and the steady-state eddy viscosity. The machine learning surrogate models for the Spalart-Allmaras turbulence model are constructed and used to solve the backward facing step problem. It is demonstrated that the machine learning surrogate models constructed by the five machine learning algorithms can effectively express the complex mapping relationship between the average turbulent flow field and the steady-state eddy viscosity. They can not only obtain reasonable simulation accuracy but also significantly accelerate the solution time of the flow field. The algorithms of eXtreme Gradient Boosting, K-Nearest Neighbours and Artificial Neural Network have better performance when considering the calculation accuracy, time cost and memory cost. It shows the great potential of applying machine learning algorithms to RANS turbulence model and also provides a new idea for industrial simulations of turbulent flows.
Short-term load forecasting model based on EEMD-LSTS-ARIMA
Authors: Guantong Huang , Lanxin Hu 
Published: December 2020

Abstract
To improve the accuracy of the short-term power load forecasting, this paper designed a model, namely, the first to use based on empirical mode decomposition (EMD) method based on the collection of ensemble empirical mode decomposition (EEMD) in order to improve signal decomposition in the process of modal aliasing phenomenon and false component, and using the decomposition method of power load data decomposition, modal components with different characteristics, and according to its characteristics can be divided into high frequency components, intermediate frequency and low frequency components, Then the Long Short-Term Memory network (LSTM) is used for load forecasting. Using the autoregressive Integrated moving average model (Arima) to correct the predicted residual error, the load forecasting model based on EEMD-LSTM-ARIMA is established. Finally, the data of Queensland, Australia is used as the research sample. On the above prediction model, the data of this region is input into the model for power load prediction. At the end of the prediction, the final prediction results are compared with the EEMD-LSTM model through root mean square error (RMSE) and mean absolute percentage error (MAPE). Through comparison, it is found that the method used in this paper has a high prediction accuracy.
Lump solutions to the (2+1)-dimensional potential Kadomtsev-Petviashvili equation
Authors: Fang Shuo , Jiao Ailing , Li Wenting , Jiang Kun 
Published: December 2020

Abstract
Based on the Hirota bilinear method, we obtained the lump solutions to the (2+1)-dimensional potential Kadomtsev-Petviashvili equation by aid of symbolic computation with Maple. Examples are given to illustrate the different appearances and movements of the solutions when different value of the involved parameters are chosen in this paper.
A Byzantine consensus based on proof-of-work of nodes’ behaviors
Authors: Dingyu Wang , Dingmin Wang , Lupin Qin , MinLong Xu 
Published: December 2020

Abstract
Blockchain is a decentralized distributed peer-to-peer system, and decision-making in the system relies on a reliable consensus protocol. Under the current consensus protocol in the blockchain system, the behavior of nodes are not regulated. Based on the existing consensus protocol, this paper analyzes the problems encountered in application scenarios, introduces a credit calculation method to regulate the behavior of system nodes, and with a simplified consistency protocol, it can promote the alliance chain system to enter a virtuous cycle, and improve the efficiency of consensus in scenarios with many wrong nodes.
Research on Query Term Expansion based on RankSVM and LDA Model
Authors: Peng Lin , Lu Guo-quan 
Published: December 2020

Abstract
The lack of semantic association will lead to poor performance of retrieval system. This paper proposed a query terms extension method based on RankSVM and LDA model. Firstly, with the aid of RankSVM’s excellent performance of sorting, high-quality initial retrieved results will be obtained. Then utilizing the LDA model to select the documents related to query term and to generate topic model for sequenced initial retrieval results.in the post-processing step, using threshold to select the highest probability among each topic and regards them as query terms set. Experiments show that the query terms expansion method proposed in this paper is superior to the current query expansion methods based on pseudo-relevance feedback in the five evaluation index, therefore this is a feasible method.
Program Semantics-based Task Decomposition
Authors: Wang Zhiming 
Published: December 2020

Abstract
Recently, automatic code generation technology has been extensively studied and gradually becomes an important part of software development. However, when facing coarsegrained, highly general task description, the current automatic code generation technology still cannot achieve good results. Therefore, we propose program semantics-based task decomposition technology. For a task description given by the user, natural language processing and deep learning technology are used to learn and understand it. Combined with the program semantics contained therein, we decompose the task into ordered subtask sequence. We evaluate our proposed model on constructed data set and achieve a BLEU-4 score of 18.13.
A Routing Algorithm for Delay Tolerant Networks
Authors: Bin Liu , Zhi-lin Wang , Ren-jie Zhu 
Published: December 2020

Abstract
Based on the limitations of traditional submission ratio of Prophet caused by the performance difference and state difference of nodes in the new service environment, and the idea of utility quantization, a Prophet-BSAS algorithm based on node state was designed. The utility parameters and utility functions of the improved algorithm were analyzed in detail, and the performance of Prophet-BSAS and other classical routing algorithms was simulated and analyzed under the typical mobility model.
A Clothing Retrieval Model Based on DenseNet and Multi-Similarity Loss
Authors: Feifei Xu , Yu Tian , Shuting Zheng 
Published: December 2020

Abstract
With the rapid development of deep learning in recent years, Deep Neural Network structure has been applied to all aspects of image algorithm, including image classification and recognition, target detection, image retrieval. Image retrieval is suffering from some challenges, such as crease, cover, background. Based on these challenges, using the local information of the image to replace the image is proposed which learns embedding vectors by local information to reduce noise interference. Inspired by the above discussion, a new deep learning model named IAPM (Integrated and Partial Model) is proposed in this paper. According to global information and local information, Joint training is carried out. For feature extraction, DenseNet is used to extract the feature vector. And the idea of transfer learning is applied to transform classification problems to retrieval problems. Meanwhile, the multi-task model is adopted to construct a framework, and Multi-Similarity Loss is used to pull the position of the embedded vector in feature space. Experimental results show its effectiveness.
A Visual transformation method of SysML model to Modelica model
Authors: Fu Chao , Liu Jihong , Yu HongYan , Xu WenTing 
Published: December 2020

Abstract
The core idea of Model based systems engineering (MBSE) is to model all the information related to the system design starting from the beginning of the system development in the integrated form, and serve as a central system model throughout the product system life cycle. In view of the lack of executable capability of the standard system modeling language (SysML) within MBSE, the system design-simulation model transformation method was studied. Visualizing the transformation process based on SysML could contribute to the low complexity of the transformation. Finally, an example of 3D printer is used to verify the proposed method.
Interaction solutions to the (2+1)-dimensional generalized Bogoyavlensky-Konopelchenko-like equation
Authors: Jiao Ailing , Jiang Kun , Li Wenting , Fang Shuo 
Published: December 2020

Abstract
By using the generalized bilinear operations with a prime number p=3, a (2+1)-dimensional gBK-like equation is introduced. classes of interaction solutions of the (2+1)-dimensional gBK-like equation are generated through symbolic computation with maple. Some images were plotted to illustrate the dynamical movement of the solutions with specific values of the involved parameters.
A new red wine prediction framework using machine learning
Authors: Ye Chao , Li Ke , Jia Guo-zhu 
Published: December 2020

Abstract
Red wine has become an integral part of people’s lives and culture. Modeling the red wine quality is crucial. We propose a new framework to predict the red wine quality ratings. MF-DCCA was utilized to quantitatively investigate the cross-correlation between quality and physicochemical data. The long-range correlations importance was ranked. We compared two machine learning algorithms with other common algorithms implemented on the red wine data set, which was taken from UC Irvine Machine Learning Repository to ensure the reliability and performance. These data sets contain 1599 instances for red wine with 11 features of physicochemical data. Our model has better performance than other results.
Analysis and Evaluation on the Network Security Defense in Power Marketing Industrial Control System
Authors: Li Jiawei , Wu Kehe , Zhang Bo 
Published: December 2020

Abstract
With the implementation of the energy Internet, ‘Internet+power’, power IOT, and digital transformation and upgrading strategy, the power marketing system has carried out the massive metering acquisition and load control terminals, which are interactive and open accessed. Also, it has enhanced the coordinated operation and control with core power control systems such as power dispatching, transportation inspection. The current protection is mainly based on traditional passive defense concepts and technologies such as isolation protection, which cannot meet the demand of high reliability, high availability, safe and stable operation in the energy Internet environment. Therefore, it is urgent to establish a new generation of industrial control system for power marketing security protection system, which breaks through technologies in control terminal widely safe access, network safe and reliable interaction, system safe and trusted operation. The proactive defense technology brings new inspiration to the research on the security protection theory of the industrial control system for power marketing, including ontology security immune enhancement and pre-warning detection. Moreover, applying the theory of proactive defense to the field of power marketing security can provide a new way to improve the security protection of power marketing system..
Cross- Scenario Person Re-identification
Authors: Jia Ruoran , Liu Shuguang 
Published: December 2020

Abstract
Person Reid is a challenging task for two factors, first one is background interference, such as changes in light, weather, posture, and camera position. Second is domain adaptive capacity, such as model train by market1501 achieve the same performance on Duke dataset. To solve the above problems, we come up with adopt human semantic to remove clutter from unwanted background information, is naturally a better alternative compare with bounding box, we adopt Local Regions Representation to extra the image features, which can preeminently improve the representation of local feature and global feature. Our proposed CSReID integrates human semantic and Local Regions Representation in person re-identification and not need to train on the evaluation dataset can achieve state of the art cross-modal performance.
Using molecular fingerprints and unsupervised learning algorithms to find simulants of chemical warfare agents
Authors: Gu Leilei , Zhang Xinxin , Li Ke , Jia Guozhu 
Published: December 2020

Abstract
The emergence of novel coronavirus highlights the importance of research and development of biological protective materials and functional protective equipment. As an important experimental material, the direct application of chemical warfare agents (CWAs) will cause great pollution to the environment. The effective search for simulants determines the process of CWAs experiments. This paper combines molecular fingerprint and unsupervised learning algorithm to develop a simulants selection framework. A selection strategy is developed based on the silhouette coefficient. The closest simulants are found (GA (TEP/DEEP), GB (DFP), GD (DEHP), HD (CEES), VX (Amiton)) under a threshold (Silhouette coefficient: 0.2). This study can effectively help researchers to find the best approximate simulant to a certain extent.
Research on the construction of information fusion oriented social work information system
Authors: Liu Pengfei , Huang Xiaolian , Zhang Weifeng , He Xiaxu 
Published: December 2020

Abstract
This paper studies the information storage and fusion need in the procedure of social work. Information management is a common problem to social work. According to the theory and method of information system development, this paper discusses the significance and design of social work information system development, and finally gives the overall implementation framework and some common function modules of information system.
Research on modulation recognition of Communication Radar Based on spread spectrum technology
Authors: Bao Dujiya , Guo Hua , Zhang Wenxia , Bao Xinxin 
Published: December 2020

Abstract
Aiming at the problem that the traditional radar signal recognition method needs to be completed under the noiseless condition, which leads to the low recognition accuracy of low SNR signal, the modulation recognition method of communication radar signal based on spread spectrum technology is studied. The phase difference and instantaneous frequency characteristics of communication radar signal are extracted after the spread spectrum processing of communication radar signal with direct sequence spread spectrum. The training sample is used to train PNN neural network, and the modulation recognition of communication radar signal is completed. Simulation experiments under different parameters show that the recognition method based on spread spectrum technology has an effective recognition rate higher than 90% for low SNR signal, which is superior to the traditional method and can be applied in practice.
Research on mapping technology of laser navigation robot without reflector
Authors: Haiying Zhang , Shengming Liu , Zhiying Jiang 
Published: December 2020

Abstract
In view of the problems existing in the construction of large sparse environment map for laser navigation robot without reflector, an incremental SLAM algorithm based on multistage ICP matching is proposed. This method constructs a new image according to the pose of the robot, and develops a regional closed-loop algorithm combined with incremental optimization method based on the graph optimization algorithm. Experimental results show that due to the low amount of environmental information, the algorithm can effectively avoid falling into local minima, eliminate the cumulative error of mapping, reduce the computational complexity, and realize accurate mapping and real-time application.