Reviewerly/exHarmony huggingface dataset:
Stochastic recursive gradient descent optimization-based on foreground features of Fisher vector

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance , first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51.
Methodological support of practical training of future teachers

Practical training includes disciplines (modules), practices and other elements aimed at preparing students for professional activities. The purpose of the article: to describe the methodological support for the practical training of students enrolled in undergraduate programs &amp;quot;Pedagogical Education&amp;quot;. Research methods: theoretical analysis of the literature on the topic, understanding the experience of training student teachers, systematization of methodological support, a survey of school teachers and students. The article presents methodological support for individual elements of practical training (the study of methodological disciplines, the implementation of term papers and graduate work, the passage of educational and industrial practices).
GRACE: Designing Sociomaterial Assemblages Unpacking Gender Equity in Computing

Abstract This chapter introduces GRACE, a FemTech sociomaterial assemblage that performs concerns related to equity in computing. GRACE is an interactive installation combining IoT, origami paper, and the history of computing performed at three events in Denmark, the USA, and France. Each event was designed to unpack equity in computing through different types of sociomaterial performances, while allowing us to collect data about the lived experiences of equity in computing. GRACE is both reconfigurable and relational. It is reconfigurable because GRACE is malleable and can take different forms (reaching out to people who are committed to the agenda as well as to people who are within the domain but not necessarily committed to the agenda) – even though its core remains the same. GRACE is relational because the nature of the installation emerges in use and thus is shaped by the relational connections created through specific use and people enacting the artefact. Through sociomaterial manifestations, GRACE seamlessly integrates the performance of equity in computing with data collection on equity in computing. We argue that designing GRACE as a sociomaterial assemblage allowed for long-term engagement with a gender-equity agenda across multiple diverse encounters and over several years.
Makerspace Methodologies &amp; Design Principles

Abstract The four FemTech design principles which underscore all our work are that design artefacts must (1) produce alternative narratives of computer science, which (2) challenge the taken-for-granted assumptions about computer science, by (3) embedding a story into the design while (4) allowing for surprising interactive opportunities. However, before we dive into the details of these principles, we contextualize the principles in the design practices by which they were made.
Hong Kong's “future” energy transition-digitalization-financialization triangle: Actors and impacts

Decentralized renewable energy installations, such as home or building solar PV, can be funded through digital fintech instruments, such as via peer-to-peer trading. This is an innovation merging two different sectors: energy and finance. But who could be the actors in this new ecosystem? How is this blending of the digital or the fintech and the renewable energy sectors perceived? This talk highlights on Hong Kong, a global financial centre, as it seeks to become an international hub for green finance and expand renewable energy contributions in its energy mix. Reporting the outcome of an expert elicitation work, the talk describes a prospective actor/institution landscape of this emergent ecosystem. We found that not only energy and finance actors will most likely be engaged in this innovation, but also those in the technology and research sectors. The general public is also seen as a key actor. The talk also surfaces expert perceptions on the possible impacts of this innovation on Hong Kong. This includes new opportunities for citizen participation in energy transition as these new investment vehicles are made available. The innovation, however, is challenged by Hong Kong's distinct electricity market regime and lack of space for technology deployment.
A New Metaverse Mobile Application for Boosting Decision Making of Buying Furniture

In applied computational science, augmented reality (AR) is recently a very popular technology as it can help people to understand the virtual world and the real world more clearly. In this paper, we develop a new mobile application using augmented reality for boosting decision-making in purchasing furniture. Pixlive Maker is also used to design augmented reality, and then it is used to integrate into our main system for 3D furniture. After that, we evaluate the system by distributing questionnaires to people who have used the application about the respondents’ experience to make a product purchase. The samples are selected by the stratified random sampling method from people who have smartphones and have been using online purchasing. Finally, the experimental results after the respondents used the augmented reality furniture application show that our proposed system is robust for boosting decision-making of buying furniture.
The Application of Neural Network algorithm in Artificial Intelligence Recognition

artificial intelligence technology is an important product of the development of science and technology, greatly facilitate people's life and production, in recent years, artificial intelligence technology without living and living artificial intelligence recognition technology is constantly improving, and the perfect process requires the organic combination of neural network algorithm and artificial intelligence recognition. Based on this, this paper analyzes the application of neural network algorithm in artificial intelligence recognition, in order to provide some reference.
Repository of Interval Models of Dynamics of Concentrations of Harmful Emissions of Motor Transport

To build mathematical models based on the obtained interval data, it is necessary to solve the problems of structural and parametric identification. The result of the identification is the interval difference equations of the dynamics of concentrations of harmful emissions of vehicles. Procedures for structural and parametric identification of such models are quite complex. Therefore, in practice, based on the experience gained in modeling these processes, it is advisable to create a repository of interval models that would describe these processes, or easily customized to specific application conditions. This task is considered in the article. The basis for repository management is the ontological model. The features of building an appropriate ontological model, analysis the shortcomings of existing tasks, which are solving with its help also presented in this paper.
Ideal lattices in cryptography

This paper describes concepts from modern cryptography, such as ideal, cyclic and NTRU lattices. The largest contribution is the generalisation of some of the claims proposed in the article Identifying Ideal Lattices by Ding and Lindner. Several results have been extended to NTRU lattices.
Software system for modeling the distribution of harmful emissions from vehicles

The article considers the problem of control and modeling of air pollution processes. To model these processes, it is proposed to use methods of mathematical modeling based on interval data. The calculation scheme of the method of identification of the parameters of the interval model based on the models of behavior of the bee colony is presented. Features of algorithmic and software implementation of the process of building this model are considered. An example of using a software application to model the processes of distribution of concentrations of harmful emissions of vehicles is given.
Book Recommendation Using Collaborative Filtering Technology

The aim of the paper is to develop approach for books recommendation based on collaborative filtering. The different algorithm of collaborative filtering has been compared. The approach enables the creation of recommendations based on feedback from other people, so that users have a chance to explore completely new products. The solution was built based on book ratings from the Amazon.com sales platform, which consists of more than 50 million observations.
The Task of Parametric Identification the Interval Models with Nonlinear Parameters

The paper formulates the parametric identification problem of static objects' interval models of as a finding solutions problem of interval nonlinear algebraic equations system in the optimization problem's form with nonlinear and discrete goal function and linear constraints on a continuous set. For the first time the parametric identification method of static objects' interval models based on the interval data analysis is offered and proved. In contrast to the existing ones, the developed method is based on the computational procedures with the self-organization and self-adaptation elements by analogy with the artificial bee colony algorithms. The proposed approach to the models' construction with guaranteed prognostic properties is characterized by lower procedures' time complexity for building such models.
The Task of Structural Identification the Interval Models of Static Objects with Multiple Parameters

In the paper formulated the static objects’ interval models’ structure identification problem as repeatedly searching of interval systems of nonlinear algebraic equations (ISNAE) solutions, in the optimization problem’s form with nonlinear goal function and nonlinear constraints. For the first time, a method of structure identification of static objects’ characteristics interval models by the analysis of interval data was substantiated and development. The method is based on the use of the artificial bee colony algorithm. The justification of the advantage of the proposed method in comparison with known ones based on genetic algorithms is given.
Model of an Autonomous Airmobile Complex for Measuring Air Pollution Concentrations by Vehicles

Air pollution has become a major concern of the world. High levels of pollutants could have negative consequences for the atmosphere, the environment, and human existence, such as rising global average temperatures, reducing atmospheric visibility, lowering air quality, and impacting human health. Vehicle emissions of carbon monoxide (CO), carbon dioxide (CO2), volatile organic compounds (VOCs) or hydrocarbons (HCs), nitrogen oxides (NOx), and particulate matter (PM) have become the primary source of air pollutants in many places (Transportation Research Board (TRB), 2002). This research explores the model and the use of an autonomous airmobile complex to accurately measure the various concentrations of pollutants emitted from cars predominantly CO, SO2, NOX, and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . The system consists of a sensor, an energy supply system, and the wireless power transmission system; these are the systems that are going to be discussed in depth in this research.
Features of Implementation the Academic Staff Performance Appraisal System

This paper describes the features of software implementation of the academic staff performance appraisal system. This system provides creating the ratings of academic staff, departments and faculties based on different activities during reporting period. This appraisal system is available on the site, has an improved system architecture and allows increasing the responsibility of each stakeholder in compiling and reviewing reports. Information about the activities of academic staff is analyzed and used by management to promote the quality and efficiency of activities, which affects the promotion of the university.
Research of Efficiency of Information Technology of Intraoperative Neuromonitoring in the Prevention of Injuries of Laryngeal Nerves

Information technology of intraoperative neuromonitoring of laryngeal nerves during thyroid surgery is presented in the article. In contrast to existing technologies, the use of a set of software based on a single-chip computer is proposed. The essence of information technology is to stimulate the tissues of the surgical wound with an electrical pulse and further fix the result of stimulation. The result of the stimulation is a sound signal that is processed by a computer. This result then indicates the location of the nerve. Mathematical models of electrophysiological properties of surgical wound tissues were also used for this purpose. The results of electrophysiological experiments in which the efficiency of the proposed technology is confirmed are given. The possible consequences of the use of the proposed complex based on which it is possible to improve the considered information technology are presented in detail.
Evolutionary Method Based on Artificial Bee Colony and Ontological Approach for Structural Identification of Interval Discrete Models of Objects with Distributed Parameters

The problem of structural identification of interval discrete models of objects with distributed parameters is considered in the paper. This paper also proposes an ontological approach as a control tool that greatly simplifies the systematic standardized methods of storage models, the process of their construction and appropriate usage in practice. This paper also describes the features of the construction of the corresponding ontological model, the pattern of choice of a nonlinear model. Relevant experimental studies have also been conducted to confirm the effectiveness of the proposed approach.
Quality Assessment of Translators Using Deep Neural Networks for Polish-English and English-Polish Translation

Most people today use commercial machine translators in various aspects of their lives: school, web translation, documentation translation, chat rooms, document translation, etc. Nevertheless, these tools do not always perform on such a great level as professional translators. In this article we show what problems we encounter with solutions like Google Translate, DeepL and Marian MT trained models when translating from Polish to English and vice versa. We analyze the results obtained in a survey, which was evaluated by a specialist. We prove that an open source tool based on Marian MT gives promising results.
Video-Based Text to American Sign Language via Transitional Motion Synthesis

This paper describes a novel approach to provide a text to American Sign Language (ASL) media, a Video-Based Text to ASL. The hearing impaired or we called as the deaf are used to communicate with sign language. When they have to face the spoken language, they have difficulties on reading the spoken words as fast as the people that do not suffer from the hearing problem. The availability of a public dataset named ASL Lexicon Dataset offers the chance to make a video-based interpreter for the deaf. After the dataset has been pre-processed, it is fed to OpenPose library to extract the skeleton of a signer and saved as JSON files. Our system requires a user to input some glosses by texts, and then it finds the JSON files and the videos for the corresponding glosses. The whole sequence of the original video is also used as a transition pool. Later, the corresponding frames of the glosses are used together with the transition pool to construct the sequence of transition frames. After obtaining the sequences, to enhance the smoothness of the motion, we also apply one cross-faded frame in between the transition. Since this algorithm is fully dependent on the transition pool, there is some limitation on making a good transition. If the transition frames with logically and visually correct motions are not available, the result may not be not satisfactory; otherwise, this system can generate smooth transitions.
Terrain Image Recognition with Unsupervised Generative Representations: the Effect of Anomalies

Recognition of real-world images plays important role in a rapidly expanding range of applications. In this work we examined characteristics of distributions of classes of aerial images of terrain obtained in real time aerial photography from the perspective of detection and rectification of outliers in the generative latent distributions of image classes. Neural network models of generative self-learning with the architecture of convolutional autoencoder were used for compression of image data and extraction of informative features via selection of most informative principal components. Influence of outliers was examined with respect to distributions of image classes in the informative low-dimensional latent representations and classification performance of models trained in such representations. It was found that removal of outliers results in significantly more compact latent distributions of characteristic types of images, with a positive impact on classification performance. The results can be used in developing methods of learning based on unsupervised generative structure in informative representations in applications and problems with a deficit of training data.
Information System for the Work of Scientific Councils with the Possibility of Voting

The paper presents an online platform for the automated operation of the Academic Council or for conducting and documenting a system of meetings of various kinds. A feature of the developed platform is the possibility of voting of the participants of the meetings with the formation of minutes based on the voting results. The developed information system is a closed online platform. In it, users get some fixed roles, which the administrator (secretary of the Academic Council) can change if necessary. The user subscribes to the channel of the relevant meeting and can see in real time the changes made by other users, or make their own adjustments. The system has a "Vote" option, and the voting process is included in the life cycle of each issue on the agenda. After the meeting, the system automatically generates protocols on voting results and the general protocol. The implementation of this system is presented on the example of the current work of the Academic Council of the faculty of computer science and cybernetics Taras Shevchenko National University of Kiev.
Data Structure and Simulation Model for Associative Reasoning

Implementing the human way of thinking in different systems is still an open issue in artificial intelligence. Part of this thinking is also the so-called associative reasoning, which is based on associations arising mainly from the simultaneous observations of individual concepts representing our world. These observations can take both the form of images and simultaneous perceptions through other human senses (e.g., sound perceptions). This paper focuses on how to represent these perceptions and their sequence and ways to implement associative reasoning based on them. A data model enabling this functionality on large volumes of data will be presented, as well as the results of experiments on it.
Polynomial Rabin Cryptosystem Based on the Operation of Addition

The analysis of the existing asymmetric encryption / decryption algorithms is carried out, their advantages and disadvantages are determined on the basis of this analysis in the paper. Mathematical and algorithmic support for the implementation of a polynomial Rabin cryptosystem based on addition without computationally costly arithmetic operations is presented. This allows us to develop reliable and effective systems for protecting information flows by increasing the dimensions of input parameters (key size, message size), while improving the robustness of the cryptosystem. The encryption / decryption scheme of a polynomial Rabin cryptosystem applying addition is presented. Analytical expressions of time complexity of the classical and proposed approaches to the implementation of the Rabin cryptosystem in the polynomial ring are obtained. Graphical dependences of the found complexities on the degrees of polynomials are given, which show the advantages of the developed method.
Evolutionary Computations Based on an Artificial Bee Colony for the Analysis of Interval Data in the Problem of Air Pollution by Nitrogen Dioxide

Evolutionary computation methods based on an artificial bee colony algorithm as one of the directions of computational intelligence are considered. It is shown that these methods are effective for the problems of analysis of interval data when the model of pollution by harmful emissions of motor vehicle exhaust gases is bild. The proposed calculational schemes made it possible to solve the parametric identification problem of the interval model of the distribution of pollution of the squat layer of the atmosphere with nitrogen dioxide. This is a component in the exhaust gases of vehicles. Verification of the constructed model was carried out on the test data obtained for the same area, but for variable conditions of traffic, which confirmed the sufficient prognostic properties of the model.
Pandemi Covid-19, Biaya Pendidikan dan Motivasi Karir dan Minat Melanjutkan Pendidikan Profesi Akuntansi

The purpose of the study was to determine the effect of career motivation and education costs on student interest in continuing their education to the Accounting Profession Education (PPAk) level with the limitations of the Covid-19 pandemic. The research population is accounting students who are taking semester 7 and obtained as many as 251 students as samples. Sampling technique with purposive sampling method. The data analysis technique used was moderated regression analysis. The results of the analysis show that educational costs have a negative effect on student interest in accounting profession education (PPAk), career motivation has a positive effect on student interest in accounting profession education (PPAk), the covid-19 pandemic strengthens the effect of educational costs on interest in accounting profession education (PPAk). ) and the COVID-19 pandemic weakened the influence of career motivation on interest in taking accounting profession education (PPAk).&#x0D; Keywords: Cost of education; Career Motivation; COVID-19 Pandemic; Student Interest