Reviewerly/exHarmony huggingface dataset:
USE OF GAMING TECHNOLOGY IN THE STUDY OF CHEMISTRY

Purposes: In article influence of a game method of training on the formation of interest at seniors in profound studying of a subject of chemistry is considered. Game technologies in teaching are used for a long time, but their importance inactivation of cognitive activity of students does not lose relevance. By means of a didactic game, it is possible to realize with success everything the leading functions of training: educational, bringing up and developing.&#x0D; Methodology: Authors describe the scenario of a lesson game of chemistry on the subject "Hydrocarbons" for students of the 10th classes, do the analysis of results of the performance of tasks at all stages of a game and give criteria of estimates.&#x0D; Results: Following the results of the questioning which is carried out among school students the conclusion is drawn that thanks to a game method at a lesson, it was succeeded to increase interest in a subject, the level of digestion of material, to make active orientation on the acquisition of knowledge and motivation of success. During the game, the creative atmosphere reigns in a class thanks to what such lessons promote the maximum realization of abilities of each pupil and develop skills of group interaction. Didactic games can be used at all stages of the process of training: when studying a new subject, fixing of the gained knowledge, at the control of results.&#x0D; Implications/Applications: Implementing new methods in the study can aid the audience to be prospered. Game theory can be the practical inefficient study of chemistry.&#x0D; Novelty/Originality: The research of the influence of didactic games on the formation of informative activity of students was conducted in the form of the quest with the subject devoted 190-year since the birth of A.M. Butlerov.
Estimating the quality of stereoscopic endoscopic systems

Abstract Stereoscopic video endoscopic systems are widely used for the control of hard-to-reach objects at the stages of production, assembly and testing of various devices. Using a stereoscopic endoscope as a measuring tool requires its geometrical calibration. The effectiveness of such systems is largely determined by the quality of an image, based on modulation transfer function (MTF) of the optical system. In this paper, we describe and test an algorithm for automatic calculating the MTF for images of a plane test object with chessboard pattern. Experimental data was obtained using two self-developed prism-based endoscopic stereoscopic systems. Coincidence of computer simulation results and results of applying the proposed algorithm shows its effectiveness for estimating the image quality provided by stereoscopic endoscopic systems.
Approach to Sensitivity Analysis of Stochastic Freeway Capacity Model Based on Applying Analysis of Finite Fluctuations

The paper introduces approach to Sensitivity Analysis based on applying Analysis of Finite Fluctuations to neural network model showing the dynamics of freeway section capacity depending on several external factors. The presented numerical examples, conducted on data from loop and radar detectors describing the capacity within long-term work zones on sections of German freeways, contain calculated scores on factors significance. There is also given the comparison between the proposed approach and Garson algorithm which is common in Sensitivity Analysis of neural network models; the similarity of both results proves the relevance of applying Analysis of Finite Fluctuations in this field.
Underground Cyber Economy and the Implication for Africa's Development

Cybercrime and the activities of the underground cyber economy has detrimental consequences for the growth, development, and wellbeing of individuals, groups, organizations, and nations of the world. Criminal activities such as hacking, identity theft, scams, fraud, and cloning on individuals, corporate bodies, and nations are attempts by cyber criminals to illegally scoop funds out of these various treasuries for personal use, all of which have tremendous consequences for personal, corporate, national, and hence, continental development. The underground cyber economy is a key factor that has enhanced the continued perpetration of cybercrime activities. Leaning on the space transition , innovation diffusion, and the gang theories, as well as resources gathered from past studies, the current study examines the underground cyber-economy and its implications for the development of Africa.
Accounting of Loops in Matrices of Graphs

Loops may present in graphs. In this paper some methods is proposed to account loops in graphs' block matrices. Method admits clear interpretation.
Cigarette Brand Recommendation Based on Consumer Modeling

Based on the consumers' consumption records, the paper extracted the features which reflected consumers' preferences, calculated the weights of different eigenvalues of each feature based on the idea of TF-IDF algorithm, constructed a consumer's consumption preference model, and then extracted the same features and established the cigarette brand feature vector. A method for calculating consumer's preference similarity was defined. A cigarette brand recommendation algorithm based on consumer's preference was implemented by calculating the similarity between the cigarette brand feature vector and the consumer's preference feature. The experimental results showed that the algorithm achieved a high acceptance rate in the cigarette brand recommendation to the consumers.
Development of a Pupil Response Pet-Robot Based on the Body Contact for Improving Familiarity

Pupil response is closely related to human affect and interest. Focusing on the pupil response in human-robot interaction, we have developed a pupil response system using hemisphere displays for enhancing affective conveyance. This system can generate pupil response like human by speech input and enhance affective expression. In this study, we designed and developed a pupil response pet-robot for improving familiarity between human and pet-robot. This robot generates the pupil response based on the body contact using small displays which represent the 3D models of pupil and iris. The effectiveness of the pet-robot was confirmed experimentally.
A Novel Approach to Detect Crimes and Assist Law Enforcement Agency using Deep Learning with CCTVs and Drones

In India, crime has always been a serious issue and it is strenuous to tackle it due to lack of technology. The current security techniques are precarious and cannot be trusted, due to the negligence of the authorities and the policy setters, there's a monumental rise in the number of crimes taking place daily like kidnapping, abduction, molestation, etc. India ranks 133 out of 167 countries in Women, Peace and Security Index with Indore reporting the highest crime rate (769.1) among the megacities in India, followed by Bhopal (719.5) and Jaipur (597.1). Thus, an improved security system near bus-stops, schools, neighborhoods, and other public places is mandatory to detect violent or abnormal activities to avoid any casualties which could cause social or economic damage. The main focus should be to aid law enforcement agencies to avert the outbreak of violence by monitoring crime scenes and alerting the respective security officials. There is a need to instigate fear and perturbation in the minds of the criminals that they will get caught on committing any type of crime. Several image-based extraction techniques with smart methods of deep learning and artificial intelligence will help support the security officials to detect, monitor and track the perpetrator. Thus, we present a smart way to reduce violence, by criminal activity detection and recognition in a particularCCTV's field of view and make the drone identify, track and chase the criminal till he or she gets apprehended.
Adversarial Graph Attention Network for Multi-modal Cross-Modal Retrieval

Existing cross-modal retrieval methods are mainly constrained to the bimodal case. When applied to the multi-modal case, we need to train O(K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) (K: number of modalities) separate models, which is inefficient and unable to exploit common information among multiple modalities. Though some studies focused on learning a common space of multiple modalities for retrieval, they assumed data to be i.i.d. and failed to learn the underlying semantic structure which could be important for retrieval. To tackle this issue, we propose an extensive Adversarial Graph Attention Network for Multi-modal Cross-modal Retrieval (AGAT). AGAT synthesizes a self-attention network (SAT), a graph attention network (GAT) and a multi-modal generative adversarial network (MGAN). The SAT generates high-level embeddings for data items from different modalities, with self-attention capturing feature-level correlations in each modality. The GAT then uses attention to aggregate embeddings of matched items from different modalities to build a common embedding space. The MGAN aims to "cluster" matched embeddings of different modalities in the common space by forcing them to be similar to the aggregation. Finally, we train the common space so that it captures the semantic structure by constraining within-class/between-class distances. Experiments on three datasets show the effectiveness of AGAT.
On the semantic equivalence of language syntax formalisms

Abstract Several formalisms for language syntax specification exist in literature. In this paper, we prove that longstanding syntactical transformations between context-free grammars and algebraic signatures are adjoint functors and/or adjoint equivalences that preserve the abstract syntax of the generated terms. The main result is a categorical equivalence between the categories of algebras (i.e., all the possible semantics) over the objects in these formalisms up to the provided syntactical transformations, namely that all these language specification frameworks are essentially the same from a semantic perspective.
Knowledge Graph Generation

Nowadays, as computer systems are expected to be intelligent, techniques that help modern applications to understand human languages are in much demand. Amongst all the techniques, the latent semantic models are the most important. They exploit the latent semantics of lexicons and concepts of human languages and transform them into tractable and machine-understandable numerical representations. Without that, languages are nothing but combinations of meaningless symbols for the machine. To provide such learning representation, embedding models for knowledge graphs have attracted much attention in recent years since they intuitively transform important concepts and entities in human languages into vector representations, and realize relational inferences among them via simple vector calculation. Such novel techniques have effectively resolved a few tasks like knowledge graph completion and link prediction, and show the great potential to be incorporated into more natural language processing (NLP) applications.
Context-Dependent Representation of Knowledge Graphs

Recently, there is a growing interest of leveraging graph’s structural information for knowledge representation. However, they fail to capture global connectivity patterns in knowledge graphs or depict unique structural properties of various graph context. In this paper, we propose a novel representation framework, Context-dependent Representation of Knowledge Graphs (CRKG), to utilize the diversity of graph’s structural information for knowledge representation. We introduce triplet context to effectively capture semantic information from two types of graph structures around a triple. One is K-degree neighborhoods of a source entity in the target triple, which captures global connectivity patterns of entities. The other is multiple relation paths between the entity pair in the target triple, reflecting rich inference patterns between entities. Considering the unique characteristics of two kinds of triplet context, we design distinct embedding strategies to preserve their connectivity pattern diversities. Experimental results on three challenging datasets show that CRKG has significant improvements compared with baselines on link prediction task.
Data visualization as a proving ground for graduate studies

column Data visualization as a proving ground for graduate studies Share on Authors: Geoffrey Draper Brigham Young University--Hawaii Brigham Young University--HawaiiView Profile , Aaron M. Curtis Brigham Young University--Hawaii Brigham Young University--HawaiiView Profile Authors Info & Affiliations ACM InroadsVolume 10Issue 3September 2019 pp 33–38https://doi.org/10.1145/3350745Published:06 August 2019 0citation100DownloadsMetricsTotal Citations0Total Downloads100Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Diagnosis of creative self-realization of undergraduates in musical art

The article presents a methodology for diagnosing experimentally identifying a vivid vividness of the formations of creative self-realization of the magistrates of the musical mystery in the process of the choir-choir movement. Today’s master student of musical mystery in Prague has expanded the horizons of musically-vikonavsky, musically-creative talents, be aware of alternative musical and pedagogical activities, deny your own selfrealization, and learn more about the Criteria have been introduced to the testimonials of the pre-approved approval. The following methods were implemented: methods of nutrition, ranking; diagnostic experiment. By a method of conducting a diagnostic experiment, a voucher of a vivid vividness of the formations of creative selfrealization of magistrates of a musical mystery was formed. So, we conducted a questionnaire with masters of the musical mystery in order to ensure that they are put before creative self-realization. Dalі, the students were blown away for profiling, according to the world’s significance, the components of creative self-realization (cognitivemotivational, gnostic-tsilovy, communicative-constructive, techno-design, creative-design). There was a great deal of discussion with the knowledge of respondents from day to day, the significance of the structure of the previous construct. Prospects for fake science rozvidok vbacha вmo in the development of experimental models of creative self-realization of magistrants of musical mystery. From now on, an analysis of the results of our achievement so that I can actualize respect for the fact that the period of fierce joining the masters of the musical mystery is characterized by their need for creative self-realization. Vrakhova’s students, on purpose, looked at selfsustainability and student initiative, as well as indicators of their creative self-realization from the choir choir.
The Construction of Interactive Environment for Sentence Pattern Structure Based Treebank Annotation

In the construction of treebanks, manual annotation is inefficient, and unable to ensure the consistency of results. Based on the existing graphical syntax annotation platform for the sentence pattern structure, this paper adds the automatic syntax analysis function and constructs a human-computer interactive annotation environment. It shows that, compared with manual mode, human-computer interactive mode can greatly improve the efficiency.
SOFTWARE IMPLEMENTATION OF THE TASKS OF THE OPEN REGIONAL ROBOTICS CHALLENGE "FAIRYTALE TOURNAMENT"

The article presents some solutions to the tasks "Ilya Muromets" and "Goldfish" those were done by the participants of the open regional robotics challenge "Fairytale tournament". The tasks can be used while teaching robotics at school. Schoolchildren may have different levels of training. The design of the robot to solve the tasks done with LEGO MINDSTORMS NXT and LEGO MINDSTORMS EV3 is offered. The solutions are done in Lego MINDSTORMS Education EV3 graphical environment.
Investigating Application of Machine Learning in Identification of Polygon Shapes for Recognition of Mechanical Engineering Drawings

This paper investigates the applications of Machine Learning in recognition of 2D drawings of machine components. Recognition of primitive geometric shapes such as polygons within engineering drawings forms basic element of such approach. Machine learning algorithms are used to identify 3 to 7 sided polygons with random shapes and segmented edges. The uncertainty induced by segmented edges poses a challenge for predicting number of sides using statistical method such as Machine Learning. Different types of datasets with varying amount of uncertainty are used. The recognition of shapes is attempted with different sets of features such as coordinates of points, slopes of lines and geometric parameters such as area, perimeter and centroid. Three machine learning models namely, Random Forest Classifier, K- Nearest Neighbors Classifier and Support Vector Classifier are adopted. The performance of these models for identification of polygons is discussed.
Conditions of Existence of the Solution of the Observation Optimization for the Kalman-Bucy Filter

In this paper, we are concerned with a problem of optimization of the linear observations which are used in the stationary Kalman-Bucy filter. Especially, we consider the optimization of the gain matrix in the observation. In the previous works the author, by introducing a change of variables using eigenvalues and eigenvectors of a symmetric matrix, the condition of optimality of these variables was obtained. In this paper, it is shown that the expression of the optimal gain obtained in the previous works is unique, and there does not exist any other gain matrix which produces the same value of the cost function. It will be also shown in this paper that under what condition the optimal gain fails to exist and that how one can avoid the situation by modifying the weight matrix in the cost function.
Multiscale Morphological Tone Mapping Operator for High Dynamic Range Images

High Dynamic Range (HDR) images have attracted a lot of attention, since they could convey more information than Low Dynamic Range (LDR) images. The dynamic range of classic monitors is about 102:1, while the dynamic range of HDR images can reach 102:1. Therefore, HDR images cannot be fully displayed on traditional low dynamic range devices. To deal with this difficulty, many tone mapping algorithms have been proposed in the literature to compress the dynamic range of HDR images. This paper develops a new tone mapping algorithm based on multiscale morphological image processing. The proposed algorithm realizes effective dynamic range compression for various types of HDR images while avoiding the halo artifacts that usually exist in the results of multiscale tone mapping algorithms. Numerous experiments show that, compared with classical tone mapping operators, the proposed algorithm could perform better in preserving details, and the resulting images are more consistent with human visual perception.
Fuzzy decision system with a single variable parameter used in the intelligent control of the processes

Abstract Based on the results obtained from the fuzzy systems presented in other published articles, a simplified fuzzy system was made, which was considered as the main factor the temperature difference. In this paper, another type of fuzzy system (single input and two outputs) has been developed and tested using the fuzzy toolbox in Matlab for viewing the resulting graphs and comparing them with the graphs resulting from the other types systems has come to the conclusion that the optimal solution is this fuzzy system with one input and two outputs.
Tibetan Sentence Similarity Evaluation Based on Vectorized Representation Techniques

Sentence similarity evaluation is an essential subject among the researching fields of Natural Language Processing (NLP), however, Tibetan related research on this subject is fairly inactive and has rarely drawn attention. In this work, we proposed an approach by leveraging vectorized representation techniques to tackle this problem by implementing two methods, namely, Euclidean distance evaluation and Jaccard similarity evaluation. Experiments indicated the performance of presented methods is satisfactory.
Destabilizing Optimization of Operating Modes of the Rectification Subsystem of Low Pressure Air Separation Plant for Simultaneous Production of Nitrogen, Oxygen and Argon

The paper focuses on the problem of energy-saving control of a large low-pressure air separation plant with a turbo-expander. The plant is designed for simultaneous production of nitrogen, oxygen and argon and operates at variable performance over a time interval when the need for air separation products is repeatedly changed. The purpose of the research is to improve the efficiency of the air separation plant, in terms of minimizing energy consumption for air separation over a time interval, using destabilizing optimization algorithms for the operating modes of its rectification subsystem, the latter being the core of the proposed plant. The study introduces the author's "concept of destabilization", which consists in expanding the range of feasible solutions to the problem by replacing in its statement some constraints like equalities with inequality constraints. This is technologically equivalent to purposefully changing some parameters within acceptable limits instead of stabilizing them, which provides an additional effect over a time interval. The authors developed methods and algorithms for controlling an object over a time interval, which provide for all possible changes in the performance of various target air separation products with a linear dependence of the optimality criterion on additional control actions resulting from destabilization. A specific example demonstrates the use of destabilization control algorithms for solving the problem of optimizing the modes of the rectification subsystem of the air separation plant over a certain time interval with an estimate of the effect obtained. Using the proposed approach and making structural changes to the existing equipment of the rectification subsystem of the air separation plant, we can significantly increase the resulting effect. Destabilization can be fully applied to other complex technological objects with liquid capacities and when a change in the liquid levels is acceptable within certain limits. Further research is needed to find out how plant performance and the time of the plant constancy influence the degree of the resulting effect, as during the plant operation the need for separation products, the moments of their switching and the time of constant performance intervals repeatedly change.