Reviewerly/exHarmony huggingface dataset:
The practice of introducing interactive technologies in the training process of future employees of internal affairs bodies

Introduction: Speaking about the relevance of the study, it is necessary to note the positive role of studying the issue of managerial decisions during the training of future employees of the internal affairs bodies in educational institutions of the Ministry of Internal Affairs of Russia while studying the academic discipline «Fundamentals of Management in the Internal Affairs Bodies». It seems that in order to strengthen the practical component of studying this issue, it is necessary to search for suitable interactive technologies for conducting training sessions, which we tried to find in the framework of the study. The purpose of the study is dictated by the need to create certain methodological teaching aids, the use of which will make it possible to introduce into the learning process the practical implementation of the study of certain issues within the framework of conducting classes in an interactive form in the discipline «Fundamentals of Management in the Internal Affairs Bodies».&#x0D; Research methods: In the course of the research, such theoretical methods as analysis, synthesis, systematization, generalization, comparison and such empirical methods as observation, conversation, description, testing, experiment were used.&#x0D; Results of the study: We saw the solution to the problem in the testing and application of interactive technologies in the form of «Case method» and the «Business game» method in the training course of the discipline «Fundamentals of Management in the Internal Affairs Bodies». The study reflects new, previously not used, methodological teaching aids developed for this specified academic discipline, containing the designated teaching methods and the results of their practical application, which showed a pronounced positive trend in mastering new knowledge, skills and abilities by learners in the educational process, which allows us to speak about the practical significance of his study results.
Contributions to Nursing Knowledge: A Dialogue With Dr. Rozzano Locsin

There are many nursing scholars who have contributed to nursing knowledge. Dr. Rozzano Locsin is one of those scholars. His many contributions to nursing knowledge include his middle-range theory technological competency as caring in nursing. In this scholarly dialogue Dr. Locsin talks about nursing and his contributions to its knowledge development.
Detect malicious websites by building a neural network to capture global and local features of websites

Abstract Malicious websites are a common way for attackers to steal user data and are a potential security threat to Internet users. With the development of machine learning techniques, malicious website detection methods based on machine learning have been proposed. However, machine learning requires manual extraction of important features and cannot detect unknown features. This leads to unstable and time-consuming predictions. With the development of deep learning, malicious website detection has been achieved by building neural network models. Neural network models can capture high-level information features directly from the data to detect classification. There are still some problems with neural network detection models that affect the accuracy of prediction results. Feature information from malicious websites is not expressed efficiently enough. The model does not capture the feature information of malicious websites well. For this reason, we propose a new malicious website detection model. Our model uses wordpiece-level features to better represent website information. Then, the model will combine the advantages of multi-filter text convolutional neural networks and multi-headed self-attention mechanism in capturing website features. The advantage is that it can capture the global and local features of malicious websites more effectively. The final captured feature vector is detected for classification by a fully connected network. We collected two datasets containing URLs of malicious websites to verify the feature capture capability of our model. From the experimental results, our model obtains 93.08% and 98.52% accuracy on the two datasets, proving the feasibility of the proposed solution.
The Impact of Internal Control on Project Management in Construction Site Among Small and Medium Enterprises in Iraq

The present study investigates the relationship between internal control and project management among Iraq construction SMEs. The current research is focused on internal control as multi-dimensional by using its four main components, which are control environment, control activities, self-assessment, and information &amp; communication, while project management was taken as unidimensional. Data were gathered from the employees who are working in construction SMEs in Iraq, either consultants, developers, or contractors. 251 questionnaires were received and analyzed by using SPSS and smart PLS-SEM to measure the research model fit and examine the hypothesis, whether accepted or not within the study context. The model measurement and structural equation model test indicated that all four main hypotheses were accepted, which means that internal control has a positive impact on the four main components of project management in SMEs in the construction sectors in Iraq. Also, the theoretical and managerial implications of this research were then discussed at the end of this article.
ASSESSING THE AESTHETIC CREATIVE CAPACITY OF STUDENTS IN LEARNING PROJECTS AT HCMC UNIVERSITY OF TECHNOLOGY AND EDUCATION

The development, expansion of exchange and integration relations in all fields such as economy, culture, science, education... has had a significant impact on the value system of society, in which have aesthetic values. Before that situation, the Document of the 9th Conference of the Central Committee of the 11th term affirmed: Currently, the issue of building, preserving and promoting a good value system of aesthetic tastes in the process of construction Vietnamese people to develop comprehensively and a new advanced culture imbued with national identity becomes urgent. Studying the change of the value system in Vietnamese culture through the study of art, aesthetic tastes and aesthetic education has both theoretical and practical significance. This article evaluates the current situation of students' aesthetic creativity through learning projects at HCM City University of Technology and Education.
Analysis of Gender Differences in the Longitudinal Causal Relationship between Cooperative Learning Preference and Creativity

A multi-group analysis across gender was conducted to confirm the longitudinal causal relationship between cooperative learning preference and creativity. In this study, the 2nd, 3rd, and 5th data of KELS 2013 provided by the Korea Educational Development Institute were used. The sample was 5,946 students. The analysis results are as follows. First, it was found that parent-child interaction and academic support in the 6th grade of elementary school had a significant effect on both cooperative learning preference and creativity. Second, the self-regression effect of cooperative learning preference and creativity and the difference in path between groups were significant. Third, cooperative learning preference had a significant effect on creativity at the same time point, and the difference in path between groups was also significant. The longitudinal analysis results of cooperative learning preference and creativity in this study can be used as information to help enhance youth creativity using learning style preferences.
Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM.

Variational autoencoders (VAEs) are a popular generative model used to approximate distributions. The encoder part of the VAE is used in amortized learning of latent variables, producing a latent representation for data samples. Recently, VAEs have been used to characterize physical and biological systems. In this case study, we qualitatively examine the amortization properties of a VAE used in biological applications. We find that in this application the encoder bears a qualitative resemblance to more traditional explicit representation of latent variables.
Semantic segmentation of remote sensing images based on dilated convolution and spatial-channel attention mechanism

The rich context information and multiscale ground information in remote sensing images are crucial to improving the semantic segmentation accuracy. Therefore, we propose a remote sensing image semantic segmentation method that integrates multilevel spatial channel attention and multi-scale dilated convolution, effectively addressing the issue of poor segmentation performance of small target objects in remote sensing images. This method builds a multilevel characteristic fusion structure, combining deep-level semantic characteristics with the details of the shallow levels to generate multiscale feature diagrams. Then, we introduce the dilated convolution of the series combination in each layer of the atrous spatial pyramid pooling structure to reduce the loss of small target information. Finally, using convolutional conditional random field to describe the context information on the space and edges to improve the model’s ability to extract details. We prove the effectiveness of the model on the three public datasets. From the quantitative point of view, we mainly evaluate the four indicators of the model’s F1 score, overall accuracy (OA), Intersection over Union (IoU), and Mean Intersection over Union (MIoU). On GID dataset, F1 score, OA, and MIoU reach 87.27, 87.80, and 77.70, respectively, superior to most mainstream semantic segmentation networks.
The Action Recognition with Multi-Clue Sparse 3-Dimensional Convolutional Networks

The current approaches of deep neural network lack capacity on action recognition, yet ineffective structure for spatiotemporal feature learning, and heavy parameters of architecture are even harsh to optimize. In this paper, we propose multi-clue sparse 3-dimensional convolutional networks(MS3D ConvNet) based on landmark works that aims to make it far known in this field. The MS3D ConvNet enhances channels of input through low, medium, and fast pathways with disparate frames of video, frame sequences considering as temporal clue sampled in terms of variance rate of action. The frames of each pathway are convolved with commonly shared kernels forming cohorts as initial results, and pile up three-way cohorts to the 3D block transmitting to subsequent 3D network. The 3D network is modified by DenseNet architecture with randomly dropping out connections among the layers, and introduces 3D convolutional kernels to deal with temporal information, eventually generating 256-dimension codes of actions. Action recognition employs centers of vector clusters of actions instead of stubborn format, which provide a robust convergence environment. Spontaneously, the MS3D ConvNet fosters capacity of action information and hardly ever sustains heavy amounts of parameters which deserves double profits. This fancy structure also leaves an end-to-end fashion training and a possibility to pre-train parameters. Besides that, we devise tailor strategy based on yolo algorithm to endow disparate factors on pixels in compliance with bounding box frontier, regarded as the means of attention. The MS3D ConvNet has been applied to three public action datasets and a self-defined dataset, which performs advanced steps on accuracy, convergence, and robustness.
Detection Of Ships in Satellite Imagery Using Deep Neural Networks with Transfer Learning Approach

With increasing trade and globalization of businesses, there is also an increasing need for an efficient, reliable and robust system for Naval Security, especially for a peninsular country like India. The developing technology and publicly available remote sensing data can be a great solution to this. The focus of this paper is to implement a Deep Neural Network model using transfer learning approach.
Intelligent Digital Ledger Voting System

The proposed methodology concentrates on the development of an approach which allows the voters to vote to their constituency from any booth participating in that particular election. It uses concepts of blockchain to realize a remote voting experience for the voters which will prove to be a relief for migrants and a less exorbitant system in terms of manpower. User Interface is provided using web application and Aadhar biometric authentication has been used to furnish the voter authentication machinery.The proposed approach is not only able to handle network attacks by intruders, data hampering attacks, client- side attacks, server-side data breach etc. But also ensures that the electoral procedures strictly follow the set of rules led down by the constitution of India.
A Brief on Blockchain and cryptocurrency system

Blockchain is ultra model technique which is making a great impact on the society for its decentralized nature. Cryptocurrency is one of the popular application of blockchain. Blockchain is decentralized in nature and each block is connected with chain using the hashing concept, so that third party will not be able to access it, The blockchain is more secure. Cryptocurrency contain secure distributed ledger and mining concept is important in cryptocurrency. Secure mining algorithm is required for cryptocurrency. This paper describes an overview of an blockchain and cryptocurrency systems.
Cloud Security

“Cloud computing” is the most important term in the IT industry. It has grown tremendously in the past few years. Although many IT companies use cloud computing, such as Google, Amazon, Microsoft and others, but it is still not totally secure because all the user’s data are stored and processed in the cloud. It continues to grow every day as new security solutions are developed. In this paper, we provide a brief overview of cloud computing and concepts behind cloud computing. We discussed some issued involving data security, Application and virtualization in cloud computing and security requirement with some solutions related to it.
Aspect-Based Sentiment Analysis with Multi-Task Learning

In the era of big data, with an increasing number of e-commerce and social media users worldwide, the demand for automated sentiment analysis systems is growing rapidly. With respect to industrial interests, aspect-based sentiment analysis (ABSA), which focuses on the sentiment at the aspect level, has become a popular research topic. ABSA includes two subtasks aspect-term sentiment analysis (ATSA) and aspect-category sentiment analysis (ACSA). This paper proposes a multi-task learning framework based on the pre-trained BERT model as a shared representation layer to jointly learn ATSA and ACSA tasks. To fully exploit the contextual information surrounding the aspects, we add a multi-head self-attention layer with a skip connection on top of the shared BERT model. Experimental results on SemEval datasets show that our multi-task learning model improves the performance of the ATSA task and outperforms baseline multi-task network and single-task models.
Accuracy Comparison of Various Movie Recommendation System Algorithms

A Movie Recommendation is significant in our social life since it enhances entertainment while also providing a tailored user experience. Using such recommendation algorithms, users may receive recommendations for a group of movies based on their interests or the popularity of the movies.Despite the fact that there are hundreds of movie recommendation systems, the majority of them either cannot recommend a movie to existing users or cannot propose a movie to a new user at all. In this paper, we compared the different algorithms to step up a movie recommendation system to improve the accuracy of user-recommended movies. This recommendation engine scours movie databases for all relevant variables, such as popularity and attractiveness, needed to make a recommendation. In many circumstances, large correlations between several categories of items can be used to produce more accurate suggestions. Experiments on real data have shown that the proposed solution is efficient and effective. This Movie Recommendation system will use a comparison among different algorithms that can be used to form a movie recommendation system, to search the movies that are similar to the customer’s taste or genre, and would also provide a combination of the recommendations that are highly rated by different customers’ experience so that the customer is not limited to a particular category or genre but is also able to explore new and different varieties as well. The system will get trained for the improvement of the recommendations through hybrid means.
Detection of Arrhythmia using Ensemble classifier in Comparison with Support Vector Machine Classifier to Measure the Accuracy, Sensitivity, Specificity and Precision

Aim: Machine Learning is used as a technique for fully paid loan payback with an increase in accuracy of prediction utilising Support Vector Machine (SVM) in comparison to XGBoost (XGB) classifier on lending club data. The components and procedures It was decided to use a sample size of 4000 for the completely paid loan repayment system forecast, with Group 1 equaling 2000 and Group 2 equaling 2000. Support Vector Machine (SVM) and XGBoost (XGB) Classifier are two strategies that are utilised in the process of classification of enhanced fully paid loan repayment with an increase in accuracy prediction. In the end, the accuracy rate of the Support Vector Machine (SVM) was found to be 94.18%, while the accuracy rate of the XGBoost (XGB) algorithm was found to be 89.34%. When compared, the findings of XGBoost (XGB) have a precision rate of 90.21%, whereas Support Vector Machine (SVM) has a precision rate of 94.55%. When compared, the Support Vector Machine (SVM) has a recall rate of 95.62%, whereas the results of XGBoost (XGB) have a recall rate of 90.45%. The Support Vector Machine (SVM) achieves a specificity rate of 95.34%, while the XGBoost (XGB) algorithm only achieves a specificity rate of 90.97%. There is a statistically significant gap in the rate of accuracy (P = 0.062). When it comes to coming to a conclusion, the Support Vector Machine (SVM) Classifier outperforms the XGBoost (XGB) Classifier in terms of finding the better classification in terms of finding the accuracy, precision, recall, and specificity for predicting fully paid loan repayment systems.
Experimental Evaluation for Text Classification using Improved Deep Learning Models

Language use on online platforms has dramatically expanded over the last ten years. Turkish text natural language processing (NLP) research, however, has not gotten much attention. This article seeks to give a thorough review of the Turkish text classification tools and models that are currently accessible. On Turkish text classification dataset, we assess proposed improved Convolutional Neural Network (CNN) model. A customization is made with the default optimizer made. An Artificial Neural Network (ANN) based models is given a comparative analysis. We demonstrate that on the available datasets, simple single layer models to outperform conventional models. Ideally, our research and experimentation with paper is concentrated in the Turkish NLP field.
Research on equipment allocation and Application of information System based on IRP

This paper USES information resource planning (IRP) method, starting from the battle plan, through the demand analysis and system modeling in two stages, through business process modeling, information modeling and system modeling of three, completed the orchestration of the information system based on the actual combat task, implements a battle plan orchestration use an information system equipment, It has effectively solved the problem of “using information” and “using systems and equipment” at the end of the army.
The algorithm and implementation of Yi character recognition based on convolutional neural network

Yi characters have unique historical value and rich cultural connotations, but the classification and recognition of Yi characters started late and the research is not deep enough. In this paper, a character recognition DCNN (Deep Convolutional Neural Network) based on DL (Deep Learning) is designed. Compared with traditional printed Yi characters recognition algorithms such as structural pattern recognition and artificial neural network, it has some shortcomings such as low accuracy and poor robustness, which not only improves the accuracy of Yi characters recognition, but also has a higher recognition rate for Chinese characters with noises. After testing, the accuracy of DCNN on training data set and test data set is as high as 0.95, which shows that the neural network model can perfectly fit the training set of Yi character recognition.
Sports Video Panorama Synthesis Technology Based on Recurrent Neural Network

Panoramic synthesis technology of sports video combined with recurrent neural network is an important content in the field of information analysis, and shot boundary detection is an important support for video analysis. Because the research of video motion information is widely used in video splicing, video segmentation, video compression, video matching, video surveillance and other fields. In recent years, video motion information analysis has become a research direction that attracts much attention in the field of computer vision. However, in practical applications, factors such as background motion, camera shake, and irregular motion of foreground objects caused by camera motion have brought great difficulties to the analysis of motion information in videos. For object detection, the last step is to predict the probability that certain regions contain specific objects. According to a fixed threshold, if the prediction probability is higher than this threshold, it is considered as a real target, and if the prediction probability is lower than this threshold, it is judged as a false target. However, there are some problems with the fixed threshold. In the case of huge scene differences or different sizes of multiple targets in the image, the fixed threshold is difficult to distinguish the high probability false target from the low probability true target.
Research on Digital Concert Performance System based on Computer Virtual Reality Technology

This paper will analyze the development status of intelligent orchestration according to the actual working situation and existing problems of the digital concert performance system, and give solutions according to the problems. In this paper, a music management platform is developed in a targeted manner, so as to realize the processing of music works, which fully involves cataloging management, transcoding services and other aspects, and further forms the association table of music works. This paper discusses the similarity between the two groups of target users and ordinary users. Under the action of this paper, the user's interest in the works can be known, and the recommended T list is finally obtained, thus a complete digital concert performance system is obtained.
A non-parallel many-to-many speech conversion method based on STARGAN model

Speech conversion is a task of converting speaker personality features while maintaining semantic information so that the converted speech has the same semantic information as the source speech and has the same speaker personality features as the target speech. Traditional speech conversion methods require parallel speech data to train the conversion model, but it is difficult to obtain parallel data in practical scenarios. To break this limitation, many non-parallel speech conversion methods have been proposed. In this paper, we propose a speech conversion method based on STARGAN model, which can achieve many-to-many speech conversion under non-parallel conditions, and the quality and similarity of the converted speech are better.