Reviewerly/exHarmony huggingface dataset:
Recognition Method of Airport Typical Motion Behavior Based on Infrared Image

Aiming at the problem of airport surface behavior recognition in low illuminance environment, based on infrared monitoring image data, the eco (efficient fluctuation operators for tracking) target tracking method is proposed to track different targets on airport surface including non-cooperative targets. The method of extracting macro motion features from tracking results is studied. On this basis, the long-term motion features are learned recursively in the framework of LSTM (long short term memory) network to realize the recognition of typical human actions on the airport surface. The experimental results on the self-made video data set show that the model can make full use of the long-term motion information in the video sequence. It is suitable for the airport scene in low illumination environment, and has a certain recognition effect.
Design and Integrate Real Time Clock and Temperature Digital Signal Processing with ARM System

Abstract The integrated circuit had been developed rapidly from microprocessor to microcontroller and System-on-Chip (SoC) just happen within a few decade. The development is continuing as the demand for the functionality increase. The Real Time Clock (RTC) is one of the peripherals most widely used inside the microcontroller. Besides, when Moore’s law is still applied, the number of transistor will increase, so does the temperature. Thus, the temperature digital signal processing (TDSP) unit had become an important peripheral to monitor the system-on-chip temperature. In this paper, these two Intellectual Property which are RTC and TDSP are designed and integrated into ARM system. The system architecture and implementation strategies are discussed. The results were run and simulated in the Synopsys software.
Reduction of Stain Variability in Bone Marrow Microscopy Images

The analysis of cells in bone marrow microscopy images is essential for the diagnosis of many hematopoietic diseases such as leukemia. Automating detection, classification and quantification of different types of leukocytes in whole slide images could improve throughput and reliability. However, variations in the staining agent used to highlight cell features can reduce the accuracy of these methods. In histopathology, data augmentation and normalization techniques are used to make neural networks more robust but their application to hematological image data needs to be investigated. In this paper, we compare six promising approaches on three image sets with different staining characteristics in terms of detection and classification.
Research and Application of Equity System Development of Rural Joint Stock Cooperative Economic Association Oriented to Big Data Technology (Blockchain Technology)

With the development of science and technology, people understand that the development of a physical object is no longer limited to books. With the advent of 5 g technology, big data technology has been everywhere in our life, and the emergence of big data makes it easier for us to do things. Nowadays, how to use big data technology to develop, research and apply the equity system of rural joint stock cooperative economic association has become a big problem. How to ensure the orderly operation of this key, one is the incentive path, the second is the constraint path, the third is the guarantee path, the three are different division of labor, the three complement each other, inseparable, together for the development of rural joint stock cooperative economic association equity system research and application. By using the method of data survey, this paper found that in the current rural joint-stock cooperative economic association, fishery cooperatives accounted for 5%, forestry cooperatives accounted for 20%, service cooperatives accounted for 35%, animal husbandry cooperatives accounted for 15% and 25% of the planting industry. Therefore, in the process of promoting the development of agricultural stock cooperation system, we should adhere to the principle of diversified forms and gradual improvement.
Professional Training Scheme in USUE: COVID-19 Pandemic Impact

Over the past decades, the Russian tertiary sector has undergone profound changes caused by economic and social factors. The paper focuses on the challenges that modern universities are facing nowadays. One of the challenges is pedagogical and methodological professional development of academic staff, which has been affected by the COVID-19 crisis. However, a rapid shift to online teaching and learning has offered new opportunities to capacity building of staff and faculty who have learned and tested new tools and systems to enable distance teaching and learning. USUE case is considered.
Development of a Vision Based Ball Catching Robot

This paper investigates an automated solution for ball catching task using a static camera and an automatically controlled mechanical system. The robot system is able to catch a ball which comes towards the ball catching plane. The robot accomplishes the ball-catching task with different success rates according to the trajectory type and speed of the ball. The thrown ball is visually tracked through a ball detection image processing algorithm. 2D synchronized motion of the ball catcher is coordinated by PID algorithm. Experimental results are conducted to evaluate the robot performance.
Healthcare and pharmaceutical industry supply chains

The healthcare and pharmaceutical industry has been among the highest priority to apply blockchain in major economies across the world. This chapter provides a detailed analysis and description of blockchain-led transformation in this industry. A special focus will be on blockchain’s roles in meeting climatic, market, operational, and regulatory conditions of drug delivery at a lower cost. This chapter also delves into blockchain’s potential to address fake and counterfeit medical equipment and drugs. It demonstrates how blockchain can help build trust in the digital ecosystem involving the healthcare and pharmaceutical supply chains. Case studies of three blockchain solutions have been used to illustrate these issues.
Violation target detection based on video streaming

Based on the increasing number of pedestrians and non-motor vehicles running red lights, the use of video stream to detect illegal targets, obtain evidence of violations, as a basis for punishment, can effectively reduce the occurrence of violations. Based on the YOLOv3 algorithm, pedestrian and non-motor vehicle detection can be obtained by combining skin color detection and face detection, and redundant target information can be filtered by location score function, which can reduce the misjudgment of pedestrians. For non-motor vehicle testing, the cyclist’s position is determined by the re-matching of face or pedestrian position with non-motor vehicle. The border regression operation is carried out on the prediction box to make the non-motor vehicle detection box contain the information of cyclists.
Methodology to Solve Definitional Tasks of Pedagogical Research

The research objective includes identifying methodological tools for solving definitional tasks of a pedagogical research. Relying on theoretical and empirical analysis, the author justifies methodology to develop a conceptual framework of a scientific-pedagogical research. Scientific originality of the study lies in the fact that the researcher for the first time proposes methodology to solve definitional tasks of a theoretical research. The findings are as follows: the author justifies methodology for solving definitional tasks, which includes invariable and variable stages, identifies scientific methods relevant to a particular stage.
An Agile Learning Management Method Based on Scrum

Challenge Based Learning (CBL) is a student-centered learning ap- proach that aims to prepare them to learn by asking relevant ques- tions and proposing solutions to real-world challenges. However, in the context of CBL in general, learning management is a diffi- culty faced by undergraduate students, and it is rarely addressed. In order to support learning management, this work proposes an agile learning management method based on Scrum to support students in a CBL learning cycle. In conjunction with the proposed method, a case study was carried out involving students from several under- graduate courses. The use of the method showed significant results in learning, in the administration of learning objectives (LOs)[1], and in the acceptance of its use by the participants.
Active Learning Approach for Intent Classification in Portuguese Language Conversations

Intent classification from conversations is a challenging task especially if messages are collected from real data since messages can contain several grammatical errors, outliers, language slang, and a lot of categories. Most of the intent classification methods use supervised learning approaches and do not consider the lack of labeled data, because supervised learning approach requires a large amount of labeled samples during its training process. In this article, we propose to reduce the sample scale to be labeled and maintain a desired classification effectiveness. Our method is based on active learning to minimizes amount of labeled data required and a convolutional neural network that obtains effective vector representations from BERT to perform accurate classification of messages. Experimental results on a large Brazilian Portuguese corpus suggest that the proposed method can achieve improvements with more than half of the training data, and accurate results with less than half of data in small dataset like as ATIS.
Greedy Causal Discovery Is Geometric

Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example, the greedy equivalence search, greedy interventional equivalence search, and max-min hill climbing algorithms. In 2010, Studený, Hemmecke, and Lindner introduced the characteristic imset (CIM) polytope, , whose vertices correspond to Markov equivalence classes, as a way of transforming causal discovery into a linear optimization problem. We show that the moves of the aforementioned algorithms are included within classes of edges of and that restrictions placed on the skeleton of the candidate DAGs correspond to faces of . Thus, we observe that greedy equivalence search, greedy interventional equivalence search, and max-min hill climbing all have geometric realizations as greedy edge-walks along . Furthermore, the identified edges of strictly generalize the moves of these algorithms. Exploiting this generalization, we introduce a greedy simplex-type algorithm called greedy CIM, and a hybrid variant, skeletal greedy CIM, that outperforms current competitors among hybrid and constraint-based algorithms.
Development of digital handout on particle wave dualism material

Abstract A valid and practical digital handout on particle wave dualism material was produced in this study. The adaptation of Rowntree product development model and Tessmer formative evaluation was used as the method on the Physics Education Study Program, FKIP, Sriwijaya University. The walkthrough and questionnaire techniques was used to collect data. The result showed the mean score for the validity of the digital handout was 4,53 for the design, content, and linguistic aspects with a highly valid category. The practicality average score in the one-to-one evaluation stage was 79,08% (practical) and in the small group evaluation stage was 84,59% (practical). The product can be used as an additional material in modern physics learning to improve students’ technological literacy.
Creating Slides from Video Lecture

Introduction. Video recordings of lectures are no longer a rarity in the conditions of distance learning. Videos may be in an inconvenient format for students or contain different artifacts due to compression, camera quality, and other factors. It is useful to have a presentation of the study material, which contains only the text from the board because such a view of the material is most like the compendium. Purpose. The item of our work is creating an algorithm for obtaining panorama slides without a teacher from video lecture. To develop this algorithm, we use the Boykov-Kolmogorov Max-flow algorithm for obtaining a mask of moving objects. In some video lectures, the camera is moved so that the teacher is in sight, so some of the recordings are not visible. To do this, we make frames stitching to get a panorama. To reduce duplication of slides due to camera shaking, we perform video-stabilization as a preprocessing step, then replace the pixels using the mask. Finally, we create slides by comparing changes in the frames, binarizingand denoising. Methods. We used the Boykov-Kolmogorov Max-flow algorithm, SIFT, Laplace operator and Otsu binarization for developing information technology. Results.As a result, we get panorama slides with the extracted text or drawings from the board, which will help to simplify the creation of e-learning materials for both new and existing lecture recordings. Teachers will also be able to quickly provide lecture material to students even if they teach several complex subjects. Conclusion. We have developed an algorithm and implemented information technology for obtaining slides from video-lecture. Now, a teacher can give students short content of video material. The next steps are automatic detection of the board, division of the board into sections, and recognition of the text and formulas written on the board.
Fast implicit active contour model for inverse lithography

We combine the ideas from level-set methods in computer vision and inverse imaging to derive a generalized active contour model for inverse lithography problems endowed with a locally implemented semi-implicit difference scheme. We introduce a cognitive analogy to move an initial guess of the interesting pattern contour by image-driven forces to the boundaries of the desired layout pattern. We develop an efficient semi-implicit numerical scheme implemented in the vicinity of the zero level-set and apply additive operator splitting (AOS) with respect to coordinate axes to solve consecutive one-dimensional linear systems of equations with the Thomas method. We demonstrate with simulation results that computation and convergence efficiency are jointly improved with reduced optimization dimensionality and a sufficient large step-size.
Analysis Method of Software Risk Based on Rough Set

The complexity of software process leads to that risk data is uncertain and rough. Based on rough set theory, this paper proposes a software project risk analysis system and analyzes the historical data of software project risk management. By using the concepts of upper approximate set, lower approximate set and reduction, this paper classifies and manages software project risks, and analyzes them through examples.This work can provide strategies for risk control, help identify weak links in risk management activities, and achieve the purpose of process improvement.
Land Property Data Logging on Blockchain Ledger

Occurrences of disaster lead to problems in information retrieval about damaged land properties as it is a time-consuming task, and one that entails a lot of bureaucracy. In this paper we will discuss a specific method using blockchain technology and specialized equipment to collect and register land property information. That information is critical for individuals, governments as well as insurance companies for evaluating risk associated with disasters. For reasons of data integrity and transparency, all of this information is available in a form on a public blockchain ledger.
Abandonment of a Software Process Improvement Program: Insights from Case Studies

Software processes improvement (SPI) based on maturity models is a well-researched theme regarding aspects such as adoption benefits, process definition, and automated support environments. However, few studies address the reasons that can lead an organization to abandon SPI. It is vital for organizations that seek to improve their processes to know what can negatively influence SPI and, thus, take actions to mitigate the risk of SPI failure, avoiding the loss of effort in the implementation stage. We present a study’s results to understand how abandonment occurs in software process improvement programs after successful assessments based on maturity models. We conducted case studies in four Brazilian software companies. The results show that SPI initiatives failed because of both internal factors (people, SPI project management, organizational aspects, and processes) and external factors (country economic crisis, outsourcing, and governmental political influence) to the organizational context.
Improving the quality of education at the municipal level to prepare for a new technological order

Preconditions of this work are stipulated by necessity to provide interrelations of forecasted targets of education and targets of development of state and society. Research is aimed at revealing features of target setting of participants in the relations in the sphere of education during preparation for new technological order. Analysis of publications devoted to the considered problem was conducted. The participants in the relations in the sphere of education were surveyed. The main results are the essence and definition of concepts of education targets and their specificity during preparation for new technological order by the participants in the relations in the sphere of education. It has been determined that all participants in the relations in the sphere of education more distinctly understand the education target in comparison with comprehension of necessity to arrange teaching in accordance with the NTI program. Herewith, the most concerned in development of education of students in NTI technologies are pedagogical staff of comprehensive educational establishments, university administration, and governmental authorities of Udmurtia. All participants in the relations in the sphere of education have higher orientation at knowledge acquisition by students in comparison with orientation at formation of personal properties (except for parents and local authorities). Consistency of targets of all participants will allow to more efficiently train students for new technological order.
Blockchain and Cryptocurrencies

In financial trading, cryptocurrencies like bitcoin use decentralization, traceability, and anonymity features to perform transactional activities. These digital currencies, using the emerging blockchain technologies, are forming the basis of the largest unregulated markets in the world. This creates various regulatory challenges, including the illicit purchase of drugs and weapons, money laundering, and funding terrorist activities. This chapter analyzes various legal and ethical implications, their effects, and various solutions to overcome the inherent issues that are currently faced by the policymakers and regulators. The authors present the result of an analysis of 30 recently published peer-reviewed scientific publications and suggest various mechanisms that can help in the detection and prevention of illegal activities that currently account for a substantial proportion of cryptocurrency trading. They suggest methods and applications that can also be used to identify the dark marketplaces in the future.
Construction of Deposit Model-oriented Knowledge Graph

Abstract It has become a hot research direction under the background of artificial intelligence to effectively organize geological big data and to mine corresponding potential value. The knowledge graph, as a new form of knowledge representation, can effectively organize and represent data. This paper presents the deposit model ontology according to the triune framework of “ ore-forming geological body, ore-forming structure and structural plane, and mineralization mark” by centering on the deposit model, sets up the knowledge graph with the “branch intersection, top-down action and step-by-step layering” method, expresses and mines big data for geological text in the form of knowledge graph, and establishes a special knowledge graph for deposit model with semantic processing capacity and open interconnection capacity, providing a routine to match geological data from new perspective and showing the integrity and relevance of genesis features of deposits on deeper level. The results show this method can effectively realize semantic correlation and constraints between deposit model ontology, as well as retrieval and reasoning of correlation objects, and prove the usefulness of the research workflow in this paper.
A Novel Fast Iterative Parallel Thinning Algorithm

In this paper, we have proposed a novel fast iterative parallel thinning algorithm which combines the trait of the well-known Zhang-Suen algorithm (ZS) and of the one-pass thinning algorithm (OPTA). In the thinning procedure of the proposed algorithm, all the pixels are checked by logical criterion conditions and template match. The usage of the logical criterion conditions is for the purpose of reducing the amounts of the match template to accelerate the speed of the processing and the applying of the template match is to avoid the excessive erosion of some certain pattern which happened in ZS algorithm. Proposed algorithm, ZS algorithm and OPTA algorithm have implemented and applied to some simple binary image for comparison. The test results confirmed that the proposed algorithm has better performance in terms of visual effect and lower number of iterations. Meanwhile, from the perspective of robust to the edge noise, the proposed algorithm also has a good performance which is better than OPTA algorithm but is not over the ZS algorithm.
Machine Learning and Data mining on the innovation of E-sports industry

AI technology brings many revolutionary innovation opportunities to the e-sports industry. With the help of data mining, we can analyze the advantages and disadvantages of competitors, and predict the trend of the situation in the future. With the help of the agent created by intensive in-depth learning, it can assist players of different levels to carry out routine training, so as to improve the overall activity of the game. With the help of AI's big data advantage, AI can assist E-sports teaching to regard E-sports specialty as an experimental platform for using cutting-edge technology to reform and innovate traditional education and provide forward-looking guidance for future education. This paper uses CNN, LSTM, and LSTM + CNN three model to predict the outcome of the game according to the heroes selected by both teams, and has achieved good prediction results.