Reviewerly/exHarmony huggingface dataset:
Selection of variables in generalized linear mixed model for smoker in Jambi Province

Abstract Smoking is one of the health problems in Indonesia. Many factors cause a person to smoke, both originating from oneself and the environment. The statistical question that arises is how to choose the factors that are most significant in influencing people to smoke. These factors are the variables that will sed in modeling. This study aims to select the variables in the compressed linear mixed model using the Lasso penalty and the Boosting function, using the EM and REML algorithms. Respondents in this study were 160 smokers in Jambi Province. Based on the AIC value, the best model obtained from the selection of variables with the Boosting function and REML algorithm. The analysis shows that work, welfare level, and family members who smoke are the factors that influence people smoking in Jambi Province.
A Chinese document parsing and code recognition system using Regex and SVM

We design a document parsing and code recognition system to promote the informatization and automation of work. Its key function is to obtain and analyze the content of the document, and then identify the equipment code in the document, thereby extracting the valuable information from the document. This allows the staff to use it more effectively or retrieve the required document faster. The proposed document parsing and code recognition system is designed based on the regex and support vector machine (SVM). Specifically, regex is used to extract suspected codes from documents. A two-class classifier trained by SVM is used to judge whether the suspected codes are real codes. The research results show that the system can be effectively applied to engineering practice, and it has high availability. Most importantly, it can improve the work efficiency of staff.
Promotion of Educational Organization Services (on the example of Ulyanovsk State Technical University)

The article describes the promotion of the bachelor’s degree marketing program (38.03.02. «Management») in Ulyanovsk State Technical University and outlines the stages of its realization. The presented experience is of some value and can be used as part of the career guidance work of institutions of higher and secondary vocational education.
Bring the Social Media to the Classroom of Portuguese as a Foreign Language in China: Possibilities and Challenges

Although introducing social media in a Foreign Language classroom is nothing new in the academic world, studies regarding Portuguese as a Foreign Language (PFL) in fact has been insufficiently studied, particularly in the case of Chinese learners. We implemented two interventions with a duration of 14 weeks and the participation of 10 Chinese students in total by using a Chinese microblogging platform, Weibo. Although obtained some evident positive results from a test performed after the activities, the participants revealed in the interview some differences brought by this tool during the learning process might not even correspond effectively to their learning expectation due to the learning strategy usually applied in daily routine, which indicates some challenges existing in PFL teaching model reform in China and in the integration of these technologies in actual didactic practice based mainly on the passive memorization by students. Therefore, before applying social media in PFL classroom, it’s urgently necessary to help students to build awareness about a full-scale learning system of Portuguese language, which does not be confined solely to the memorization of vocabulary and grammar rules, and emphasize the importance of the social nature and communicative practicability of languages in real-life occasions.
Hybrid Genetic Algorithm and Simulated Annealing for The Selection of Web-Based Beef Cattle Feed Composition

The nutritional requirements for the fattening process in each beef cattle differ according to body weight and body weight gain targets. Inappropriate feed composition can be detrimental to breeders because the bodyweight gain target is not achieved, and improper expenditure of feed funds. Genetic algorithms can be used to search for feed composition solutions, where the nutrients produced are close to the nutrients needed by beef cattle. Genetic algorithms have several disadvantages, one of which often occurs premature convergence, where genetic operators cannot produce offspring better than their parents. Premature convergence on genetic algorithms can be overcome by hybridizing local search algorithms, one of which is Simulated Annealing. Simulated Annealing is a local search algorithm that functions as a counterweight to genetic algorithms, where genetic algorithms are able to explore global areas, while simulated Annealing is able to exploit local areas. Comparative testing of hybrid genetic algorithm and simulated Annealing with a simple genetic algorithm shows that the fitness value of the hybridization method is better than the simple genetic algorithm. The best fitness of the hybridization method is 0.15934987829563, and the best fitness is a simple genetic algorithm of 0.15869962195529. The hybridization method produces better fitness because of the role of simulated Annealing in exploiting individuals on genetic algorithms so that the composition of feed solutions can be closer to the optimal solution.
Feature Selection based Reduction in Dimensions and Indexing of Images for Efficient Image Retrieval

CBIR prototype recovers images from massive databases identified by the interrogatory images provided by the client. The stocks of CBIR prototypes extract the limited amount of feature variable sets that impound the retrieval adequacy. CBIR is chiefly used in the domain of data retrieval where it is not necessary to feed information in order to search for a specific image stored in the database. Instead, feeding an image that akin to the one that is being traced would suffice. This proposed work eliminates the traditional approach of storing the images in the database which is tagging the images as per their content and retrieving them using those keywords. Content-based image recovery (CBIR) is attractive because searches that depend simply on metadata are subject to annotation aspect and fulfillment. The proposed framework efficiently performs most similar image retrieval from the database corresponding to the test query image. Our approach outturns state-of-the-art attainment on the Corel 10K dataset while being much fast at particular to test time. The results obtained were higher-up to other state-of-the-art CBIR prototypes analogous to precision.
BLU: An Interactive Loving Pet Robot

In this research, it has been focused on developing an intelligent interactive robot called BLU (Beloved Until) which is capable of working as a home assistant, security bot, and entertainment partner. BLU is equipped with advanced gears and a number of smart sensors for uncomplicated maneuvers, environment sensing and responding, and relevant data collection. This robot uses various communication protocols to accept the human command and performs a set of uncomplicated assistance in object handling, appliances management, and ambiance control. It can also work as an entertainment partner and security assistance through its advanced face recognition technology and provides security alarms in case of any unwanted alteration in its control environment. In this paper, it has been concentrated on developing three basic algorithms for BLU for its operations and functional improvements. The paper has also been acknowledged a number of limitations in the work and discussed the scope for future enhancements to build BLU as a robust mechanical assistance with improved sensing capabilities and artificial intelligence.
The Creative School

The Creative School project explores the possible mobilisation of digital cultural heritage and engagement with maker spaces models, as tools to create unusual and exciting learning opportunities. The project is developing learning modules for children and school teachers, promoting self-directed learning, critical and visual thinking skills by using cultural heritage content made available by the partner organisations. Its main outputs are: analysis of training and information needs of teachers, educators, children and also parents; development of a set of training materials focusing on the development of thinking skills through engagement with cultural heritage; development of guidelines and recommendations aimed at influencing policy makers and curriculum decision makers. The main beneficiaries of the project include primary and secondary school teachers, who, through engaging with the project will become equipped with the skills necessary to facilitate pedagogical strategies for creativity and critical thinking. Children and young people involved as participants in the Creative School project will develop the skills required to respond to the challenges offered by the Creative School curriculum.
A Forwarding Prediction Model of Social Network based on Heterogeneous Network

Weibo and other online social networks have be-come the basic platform for information dissemination and diffusion. Prediction of information forwarding in social networks has attracted a lot of research work, especially how to effectively consider the characteristics of users and information, as well as the interaction between them. In this paper, we construct a weighted heterogeneous network based on users and information, and propose an improved heterogeneous network graph representation algorithm Mpath-wMetapath2vec to generate low dimensional representation for forwarding prediction. The experimental results on Weibo dataset show that our model outperforms the algorithms without considering node features and other graph vector representation algorithms.
GAN-based Wireless Channel Recognition Enhancement in Aerospace Communication System

Abstract In the field of communications, wireless channel identification is of great significance for spectrum identification and spectrum resource scheduling, and is an indispensable link in cognitive radio technology. However, the poorness of the aerospace data set will affect the recognition accuracy. This paper studies the application of neural network in aerospace communication wireless channel recognition scenarios. Then, we propose a sample set expansion method based on GAN (Generative Adversarial Networks) to enhance the cognitive ability of neural network. Finally, we compare the accuracy of the channel recognition model before and after the data set expansion. The results show that in the case of small sample data sets, the use of GAN-based data expansion method helps to improve the accuracy of the channel recognition model.
Transforming Audience into Spectator/Actor: Assimilating VR into Live/Theatre Performance

Virtual Reality (VR) is a medium that is used to teleport audience virtually from one place to another in the physical world or into a virtual world. VR does prove to bring a different and more enhancing perspective to the audience by immersing them into the performance. This paper is a continuation of a prototype project on the early development and use of a three-dimensional theatre prototyping in order to explore the technical requirements for the application to a VR theatre experience, which is a part of more extensive research to use VR as an assistive technology in order to enhance audience access and propinquity to a live performance. This paper explores and investigates the relative position of the audience on the stage (virtually transported to the stage using VR) with respect to their traditional off-stage seating position. The evaluation between the two situations is based on perceptual experience along the lines of the field of view of the audience, obstruction and framing. This experiment is a part of a larger research and its evaluation will be critical to the result of integrating VR inside theatre performance. The study of the larger research was to introduce VR into the theatre and induce a state of deeper engagement from bodily presence. However, this experiment not only introduces VR into the theatre space but also helps to evaluate how the virtual presence of an audience can induce a sensation of being inconspicuous yet close at hand and quite removed from the audience. The project aims to exploit the idea of having the stage cameras either mounted on a pole (stage) or on the actor (participant) and comparing them with the traditional audience perspective using the technique of video analysis. This paper will account for the findings and comparison between the audience position outside the stage and on the stage using VR technology, providing the necessary foundation of assimilating VR inside of live performance.
Design and Analysis of Decentralized Interactive Cyber Defense Approach based on Multi-agent Coordination

Since the current cyberspace is becoming changeable and complex over times, the situation of cyber security is becoming increasingly severe, and one of the important issues is that there is still lack of a general applicability defense model for open and dynamic networks. Recent research suggests that the evolutionary game methods have the advantage of improving the defensive capabilities based on the internal decision and learning mechanisms. In this paper, by exploiting the advantage of collaborative decision-making in multi-agent system, we constructed the dynamic cyber defense problem into a decentralized multi-agent cooperative decision framework, whose core idea is the initiative decentralized interactions among defense agents. Then, we contributed a heuristic imprecise probabilistic based interaction decision algorithm, HIDS, that is, which utilizes the multidimensional semantic relevance among observation, tasks and agents, so that agents can continuously improve cognition and optimize decision-making by learning interactive records. In addition, we analyzed the equivalence and the transformation conditions between the proposed model and the existing decision models, and combined the evolutionary game with the nonlinear stochastic theory, then the evolution process of the defense policies are analyzed. Finally, the performance comparison of the proposed algorithm and the influence of different intensity random disturbances on the evolution process are analyzed.
Evaluating Automated System Interventions Against Email Harassment

Email and other forms of electronic communication are becoming increasingly more essential to our everyday lives. However, with this growth comes the paralleled increased risk of email harassment, exacerbated by the current lack of platform support for managing these harmful messages. This paper explores different interfaces for the automated detection and management of email harassment using artificial intelligence in order to investigate what degree of platform intervention email users prefer when navigating their email platform. Through conducting user studies involving three different email platform prototypes based on the Gmail platform, we employ mixed-method analysis to evaluate how varying levels of platform intervention affect users’ perceived sense of safety, agency, and trust with their email platform. Our primary findings suggest that users generally benefited from each of the system intervention strategies and desired higher intervention features when combating email harassment, as well as ways of managing this intervention based on their unique preferences.
Estimating Policy Functions in Payment Systems using Reinforcement Learning

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Classification of Brodatz Dataset Samples (36-64) Through Second-Order Statistical Parameters (B)

In this research paper, we have extended the approach of evaluating the texture features of the Brodatz dataset for the samples varying from 36-64. Here we have mathematically analyzed the texture visual features for the Brodatz dataset since we are aware of the fact that contrast, correlation, energy, and homogeneity are considered as the texture features which can affect human vision, so we have calculated the values of the features of texture feature for visual parameters only. As a result, we have obtained information about the samples which has reached the maximum and minimum feature value. Here we have also shown the grey level images of the dataset.
Evaluation of Panoramic Image Projection for “Ulaanbaatar GigaPixel Panaroma”

As a virtual tour and virtual reality technology advances, resolution with gigapixel panoramic images becomes available. Gigapixel equirectangular panoramic image needs to load and access without any delay through the internet by any device like mobile, tablet, or a computer. Also, several popular visualization formats like equirectangular projection were developed to map a real scene to a panoramic image. However, the most popular panoramic visualization technique equirectangular projection may not the best choice. Visualizing equirectangular images as spherical panorama has distorted nadir and zenith that can be affecting the viewing experience. So, we must consider image quality. In this research, we show the result of the evaluation for the projection formats using various comparison method to find which projection format (equirectangular projection, cubemap projection, and octahedron projection) and visualization technologies provide better image quality and speed of loading over Internet. We exclude other format that has lower quality than the equirectangular projection format used in this experiment.
Comment: On the History and Limitations of Probability Updating

Gong and Meng show that we can gain insights into classical paradoxes about conditional probability by acknowledging that apparently precise probabilities live within a larger world of imprecise probability. They also show that the notion of updating becomes problematic in this larger world. A closer look at the historical development of the notion of updating can give us further insights into its limitations.
Development of an Algorithm for Predicting the Possible Social Circle of Students

This article is devoted to the study of the relationships of people in society, using the example of the student community. The focus is on creating a list of criteria that can be used to predict the probability of people communicating. This research can be divided into two semantic parts: theoretical and practical. In the theoretical part, the criteria for analyzing people's interests are formulated. In the second part, the algorithm is implemented programmatically.
Multi-source cross-project software defect prediction based on deep integration

Abstract Cross-project defect prediction (CPDP), training a machine learning model by using training data from other projects, has attracted much attention in recent years. This approach provides a feasible way for small-scale or new developed project with insufficient training data to carry out defect prediction. This paper focus on the bottleneck issue of CPDP, poor accuracy, and propose a deep learning model integration-based approach (MTrADL) for CPDP. This paper consists of two main phases. First, the similarity between target project and source projects is measured by the modified maximum mean discrepancy (MMD) and the top K source projects with high similarity to the target project are selected as training data. Second, for the selected training data, this paper use convolutional neural network (CNN) to build the defect predictor. Each selected training data corresponds to one CNN predictor. Then, multiple predictors are integrated to get the final prediction result. To examine the performance of the proposed approach, this paper conduct experiments on 41 datasets of PROMISE and compare our approach with three state-of-the-art baseline approaches: a training data selection model (TDS), a two-stage transfer learning model (TPTL), and the multi-source transfer learning model (MTrA). The experimental results show that the average F1-score of our approach is 0.76. Across the 41 datasets, on average, MTrADL respectively improves these baseline models by 39.8%, 28.50%, and 10% in terms of F1-score.
Cloud Computing and Edge Computing for Risk Modeling and Analysis of Urban Community

How to realize the deliberative democracy of urban communities in cyberspace, that is, how to promote the realization of deliberative democracy in urban communities in cyberspace, is still an urgent problem to be studied at present and in the future. Traditional methods generally use simplified finite element model to reduce the demand for computing resources to achieve performance analysis, and rely on human experience to implement necessary intervention in the process of optimization to achieve performance optimization, resulting in unsatisfactory calculation time, accuracy and practicability. On the one hand, the cloud platform with the characteristics of elasticity, dynamic and easy expansion is used to solve the large-scale electromagnetic finite element and multi sample task calculation problems faced by electrical equipment optimization design, so as to reduce the calculation time; on the other hand, the support vector machine has superior data feature expression ability and strong generalization ability when dealing with small sample data and nonlinear problems, Support vector machine (SVM) is used for nonlinear regression analysis of electromagnetic model, and intelligent algorithm is used to optimize the surrogate model to improve the accuracy of the target value.
Matching Application of Service Consumption: Yattekoh

The following topics are dealt with: medical signal processing; learning (artificial intelligence); neurophysiology; diseases; geriatrics; biomechanics; feature extraction; medical image processing; electroencephalography; health care.
Improved Switching Vector Median Filter for Removal of Impulse Noise from Color Images

Image noise is defined as the random variation of color information or intensity in images. It is recognized as a type of electronic noise. Impulse noise is a “high level-low level” noise that affects an image abruptly. A filtering technique defined as an improved switching vector median filter (ISVMF) is proposed for removing impulse noise from color images in this manuscript. The performance analysis has been done for varied noise densities. Though performance has been shown in terms of Lena images, it delivers improved results irrespective of variation of images. The performance comparison has been done on the basis of standard metrics like peak signal to noise ratio (PSNR) and structural similarity index (SSIM), etc. It is observed that the proposed improved switching vector median filter (ISVMF) provides better results both visually and statistically as compared to the other conventional filtering techniques. This better performance may be due to the fact that the threshold has been localized rather than generalized in the filter resulting in better homogeneity across the various sections of the image.