Reviewerly/exHarmony huggingface dataset:
Model of Computer Automatic Test Paper Based on Genetic Algorithm

With the widespread application of computer technology in the field of education, online testing has become an important means of new educational measurement. The online examination needs to be supported by the convenient and quick computer automatic test composition. The computer automatic test composition depends on the construction of the test question bank system and the test composition model. This paper studies the computer automatic test composition model based on genetic algorithm to solve the key problem of computer automatic test composition. Genetic Algorithm is a method of global search for optimal solution. According to the execution flow of Genetic Algorithm, this paper builds a Genetic Algorithm model with “selection operation, crossover operation and genetic operation” as the core. The computer automatic test paper is actually a multi-constraint solution problem. This paper builds an automatic test test model with “difficulty coefficient, knowledge points, discrimination and frequency of use” as the core.
Clustering Analysis of Borrowing Data of University Library based on K-means Algorithm

With the continuous progress and development of data mining, the application of data mining technology has been involved in all aspects of people. The data mining of the borrowing data of university library can master the reading trend of readers on the whole, and make scientific prediction according to the trend, so as to improve the management level of university library and the reading atmosphere of readers, which is beneficial to the construction of library collection. In this paper, K-means algorithm is mainly used for clustering analysis of borrowing data, and the clustering characteristics of readers and books are analyzed, so as to provide more rigorous and scientific research methods and ideas for library service innovation and library department cooperation.
To hash or not to hash: A security assessment of CSP’s unsafe-hashes expression

More and more people use the Web on a daily basis. We use it for communicating, doing bank transactions, and entertainment. This popularity of the Web has made it one of the main targets of attacks, most prominently Cross-Site Scripting (XSS). To mitigate the effect of those attacks, the prevalence of the Content Security Policy (CSP) is increasing. Such a policy allows developers to control the content that should be allowed on their Web applications precisely. Because this content includes JavaScript (via the script-src directive), it can also be an effective tool to mitigate the damage of markup injections such as XSS. Developers can specify fine-grained policies for scripts to only allow trusted third parties and disallow the usage of functions like eval and its derivatives that directly execute strings as code. As the whole Web is still evolving, so is CSP. The experimental source-expression unsafe-hashes aims to ease the adoption of secure CSPs, by allowing trusted scripts to be used as inline event handlers for HTML tags, which is currently only possible by blindly allowing all inline scripts to be executed. Our goal is to analyze if this expression is able to improve the security of a Web application or if it mainly provides a false sense of security because it still enables attackers to bypass the CSP. We built an automatic crawler utilizing dynamic JavaScript analysis using taint tracking and forced execution to detect security vulnerabilities of inline event handlers. This crawler visited 753,715 unique URLs from the Alexa Top 1,000 domains up to a maximum of 500 URLs per domain. We collected a total of 735,105 individual event handlers, where 443 of those had attribute values that flow into a dangerous JavaScript sink. Our manual analysis of the event handlers revealed that 370 of those handlers on 34 different domains are still vulnerable in presence of a CSP that contains the unsafe-hashes expression. We show that attackers can exploit these flows with only partial injections, such as adding new attributes to existing tags in most cases and discuss the impact of our findings on the future of the CSP standard.
Certified Parsing of Dependent Regular Grammars

Parsers are ubiquitous, but formal reasoning about the behavior of a parser is challenging. One key challenge is parsing dependent formats, which are difficult for traditional techniques to handle because parse values can influence future parsing behavior. We present dependent regular grammars, which extend regular languages with data-dependency by generalizing concatenation to monadic bind. Even this small tweak adds significant expressive power; for example, conditional parsing and dependent repetition are both implementable using monadic bind.However, it is not obvious how to actually parse dependent regular grammars. We implement a Brzozowski derivative based matching algorithm, and we show how many popular parser combinator functions can be implemented in our library while retaining the same simplicity as traditional parser combinators.We implement and formalize these grammars in Coq, as well as a derivative-based matching algorithm. We prove soundness and completeness of the derivative operator in the standard way. We also implement a variety of popular parser combinator functions and give formal specifications to them. Finally, we implement as a case study a verified netstring parser, and prove functional correctness of the parser.
System Description on Third Automatic Simultaneous Translation Workshop

This paper shows my submission to the Third Automatic Simultaneous Translation Workshop at NAACL2022.The submission includes Chinese audio to English text task, Chinese text to English text tast, and English text to Spanish text task.For the two text-to-text tasks, I use the STACL model of PaddleNLP. As for the audio-to-text task, I first use DeepSpeech2 to translate the audio into text, then apply the STACL model to handle the text-to-text task.The submission results show that the used method can get low delay with a few training samples.
An Annotated Dataset and Automatic Approaches for Discourse Mode Identification in Low-resource Bengali Language

The modes of discourse aid in comprehending the convention and purpose of various forms of languages used during communication.In this study, we introduce a discourse mode annotated corpus for the low-resource Bengali (also referred to as Bengali) language.The corpus consists of sentence-level annotation of three discourse modes, narrative, descriptive, and informative of the text excerpted from a number of Bengali novels.We analyze the annotated corpus to expose various linguistic aspects of discourse modes, such as class distributions and average sentence lengths.To automatically determine the mode of discourse, we apply CML (classical machine learning) classifiers with ngram based statistical features and a finetuned BERT (Bidirectional Encoder Representations from Transformers) based language model.We observe that fine-tuned BERT-based model yields better results than CML classifiers.Our created discourse mode annotated dataset, the first of its kind in Bengali, and the evaluation, provide baselines for the automatic discourse mode identification in Bengali and can assist various downstream natural language processing tasks.
Construction of Sanda Teaching risk assessment index system using neutrosophic AHP method

To build a strong country, build strong teaching. But teaching surrounding multiple risks. So this paper aims to identify and assess risks in teaching. The assessment risks in sanda teaching is a critical task and contain multiple conflict criteria. So we use Multi-Criteria Decision Making (MCDM). In this paper, we use an Analytical Hierarchy Process (AHP) to rank and compute each criterion's weights. We use five main and twenty sub-criteria. These criteria were evaluated under a neutrosophic environment—an example provided to present the outcomes of the proposed model.
Bimodal Distribution Removal and Genetic Algorithm in Neural Network for  Breast Cancer Diagnosis

Diagnosis of breast cancer has been well studied in the past. Multiple linear programming models have been devised to approximate the relationship between cell features and tumour malignancy. However, these models are less capable in handling non-linear correlations. Neural networks instead are powerful in processing complex non-linear correlations. It is thus certainly beneficial to approach this cancer diagnosis problem with a model based on neural network. Particularly, introducing bias to neural network training process is deemed as an important means to increase training efficiency. Out of a number of popular proposed methods for introducing artificial bias, Bimodal Distribution Removal (BDR) presents ideal efficiency improvement results and fair simplicity in implementation. However, this paper examines the effectiveness of BDR against the target cancer diagnosis classification problem and shows that BDR process in fact negatively impacts classification performance. In addition, this paper also explores genetic algorithm as an efficient tool for feature selection and produced significantly better results comparing to baseline model that without any feature selection in place
Applying Euler Diagrams and Venn Diagrams to Concept Modeling.

This article introduces Euler diagrams and Venn diagrams as business analysis instruments. Application of these visual schematics to support development of vocabulary and concept models can help business analysts deal with the complexity of relationships between concepts. It also can help contributing subject matter experts overcome a perplexity commonly observed with intricate aspects of concept modeling. Visualizing typical relationships between concepts with circles that include, exclude, and intersect each other results in simple visual schematics that assist in conducting concept analysis and modeling activities. An implementation example is provided, based on a vocabulary development in the public health domain of immunization information systems.
Determination of edge coordinates of cylinder in diameter measurements by the projection shadow method

A method for determining the edge of a cylinder by the projection shadow method is proposed. The method is distinguished by the possibility of measuring the diameter of cylindrical objects even in the conditions of a limited dynamic range of the photodetector without the appearance of systematic deviations in the measurement results. The method is based on determining the position of the edge of the object in the image by the position of the maximum of the derivative of the intensity dependence.
Research on the application of computer virtual reality technology in tennis visual communication system

This paper is a tennis match broadcasting system developed based on computer big data technology. Based on computer big data algorithm and using Mariana. 5D system and AutoSports platform, it can broadcast tennis matches intuitively. This paper studies the server of the computer broadcasting system. and client system, and developed and tested a tennis match automatic broadcast video transmission device, so as to detect the moving foreground target by using the background frame difference method through related applications, and developed an advanced computer big data tennis match broadcast. The system effectively exerts the application and research of the visual communication effect technology of sports tennis live broadcast.
Aspect Root-Oriented Latent Structures with Graph Convolutional Networks for Aspect Sentiment Classification

Sentiment Analysis based on Aspects (ABSA) is aim to determine the degree of polarity of a set of views. In recent work, dependency trees were combined with graph neural networks (GNN). However, dependency trees can be errors. In order to use syntactic information more effectively, we propose that relies on the root node of the aspect tree and extracting potential syntactic trees from language model (PTM). Hence, our proposed model (RLGCN) can compensate for the inaccuracy of the ordinary dependency trees. The proposed method is proven effective in four datasets.
Design and Implementation of Intelligent Robot Experimental Platform Based on Virtual Simulation Technology

Intelligent robots are programmable actuators with a certain degree of autonomy that can move within their environment and perform desired tasks. Intelligent robots mark the highest level of mechanical automation, which is equivalent to equipping robots with “brain chips”, which are more intelligent and have stronger capabilities in cognitive learning, automatic organization and comprehensive processing of fuzzy information. Based on the virtual simulation technology, the intelligent robot experimental platform is developed, and the highly simulated virtual experimental environment and experimental objects are constructed to solve the problems that the real scene does not have or is difficult to complete. This paper analyzes the related technologies of intelligent robots, studies the fuzzy control principle and mathematical model, constructs the pseudo-random number generation algorithm and test, designs the intelligent robot simulation experiment process, and solves the key problems of the intelligent robot experimental platform.
Thesis Intelligent Evaluation Model based on Heterogeneous Graph Neural Network

Heterogeneous graph neural network learning hopes to express meaningful graph information through the neural network. Based on the obtained graph information to be applied to connection prediction, recommendation system, etc., this article learns from the academic social network based on HetGNN, combines the journal information and paper information, and expresses it in the form of feature vectors. According to the network learning results based on the VPR model, the Het-VPR model is proposed to apply the neural network to the paper evaluation, and very good results are obtained in the paper evaluation results.
Application of Model Predictive Control to Trajectory Tracking of Mecanum Wheels

The paper builds a ROS-based Gazebo simulation environment. The move_base function package provides reference trajectory and vehicle position information to the MPC controller which calculates a series of future optimal speed information and publishes the current optimal speed to the PID controller. The PID controller corrects the vehicle speed information to ensure that the vehicle body accurately reaches the required vehicle speed. After experiments, the scheme proposed in this paper is proved a feasible way to implement accurate and quick trajectory tracking control in the simulation environment.
Color Texture Image Recognition based on Deep Learning

Traditional image recognition mainly preprocesses the image based on the basic characteristics of the image, and then classifies the image after processing, but the whole process is complicated and requires a lot of resources to be invested, and the recognition effect may not meet people's expectations. The introduction of deep learning (DL) algorithms has further improved the efficiency of texture image recognition, and can also improve the time-consuming and labor-intensive shortcomings of traditional image recognition (IR). Deep learning imitates the hierarchical structure of the human brain neural network, removing invalid information layer by layer from the input information, and extracting effective information. Applying DL to the field of IR can achieve better target feature extraction and higher classification accuracy, and further improve the accuracy of image recognition.
Optimal Design of Computer Network Reliability based on PSO-Fcm Intelligent Algorithm

With the wide application of computer networks, in order to provide users with satisfactory services, it is necessary to ensure the quality of network services, and reliability is an important indicator to measure service quality, and the research on network reliability has attracted much attention. A large body of literature shows that intelligent algorithms have certain advantages in solving general optimization problems. Among them, PSO-FCM shows strong optimization ability and good performance in solving general optimization problems, and has been widely used in many fields. This document uses PSO-FCM to update the solution model through simulation and determines that the minimum computer network connection cost is 634, and the maximum guaranteed computer network reliability is 6.2. The best results outperform the reliability of traditional computer networks.
Building API Ecosystem in Blended Learning by Service Oriened Architecture

Over the past few years, blended learning has become an increasingly popular, convenient and affordable way of learning. This is thanks to the flexibility of blended learning approaches in terms of time and place, where individuals can adjust their schedules to learn. However, rapid changes in the education sector can affect the design, development and deployment of educational resources. Therefore, this study proposes a Service Oriented Architecture (SOA) approach that loosely couples different system components to reduce development time and costs. Furthermore, we propose a prototype learning application with an API ecosystem that considers the attractiveness and validates the functional and non-functional requirements of the system.
Comparison on Visual Trackers: A Taxonomy

Visual objective tracking is an important research problem in computer vision. Recent years have seen a significant increase in deep learning style correlation filtering due to developments in the field of deep learning and computer arithmetic power. The purpose of this paper is to present an overview of the approaches based on deep learning processing. We classify different articles through their network perspectives, and for each approach in the classification, including correlation filter, Siamese network, Multi-domain network. First introduce the problems handled by each approach, then analyze the process of the methods they use, and finally summarize their derived final results. After reading several papers, we conclude that these deep neural network-based approaches have improved the performance of visual tracking algorithms in recent years. In addition, we also analyze the future trends of visual target tracking and the problems that remain to be solved, and these works will help the future direction of development.
Application of Artificial Neural Network in Student's Behavior Analysis in English Class

The Convolutional Neural Network (CNN) has outstanding performance in images identification and facial recognition. It is helpful in analyzing students' behaviors in English class, judging whether they are focused or unfocused in class; through the facial expressions, CNN could identify the five basic emotions in English class and by processing the pictures of eyes and mouth, it is conducive to judging students' fatigueless in class.
The Effect of Using Scrabble Mobile Phone Games to Develop Students' Vocabulary Knowledge

The purpose of this research was to obtain empirical evidence on the effect of using scrabble mobile phone games to develop students&#x0027; vocabulary knowledge. In this research, a quantitative method was chosen, and it was modified into a quasi-experimental study with multiple choices as the instrument of this research. Students from the seventh grade of a junior high school in Jakarta were chosen through purposive sampling as the sample from the research population. 60 students were divided into two classes. 30 students from VII A were selected as the controlled class, and 30 students from VII C were selected as the experimental class. The data were collected from the result of the pre-test and post-test of both classes. According to Setyawarno, the data was analyzed using a normality-test then proceeded with a homogeneity-test to conduct a T-test. The study found a difference in the mean score of the two classes from the post-test result, in which the experimental class got a higher score than the controlled class. The post-test result of the experimental class was 77.67, while the post-test result of the controlled class was 80.93. Moreover, the study found that the T-observation was 13.509, and the T-table with df 58 insignificant level degree of freedom 5&#x0025; was 1.67. The data result proved that T-observation is higher than T-table., indicating that the null hypothesis (H0) was rejected and alternative hypothesis (Ha) was accepted. Thus, it proved that using scrabble mobile phone games was effective at a moderate level to develop students&#x0027; vocabulary knowledge.
The Power of Fusion: A Multimodal Behavioral Biometric Authentication Framework on Smartphones M2auth

Most of proposed behavioral biometric authentication systems on smartphones are unimodal based, which rely only on a single source of information such as gesture. Unfortunately, the unimodal based system is suffering from some problems such as noisy data and non-universality. Moreover, it provides a lower authentication accuracy in compare with the physiological biometric systems such as Face recognition. In this paper, we propose m2auth, a multimodal behavioral biometric authentication framework on smartphones. m2auth is used to authenticate smartphone users based on three different modalities, gesture, keystroke and sensor. Fusing different behavioral biometric modalities can improve the authentication accuracy and address these problems. Instead of relying on only one modality to perform the authentication process, we use two fusion scenarios, one on feature level and the other on decision level fusion. To evaluate m2auth, we performed a field study in which we gathered behavioral biometric data from 52 participants in a real-life settings without intervention, i.e.~unconstrained environment. We conducted the evaluation for each authentication modality individually based on verification matching mode using real-world dataset. Then we show how the fusion module enhanced the unimodal authentication performance from 85\% to 99.9\% with approximately zero false acceptance rate. This perfect result was achieved based on the hypothesis, if one modality failed to classify a sample, others may not. We leveraged the notion of complementarity to lead us to this perfect decision.
Vectorized Deep Learning

In a series of lemmas and corollaries, I proved that under certain reasonable assumptions, you can classify and cluster datasets with literally perfect accuracy. Of course, real world datasets don't perfectly conform to the assumptions, but my work nonetheless shows, that worst-case polynomial runtime algorithms can produce astonishingly high accuracies. This results in run-times that are simply incomparable to any other approach to A.I. of which I'm aware, with classifications at times taking seconds over datasets comprised of tens of millions of vectors, even when run on consumer devices. Below is a summary of the results of this model as applied to benchmark datasets, including UCI and MNIST datasets, as well as several novel datasets rooted in thermodynamics.
Research and development of quantum neural networks, their effective dimension and trainability

Over the past few decades, there has been a significant breakthrough in the field of quantum computing. Research is of growing interest, which has recently led to the development of quantum information systems prototypes and methods for their development. The paper describes the possibilities, advantages and disadvantages of quantum neural networks. The analysis of throughput, learning complexity and comparison of a classical feed-forward neural network and a quantum neural network with two different feature maps is performed.