Reviewerly/exHarmony huggingface dataset:
Online Learning on Children's Language Development During the COVID-19 Pandemic

The period of the COVID-19 pandemic impacted education, especially on early childhood education aged 5-6 years. This study aims to determine the effect of online learning on developing language aspects of children aged 5-6 years. The method used is True Experimental Design by comparing face-to-face learning at school and online education at home. The research subjects consisted of two groups, namely experimental and control, with 40 PAUD children from Kindergarten Schools in Surabaya and Gresik. Based on the Asymp.Sig value shows that online learning significantly affects early childhood language development during the Covid-19 pandemic. The reason is, face-to-face learning cannot be done optimally, with reduced learning hours during the Covid-19 pandemic. Conversely, online learning can be done anywhere and anytime, even though the place and distance are different. 0.000 0.05. Online learning that is carried out has the advantage of not having space and time boundaries so that the need for increasing children's speaking, reading, and writing skills can be met.
A Tool-Based Approach to Assess Simulation Worthiness and Specify Sponsor Needs for SMEs

Many small and medium-sized enterprises (SMEs) still refrain from using discrete-event simulation (DES) to plan, implement and operate their manufacturing systems. Given the increasing relevance of DES - e.g., as the basis for digital twins - a need for action is therefore identified. While the reasons for the seeming aversion to DES in the context of SMEs are well-researched, the following work's main objective is to present a tool-based approach that aims at supporting SMEs overcome the hurdles of the early stages within a structured simulation study. This is done through the assistance of users in identifying issues that are simulation-worthy as well as approaching the possible tendering and development of accurate problem specifications. Furthermore, the research methodology and the results of the commenced Delphi study are outlined. A critical reflection and an outlook on the topic are provided as well.
Demand Predictability Evaluation for Supply Chain Processes Using Semantic Web Technologies Use Case

Semantic Web technologies provide the possibility of a common framework to share knowledge across supply chain networks. We explore Semantic Web technologies to evaluate processes' demand predictability through a use case. First, we create an ontology describing the relevant domain concepts and data and define competency questions based on the need of the use case. Then, we map the data to the ontology in a knowledge graph. We design a chain of SPARQL queries to retrieve and insert information from the knowledge graph to answer the competency questions. We calculate the underlying demand for supply chain processes using aggregations and the created semantic description. We successfully computed a pre-defined metric for demand predictability for different time scopes and process groups: the mean of yearly coefficient of variation. Using this approach, one could perform predictability evaluations for relevant indicators of end-to-end supply chains by further data integration in the semantic framework.
The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network

How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis.
Collective argumentation with topological restrictions: the case of aggregating abstract argumentation frameworks

Abstract Collective argumentation is the process of reaching a collective decision that is acceptable to the group in a debate. We introduce the notion of topological restriction to enrich the study of collective argumentation. Topological restrictions are rational constraints assumed to be satisfied by individual agents. We assume that in a debate, for every pair of arguments under consideration, every agent indicates whether the first argument attacks the second, i.e. an agent’s argumentative stance is characterized as an argumentation framework, and only argumentation frameworks that satisfy topological restrictions are allowed. The topological restrictions we consider in this paper include various topological properties in the literature, such as acyclicity, symmetry, coherence and determinedness, as well as three topological restrictions that generalize classic social-choice-theoretic domain conditions. We show that when the profile of the argumentation frameworks provided by the agents satisfies topological restrictions, impossibility results during aggregation can be avoided. Furthermore, if a profile is topologically restricted with respect to restrictions that generalize domain conditions, then the majority rule preserves several desirable properties during aggregation.
Anomaly Detection through Density Matrices and Kernel Density Estimation (AD-DMKDE)

This paper presents a novel anomaly detection method, called AD-DMKDE, based on the use of Kernel Density Estimation (KDE) along with density matrices (a powerful mathematical formalism from quantum mechanics) and Fourier features. The proposed method was systematically compared with eleven state-of-the-art anomaly detection methods on various data sets, and AD-DMKDE shows competitive performance. The method uses neural-network optimization to find the parameters of data embedding, and the prediction phase complexity of the proposed algorithm is constant relative to the training data size.
Modeling Reasoning and Argumentation for the Development of Intelligent Assistants

The article develops model-theoretic methods for formalizing reasoning and argumentation. An important application of these studies is the creation of intelligent assistants. One of the very serious problems that arise when using Model Theory to formalize and model domains is that submodels, isomorphic embeddings, and homomorphisms are considered only for models that have the same signature. This article proposes an approach to solving this problem, develops the Model Theory of Subject Domains. The formalization of reasoning is based on the representation of knowledge about situations and subject domain cases in the form of partial models. The existence of a one-to-one correspondence between partial models and their atomic diagrams is shown. Reasoning and argumentation are modeled on the basis of a reasoning graph, at the vertices of which there are partial models, and transitions are carried out using homomorphisms and isomorphic embeddings. A model-theoretic formalization of emotional estimations that arise in the user in the process of dialog with an intelligent assistant is proposed.
Elimination of Systematic Distortions in the Signals of LTI Systems using Test Measurements

Methods of eliminating systematic errors in signals distorted during processing by linear time-invariant systems using test measurements are proposed. The use of test measurements makes it possible to restore signals of processing systems of any complexity without determining their hardware functions, which usually belong to the class of generalized functions. Tikhonov’s regularization technique, used in methods with test measurements, allows solving ill-posed and ill-conditioned problems when restoring real non-deterministic signals. The reconstruction of continuous signals and discrete data by direct and spectral (using Fourier transforms) regularization techniques is considered, the features and areas of application of these techniques are described, and the recovery errors are estimated. The criteria are analyzed and recommendations are given for the selection of test signals and regularization parameters. The possibility of using test measurements for simulating the response of linear time-invariant systems to given actions is considered. Examples of reconstruction of 1D signals and restoration of 2D images are presented.
Using Parallel Computing to Optimize Optical Anisotropy Detection Methods Using OpenCL Technology

In this paper, we propose a way to optimize the processing of data obtained by analyzing images of various materials for the presence of optical anisotropy properties. A variant of calculation optimization by means of data processing transfer from a central processor to a GPU is offered. As an example of calculation, the processing of a set of images obtained from the camera of an optical polarization microscope used for detecting optical anisotropy of diamond plates is considered as an example of calculation. The problems and limitations arising from the transfer of computations from the CPU to the graphics card and the subsequent parallel computation are investigated. The time required to process different types of images is compared. The results are presented as diagrams, the ratio of CPU and graphics card processing time spent on obtaining the results of optical anisotropy detection is obtained. A program model that performs parallel computation using OpenCL technology on the GPU is described.
On the improvement of McEliece cryptosystem based on LDPC codes

In this paper an improvement of the McEliece cryptosystem based on QC-LDPC codes is considered. The key idea of the improvement is to use soft-decision decoding algorithm of LDPC codes in decryption step. This allows to reduce a public- key size in comparison with the McEliece cryptosystem based on QC-LDPC codes decoded by hard-decision bit-flipping decoder. In addition, the analysis of the dependence between key size and security in bits for the proposed cryptosystem was conducted.
Video System With Automatic Tracking of the Object of Interest

The article presented a comparison of SIFT, AKAZE, BRISK, ORB methods of searching feature points and CSRT, KCF, GOTURN methods of tracking by the criterion of speed (the number of processed frames per second), as well as by the number of feature points found from the specified error value. The software for tracking the position of the object of interest in the video stream was developed based on these methods and using the Python programming language. A graphical user interface (GUI) for the software for tracking the object of interest was created using the Qt Designer software. A system consisting of a camera, a camera bracket, servos controlling the camera position, a microcontroller and a personal computer was built. A program for STM32F1 microcontrollers responsible for changing the camera position by servos was written using C++ programming language. The dependences of the camera rotation angles on the time when tracking the object were constructed. The trajectory of the tracked object was also constructed. Various data can be obtained from this trajectory, for example, you can find out the direction of movement, calculate speed and acceleration.
Visual Word based Neural Tree for Interpretable Recognition of Images

It is necessary to have an explanation of the model prediction to increase confidence in the decision making in a broad range of image recognition tasks, especially medical image recognition.In this paper, we proposed a new method of interpretable image recognition. The basic idea was to use a combination of convolutional neural network, similarity based decision tree, and a trainable bag of visual words. During recognition, the model compared parts of the image with trained templates and made a decision based on the similarity between them. The recognition process was presented as a sequence of logical decision rules which can be easily understood by specialists in the applied area. This technique is similar to the human way of visual recognition. Testing on two datasets showed a competitive recognition quality of the proposed method, compared to a classical convolutional network ResNet50. At the same time, the method provided a possibility of interpreting the prediction.
Monitoring Information About International Conferences Using Natural Language Processing

Research in the field of solving applied problems of extracting information and text using natural language processing continues to be used worldwide in a variety of areas. The development trend of research in the field of solving applied problems of extracting information and text using natural language processing in the field of working with publication events of scientific articles is analyzed in this article The problem of automated search for information about international conferences is considered. A solution to the problem is proposed, including the extraction and processing of unstructured text, the work with templates for general-level sentence models, the work with templates for extracting named custom entities and with tags for extracting information for searching for international conferences. Based on the results obtained, it is possible to judge the possibility of using a decision-making scheme by researchers, which allows obtaining data to select the most relevant international conference that meets the request of a group of researchers, authors of future scientific publications.
Efficient Algorithms for Time Series Prediction Method

This paper describes the effective algorithms for calculating the Krichevsky measure and the R measure which are used in the time series prediction method. This prediction method is based on universal coding and first was proposed in 1988. The reliability and potential of method’s application was investigated in the context of many real tasks, however, the prediction calculation speed was the main problem. The algorithms proposed in the work allow to calculate the predicted value online and have the minimum possible complexity which significantly expands the possibilities of applying the method itself.
Tuning deep learning hyperparameters for magnetic resonance fingerprinting recognition

We present a novel deep learning approach for breast cancer detection. In the proposed method we simulate the magnetic resonance fingerprints obtained from pseudorandom radiofrequency pulse sequences using the population average breast tissue composition. We analyze the magnetic resonance response using deep neural networks. Two types of network architectures are considered: fully connected networks and residual-type networks. A number of networks hyperparameters sets are considered and network performance is assessed. A maximum accuracy of 0.82 and 0.81 with fully connected and residual networks is achieved with under the selected hypermarameter sets, which exceeds the accuracy with suboptimal training conditions by more than 0.30. After the hyperparameters were set, the classification accuracy exceeded 0.95.
Analysis of Competency Questions for Automatic Generation of Lexico-Syntactic Ontology Design Patterns

he work considers an approach of automatic generation of lexico-syntactic ontology design patterns based on analysis of competence questions expressed in natural language. Process of the generation of lexico-syntactic patterns includes generation of the scientific dictionary, extraction of the entities of the ontology, formation of the structure of extracted relations, such as Data Property and Object Property, and a set of restrictions, including semantic, grammatical and positional restrictions. We use competency questions for specifying grammatical and positional restrictions and extract the necessary grammatical restrictions for each specific relation. For the experiment, we used the “Decision support in weakly formalized areas” ontology, a corpus of scientific texts of the same subject area. During the experiment, we got the following results: the F1 score was 0.77 for attributes and 0.55 for relations. Comparison of the results obtained for patterns without grammatical restrictions and the results obtained for patterns with grammatical restrictions showed that adding restrictions improves extraction of ontology entities.
Experimental Stand for the Study of Analytical Methods of Contact Tracing in Context of Anti-Epidemic Measures Based on the Use of Information and Analytical Security Sistems and Synthesized Data Arrays

The article considers the possibility of solving the educational task of searching for persons who have been in contact with an infected person. Practical use of information and analytical security systems. Software for the synthesis of training arrays of conditionally real data on the network interaction of users is proposed.
A New Approach to Enhance Learning Data Sets for Object Classification

In this paper, we propose a novel solution to the problem of insufficient training data in complex image classification tasks. The presented method efficiently recommends new learning samples with the use of contour-based descriptors and Scalable Vocabulary Tree (SVT) technique. The SVT is built from SIFT features and may be used for identification and localization of objects of interest in the analyzed query images. In conditions where the training data set is not large enough, the SVT's classification accuracy is satisfactory and comparable to that achieved by the dedicated CNN network. However, the SVT approach surpasses the classic convnet approaches due to its incremental learning capability. The classification certainty is further increased thanks to contour-based analysis using Point Distance Histogram (PDH) descriptor. At this stage, the PDH representation of the analyzed object is matched with the information stored in the pattern database. This database contains a set of 360 shape outlines for each object that are extracted from its 3D model. This leads to higher classification accuracy of the presented approach.
An Algorithm for FPGA-based Segmentation of Binary Images

This paper proposes a new single-scan algorithm for segmenting the independent regions from binary images, evaluating their boundaries, and coordinates of centroids. Its main benefit, compared to the known solutions, is that it does not require changing the original image or creating a new one with a similar size to store labeling information. Particular attention in the development of the algorithm is paid to reducing both the amount of memory needed to implement it and the number of records in memory during the scan. This makes it perfect for further FPGA implementation.
The potential of using virtual reality in the field of security control in public space

This paper discusses the potential use of virtual reality technology in the security and protection of the population. Experimental using the properties of the scanning device, the actual public environment is recorded in a panoramic 2D image and subsequently adjusted for the needs of display in a virtual environment. The text also lists selected safety requirements. The article discusses the conclusions and potential of the application as a secondary tool in security issues.
Analysis of the Influence of Students’ Identity on Academic Performance

This paper presents the results of ongoing work. Today, personalization is one of the main trends in education. Universities collect and analyze data about the students' identity and their needs in order to develop specialized systems that support the personalization of the educational process. The purpose of the current work is to verify the applicability of the "Learner model" that was designed previously. This model is one of the main component of universities' intelligent learning system architecture. This model is based on the results of psychometric test. We increased the sample of students by including new observations and new predictors. Having performed preliminary data preparation, we obtained linear regression models. The results of psychometric tests were used as independent predictors (regressors). We tested students from the 2nd to 4th year of the Faculty of Applied Mathematics and Informatics. The dependent variable is the general ECTS score, as well as the average score for the groups of disciplines (mathematical, work-oriented, humanitarian). We compared the resulting models with those obtained using a smaller sample. In general, our conclusion was confirmed.
Deep Neural Network and Text Processing: A Literature Review

Deep learning is a powerful representation training algorithm which has been used to understand context clues. This paper has provided review of past research on neural networks in their use of in text analysis. Neural networks were observed to use a number of computational layers to understand hierarchical representations of the data, resulting in cutting edge results in a range of domains. This article carried out an empirical assessment of vital deep learning related techniques and frameworks to investigate their use in varied NLP tasks, as well as contextualizing, making comparisons, and comparing the various models and gives a clear knowledge of the relevant facets of deep neural network use in NLP.
Teleoperation and Autonomous Mode of Transport Mobile Robot with Mecanum Wheels

In this paper a review of a mobile transport robot with mecanum wheels has been made. The robot control hardware is described. The research and development of methods for remote control and autonomous control are presented. Software algorithms have been developed for implementation of the described methods. Experiments have been conducted related to remote control and autonomous mode of a transport mobile robot in real time, using an educational mobile robot with mecanum wheels. The results of the conducted experiments are presented. The results show that the proposed methods and algorithms are able to provide tele-operation of the robot and autonomous movement.