Reviewerly/exHarmony huggingface dataset:
EDUCATIONAL POLICY OF RUSSIA IN THE FIELD OF HIGHER EDUCATION

The article discusses topical issues of education in higher educational institutions of Russia.It is shown that the world civilization is undergoing unprecedented changes in history and has come to a period when its progress is determined by its intellectual and educational power.Social progress has only one wayknowledge, and there is only one way to overcome the emerging obstacles -intellect.Because of this, the analysis of the formation and development of general and vocational education is of particular interest, since it creates the preconditions for understanding the patterns of this process and at the same time provides guidelines for reflecting on the contours of modern state educational policy, the effectiveness of which depends on understanding the essential properties of its object -the education system.It is necessary to bear in mind the fact that educational policy is not something forever given, it undergoes changes in dependence from the period of development experienced by the country, therefore, a theoretical analysis of its positive and negative aspects is necessary.
Development of professional-pedagogical focus of a teacher in vocational training

Nowadays, the requirements of the state and society for the training of a vocational education teacher have undergone significant changes and have indicated the need for highly educated competent specialists. The purpose of the article is to conduct a study to identify the development of professional-pedagogical focus of the teacher of vocational training in a pedagogical university. The essence of the professional-pedagogical focus in close interconnection with the professional competence of future graduates is revealed in the work. The analysis of scientific literature made it possible to identify the components of the studied trend: motivational, value, cognitive, reflective. The development of each component was considered in the process of preparing a teacher for vocational training at a pedagogical university. Students apply the obtained theoretical knowledge during the projects development, participation in games, discussions and solving case assignments. The project method is used as the most commonly used method in the professional focus development. To contribute to it students carry out a project in the course Pedagogical technologies. The features of preparing students in these conditions are highlighted. The analysis showed that the methods used for the focus development help to achieve success in  interest creation as well as professional competence, further improvement of this process will achieve better results in preparation of a teacher of vocational training.
Dynamic Path Generation for Multirotor Aerial Docking in Forward Flight

In-flight docking between unmanned aerial systems (UASs) is an essential capability for extending collaborative long-range missions. This work presents a planning strategy for a smaller multirotor UAS to autonomously dock with a non-stationary carrier/leader UAS in forward flight. Our method assumes the leader aircraft to be another multirotor, and first projects the hypotheses for its pose forward in time. Using a multi-objective cost function, we then solve an optimal control problem to obtain an interception trajectory to all these possible locations. We employ a cost formulation that allows us to generate piecewise smooth curves that favor different objectives during the course of the mission. Through a greedy strategy, the paths are iteratively refined online as the prediction is improved with new observations. We demonstrate and evaluate our method through a series of physics-based simulations with different operating conditions for both vehicles.
Relationship between precursory signals and corresponding earthquakes using different spectral analysis techniques

The origin of precursory signals that precede the P-waves is controversial, as it may be caused by ground effect (seismic nucleation phase) or instrumental effect due to a digitizer. Such signals cause errors in the automatic detection of P-wave arrival times and consequently decrease the accuracy of earthquake parameter estimations. In this paper, we proposed spectral analysis techniques to distinguish between different types of precursory segments, and to find if there is a relation between existence of these precursors and their corresponding earthquakes. These algorithms are applied to 1015 local events recorded by eleven permanent seismic stations belonging to the Egyptian National Seismic Network. Manual inspection of these events reveals the existence of these precursors. The investigations provided evidence that one type of these precursors originated because of an instrumental effect. This study shows the underlying causes of the precursory segment’s appearance prior to P-wave impulses. Many events had similar parameters; however, some of them had such precursors while others did not, in this inspection, multi resolution analysis details of the Discrete Wavelet Transform have been used. When large amplitudes for high frequency P-wave detail exceed 104 counts (1 count = 1 nm/s), the appearance percentage for the precursory segment is 100%.
A Two-Step Approach for Automatic OCR Post-Correction

The quality of Optical Character Recognition (OCR) is a key factor in the digitisation of historical documents. OCR errors are a major obstacle for downstream tasks and have hindered advances in the usage of the digitised documents. In this paper we present a two-step approach to automatic OCR post-correction. The first component is responsible for detecting erroneous sequences in a set of OCRed texts, while the second is designed for correcting OCR errors in them. We show that applying the preceding detection model reduces both the character error rate (CER) compared to a simple one-step correction model and the amount of falsely changed correct characters.
Statecharts and Agent Technology: The Past and Future

This work aims to bring forward the intersection between the world of statecharts and that of agent technology. We begin by disambiguating the different terms related to statecharts, i.e. state machines and finite state automata/machines. Subsequently we review their impact to agent technology, mainly in the area of Agent-Oriented Software Engineering. Our findings are that multi-agent systems modeling has used and, some times, extended the language of statecharts, mainly for modeling agent interaction protocols and for coordinating the different modules of an agent. We conclude with some future directions related to the use of statecharts by the Multi-agent Systems community in the coming years.
Sistem Pakar Menentukan Tingkat Depresi Pekerja Yang di PHK Menggunakan Metode Certainly Factor

Certainty factor shows a measure of certainty about a fact or rule. As well as providing a good solution for many people. Depression in laid-off workers is a very serious problem experienced by everyone who experiences termination of employment from their place of employment, this will have an impact on increasingly uncontrolled behavior changes. The results of the level of worker depression were obtained from the certainty factor algorithm by identifying the symptoms they experienced. And certainty factor works well in analyzing the symptoms caused by the depression.
Olympiad at a technical university: students’ opinions and development prospects

Introduction. Learning foreign languages at a technical university is an integral part of the educational process. A highly qualified specialist must have both knowledge in his/her specialist field and the skills of mastering foreign language competence. The purpose of the study was an attempt to analyze the data obtained through a sociological survey of students regarding the holding of the annual Olympiad in a foreign language at a technical university. Materials and methods. The material of the study included the answers of 114 students of the Stupino branch of the Moscow Aviation Institute (National Research University) from the first to third full-time courses by filling in Google forms. The use of the data obtained allowed revealing the attitude of students towards the English Olympiad held at the university since 2014. Results. The significance of participation in a foreign language Olympiad was assessed by 23% of the respondents. Among the respondents, there were 73% of the respondents who consider preparation for the Olympiad an important element reflecting the student’s knowledge and influencing the results of the Olympiad. Besides, 78% of respondents have already taken part in similar events. Thirty-nine percent of the surveyed students discovered new opportunities in continuing to learn the language. It is a fact that some respondents (36%) do not think that preparation for and participation in the Olympiad can increase the student’s motivation when learning a foreign language. Conclusion. Olympiad activities and preparation for this event make it possible to train a socially successful, creatively active, and demanded young specialist, acting as an activator of a competitive and developing educational environment.
Improving Semi-Supervised Clustering Algorithms with Active Query Selection

Semi-supervised clustering algorithms use a small amount of supervised data in the form of pairwise constraints to improve the clustering performance. However, most current algorithms are passive in the sense that the pairwise constraints are provided beforehand and selected randomly. This may lead to the use of constraints that are redundant, unnecessary, or even harmful to the clustering results. In this paper, we address the problem of constraint selection to improve the performance of semi-supervised clustering algorithms. Based on the concepts of Maximum Mean Discrepancy, we select a batch of most informative instances that minimize the difference in distribution between the labeled and unlabeled data. Then, querying these instances with the existing neighborhoods to determine which neighborhood they belong. The experimental results with state-of-the-art methods on different real-world dataset demonstrate the effectiveness and efficiency of the proposed method.
Neural Networks for Natural Language Processing

Bringing human cognitive intelligence (i.e., thinking, reasoning, and action) to artificial systems has always been a hot topic for researchers. In this process, they came up with idea of neural networks that try to emulate how the neurons of the human brain work. Although they are still very far from human cognitive capability, artificial neural networks hold a very promising position in the area of ML, and have transformed the way NLP applications are developed.
Citation Recommendation with Random Walking

Citation recommendation is a research problem that has a lot of potential impact in both academia and practice. In this paper, we present a method of citation recommendation based on the content of documents. Our method does not require the metadata of the documents. We evaluate our method against some real-world datasets. The experimental results claim the advantages of our proposed approach compared to the other techniques.
Application of Blockchain Technology in the Field of Medical Devices

In recent years, Blockchain technology has been mentioned for many times in the fields of finance, Internet and energy, and it is a distributed data ledger that is decentralized, authentic, reliable, traceable open and transparent. Blockchain is constantly figuring out how to integrate with other industries. This paper analyzes the current situation in the field of medical devices of China by combing the Annual Report on Medical Device Adverse Event Monitoring from 2013-2019, and using the compound annual average growth rate. The analysis found that this field has pain points such as lack of third-party regulation and difficulty in traceability, and then it proposed the specific application of blockchain technology in the field of medical devices to solve the problems in this field.
Assessing the Potential of Implementing Blockchain in Supply Chains Using Agent-based Simulation and Deep Learning

In the past decade, with the increase of data analytics and technology and the growth of e-commerce, many new applications have been developed. One example of these applications is Blockchain. However, Blockchain has faced some vulnerabilities and security issues, and there are no clear guidelines to assess its benefits. This research assesses the implementation of Blockchain in the supply chain by using Deep Learning and agent-based simulation. This chapter utilizes a case study in e-commerce to assess the proposed methodology.
Innovation and SMEs patent propensity in Korea

This paper analyzes the patent propensity as an outcome of innovative activities of regional SMEs. To achieve the aims, we apply robust regression analysis to estimate the models to test 5 research hypotheses using 263 firm level data located at Gwangju region in Korea. Our empirical results show that a firm's industry characteristics, such as machinery and automotive parts industry, is negatively related with propensity to patent innovation. Also, unlike expectations, the InnoBiz firms designated as innovative SMEs by the government are not performing differently than general firms. Only the CEO's academic credentials are positively related with propensity to patent. From the findings, we can conclude that patenting propensity is not directly related with a firm's characteristics but mainly to CEO's managerial strategy. Also, we cannot find evidence for policy effectiveness from public support given to InnoBiz firms as part of the state policy to nurture photonic industry to boost regional economic development. Given the lack of strong policy effects, a new industry policy should be considered to actively promote SMEs innovativeness.
Towards a Notion of Basis for Knowledge-Based Systems—Applications

In the paradigm of Knowledge-Based Systems (KBS), the design of methods to simplify the reasoning leads to more efficient processes. A point of view that provides valuable insights is the algebraic one. In this work, a notion of basis (and dimension) for Knowledge Bases in Propositional Logic associated with knowledge forgetting is introduced. It is based on ideas that come from the translation of such logic in (Computer) Algebra, particularly from the interpretation of variable forgetting. In this paper, the concept of weak base is defined as a set of variables sufficient to decide the consistency using variable forgetting. Several applications of weak bases are presented in order to show their usefulness in KBS reasoning and to justify their study and use in solving problems within this topic.
A Framework for Multi-lingual Scene Text Detection Using K-means++ and Memetic Algorithms

Recent years have witnessed an exponential surge in interest to explore the domain of scene text detection as well as analysis in natural scene images. However, owing to the complexities arising due to various factors, it can be said that existing techniques may fail at times while attempting to detect text components. This paper presents a system wherein an image is taken as input and its color components are extracted at first. Next the intensity values from each color channel are grouped together using K-means++ clustering algorithm. Memetic algorithm is then applied to get an optimal set of candidate components from the color maps while eliminating the background. The spurious components are removed on the basis of their dimension and entropy measure. This system is experimentally evaluated on two standard datasets namely MLe2e and KAIST, and on an in-house dataset of 400 images, all having multi-lingual texts. The results obtained are comparable with some state-of-the-art methods.
Culturally-Endorsed Leadership, Social Cognition, and Entrepreneurial Orientation: An Empirical Inquiry

Entrepreneurial orientation can not only be influenced by leadership, culture, but also through social cognition induced through Job Demands and Resources. Moreover, higher Entrepreneurial orientation could further lead to job satisfaction. These intricate relationships were never explored before. Therefore, we proposed a theoretical framework combining (Stephan & Pathak, 2016) and (Ralph Kattenbach, 2018) models, and modifying them to include the concept of Job satisfaction. Stephan & Pathak (2016) investigated the impact of Culturally-endorsed Implicit Leadership Theories (CLT) on individual entrepreneurship. CLTs are culture-level concepts build on individual-level implicit leadership theory proposed by (Robert G. Lord, 1991). (Ralph Kattenbach, 2018) modified (Bandura, 1997) social cognitive theory (SCT) to include Entrepreneurial orientation. SCT itself was a modified version of the Job Demands-Resources Model proposed by (Demerouti, 2001). The empirical validity of the proposed theory was established by means of a survey based on a close-ended Likert scale type questionnaire that collected data from 200 corporate sector employees based in Karachi. It was later analyzed using structured equation modeling and confirmatory factor analysis. These results showed that both cultural factors i.e. Uncertainty Avoidance and Collectivism have a significant and positive influence on both Self-Protective and Charismatic leadership. However, the effect of leadership on entrepreneurial orientation (EI) was not empirically significant. Similarly, the effect of both social cognitive factors i.e. Job Demand and Job resource on EI was positive and significant. EI and job demand had an insignificant effect on job satisfaction. However, job resource has a significant positive impact. This implies that job resource factors have a direct influence on EI as well as Job Satisfaction. But EI itself has no direct link with the job satisfaction of employees. Hence, measuring and analyzing an organizational culture in combination with its employees’ job demand and resource factors may lead to higher job satisfaction.
U-YOLO: higher precision YOLOv4

YOLO is a milestone algorithm of object detection, which is the first One-stage detector in deep learning era. In spite of its great improvement of detection speed, the detection accuracy is somewhat insufficient, especially for small targets. In this paper, U-shaped module based on YOLOv4 (U-YOLO) is proposed. First, multi-level features extracted by CSPDarknet using Feature Pyramid Network (FPN) are fused. Then, the fused features is fed into multiple U-shaped modules. Finally, feature maps consisting of the features from different U-shaped modules are gathered up to construct a feature pyramid for object detection. Experiment shows that the U-shaped module can improve the accuracy of YOLOv4.
Implementation of the C4.5 Algorithm to Predict Student Achievement at SMK Negeri 6 Surakarta

Data mining is a knowledge used to get information from multiple data. C.45 Algorithm is one of data mining algorithm to classify data to many categories. Implementation of data mining not only could be used in industrial section but it could be used to in educational section (educational data mining) to help teacher and student improve their learning quality. This research purposed to know the implementation of data mining to predict student achievement from many factors could be effected . The research use Knowledge Discovery in Database method and it would be analyzed by accuration calculated from classify model that be form. Result of the research is the rules that formed by the decision tree and it could predict student achievement . Teacher could use it to give special treatment to student who got not good prediction.
On financial innovation in developing countries: The determinants of mobile banking and financial development in Africa

This study addresses the question of financial innovation in developing countries by focusing on the determinants of mobile banking development in Africa. Data on mobile banking across countries is partially constructed by the author, to which is applied panel data and cross-sectional econometric technics, to investigate the determinants of mobile banking development. Statistical analysis reveals that there is a positive link between the different proxies of mobile banking and an indicator of mobile banking development using the principal component analysis technique is constructed. Overall, the estimation of the four models highlights the following determinants: human capital development, credible monetary policy, infrastructure development, remittances facilitation, urbanization, trade openness and the facilitation of access to domestic credit from the banking sector. Also, the results of our robustness check are uncontroversial. A general recommendation to African governments is that they should pursue good performance in the light of the determinants highlighted above by implementing specific and well conditioned economic policies.
How Human and Organizational Factors Influence Software Teams Productivity in COVID-19 Pandemic: A Brazilian Survey

Software companies have adopted remote work to maintain practitioners safe during the COVID-19 outbreak worldwide The remote environment in pandemic time brings some challenges, mainly for practitioners with limited experience with this work modality Moreover, there are several factors in the environment that together with the change in the work routine can directly impact the teams productivity, such as human and organizational factors To investigate this problem, we conducted a survey applied to software teams in Brazil to analyze how such factors can influence their productivity in the remote environment during the COVID-19 pandemic The survey was guided by human and organizational factors identified in the literature that affect the productivity of software teams, such as: work environment, collaboration and organization of teams, communication, motivation, among others Our survey obtained 58 participants from all Brazilian regions As main conclusions, we identified that 74 1% of the participants consider their productivity remains good or excellent, and 84 5% feel motivated and have easy communication with their co-workers Furthermore, the main factors influencing the productivity are external interruption, environment adaptation, and emotional issues © 2020 ACM
Multicamera Video Stitching for Multiple Human Tracking

In this paper, a novel approach is proposed to track humans for video surveillance using multiple cameras and video stitching techniques. SIFT key points are extracted from all camera inputs. Using k-d tree algorithm, all the key points are matched and random sample consensus (RANSAC) is used to identify the match correspondence among all the matched points. Homography matrix is calculated using four matched robust feature correspondences, the images are warped with respect to the other images, and the human tracking is performed on the stitched image. To identify the human in the stitched video, background modeling is performed using fuzzy inference system and perform foreground extraction. After foreground extraction, the blobs are constructed around each detected human and centroid point is calculated for each blob. Finally, tracking of multiple humans is done by Kalman filter (KF) with Hungarian algorithm.
Cryptocurrencies and Monetary Policy

This policy contribution was prepared for the Committee on Economic and Monetary Affairs of the European Parliament (ECON) as an input for the Monetary Dialogue of 9 July 2018 between ECON and the President of the ECB. The original paper is available on the European Parliament’s webpage (here). Copyright remains with the European Parliament at all times. This Policy Contribution tries to answer two main questions- can cryptocurrencies acquire the...