Reviewerly/exHarmony huggingface dataset:
Development of Prototype of Natural Language Answer Processor for e-Learning

Automated knowledge control is an important component of e-Learning systems. Moreover, as an analysis of the most recent publications shows, the possibility of automated free natural language answers assessment is almost not represented in modern e-learning systems. Existing educational technologies either support only test approach to knowledge control, or when processing a natural-language answer, its semantic structure is not taken into account sufficiently for accurate assessment. This paper presents a software prototype that implements an algorithm for semantic processing of natural language answers. The basis of the algorithm is a theoretical pragmatically-oriented model proposed by D. Suleymanov, where main methodological principles are the principle of context determinism and the principle of meaning expectation. The implemented prototype was evaluated in order to verify its compliance with theoretical model and obtain the data necessary for further development of model and algorithm.
A VCA-Based Approach to Enhance Learning Data Sets for Object Classification

This paper presents a novel approach to solving the problem of poor learning data in complex object classification task. It efficiently combines the Visual Content Analysis technique known as the Scalable Vocabulary Tree (SVT) and contour-based descriptors to recommend new training samples. The SVT technique uses the SIFT features to identify and accurately localize objects of interest within the visual content of the processed query images. Despite the small learning data set its classification accuracy is pretty good and matches the accuracy of a dedicated CNN network trained under the same conditions. However, due to the ability of fast and effective incremental learning, it overcomes the convnet type networks. Contour-based classification based on Point Distance Histogram (PDH) is utilized then to increase the classification certainty. During this stage, the PDH descriptors representing a given object of interest are matched against descriptors stored in the pattern database, where each object is represented by a collection of 360 pattern outlines extracted from its 3D model. As finally reported, such an exact pattern representation allows for achieving a high classification accuracy of the entire approach.
Особливості психологічної стійкості студентів-психологів

The study developed a comprehensive analysis of the concept of speed and its composition. The level of stability of psychological work and psychological work has been empirically investigated in comparison with the level of introduced phenomenon in the graduates of other university employees. They were separated between different psychological characteristics and individual psychological characteristics of work.
Simulasi Persamaan Difraksi Fraunhofer pada Celah Lingkaran dengan Menggunakan Visual Basic for Application (VBA) Spreadsheets Excel

The research aims to visualize the Fraunhofer Diffraction Equation in the Circle Slit using Excel Spreadsheets and Macro Visual Basic for Application (VBA). The results of this study indicate that Microsoft Excel is able to display a visualization of fraunhofer diffraction shapes on the circle slit. The results of this study can also show a simulation of the formation of fraunhofer diffraction in a slit circle with a gap width (a) and a different wavelength (λ).
Characterization of Events

Many experiments collect quite a bit of information from each event. Often it is desired to classify these events into several different classes. For example, in a neutrino experiment, it may be useful to collect neutrino events into \(\nu _e\) charged current events, \(\nu _\mu \) charged current events and other events. The simplest method is to make a series of cuts on the data. This has the advantage of clarity, one can see what is happening with each cut. However, since there are often correlations between the various pieces of information, it may not be efficient. Modern methods, such as neural nets and boosted decision trees (BDT) take the correlations into account and are more efficient. They use samples of known events to train the network and then run the trained network on the data events.
Computational Study of DFMB Algorithm for Unmanned Vehicle in Static 2D Environment

In this paper, a Distance Function Modified Bug (DFMB) Algorithm with a random clockwise or anti-clockwise movement has been presented; offering optimum path length and less run time; which can be used for unmanned vehicle path tracking. Percent deviation in the run time of the algorithm has been compared with the other existing algorithms. In a static 2D environment where an obstacle is present; the traditional Bug approach is to first seek the shortest path from start to goal location and follow that path. If the path passes through any obstacle, then it starts wall following behaviour to find the shortest way to the goal. In our approach, we have introduced an improved distance calculation method with some newly developed parameters. The DFMB algorithm offers a smooth trajectory from source to goal location with obstacle avoidance in a given 2D environment without blindly following the boundary of the obstacle. Various simulation results and suitable comparative analysis have been presented to prove the superiority of the algorithm. We have done the effective realization of the algorithm, using MATLAB.
Glowing Window-Based Feature Extraction Technique for Object Detection

Traditionally, a number of techniques are available for extracting the features and we use glowing window approach for detection of an object. In glowing window approach firstly to crop, resize, and skeletonization of an image then find center, boundary values, and other features such as chord feature, connected and non-connected features, and internal boundary values and external boundary values of an image in this work three types of windows are extracted, i.e., internal window, external window, and glowing window. Internal window is inside the object and external window is beyond the boundary values of window. From these windows, find the glowing window and that window is further divided into sub-windows on the basis of their hierarchy (H). If value of H is equal to 2 then an object is sub-divided into 4 sub-windows and if H is equal to 4 then an object is sub-divided into 16 sub-windows. The bitmap image of a shape has been used for getting the statistical measures of distribution of points. These extracted features are further used for classifying an object by finding total number of black pixels in each rectangle, i.e., internal window, external window, and glowing window of an image. We used mpeg7 dataset for experiments with different training and testing dataset strategies, i.e., strategy A is with 50% training and 50% testing dataset, in strategy B 60% training and 40% testing dataset, and so on. For getting accuracy used linear-SVM and k-NN classifier. We got 94.2% accuracy by using D strategy and using Linear-SVM classifier.
An Underwater Color Image Enhancement Model Based on Comprehensive Processing

The propagation of light in water is affected by the absorption of water and the scattering of particles in water, which leads to the problems of low contrast, blur, color distortion and so on, the traditional underwater image enhancement algorithm is prone to over compensation of red component. In view of the above problems, an underwater color image enhancement method based on comprehensive processing is proposed. Firstly, the improved preprocessing method is used to equalize the color of the image through the bright channel and the blue-green channel of the underwater image, and stretch the hue and saturation components in the HSV color space to eliminate the color distortion of the underwater image and compensate the color attenuation; Secondly, contrast limited adaptive histogram equalization is used to improve image contrast; Then, sharpening is used to enhance image edge, improve the image clarity and visual effect; Finally, the sharpened image is smoothed by guided filtering. The experimental results show that the contrast and clarity of the underwater image are effectively improved and the problem of color distortion is solved.
Adaptive Features Fusion Correlation Filter for Real-time Object Tracking

Recently, visual object tracking has encountered many challenges. The response maps of multiple templates are linearly combined with a fixed weight. Meanwhile, the model updating strategy is realized by linear interpolation of continuous frames. It is easy to introduce too much background information and cause model pollution. Herein, we propose a novel tracking approach. The four features including fHOG, local binary pattern (LBP), color, and gray features are integrated into the tracker, which can make the model have strong expression ability. The fusion weight has been adaptively assigned according to the response peak value and smooth constraint of confidence maps (SCCM) indicator. The experimental results show that the overlap precision score (OPS) and distance precision score (DPS) are improved compared with some existing tracking algorithms. The average running frame rate can meet real-time requirements.
Two New Challenging Resources to Evaluate Natural Language Interfaces to Databases Generated Based on Geobase and Geoquery

Databases and corpora are essential resources to evaluate the performance of Natural Language Interfaces to Databases (NLIDB). The Geobase database and the Geoquery corpus (Geoquery250 and Geoquery880) are among the most commonly used. In this chapter, the authors analyze both resources to offer two elaborate resources: 1) N-Geobase, which is a relational database, and 2) the corpus Geoquery270. The former follows the standard normalization procedure, then N-Geobase has a schema similar to enterprise databases. Geoquery270 consists of 270 queries selected from Geoquery880, preserving the same kind of natural language problems as Geoquery880, but with more challenging issues for an NLIDB than Geoquery250. To evaluate the new resources, they compared the performance of the NLIDB using Geoquery270 and Geoquery250. The results indicated that Geoquery270 was the harder corpus, while Geoquery250 is the easier one. Consequently, this chapter offers a broader range of resources to NLIDB designers.
Application of Computer “Virtual Reality” Technology in College Physical Training

With the continuous progress and development of the society, the computer technology continues to progress, one of the new technology has been widely used, is the computer “virtual reality” technology, it is an important part of the computer technology, has become an important new technology indispensable in today’s society. At the same time, it can be applied in physical education teaching to help improve the teaching structure and increase new teaching experience. We can understand the characteristics of “virtual reality”, analyze and utilize its advantages, and fully combine college physics training and teaching, so as to improve the skills and training quality of college athletes, improve the quality and efficiency of college physics teaching, and promote the healthy development of modern college physics teaching.
Count cells by weighted random forest

Cell counting is always playing an important role in microscopy and many methods have been published to make the counting task automatic with high accuracy. Random forest (RF) is an effective ensemble model to implement the task. However, due to the random selection of samples and features, the base learners may differ much. The base learners with low accuracy will affect the final prediction. In this paper, a weighted averaging random forest is proposed to mitigate the problem. According to the evaluations on all training images, each base learner will be assigned a weight factor. It makes the base learners with high accuracy contribute more. The proposed weighted random forest is a linear combination of base learners with different weights and performs more robustly with accuracy on the standard VGG cells dataset.
Generating Minimal Nondeterministic Finite Automata Using a Parallel Algorithm

The goal of this paper is to develop a parallel algorithm that, on input of a learning sample, identifies a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA or the range of possible sizes of such an NFA, that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose a parallel algorithm to solve the problem. The results of computational experiments on the variety of test samples are reported.
An Automatic Classification of Genetic Mutations by Exploring Different Classifiers

The proposed work solely focuses on transforming the manual task of pathologists in classifying a test mutation to a task automatically done by a machine. We collected the dataset from a Kaggle competition which distributes the three features, Gene, Variation and Text into nine different classes. These classes are provided by genomic researchers which state whether a particular mutation is a driver (cancer causing mutation) or a passenger (neutral mutation). Our model was able to perform the labour intensive work of classification, thus saving time, diminishing possibility of human error and risks of wrong analysis. As the score was not too high, we have given a probabilistic output and hence our model is interpretable. Researchers need to analyse only around two classes with highest probability for classification ignoring all other classes. We have used different encoding and embedding techniques to convert text entry to numerical form, after which they are fed as input to different classifiers. Logistic Regression classifier with Term Frequency—Inverse Document Frequency encoding technique fetched the maximum accuracy, i.e. 67.27%. Attention had been given to enhance this accuracy with Word2Vec and Doc2Vec embedding, but it only decreased due to some issues with our dataset as discussed later. However, with a better dataset, our model can ensure better accuracy.
Research on Key Technology of Resource Scheduling Based on Trust in Virtual Computing Environment

This paper aims at the resource scheduling problem in public environment, in consideration of the real situation of virtual computing environment experiment platform. It introduces the trust mechanism in the experimental platform to ensure the trust issue of resource scheduling problem in public environment; through the gradual in-depth analysis of resource properties and application, this paper realizes the gradual fining match and scheduling between resource and application, so as to enhance the overall performance of the virtual computing environment.
Acoustic Pattern Recognition Technology Based on the Viola-Jones Approach for VR and AR Systems

The ability to solve problems of graphic images recognition in virtual reality (VR), augmented reality (AR), mixed reality (MR) and cross reality (XR) systems is highlighted as one of the most important. The urgency of solving problems of recognition and classification of acoustic images has been substantiated, which will bring the quality of VR, AR, MR and XR systems closer to real reality (RR). Independent solution of graphic and acoustic patterns recognition problems using heterogeneous algorithmic and software tools is attributed to the disadvantages of modern systems. The study proposes an approach that allows the use of unified methodological and software tools for the simultaneous solution of graphic and acoustic patterns recognition problems. The proposed approach is based on converting acoustic information into graphic information using 2D-images of dynamic sonograms. This allows the recognition of acoustic patterns using unified algorithmic and software tools. It is proposed to use the Viola-Jones technology as such a unified tool. It is shown that the implementation of a two-stage determination of similarity measures of primitives and areas of the original image makes it possible to increase the speed of algorithms. For this purpose, at the first iteration, it is proposed to use not the graphic primitives themselves, but their coordinate projections. In the study, by analogy with Haar’s features, parametrizable acoustic primitives were developed, presented in the classical graphical version, as well as in the form of coordinate projections.
The Experience in the Implementation of a Regional System for Supporting the Professional Activities of Teachers in the Educational Sphere of Chechen Republic

This work is devoted to the problem of improving the professional activity of teachers of the regional education system. The paper presents the system for supporting the professional activities of the teaching staff of the Chechen Republic developed by the author, which is a regional version of the implementation of the national system of teacher growth. This system is focused on a teacher’s professional and personal development and on building an individual trajectory for advanced training, taking into account the characteristics of the teacher's personal sphere, their cognitive style. Algorithms of the procedural part of the system have been developed, allowing to represent the sequence of actions during the implementation of the system, which makes it possible to reproduce this experience in the conditions of any regional educational sphere. The fundamental difference between the proposed system and the existing one lies in the introduction of a psychological component and the development of an individual trajectory for advanced training for each teacher.
Bio-inspired information technology for multi-version design of highly reliable software systems

Abstract The main result of the research is the creation of a new model of multiversion design of highly reliable software systems, which allows taking into account the real-time mode in the operation of programs that are part of reliability critical applications. The problem of multiversion design of the highly reliable software systems is presented in the form of the well-known problem of covering the set, which made it possible to use bioinspired algorithms, in particular, ant colony algorithms, to solve it. The decision rule of the ant colony algorithm has been modified, which provides support for the implementation of bioinspired information technology, taking into account changes in the structure of the software system in real time. The proposed model and the performed modifications of the ant colony algorithm are the basis for the creation of new tools that implement software and algorithmic support for the effective multiversion design of the highly reliable software systems in real time.
Principles and Techniques to Optimize Digital Communication Systems from End to End

Digital communication systems, serving to reliably transmit information in binary form, exhibit a complex input to output response that is part stochastic, part deterministic, which depends not only on the communication channel but also on the underlying hardware. The amount and type of stochasticity is typically unspecified, and the deterministic in-to-out response is most often unknown, where both phenomena are only coarsely and improperly modeled in practice. Common methods for communication rely on the use generic textbook modulation formats and demodulation schemes to imprint and extract bits to and from waveforms, yielding modulation and detection schemes that are neither optimized nor tailored for the system at hand. As a result, conventional bits-to-waveform and waveform-to-bits mapping and demapping methods leave performance on the table and do not exploit the full communication capabilities of the system. Learning to communicate through the physical layer presents several challenges. In this paper we review the fundamentals of end-to-end learning for digital communication systems. We review different techniques to learn to recover at the system's output the symbols or bits presented at its input as well as different metrics to minimize to achieve this goal, with interpretations of the underlying signals. Techniques to train the transmitter are addressed as well as other challenges of end-to-end learning for digital communication systems.
Multicriteria decision making for the selection of the best renewable energy scenario based on fuzzy inference system

This chapter aims to use the fuzzy inference system to aid the decision-makers/stakeholders to select the best renewable energy scenario among multiple alternatives. First the principle of fuzzy set theory and fuzzy inference system has been presented, and then Fuzzy Logic Toolbox of Matlab is used to show how it can be used to select the best scenario among multiple alternative renewable energy systems, and an illustrative case with 13 renewable alternatives for electricity generation have been studied. The proposed method is simple and scientific for the decision-makers/stakeholders to determine the best renewable energy system accurately, and it can also incorporate the opinions of the decision-makers in the fuzzification process and the ‘IF-THEN’ rules.
Design and Implementation of Intelligent Pop-up Site Recommendation System

With the gradual disappearance of the online traffic bonus, as a new offline customer promotion method, pop-up activity is booming due to its fast presentation speed, sufficient word-of-mouth effect, and novel experience. The choice of sites is the key step to start the pop-up activity. At present, the selection of pop-up sites depends heavily on the experience of executives and there is no systematic theoretical guidance. So how to recommend a suitable pop-up site to merchant users has become a huge challenge.In order to improve the fluency of the pop-up site recommendation service, reduce the waiting time for merchant users, and make full use of existing data, the pop-up site recommendation system introduces an algorithmic intelligent recommendation module, which combines multiple recommendation methods to recommend pop-up sites for merchant users, effectively alleviating the cold start problem and data sparseness problem and improving the quality of recommendation results.
The challenge-based learning to students’ spatial mathematical ability

Abstract The low ability of students thinking in geometry requires a new perspective in creating a more challenging and better mathematics learning process. This study aims to improve mathematical spatial ability through challenge-based learning using the Quasi-Experiment Method and Non-equivalent Pretest-Posttest Control Group Design. The instruments used PAM test and spatial thinking ability test. Analysis of quantitative data used t-test, Mann–Whitney U test, and Adjusted Rank Transform, two-way ANOVA. The study conducted at a university in Bandung, Indonesia. It involved 62 prospective Madrasah Ibtidaiyah teacher candidates as samples, which categorized into two classes, 32 class B as an experimental class and 30 class A as a control class. The findings show: There is a higher increase in mathematical spatial abilities of students who obtain challenge-based learning than students who obtain expository learning based on the overall student sample and students’ initial mathematical knowledge. There was an interaction effect between type aspects (challenge-based learning and expository learning) and initial mathematical knowledge on students’ mathematical spatial abilities. Challenge-based learning can facilitate the process of conflict, discovery, social interaction, and the reflective process of students. The material is easy to understand and exciting to learn. Thus, spatial ability is better than expository learning.