Reviewerly/exHarmony huggingface dataset:
Blended learning module for universities and extended education programs

The paper is devoted to the actual problem of the implementation of educational programs of higher education in the conditions of digitalization and the transition to online forms of teaching. As a basic model, a mixed form of teaching is considered, when all the information part of the educational course is brought online, but at the same time all practical classes are carried out in the classroom in the traditional contact form. The paper examines in detail the procedure for creating, implementing and operating an educational module implemented in a mixed hybrid format and actually provides a teaching technology that can be widely replicated in a wide variety of educational fields. As an example, the study considers the educational module of the block Theoretical and probabilistic methods of data analysis, which includes video content of 100150 clips lasting 810 minutes, a communication medium, printed materials and step-by-step operating instructions. The issues of content production, its time and content formats are considered. In addition, the issues of creating and operating a communication environment in which the educational process takes place are considered. Special attention is paid to the issues of monitoring the activities of all participants in the educational process: both students and teachers, as well as content creators, a media group working on its production and maintenance. In addition, the risks of the project are analyzed, recommended risk management measures are proposed. The module is implemented in the Blended Learning format and is designed to be used in educational programs of higher professional education and in extended education programs. Its subject matter (probability theory and data analysis) is presented in such a way that, once implemented, it will be in demand in a wide range of educational areas: from sociology, biology and medicine to economics, energy, construction and mechanical engineering.
Learning About Catcalling: an Interactive Virtual Gallery Concept Raising Awareness for Street Harassment

In this paper, we describe a virtual reality (VR) application that educates and sensitizes visitors for street harassment, a globally prevalent form of violence predominantly targeted against women. The phenomenon, also known as Catcalling, recently gained renewed attention in public discussions initiated by social media activists. Combining VR with interactive instruction settings known from museums might be a promising way to effectively reach victims, bystanders, and aggressors alike and obtain a lasting attitude and behavior change. We present a virtual gallery where visitors can explore a variety of curated interactive multimedia material intended to inform, raise awareness, inspire empathy, perspective-taking, and behavioral change. With interactions such as a self-assessment test, as well as opportunities for visitors to leave feedback and thoughts in the gallery, this virtual world can be used as a sensitization tool. Based on a first evaluation with experts <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\left(N\,=\,16\right)}$</tex> , the original proof-of-concept prototype was extended and evaluated by a larger group including students <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\left(N\,=\,50\right)}$</tex> .
Designing the Model of Student Support System in the Virtual Learning Environment: A Necessity in the Face of the Corona Crisis

Designing the Model of Student Support System in the Virtual Learning Environment: A Necessity in the Face of the Corona Crisis
DEEP LEARNING TECHNOLOGY IN PATTERN RECOGNITION

The article discusses the methods of face recognition based on convolutional neural net-works, the problems of face recognition in the presence of interference or face masking, the main stages of training neural networks and the process of actual recognition.
Inter-personal Relation Extraction Model based on Dependency Parsing and Bidirectional Gating Recurrent Unit

Relationship extraction is a fundamental component of various information extraction systems. Traditional relationship extraction methods are mainly rule-based methods and machine learning methods. Rule-based methods require induction and analysis of the corpus, followed by extraction of relationship extraction rules and finally pattern matching. The machine learning approach requires a large amount of manually annotated train data and manual extraction of features. However, these methods require a lot of statics and higher time costs. Considering these issues in the traditional relationship extraction methods and the linguistic characteristics of Chinese text, this paper proposes a new deep neural network structure. Firstly, the dependency relationships between sentence components are analyzed by using dependency parsing, which reveals the syntactic structure of the sentence and enhance the potential semantic information. Secondly, the important semantic information in the sentences is captured by using the sentence-level attention mechanism. Finally, the Bidirectional Gating Recurrent Unit model is used to simultaneously capture the contextual information of the text, and to improve the performance of relation extraction. The experimental results show that the model proposed in this paper is more effective than existing methods.
Evaluation of Machine Learning Models for Real-Time Sign Recognition

Development of a real-time sign recognition system is a critical step in enabling communication between the speech impaired and normal individuals. This paper proposes a vision-based model for sign recognition using Machine Learning models. A local dataset is created by capturing the images under various light and background conditions by employing a webcam. AlexNet architecture and MATLAB are used to extract the features from captured images and to train several Machine Learning models for their performance evaluation. The images captured in real-time are processed to extract the features and then are directed to the classifier to predict the Sign class. The text to voice conversion module plays the speech based on the output of the classifier. An accuracy of 91.7 % is achieved with Support Vector Machine (SVM) learning model on the private dataset. Sign recognition with the proposed model is more reliable and cost-effective when compared to non-vision based approach.
A content spectral-based text representation

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.
Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking.
Data Security Protection Mechanism of Video and Image Feature Modeling Based on Domestic Crypto Algorithm

Abstract Video and image monitoring is increasingly appearing in our home, travel and other aspects. Through video and image analysis and comparison of video and image monitoring data, it can provide strong analysis support capabilities for social security prevention and control, traffic command, safety production and so on. Video and image feature modeling is a necessary prerequisite for video and image analysis and comparison. Video and image feature modeling forms video and image feature data. Video and image feature data reflects the most essential information of various elements such as people, vehicles and objects. However, video and image data is easy to be damaged, changed and leaked in the process of collection, aggregation, analysis, modeling and storage, Facing data security risks. This paper proposes a set of video and image feature modeling data security protection mechanism based on domestic algorithm to realize the whole process encryption protection of data acquisition, transmission and storage.
A Situation-aware Scenario-based Testing Strategy

Nowadays, software applications have become very complex since, in some particular situations, they depend on other systems or services to perform their functions correctly. In other words, this means that a system is not isolated and is influenced by context entities. Due to its inherent complexity, some existing software testing approaches are not effective enough to verify and validate particular situations in which context entities are relevant to consider and model. Therefore, test designers should consider testing strategies that may be useful to model different situations in which context entities may influence the testable entity. In an attempt to contribute to this area, we propose a systematic and integrated scenario-based testing strategy. This strategy is integrated since contains process and method specifications that are semantically enriched using ontologies that belong to an ontological architecture. In addition, this strategy is situationaware because the situation must be modeled and considered to produce test cases.
Research on stereo Matching algorithm based on neural network

In order to achieve high precision stereo matching in the case of limited computing resources, an end-to-end parallax computing network is proposed by improving DispNet network. The network uses code-decoding structure, adds residual structure and cost calculation method based on matrix multiplication to optimize the network structure, and adds ECA module to the parallax estimation network for end-to-end training, adaptively enhances the important channel information in the matching cost body, and improves the learning ability of the network. The experimental results show that the improved parallax computing network can achieve dense and high-precision parallax prediction in real indoor and outdoor scenes, with good generalization ability, and significantly improve the prediction accuracy of weak texture, fine structure and other ill regions, which fully proves the robustness and effectiveness of the proposed model.
A study on practical communication application for beginners of Japanese

The goal of our study is to support the first steps of language acquisition and communication for beginners of Japanese using portable terminals so that the restrictions on social activities due to language issues can be reduced as much as possible. This paper describes the details of the proposed application as well as evaluations and discussions on practicality.The application focuses on the enjoyment of the game and a sense of accomplishment so that users can keep learning with motivation and enjoyment. There are three main types of games: those for correct pronunciation, for vocabulary and for situational communication in Japanese.
A step-by-step guide to random forest model using orange data mining in the field of periodontitis

Objectives:The purpose of this study was to show a procedure for a random forest (RF) analysis which predicts periodontal disease status by using R and Orange Data Mining software, and helps us to understand how to apply the RF technique for dental research.Methods: Oral examination data of the 7th Korea National Health and Nutrition Examination Survey were used.A RF model was adopted to analyze the data where the target variable was periodontal disease status and the features were gender, age, education level, marital status, alcohol consumption level, smoking status, brushing before sleep, hypertension, and diabetes-related variables. Results:The important features of the RF analysis were in the order of age, marital status, and prevalence of hypertension and diabetes.The accuracy of the RF analysis was 73% which is not high enough for use in the clinical field.Conclusions: The RF technique is an ensemble method used to predict periodontal disease status which produces higher accurate outputs than a single method.This study provides a step-by-step guide using Orange Data Mining for researchers who want to study machine learning techniques.
Construction of human motion optical capture system

With its incomparable advantages over other motion capture technologies, the application of optical motion capture systems has become more and more extensive, and it has become a major technical means for obtaining motion data. This paper mainly studies the camera calibration and 3D reconstruction technology of the optical motion capture system based on the marker, and designs an optical motion data capture system, which describes the acquisition of three-dimensional motion data, as well as the processing and analysis of motion posture feature data. By summarizing the methods and techniques of camera calibration, marker point placement, and motion drive, it effectively solves the problems of data jitter, data point loss, motion distortion, etc. generated during the motion capture process.
A permission generation and configuration method based on Rules and FP-Growth algorithm

With the development of computer technology, lots of enterprises had begun to build a data platform, and the data and its services already paly the import role in enterprises. However, the guarantee the data security is the primary task of platform, and data access control, especially the fine-grained access control model, had become an important means to enhance the security of platform. In this paper, we propose a data access permission configuration method based on rules and FP-growth. Specifically, FP-Growth algorithm is first used to obtain the frequent items and the association relations of data, which can be transformed into the enumerable permission configuration items. Then, the correspondence and frequency of data items are calculated to acquire the frequent items, the permission configuration acting on the data table columns is obtained according to the frequency of used data items. By filtering the strong association relation, the data items that are more closely related in the association relation and the corresponding data item values are finally obtained, and they are converted into the permission configuration that acts on the rows of the data table. The proposed method has been tested and verified to meet business needs, and the performance consumption is below the threshold. Moreover, it is feasible to utilize classical data mining algorithms to generate permission configuration, which has begun to apply the Blueking Data Platform.
Game Center: Configuring and Getting Started

In the last chapter, we learned how to configure Game Center in App Store Connect and began working with the sample project, UFOs. In this chapter, we will discuss integrating Game Center into our app and get our hands dirty with some code.
HTS: High-Quality Training Set Selection for Pedestrian Detection

Training a pedestrian body detector generally involves the supervision of body bounding-boxes. We find in some scenarios the supervision is not clear due to mixing background or overlapping persons. Such informative but noisy supervision imposes a challenge to learning a well-performed detector. In this work, we resolve this problem from the perspective of data selection and propose a learning-based high-quality training set selection strategy (HTS). Our strategy aims at selecting (preprocessing) a training set with best trade-off between useful information and noise from the original training set. Our strategy is composed of three steps. First we design a metric trade-off quality to evaluate the trade-off in a training set. Then we search a training set that achieves best trade-off quality based on parameterizing the training set. Finally the learned parameters are applied to the original training set by a novel re-weighting scheme. Due to improved handling of the noisy data, our proposed strategy shows consistent superiority over the typical data selection strategy for different pedestrian detectors on different datasets (Caltech and CityPersons datasets) without any specifically designed network architecture or bells and whistles.
Fast Simulation of Attenuation Relations of Ground Motion Parameters and Seismic Intensity Map in Shandong and Adjacent Areas

The observed data of 151 sets of strong motion seismographs and 126 seismic equipment in Shandong Province were used. According to the site conditions, Shandong and its adjacent areas were divided into bedrock exposed areas and soil covered areas. The attenuation characteristic relations between ground motion parameters-peak velocity and peak acceleration-in Shandong and adjacent areas were studied through least squares fitting, and the corresponding relational expressions were given. In case of a strong motion, the earthquake early warning system could realize the fast estimation of earthquake magnitude using the data in the first 3s. Moreover, the peak acceleration and peak velocity values could be rapidly obtained according to the abovementioned attenuation characteristic relations. Secondly, the seismic intensity could be estimated fast through the calculation method of seismic intensity in the instrument based on the technical standard systems related to seismic intensity rapid reporting and early warning. Moreover, the seismic intensity distribution map could be rapidly simulated by introducing the thought of triangular mesh generation.
Fusing Multi-Modality Information for 3D Road Obstacle Detection

Considerable resources have been devoted to developing self-driving systems in industry and academia, for which three-dimensional object detection is critical. The commonly used LiDAR-based methods, in which point clouds serve as the input representation, are marred by the problems of sparsity and inhomogeneity, which make small or distant objects difficult to detect. Accordingly, we propose a LiDAR-based road obstacle detection method assisted by RGB images, which operates as follows. First, a depth completion network is used to transform RGB images into dense depth maps that can be used to create a pseudo–point cloud through matrix operations. Subsequently, both pseudo point cloud and real point cloud are transformed into a pillar form for a pillar-wise feature encoder; this is executed to generate a two-dimensional (2D) feature tensor. Finally, a standard 2D convolutional neural network detection architecture is used to learn features. This method increases the number of point features to remedy the sparsity and inhomogeneity of the original point cloud. Our method had an improvement compared with its LiDAR-based counterpart in experiments.
A Framework for Pre Processing, Recognizing and Distributed Proofreading of Assamese Printed Text

The paper provides a brief outline of the framework established for digitizing, recognizing and proofreading printed Indic documents with Assamese as a target language. The establishment of such a framework is essential as it depicts the workflow for the digitization and archival of the scanned text and it has a high impact on the end result. The main idea behind the framework is to build the foundation for an automated text correction engine which provides suggestions based on the experience set generated using manual text correction procedure and machine learning approaches. Most of the works already done in this domain is based on the dictionary approach which has its own shortcomings like inability to correct real-word errors, redundant queries, large size, non-exhaustive collection etc. Hence, in this research, the dataset will be built from the scratch based on the experience gathered during digitization which in-turn shall contribute in increasing the accuracy of the OCR engine by means of post-processing.
Robust order selection of mixtures of regression models with random effects

Finite mixtures of regression models with random effects are a very flexible statistical tool to model data, as these models allow to model the heterogeneity of the population and to account for multiple correlated observations from the same individual at the same time. The selection of the number of components for these models has been a long-standing challenging problem in statistics. However, the majority of the existent methods for the estimation of the number of components are not robust and, therefore, are quite sensitive to outliers. In this article we study a robust estimation of the number of components for mixtures of regression models with random effects, investigating the performance of trimmed information and classification criteria comparatively to the performance of the traditional information and classification criteria. The simulation study and a real-world application showcase the superiority of the trimmed information and classification criteria in the presence of contaminated data.
A set of nets planarization with two chains of contacts

An efficient approach for finding all maximal and close to them planar subsets of nets with contacts on two chains has been developed. It is useful for the physical design automation of electronic devices, spefically for the printed circuit board, system-on-a-chip, integrated circuits, thin and fine ball grid arrays, and others. The proposed approach, unlike other methods, uses of two types of relations between contacts on chains: strict direct and strict inverse. Based on these relations the two graphs are constructed. The solution of the problem is to find the longest paths on the graphs. Each such path corresponds to the one of possible optimal solutions, i.e., the largest number of nets that can be layed without intersections. The method allows to find different solutions, as all optimal and close to them, which creates the conditions for considering other design requirements besides planarization.