Reviewerly/exHarmony huggingface dataset:
Technology of creation of electronic educational resources for training specialists in digital economy

The professional training of specialists in the field of the digital economy is a promising direction in the development of economic education. The transition to digital technologies in management, banking, manufacturing, commerce, communications determines the emergence of new requirements for the professional and general competencies of university graduates. It is necessary to create an innovative vocational training system based on the use of electronic teaching aids and digital technologies. The implementation of this task should begin with the development of scientifically determined approaches to the creation of teaching aids that take into account all kinds of efficiency factors. This determines the importance of the task of scientific and methodological substantiation of the technology for creating electronic educational resources for the training of specialists in a new format. The paper proposes an analytical approach to determining the content and structure of the basic component of the methodological system an electronic textbook. The expediency of its use in order to develop necessary competencies of the individual and group levels is substantiated. It is proposed to use a matrix form of information representation and elements of matrix and vector analysis to structure the material. A mathematical model has been developed that describes structural relationships between the concepts of the thesaurus. The structural elements in this model are the glossary concepts of an academic discipline. The possibilities of using the model for unification and automation of information processing during the selection of textbook content in various educational fields are discussed. Examples of using the proposed model in various subject areas are given. The authors describe how to represent a model based on graph theory and methods for solving the problem of the presence of cycles in this semantic structure. The main conclusions and results of the study can be used for the practical development of electronic learning tools, automating the process of structuring their content and methods of use in the educational process.
Meta-Learning with Context-Agnostic Initialisations

Meta-learning approaches have addressed few-shot problems by finding initialisations suited for fine-tuning to target tasks. Often there are additional properties within training data (which we refer to as context), not relevant to the target task, which act as a distractor to meta-learning, particularly when the target task contains examples from a novel context not seen during training.We address this oversight by incorporating a context-adversarial component into the meta-learning process. This produces an initialisation which is both context-agnostic and task-generalised. We evaluate our approach on three commonly used meta-learning algorithms and four case studies. We demonstrate our context-agnostic meta-learning improves results in each case. First, we report few-shot character classification on the Omniglot dataset, using alphabets as context. An average improvement of 4.3% is observed across methods and tasks when classifying characters from an unseen alphabet. Second, we perform few-shot classification on Mini-ImageNet, obtaining context from the label hierarchy, with an average improvement of 2.8%. Third, we perform few-shot classification on CUB, with annotation metadata as context, and demonstrate an average improvement of 1.9%. Fourth, we evaluate on a dataset for personalised energy expenditure predictions from video, using participant knowledge as context. We demonstrate that context-agnostic meta-learning decreases the average mean square error by 30%.
Use of Semantic Networks to Search for Information

The paper examines the problem of semantic web, which is able to learn the origin and prerequisite for understanding the context and ideas. All commonly used search algorithms in a huge number of documents are based on the number of occurrences of the search phrase. This approach makes it impossible to find information about lesser known terms that are similar to or the same as the best known. Therefore, the concept of transforming a web page from Web documents to Web Data was created. This concept is called semantic web. In the future, the site should contain structured data about the meaning of the stored data. Part of the research in the theoretical sections is the evaluation of technologies included in the semantic web layer. There are stable formats for recording knowledge—RDF, RDFS, OWL and SPARQL protocol. Structured data can be attached to standard HTML documents by RDF and microformats and extracted by GRDDL. Many dictionaries such as FOAF, DOAC and SIOC are widely used. Part of the work is the application, which is located in the practical sections.
Modern methods of forming the motivational sphere of students in music classes

This paper discusses the concept of “motivation” in relation to the process of music education. The most effective ways of teaching are highlighted, which allow achieving a high level of motivation among students learning music, among which the method of active learning is recognized as the most effective for achieving this goal. The system of various motives that induce the student to educational activity is considered. Particular attention is paid to the process of formation and development of motives and needs; emphasis is placed on the importance of a personal approach to learning, taking into account the individual characteristics and abilities of each student. The personality of the teacher and the nature of his relationship with students are recognized as one of the fundamental factors affecting the success of training. The main pedagogical conditions that contribute to the increase of the motivational sphere of students are highlighted, such as: a personal approach, the creation of a special motivational atmosphere, the aspiration for the transition to self-education and self-development, the choice of the most effective forms of work. The conclusion is made about the necessary presence of internal motivation for the process of learning a subject (in this case, music) for productive educational activity. The problems in the field of music education are revealed, which consists in the lack of psychological knowledge among music teachers about the specifics of the formation of the motivational sphere of students.
Development of an Adaptive Fuzzy Control in Matlab Simulink Environment

The present paper describes the development of an adaptive control of a system of interconnected plate heat exchangers based on fuzzy controllers. The process flow diagram of heat exchangers operation and their relationship, are presented. The development of each fuzzy controller was carried out in the Matlab environment, and control rules were developed for each of them, taking into account the achievement of the task of heating the fluid to the required temperature. The feasibility of using fuzzy controllers in an operation system of heat exchangers is also justified. An adaptive control scheme for plate heat exchangers is developed and studies of the compiled model in the Matlab Simulink environment are conducted, graphs are presented, and input and output data is processed.
Query complexity of unitary operation discrimination

Discrimination of unitary operations is fundamental in quantum computation and information. A lot of quantum algorithms including the well-known Deutsch-Jozsa algorithm, Simon's algorithm, and Grover's algorithm can essentially be regarded as discriminating among individual, or sets of unitary operations (oracle operators). The problem of discriminating between two unitary operations $U$ and $V$ can be described as: Given $X\in\{U, V\}$, determine which one $X$ is. If $X$ is given with multiple copies, then one can design an adaptive procedure that takes multiple queries to $X$ to output the identification result of $X$. In this paper, we consider the problem: How many queries are required for achieving a desired failure probability $\epsilon$ of discrimination between $U$ and $V$. We prove in a uniform framework: (i) if $U$ and $V$ are discriminated with bound error $\epsilon$ , then the number of queries $T$ must satisfy $T\geq \left\lceil\frac{2\sqrt{1-4\epsilon(1-\epsilon)}}{\Theta (U^\dagger V)}\right\rceil$, and (ii) if they are discriminated with one-sided error $\epsilon$, then there is $T\geq \left\lceil\frac{2\sqrt{1-\epsilon^2}}{\Theta (U^\dagger V)}\right\rceil$, where $\lceil k\rceil$ denotes the minimum integer not less than $k$ and $\Theta(W)$ denotes the length of the smallest arc containing all the eigenvalues of $W$ on the unit circle.
"My avatar and me"

Innovation in Music: Future Opportunities brings together cutting-edge research on new innovations in the field of music production, technology, performance and business.Including contributions from a host of wellrespected researchers and practitioners, this volume provides crucial coverage on a range of topics from cybersecurity, to accessible music technology, performance techniques and the role of talent shows within music business.
Ethics of FinTech and Trading

A key promise, responsibility, and assumption underpinning democracy is that its participants are bound by a social contract that delivers win outcomes for those who adhere to its rules. But our social contract is under threat. Governments and large corporations are today being tempted to abandon the ancient conventions of our social contract by the allure of emerging technology that promises the leaders of large corporations and institutions greater control over LIQUIDITY + DATA. It has traditionally been inaccessible because the digital integration required to create the market was not possible. People intermediated transactions and played an important role in safeguarding important conventions. Machines, however, do not always adopt our conventions. And a big question is, who really owns the data rights anyway? In the absence of concerted action by democracies, the nation the largest source of aggregate demand will set the global standards for the ownership rights of data released from silos by digital integration. Unless democracies work together, with Joe Biden’s election as President of the United States there is now an opportunity to convene the world’s democracies in a call to arms to defend freedom, including our human right to be able to make informed choices.
Communication encryption scheme in multi-embedded systems based on distributed architecture

With the increasing use of Internet-based embedded devices, multi-embedded systems appear in the form of distributed architectures, forming a large-scale intelligent system. However, this trend inevitably connects the information and infrastructure of a large number of embedded systems to the Internet, so that the data security threats faced by many. In this article, by discussing the requirements for node user access control on embedded devices, the inherent properties of distributed multi-embedded system environment requires support for multi-authority attribute-based encryption (ABE) to achieve access control for each node. Therefore, a secure node access control solution is raised for data transmission in the IoT environment. This solution is a hidden user access control solution. It sustains multi-authority ABE and is highly scalable. In addition, we certificated that the proposed solution provides greater functional characteristics while its performance is comparable to or better than existing cloud computing solutions.
Adaptive Spatio-Temporal Regularized Correlation Filters for UAV-Based Tracking

Visual tracking on unmanned aerial vehicles (UAVs) has enabled many new practical applications in computer vision. Meanwhile, discriminative correlation filter (DCF)-based trackers have drawn great attention and undergone remarkable progress due to their promising performance and efficiency. However, the boundary effect and filter degradation remain two challenging problems. In this work, a novel Adaptive Spatio-Temporal Regularized Correlation Filter (ASTR-CF) model is proposed to address these two problems. The ASTR-CF can optimize the spatial regularization weight and the temporal regularization weight simultaneously. Meanwhile, the proposed model can be effectively optimized based on the alternating direction method of multipliers (ADMM). Experimental results on DTB70 and UAV123@10fps benchmarks have proven the superiority of the ASTR-CF tracker compared to the state-of-the-art trackers in terms of both accuracy and computational speed.
Ball detection for KRSBI soccer robot using PeleeNet on omnidirectional camera

Kontes Robot Sepak Bola Indonesia (KRSBI) is an annual event for contestants to compete their design and robot engineering in the field of robot soccer. Each contestant tries to win the match by scoring a goal toward the opponent's goal. In order to score a goal, the robot needs to locate the ball. We employ an omnidirectional vision camera as a visual sensor for a robot to perceive ball. We calibrate streaming images from the camera, in order to remove the mirror distortion. We deploy PeleeNet as our deep learning model for object detection. We fine-tune PeleeNet on modified PASCALVOC 2007-2012 dataset with the additional ball object. Our experiment results show PeleeNet has the potential to be deployed as a deep learning mobile platform for KRSBI as the ball detection architecture. It has a perfect combination of memory efficiency, speed and accuracy.
The global goal of media education in the information society

The following paper actualizes the problem of media education in the conditions of modern Russian society, allowing to master the skills and abilities to work effectively with information. It is substantiated that media education contributes to training the younger generation, first of all, for life in modern conditions, for effective perception of information and its comprehension, for understanding the impact of various types of information on the psyche, as well as for mastering modern technical means of nonverbal communication. The role of media education in the transformation of young people into creators of their own content, their development as harmonious individuals - development of emotions, intelligence, development of worldview, familiarization with moral values. The role of mass media in the development of media culture and information literacy of the population in general and of children and young people in particular is indicated. It is substantiated that the global goal of media education in the conditions of the information society is to build an ideological foundation of society. It is argued that one of the important tasks aimed at achieving the global goal of media education is the formation of patriotism, as one of the basic Russian values. The relationship between patriotic education and the media education process is indicated. The content of the multimedia patriotic media education project (festival) of the federal scale, the First All-Russian Media Competition Russian Cosmos, promotes development of the individual through media, acquires historical knowledge of local lore and familiarizes with the greatest pages of the history of his/her native country. The conclusion is made about the important role of media education for training children, adolescents and young people to live in a new technological structure, to work in conditions of the multipolarity of the virtual world and an endless stream of multidirectional information.
Image Approximate Copy Copyright Detection Technology and Algorithm for Network Propagation Under Big Data Condition

This paper adopts the viewpoint of image copy detection which combines local and global features, gives full play to the matching advantages of different image features, dynamically adjusts the weight of feature fusion according to the expressive ability of different image features and the image content characteristics of different image data sets, calculates the optimal weight allocation value, and proposes the dynamic update weight allocation calculation. This method further improves the accuracy and comprehensiveness of image approximate copy detection.
Research on Component Classification Strategy Based on Convolutional Neural Network

With the continuous improvement of technical system in recent years, the rise of software component technology drives the current development of the manufacturing industry, the construction of component library is also carried out for the application of current software construction technology, the application of software component technology and the construction and expansion of component library, both improve the efficiency of the construction and retrieval in the current software reuse process, but in recent years, the software component technology is in the process of application, due to the lack of comprehensive and comprehensive management strategies, there are some problems in the application of this technology. The application of convolutional neural network technology can better improve the application effect and application efficiency of software construction technology, therefore, this paper will conduct a detailed study on the construction of classification strategies based on convolutional neural networks.
Learn More, Forget Less: Cues from Human Brain

Humans learn new information incrementally while consolidating old information at every stage in a lifelong learning process. While this appears perfectly natural for humans, the same task has proven to be challenging for learning machines. Deep neural networks are still prone to catastrophic forgetting of previously learnt information when presented with information from a sufficiently new distribution. To address this problem, we present NeoNet, a simple yet effective method that is motivated by recent findings in computational neuroscience on the process of long-term memory consolidation in humans. The network relies on a pseudorehearsal strategy to model the working of relevant sections of the brain that are associated with long-term memory consolidation processes. Experiments on benchmark classification tasks achieve state-of-the-art results that demonstrate the potential of the proposed method, with improvements in additions of novel information attained without requiring to store exemplars of past classes.
Motivation Features of Students Learning Activity Depending on Mode of Study

The article presents the results of empiric research of motivation profiles of students of Psychology Department of Novosibirsk State Pedagogical University who are getting education as full-time students on the secondary education basis and as full-time and part-time students on the secondary vocational education basis. It reveals the general characteristics of the students motivation profile: the learning activity of all first-year students is connected with the motive of obtaining knowledge, while at the final phase of studies it gives way to the motive directed at getting the diploma, whereas the most represented motives are «to become a highly qualified specialist», «to ensure the success in future professional activity ». The article marks the specificity of motivation profiles depending on the year of studies and the students mode of study. The difference between the full-time and part-time students is expressed in insignificance of the external motivation for the latter. The leading ones for them are cognitive motivation, motivation of accomplishment, self-development and self-respect. In motivation profile of the full-time students the most expressed are the introjected and external motivation, in the forth year the leading role is performed by amotivation. The least significant motives are «implementing pedagogical requirements» and «readiness for classes».
Linear space string correction algorithm using the Damerau-Levenshtein distance

Abstract Background The Damerau-Levenshtein (DL) distance metric has been widely used in the biological science. It tries to identify the similar region of DNA,RNA and protein sequences by transforming one sequence to the another using the substitution, insertion, deletion and transposition operations. Lowrance and Wagner have developed an O ( mn ) time O ( mn ) space algorithm to find the minimum cost edit sequence between strings of length m and n , respectively. In our previous research, we have developed algorithms that run in O ( mn ) time using only O ( s ∗min{ m,n }+ m + n ) space, where s is the size of the alphabet comprising the strings, to compute the DL distance as well as the corresponding edit sequence. These are so far the fastest and most space efficient algorithms. In this paper, we focus on the development of algorithms whose asymptotic space complexity is linear. Results We develop linear space algorithms to compute the Damerau-Levenshtein (DL) distance between two strings and determine the optimal trace (corresponding edit operations.)Extensive experiments conducted on three computational platforms–Xeon E5 2603, I7-x980 and Xeon E5 2695–show that, our algorithms, in addition to using less space, are much faster than earlier algorithms. Conclusion Besides using less space than the previously known algorithms,significant run-time improvement was seen for our new algorithms on all three of our experimental platforms. On all platforms, our linear-space cache-efficient algorithms reduced run time by as much as 56.4% and 57.4% in respect to compute the DL distance and an optimal edit sequences compared to previous algorithms. Our multi-core algorithms reduced the run time by up to 59.3 % compared to the best previously known multi-core algorithms.
Hybrid Attributes Technique Filter for the Tracking of Crowd Behavior

In this chapter, the authors propose two algorithms based on the device of attributes for tracking of the abnormal behavior of crowd in the visual systems of surveillance. Previous works were realized in the case of detection of behavior, which uses the analysis and the classification of behavior of crowds; this work explores the continuity in the same domain, but in the case of the automatic tracking based on the techniques of filtering one using the KALMAN filter and particles filter. The proposed algorithms he the technique of filter with particle is independent from the detection and from the segmentation human, so is strong with regard to (compared with) the filter of Kalman. In conclusion, the chapter applies the method for tracking of the abnormal behavior to several videos and shows the promising results.
Study of the TP transformation via the inverted pendulum example

This study decribes the Tensor Product (TP) based model construction through the nonlinear dynamic system of the inverted pendulum. It presents the steps of TP modeling, various weighting functions and the Linear Matrix Inequality (LMI) based approach. LMI control has been used to stabilize the nonlinear system. This study shows the quasi-Linear Parameter-Varying (qLPV) state-space modeling and the Higher-Order Singular Value Decomposition (HOSVD) based TP model transformation. Some research in this issue already exists, but only the furuta, rotary, single and parallel-type double pendulum have been examined. In this paper the TP model transformation of the inverted pendulum is analyzed in terms of stability.