Reviewerly/exHarmony huggingface dataset:
Study on Consecutive Interpretation Automatic Scoring Model Based on Neural Network Algorithm

With the continuous development of artificial intelligence and the continuous improvement of hardware performance, the era of intelligence is coming rapidly. Using AI technology to help people deal with various practical problems has become increasingly important and urgent. Among them, the field of alternate interpretation has become a deep water area that artificial intelligence technology needs to conquer. In oral English teaching and automatic scoring, in recent years, reading questions, with questions to answer the automatic scoring system has reached the practical level, and reply, interpretation and a given range of automatic scoring method research is in the initial stage, related research results are less. Interpretation test is a comprehensive test of foreign language application ability, including foreign language thinking ability and language organization ability. Research and development of an effective Chinese-English interpretation automatic scoring system can not only provide students with an interpretation practice platform, but also assist teachers in teaching and release the pressure of teachers on teaching and marking. The paper provides the feasibility test of real test data in a provincial self-study test; using the literature research method, investigation method and quantitative analysis method, the average consensus rate of automatic score model results is 77%, which ensures the effectiveness and reliability of automatic score model based on neural network algorithm and can be fully utilized in the future.
Performance Parameters and Running Speed of Computer System Based on Big Data

Speed is the most important parameter of computer performance, and all methods devoted to improving computer performance aim to speed up computation. The development of multi-core parallel programs has made the improvement of computer performance a research hotspot. In this context, this paper analyzes the selection of computer system performance parameters and the influence of multi-core architecture on system running speed, and tests serial and parallel Monte Carlo in big data environment. The CPU utilization under the algorithm and the crawling effect of single-thread and multi-thread crawler are tested, and it is found that multi-core parallelism can improve the performance and speed of the computer system. Therefore, the parallelism of the computer system is fully utilized to improve the computing performance.
Impact of Activation Functions of Neural Network in Mammographic Masses

Computational intelligence is a well-established paradigm that has been creating new hypotheses based on good biological understanding. Defining computational intelligence is not an easy task because this area belongs to multiple disciplines like Artificial Intelligence, Soft Computing, Machine Intelligence, Machine Learning and many others. Research in this area include the computational techniques like support vector machines, fuzzy logic, neural networks along with a wide range of applications as modelling and pattern recognition. These methods have been applied to solve many complex and diverse problems. To solve the above mentioned complex problems, neural networks are used. The present paper will focus on relevance of neural network in mammographic masses dataset to determine its performance analysis using various activation function while performing the classification task.
ELECTRONIC DEVICES AS TOOLS IMPLEMENTATION OF PERSONALLY ORIENTED PEOPLE SITUATIONS IN PROFESSIONAL TRAINING MUSIC TEACHER

In the article ways of stimulation of pedagogical situations are considered based on strategy of individuality developing training with application of computer technologies. Authors describe variants of the organization tasked with the approach in vocational training the teacher of music. Efficiency of use of electronic devices as toolkit of the organization of a individuality-guided situation in formation o f such parameters of the person o f the teacher as ability to self actualization, a selfestimation, to project new creative tasks comes to light.
Big Crime Data Analytics and Visualization

Crime is one of the most permanent and troubling issues for societies and law enforcement agencies, and it costs dearly in several ways. In recent years, criminal justice agencies headed for Deep Learning, Data Mining, and Machine Learning (ML) to help in the crime-fighting process by taking advantage of historical crime data in order to identify and detect crime patterns and hotspots, predict crime occurrences in the future, and apprehend criminals and suspects. A major challenge facing researchers and law enforcement agencies is the rapid volume growth of crime data and how it can be analyzed accurately and efficiently. In this paper, state-of-the-art big crime data analysis and visualization have been done on two real crime datasets of two US cities, Chicago and San-Francisco. Some interesting and hidden patterns and correlations have been detected.
AI Design Approach for Urban Plaza: Human Behavior-Based Algorithm

The model of human behavior concerning urban plaza design emerges that designers mostly approach it with their intuitions. However, the built environment and how humans interact with it have various hidden consequences on users’ behavior. Besides immediate movement patterns and physical interaction, social interactions can be dictated by the built environment’s design. In recent years, with the expansion of design research, human behavior, and technology advancement, machine learning (ML) and multi-agent modeling/system (MAS) have become the driving factors in the evidence design field. Conventional urban designs were driven just by the practicality of the design and in order to satisfy the needs of the program of the space. Moreover, the data collection methods have been done through traditional methods of manual observation, user survey, and cellphone tracking, which are either obsolete, have user errors, or are not suitable for this design purpose. This paper is the first phase of a study investigating the potential of implementation of artificial intelligence as a means of intelligent data-driven design approach for open urban spaces. The aim of the research is to promote the user experience’s positive affordance and enhancing urban open spaces’ livelihood through ML and MAS. This paper aims to investigate the use of visual object recognition algorithms of histogram of oriented gradients (HOG) and edge histogram descriptors (EHD) for human detection in urban plazas. We investigate the impact of each stage of the computation on performance results and conclude that EHD is inaccurate due to the absence of adequate descriptors. HOG results were proven accuracy of 89% on a preprocessed image of MPII Human Pose Dataset containing over 500 annotated human images with a wide range of pose variations and backgrounds.
Research on the Information Management System of Small and Medium-sized Enterprises under the Computer Internet Technology

This article introduces the three aspects of customer management, inventory management and financial management involved in the information management platform of small and medium-sized companies. It focuses on the information platform architecture and implementation methods based on the internationally accepted File Maker Pro file database. The article uses cloud computing model to realize cloud computing of the overall structure of enterprise management information system, and introduces the system construction of management information system for small and medium-sized enterprises in the cloud computing environment and the implementation plan of enterprises in the cloud computing environment.
A pulse extraction method based on statistical histogram

Pulse compression signal is widely used in radar detection because of its outstanding anti-interference ability. The parameter measurement of pulse signal by signal analyzer is the basis of accurate detection, and the extraction of single pulse can reduce the complexity of pulse measurement. The commonly used neural network extraction methods have high algorithm complexity and are difficult to implement in instruments. In this paper, a pulse extraction method based on statistical histogram is proposed to achieve pulse power level classification. The paper estimates the pulse top power, base power and pulse amplitude, and propose the algorithm flow of pulse endpoint position extraction. Using MATLAB simulation, it is proved that the relative error of the parameter estimation results of the algorithm in this paper is less than 0.2%. The algorithm complexity is reduced by 20% compared with the traditional method.
Analysis of the Influencing Factors of Users’ Adoption Behavior in Social Q&amp;A Community Based on Machine Learning Regression Algorithms

Users are the core component of social Q&#x0026;A communities and influence the future development of the communities. Therefore, it is particularly important to ascertain the influencing factors of users&#x2019; adoption behavior. Based on the information adoption model, 41,832 Q&#x0026;A data are collected from the Zhihu community between May 27, 2011 and March 14, 2018. The data comprises three thematic areas: children education, medical care, and Internet finance. This study explores the impact of 122 features on the adoption behavior. Four machine learning algorithms are selected. They are multiple linear regression, Lasso regression, Ridge regression, and Xgboost regression. By comparing R<sup>2</sup> and RMSE of the models, the optimum model is used to filter the top 20 important features of each theme. The results show that 1) compared with the traditional model, the Xgboost model has better prediction performance, with a maximum accuracy rate of 63.9%; 2) answer content, answer background, respondents, and answer forms all affect adoption behavior; 3) users of different themes pay different attention to the features of each category. These results indicate that clarifying the scope of the themes and distinguishing them can improve models&#x2019; prediction accuracy of model. On the other hand, the research also proves the application value of the information adoption model and the Xgboost regression model.
Traffic Congestion Management using Camera and Geolocation

As the world's population grows, traffic congestion has become a severe issue. The paper examines the issue of traffic congestion as well as proposes a solution. According to the solution, one of the primary causes of traffic management inefficiency is insufficient information of real-time traffic congestion to the police and lack of knowledge of local routes and neighborhoods. This paper deals with how people report the traffic and that data can be shared with traffic cops which allows traffic personnel to quickly and effectively track the traffic congestion and resolve it. It will help in the reduction of traffic congestion by enlisting the assistance of traffic cops and improving data management.
Chinese Named Entity Recognition Based on Character Level Multi Feature Fusion

For the task of named entity recognition, this paper proposes a character-level multi-feature fusion named entity recognition method based on the IDCNN model, which can adjust the receptive field range through parameters. A distributed representation method combining radicals and character features is proposed to solve the problem of insufficient representation of Chinese characters' latent features. The character-level feature representation is input into IDCNN for feature extraction. IDCNN can make full use of the parallel ability of GPU under the premise of extracting long-distance semantic information, and finally improve the accuracy of entity label prediction through the CRF layer. In order to verify the effectiveness of the model, this paper conducts experiments on the commonly used MSRA data sets and Resume data sets. The experimental results show that the results of the model on MSRA and Resume data sets surpass those of Lattice LSTM, LR-CNN, and PLET. Compared with the PLET model with the best results, the F1 value of the model in this paper is increased by 0.55% and 0.03%, respectively.
Research on the Influencing Factors of Foundations’ Fundraising Ability in Universities Based on Neural Network

The education foundations in universities conform to the requirements of college and university construction in the new era, and provide a strong impetus for the construction of double first-class universities, the cultivation of talents, and the development of disciplines. The exploration of the education foundations&#x2019; fundraising ability in universities is of great practical significance. At present, domestic and foreign related researches are mostly limited to the level of theory and data statistics. There is a lack of research on the willingness to donate from multiple factors under different donation channels from the perspective of modeling. Neural network has good fault tolerance and strong self-learning, self-organization and self-adaptability. It is advanced in the processing of multiple data and the construction of nonlinear and complex relationships. Therefore, it is an ideal prediction method. Based on the neural network model and starting from multivariate factors, this paper analyzed the donation data samples of one foundation by constructing a mathematical model to simulate the donation process, and analyzed the influence of different factors on the donations of college and university foundations through the experimental results. The purpose is to put forward instructive suggestions for the fundraising of the foundations in universities.
Design and Implementation of an Auxiliary Software Platform for Prediction of Coal Shipping Price Index

This paper examines the index calculation model and illustrates the technology used to determine the price of coal for coastal shipping. The software hierarchy design of the shipping price index forecast and analysis system is carried out using the MVC model. Additionally, the software&#x2019;s function module is designed. Echarts technology enables data visualization, and the data cache mechanism alleviates server load during data retrieval. Additionally, the system is created and implemented. It enables the use of more representative sample routes when compiling freight indexes, as well as index prediction, analysis, and report document generation.
Lung CT super-resolution reconstruction based on Laplace residuals

In order to obtain clearer CT images at low doses, this paper proposes a super-resolution reconstruction method of lung CT images based on Laplacian pyramid residual network. The SRResNet network is connected in parallel to solve the problem that the traditional network model adopts a single scale. At the same time, the BN layer in the SRResNet residual module is deleted, and the feature information between the residual blocks is deeply fused through the dense series connection between the residual blocks. Enhance the network's perception of image features. The experimental results show that the lung CT image reconstructed by the algorithm proposed in this paper has richer details and clearer edges.
Intelligent Early Education Auxiliary System Based on Vision Algorithm

With the continuous internationalization of the market economy and the continuous progress of technology, the early education industry follows the development, and has more stringent requirements for visual algorithms. Based on the increasingly intensified market competition, the diversity of customer demand in the market competition environment is becoming more and more significant. With the continuous strengthening of national reform and continuous optimization of economic structure, the development and design of intelligent early education auxiliary system is the same. This paper studies the meaning, concept, classification and future development direction of intelligent early education auxiliary system based on visual algorithm, and lists the application cases of visual algorithm in early education industry. / auxiliary system in basic system, increase the large data model, sorting out the customer relationship, customer information, customer demand, customer value model is set up, customer usage range model, customer iteration model of intelligent/auxiliary system, from the data prove that intelligent/auxiliary system based on visual algorithm development and design of scientific, reasonable, In this way, the intelligent early education auxiliary system can be truly intelligent and scientific. Based on visual algorithm, the advantages of the comprehensive utilization of big data model, through the huge amounts of data to analyze customer value, customer value mining can improve space, improve intelligence/auxiliary system based on visual algorithm development and design of function and demand, improve their own quality, tested, check students' scores, edit the student information, subjects to change, The scores of question bank management were 89.4%, 96.5%, 90.4% and 90.2%.
CANet: Convolutional Attention-based Network for Multivariate Time Series Anomaly Detection

Anomaly detection of multivariate time series has been an active research topic for decades owing to the broad application of time series in various fields including e-commerce, smart city, robotic systems, etc. Accurate detection is therefore of great importance to the operational stability of a system or corporation. In this paper, we propose a new model composed of convolutional neural networks and a modified attention mechanism. Snapshot pictures of multivariate time series are adopted as the input for convolutional process. Convolutional blocks are responsible for spatial information extraction. And the modified attention mechanism can well assign weights to input segments since this mechanism emphasizes important sequences while depressing irrelevant segments. The comparison work with multiple baselines is carried out on datasets collected from the public archive and real world. Experimental results of multiple evaluation metrics prove that our model not only obtains higher accuracy but also gains a better recall rate.
Enterprise Information System Structure Partition Algorithm Based on Link Feature

In the diversified market competition environment promoted by emerging information technology, diversified business functions run collaboratively based on the shared data, that is, it has become the norm for enterprise data to be shared by various business functions. Therefore, there is a need for the partition of enterprise information system structure based on business functions and data usage connections to improve the collaboration efficiency of various business functions based on shared data. This research proposes a system structure partition algorithm based on link feature (SPLF). The SPLF algorithm takes non-interference sequence as the theoretical basis, and introduces link feature and link shared degree of sets as a measure of similarity between entities or sets. The SPLF algorithm includes two stages: entity link feature calculation and pre-clustering stage, and two-dimensional entity set clustering stage. Experiment shows that the clustering results produced by the SPLF algorithm have high cohesion and accuracy, and can effectively aggregate discrete original data from two dimensions in parallel, and realize sub-system partition according to the clustering results. The SPLF algorithm adopts the idea of joint clustering, and takes the sharing of links as the basis for similarity measurement, and extends the traditional clustering method from one-dimensional to two-dimensional. Based on the SPLF algorithm, this research proposes an enterprise information system structure partition framework, which structurally solves the problem of enterprise business function and data organization under the background of big data, and provides an effective solution for data sharing and cooperation for various business functions of the enterprise, so as to improve business processing efficiency and competitiveness.
FQCSpark: Efficient Spark-based Parallel Compression Algorithm for FASTQ Genome Sequences

The rapid development of Next-Generation Sequencing (NGS) technologies has posed serious challenges to the storage and transmission of genomic data, and the bioinformatics community urgently needs efficient genome compression algorithms to support genome analysis. The existing acceleration approaches for genome compression algorithms are mostly multithreading and limited to a single machine, which cannot adapt to the demand of large-scale genome compression in the distributed environment of cloud computing. In this paper, we propose a Spark-based efficient parallel compression algorithm for FASTQ genome sequences - FQCSpark. Experimental results show that FQCSpark outperforms existing algorithms with good compression ratios by several times in speed. This is due to the fine-grained degree of parallelism design and well-designed parallel operator flow. More importantly, the degree of parallelism design in this paper is also applicable to other algorithms which compress blocks independently. Meanwhile, FQCSpark provides good compression ratios, especially on the S.cerevisiae dataset, which is 15.4% better than the latest open-source genome compression tool - Genozip. FQCSpark is the first known Spark-based parallel compression algorithm for FASTQ genome sequences.
Effort Estimation for Architectural Refactoring of Data Tier Software

Architectural refactoring requires substantial effort. We introduce an approach and a tool to predict this effort prior to refactoring. We focus on PL/SQL programs that are developed as data access tiers of business software. There are two types of common refactoring needs for these programs. First, some of the modules might need to be migrated to a separate database. Second, some of the modules in the data tier might need to be migrated to the application tier. In both cases, the refactoring effort is proportional to the amount of coupling between the migrated modules and the rest of the modules in the database. Our tool can parse PL/SQL programs to reveal this coupling based on an analysis of SQL queries. Unlike prior studies, our tool can analyze queries that are created dynamically and that use multiple tables as well as PL/SQL-specific features. We evaluate our approach with an industrial PL/SQL program from the telecommunications domain. Our results are approved to be accurate by domain experts.
Design of Public Landscape Environment Optimization System Based on Niche Genetic Algorithm

Public landscape is a settlement landscape formed under human intervention, which is both cultural resources and ecological environment resources. Niche genetic algorithm is the most widely used algorithm in modern times. It greatly expands the space of system design and optimizes all links in public landscape environment optimization system design. This emerging technology also meets a wide range of public landscape needs. Starting from the influence of environmental factors and niche genetic algorithm, this paper discusses various links of modern public landscape environmental design in urban environment, deeply discusses the relationship and background between niche genetic algorithm and optimizing public landscape environmental design system, and analyzes the limitations of public landscape design at present, Combined with the research of niche genetic algorithm at home and abroad, this paper analyzes the performance links and characteristics of public landscape environment design system, focuses on its application principles and aesthetic evaluation in improving the efficiency of public landscape environment optimization system, and analyzes and discusses the advantages of this emerging tool in the use of public landscape environment optimization system.