Reviewerly/exHarmony huggingface dataset:
Analyze influence factors in customer’s insurance transaction by decision tree model

In recent years, the development of the digital is rapidly in the world. In a variety of technologies gradually mature, the Internet and mobile device popularization, the IOT and cloud computing services, driving the growth of all kinds of data, so that the data greatly increased and diversified. The value of these data can be used to predict the consumer&#x2019;s behavior, difference the user groups to study out efficient marketing strategies, and create differentiated competitiveness.In order to predict the consumer&#x2019;s behavior of buying insurance products, the research collected 4474 insurance transactions from a bank in Taiwan Tainan. After the data pre-processing, the available transaction number is 3430. In these organized transactions, we let the classification of insurance products as the dependent variable, and the attributes of customers as independent variables. Then, using the correlation analysis by chi-squared test to carry out un-relevant factors. Analyzing the influence factors by decision tree machine learning model. According to the analysis result of the decision tree model, the accuracy rate almost close to 70%, and the most important influence factors are the actual insurance fee and currency. These two influence factors can be used as a reference for the bank in Taiwan Tainan to precise the marketing.
A Fast Algorithm for Deriving Frequent Itemsets

When mining frequent itemsets (abbr. FIs) from dense datasets, too many itemsets are generated and results in the mining task from a large amount of execution time and high memory consumption. Frequent closed itemset (abbr. FCI) is a lossless and concise representation of FIs. Mining FCIs can not only greatly reduce the execution time and memory consumption, but also retain the complete information all of FI. Although many studies have proposed different mining FCI algorithms, but they have less developed methods that can effectively derive all FIs from FCIs. Form this point of view, this study proposes a novel efficient algorithm named DFI-List for efficiently deriving FIS from FCIs. The algorithm adopts the methodology of depth-first-search and divide-and-conquer to derive all FIs from FCIs. DFI-List efficiently derives all the FIs with vertical index structure called Cid List and uses SC Table to quickly find the support count of the derived FI. Experimental results show that the execution speed and memory consumption of the proposed algorithm with the proposed strategy is better than of the state-of-art algorithm.
Online Teaching System Combining Information Feedback and Teaching Evaluation

Traditional teaching model is not efficient, as teachers cannot quickly change the focus of teaching based on students' misunderstandings, and students cannot promptly reflect the problems encountered to teachers. Fortunately, the rapid development of information technology brings us many new concepts, such as video teaching, live teaching, and big educational data. They profoundly change the traditional teaching methods and further promote the development of e-Education. Therefore, this article proposes an online course teaching system that combines information feedback and teaching evaluation. Specially, students feedback their questions to teachers through the feedback module which collects students' questions to generate teaching evaluations for this class. At the same time, teachers can check the teaching effects of this class through the evaluation module, reply to students' questions, and adjust subsequent teaching content. Our proposed model enables teachers to grasp the key points of teaching and improves students' learning efficiency. Finally, we implemented the system with Java Web technology and applied the system to the actual teaching process. The experimental results show that the combination of information feedback and teaching evaluation can significantly improve the teaching effect.
OFHR: Online Streaming Feature Selection With Hierarchical Structure Based on Relief

Hierarchical classification learning, an emerging classification task in machine learning, is an essential topic. In which various feature selection algorithms have been proposed to select informative features for hierarchical classification. How-ever, existing hierarchical feature selection algorithms consider that the feature space of data is completely obtained in advance, and neglect the uncertainty and dynamism, i.e., feature arrives dynamically in an online manner. In this paper, we present an online streaming feature selection framework with hierarchical structure. First, we apply the closeness matrix between internal nodes to the Relief algorithm, which can calculate the weights of the dynamic features. Second, significant features are dynamically selected for each internal node by considering the hierarchical relationships and feature weights between nodes in the tree structure. Moreover, we perform redundant analysis of features by calculating the covariance between features, and then obtain a superior online feature subset for each internal node. Finally, the proposed algorithm is compared with six online streaming feature selection methods on six hierarchical data sets. The experimental results prove that our algorithm can improve the classification accuracy of the classifier by 10% compared to the suboptimal algorithms, which indicates that the algorithm outperforms other comparative algorithms in hierarchical data sets.
Uncertainty-oriented Intelligent Simulation Test System and Its Framework

With the development of simulation technology, simulation test has become an important way of assessment and evaluation of weapon equipment. But it is the development need for the enhancement of test credibility to reflect the uncertainties in battlefield environment. By studying the characteristics of the uncertainty of combat activities, this paper puts forward the relationship between the certainty and uncertainty of the combat simulation test, establishes the intelligent simulation framework of complex testing system and its main elements, and analyzes the way to realize the combat uncertainty during simulation testing. It provides a basis for construction of combat simulation test system.
Object Detection Algorithm Based on Alternate-Attention

The one-stage object detection algorithm, YOLOv3, has a fast detection speed and can meet real-time requirements. But giving the same attention weight to all grids during detection will result in the inability to highlight the detection subject, so the positioning accuracy of the bounding box is still room for improvement. In order to improve the detection accuracy, this paper proposes an Alternate-Attention mechanism, using the global pooled attention mechanism to highlight the overall characteristics, and the self-attention mechanism to reflect the self-weight relationship between features. The two attention mechanisms are alternated and applied to the two dimensions of channel and space, and finally enhance the features extracted by Darknet-53. Experiments on the PASCAL VOC2007 dataset shows that this algorithm can effectively improve the detection accuracy. Compared with Faster RCNN, YOLO series and SSD series algorithms, the mAPlouo.5 value of this algorithm is higher, up to 80.24.
Trajectory-Based Dynamic Handwriting Recognition Using Fusion Neural Network

We propose a fusion network model for handwriting recognition. The model consists of a feedforward fully connected neural network (FNN) and a convolutional neural network (CNN). For a given handwriting trajectory, we generate two types of inputs for the FNN and CNN networks, respectively. Each of the networks produces a confidence vector for a handwriting trajectory. Subsequently, the fused result is the element-wise product of the two confidence vectors. We evaluated the proposed fusion network on two data sets, namely RTD and 6DMG, which contain alphabetic and numeric handwriting data. Five-fold cross validation was adopted. The average accuracy of our fusion network achieved 99.77% on the alphabetic data and 99.83% on the numeric data of the 6DMG data set, and 99.61% on the RTD data set. Finally, we compared the fusion network with three state-of-the-art techniques.
Research on Sports Data Acquisition and Transmission Technology Based on VR

Motion data acquisition and transmission technology is widely used in VR field. In recent years, with the development of related technologies, its application in the VR field has become increasingly perfect. In this regard, this paper will give a detailed overview of the relevant sports data collection methods and transmission technology, including the technology and principle of riding speed, human heart rate and turning angle, as well as the wired transmission technology and wireless transmission technology suitable for VR field, such as Bluetooth and WIFE data transmission technologies. In addition, this paper summarizes the methods and technologies in the existing literature, and compares the performance of these technologies. Finally, the shortcomings and future research trends of motion data acquisition and transmission technology for VR are summarized.
Dynamic Span Selection for Mandarin Articles Using Contextual Relations and Orthography

Span selection is an important prerequisite for many natural language processing tasks. Existing methods usually generate phrase-like spans from entire articles without leveraging the topics or the key points within each paragraph that usually lie behind sentence generation during the writing processes. This study looks at multi-sentence span selection for generating multiple, independent, key-point spans with complete endings for news articles. The proposed span selection model consists of a context relation model and an end span model that merge context-related sentences within a span. The context relation model captures the topics shared between sentences, and the end span model utilizes the embeddings of Zhuyin, the orthography of Mandarin, and the cross attention between words and Zhuyin to effectively capture the end positions of the spans. To evaluate the proposed framework, we construct a news report dataset in Mandarin. Experimental results show that the proposed model not only improves performance, but is also better than previous approaches and close to human span production. The proposed Zhuyin embeddings and cross-attention also improve on BERT&#x2019;s end sentence detection performance in Mandarin.
QoS Correlation-based Service Composition Algorithm for Multi-constraint Optimal Path

With the development of network service integration, in order to obtain a better quality of service (QoS) guarantee, aiming at the characteristics of integrated network service composition and correlation, this paper proposes a QoS correlation-based service composition (QCSC) approximate algorithm based on multi-constraint optimal path selection (MCOPS). It analyses the QoS correlation criteria, correlation ratios, and Skyline algorithms to calculate the optimal path by dynamic programming, record the path nodes, and obtain the optimal service composition path that meets the user's demand. Simulation results demonstrate the good performance of the proposed algorithm in both the average calculation time and the solution path quality. Accordingly, it can meet the QCSC requirements in the cloud environment.
A Framework for Effective Knowledge Extraction from A Data Space Formed by Unstructured Technical Reports using Pre-trained Models

The transformation of unstructured data into triples is a key task in knowledge graph construction. It remains a great challenge to complete this task on technical reports. In this work, we propose a framework for effectively structuring data structuring in knowledge graph construction from a data space formed by technical reports. This framework specifically consist of two pre-trained language models to provide the embed dings and a sequence labeling model to tag the entity labels. The pre-trained models, i.e. the Flair embedding and the BERT model, are employed to combine the output vectors to downstream tasks. To evaluate the proposed method, we conduct named entity recognition experiments using the status reports of complex equipment in nuclear power plants. The evaluation shows the framework achieves remarkable improvement on F1 score. This paper details the framework, the experiments, and the evaluation of the proposed method.
Phishing Website Detection Based on Multi-Feature Stacking

Aiming at the problems of low accuracy and high computational resource consumption of most current phishing website detection technologies, a stacking phishing website detection method combining four base learners is proposed. And an HTML string embedding feature is designed based on the Transformer to replace artificial features, which converts the HTML document of the website into a multi-dimensional vector, and combines filtered URL features to detect the phishing website. On the 100,000-level data set, the accuracy rate reaches 98.52&#x0025;, and the F1 value reaches 98.81&#x0025;. And after introducing the HTML string embedding features designed in this article, compared to only using URL features, each performance index has been significantly improved.
FOCM: Faster Octave Convolution Using Mix-scaling

Octave convolution that separates the feature maps for different resolutions is an effective method to reduce the spatial redundancy in Convolution Neural Networks (CNN). In this paper, we propose a faster version of octave convolution, FOCM, which can further reduce the computation cost of CNNs. Similar to the octave convolution, FOCM divides the input and output feature maps into the domains of different resolutions, but without explicit information exchange among them. In addition, FOCM utilizes the mix-scaled convolution kernels to learn different sized spatial features. Experiments on various depth ResNet with ImageNet data-set have shown that FOCM can reduce 33.9% to 46.4% operations of the original models, and save 11.1% to 21.7% FLOPS of the models using octave convolutions, with similar top-1 and top-5 accuracy.
Discriminative Feature Extraction and Enhancement Network for Low-Light Image

Photos taken in low light conditions will cause a series of visual degradation phenomena due to underexposure, such as low brightness, loss of information, noise and color distortion. In order to solve the above problems, a discriminative feature extraction and enhancement network is proposed for low-light image enhancement. First, the shallow features are extracted by Inception V2,and the deep features are further extracted by the residual module. Then, the shallow and deep features are fused, and the fusion results are input into the discriminative feature enhancement module for enhancing. Specifically, the residual channel attention module is introduced after each stage to capture important feature information, which helps to restore the color of low-light images and reduce artifacts. Finally, the brightness adjustment module is used to adjust the brightness of the image. In addition, a hybrid loss function is designed to measure the loss of model training from multiple levels. The experimental results on the LOL-v2 dataset show that the proposed algorithm can reduce noise while improving image brightness, reduce color distortion and artifacts, and is superior to other related algorithms in objective indicators. The result maps are more real and natural in subjective vision.
The Transformation of RDB to NoSQL DB

In the past, Relational Database (RDB) is widely used to access data quickly because of high data accuracy and consistency. However, the Big Data era forces many industries to change RDB in order to process a huge amount of data in time. NoSQL (Not only SQL) database (NoSQL DB) is a tailor-made DB. Various industries can choose a suitable NoSQL DB to enhance their competitiveness according to their operational needs. But the data in RDB still need to be saved for company&#x2019;s operation. How to convert data from RDB to NoSQL DB is an important issue. This paper proposes an algorithm to transfer an RDB to the Wide Column Store DB (WCSDB), one of the most popular NoSQL databases. The experiment results show that WCSDB performs better than RDB.
Color Calibration Method for Images Taken by Different Imaging Conditions Which Suppresses False Color

Owing to different imaging conditions, color reproduction of images is usually different; hence, color calibration methods have been developed to unify color reproduction. Iterative distribution transfer (IDT) is one such method that can achieve good color calibration for most pixels of an input image by matching the color distribution of a reference image. This method, however, may lead to unnatural colors, called false colors. In this study, we propose a new color calibration method, which effectively suppresses false colors, whose result is similar to that obtained via the IDT. The input image is projected to specific powers called bases, which are multiplied by the corresponding projection coefficients and added to obtain the projection result. The projection coefficients can be obtained by minimizing the objective function, which is the mean-square error between the projection result and the IDT result. Considering that a false color in the IDT results is expected to be similar to the color of an adjacent pixel, a color-similarity-related item is added to the objective function to address this. Color similarity can be measured by the chromatic aberration between the adjacent pixels of the input image. By ensuring that similar colors in the input image remain similar in the output image, false colors can be suppressed. For the experiment, we use false color index, Kullback-Leibler divergence, and visual evaluation to evaluate the calibration results, which consistently proves that this method offers a better color calibration effect compared with other methods.
Research on Collaboration between Automotive Industry Value Chain and Blockchain Technology

With the gradual strengthening of the current links between the value chains of the automotive industry, the business collaboration between the value chains has formed a network structure that includes vertical and horizontal synergy relationships. Therefore, multiple value chains currently have problems such as difficulty in data coordination and data interaction. There are data islands between the value chains. Blockchain is a distributed infrastructure and computing paradigm based on the P2P network. Using blockchain as the underlying technical support for multiple value chain collaboration can strengthen the interaction between multiple value chain data and meet multiple chain business collaboration need. This research maps the networked multiple value chain to the multiple blockchain, studies the storage method of the business data in blockchain, and breaks the data isolation state between multiple value chains.
Improved Method of Multi-objective Particle Swarm Algorithm Learning Factor Based on Fitness Change

Aiming at the problem that the inertial component of particles could not guide the particle to the right direction when the fitness became poor, a multi-objective particle swarm algorithm learning factor improvement method based on the fitness change was proposed. The large learning factor improved the multi-objective particle swarm algorithm. In the simulation experiment, the improved algorithm PSO-AIC1C2 and the PSO-S, PSO-AIC1 and PSO-AIC2 with c<inf>1</inf>and c<inf>2</inf>obtained by splitting this algorithm were fixed with c<inf>1</inf>and c<inf>2</inf>changed separately, and then compared with other PSO improvements. The algorithms MOPSO, SMPSO, and dMOPSO are compared. Experiments showed that increasing c<inf>1</inf>could improve the performance of the algorithm, and increasing c<inf>2</inf>would cause the convergence of the algorithm to deteriorate. In most test functions, PSO-AIC1C2 had obvious advantages in convergence and distribution indicators. The improved method proposed had certain guiding significance for the study of learning factors of particle swarm optimization in the future.
Keyword-based Data Augmentation Guided Chinese Medical Questions Classification

For the existing data of medical and health questions, the majority of them are so inarticulate short texts with few terms that the text features are sparse, posing a daunting challenge to relevant classification effort. Against this background, to enlarge the terms and datasets of short tests, this paper proposes a keyword-based data augmentation algorithm, which can be used in two ways: (1) With regard to short texts featuring few terms, for the purpose of keyword expansion, keywords are extracted by topic model and trained through domain knowledge-assisted word vector model to obtain synonyms of expanded keywords, so as to expand the original keywords; (2) with regard to incomplete health questions, the synonyms are used to replace original keywords. Then the augmented samples obtained by the above two methods are sent to the classifier. As a result, the algorithm in this paper significantly improves recall, precision and macro value compared to those without data augmentation.
Application of Real-time Sentiment Analysis Based on Mini-Xception in Classroom Teaching

The in-depth understanding and application of teacher and student data by learning analytics technology provide a new development perspective for the education field, in which emotional data play a vital role in evaluating teaching quality and learning effects. Currently, sentiment analysis technology is developing rapidly, but its application in the educational field is lagging. Most of the research is based on sentiment analysis of published texts on social media or videos recorded by students, which may lead to problems such as incomplete feedback content and delayed feedback analysis. Based on the mini-Xception framework, this paper implements the real-time identification and analysis of student sentiment in classroom teaching. Through the feedback results, teachers can fully understand the degree of student engagement and provide reasonable suggestions for subsequent teaching progress. The experimental results show that this method has high recognition accuracy for the real-time detection of seven student sentiments, and the average accuracy is 76.71 %. Compared with after-class student feedback or real-time text sentiment analysis, it can better reflect the real time and high efficiency of information feedback. It provides a basis for teachers to control the teaching rhythm and evaluate the teaching effect and is an effective method for realizing personalized teaching.
Java Curriculum Design Concept that Integrates Design Thinking and Heuristic Teaching

Design thinking is a methodology. In course teaching, design thinking is used to provide a practical solution to problems, focusing on cultivating students' innovative ability and solving complex problems. Heuristic teaching is based on the reality of students. Teachers play a guiding role in teaching tasks, inspire students' thinking in various ways, focus on tapping students' learning potential, and mobilize students' enthusiasm for learning. This paper proposes a teaching method that integrates design thinking and heuristic teaching, using project-driven teaching tasks. In the teaching process, teachers adopt heuristic teaching methods, pay attention to cultivating students' design thinking, and guide students to complete tasks. Compared with traditional teaching methods, the combination of design thinking and heuristic teaching has certain advantages, which not only improve students' innovative thinking but also stimulates students' subjective initiative in learning. It improves students' learning efficiency and promotes teaching reform to some extent.