Reviewerly/exHarmony huggingface dataset:
Academic Development in the Era of Globalization of Scientific Communication

Global competition in the scientific and educational services market make universities to stimulate academic development of scientific and pedagogical personnel on a systematic basis using modern digital solutions. The leadership in research (in important part of universities rankings) is impossible without development of new methods, tools of scientific communication, which requires constant modernization of the training system of researchers. Information technologies and the advanced use of information and analytical systems has become an integral part of research activities, which requires the development of scientific and methodological background for researchers’ training. This article is aimed at analyzing experience of Russian universities, research and educational organizations on the academic culture development, substantiating methodological prerequisites and practical recommendations for the formation of effective scientific communication skills. The research methodology is based on the study and generalization of the accumulated international and Russian experience in the area using statistical and scientometric tools. The authors proposed the content of descriptors of research competencies on the example of a master course. It is concluded that the introduction of systematic academic development should take place at each stage of higher education, which will be a factor in increasing the global competitiveness of universities.
How Much of Enhanced UD Is Contained in UD?

In this paper, we present the submission of team CLASP to the IWPT 2020 Shared Task on parsing enhanced universal dependencies. We develop a tree-to-graph transformation algorithm based on dependency patterns. This algorithm can transform gold UD trees to EUD graphs with an ELAS score of 81.55 and a EULAS score of 96.70. These results show that much of the information needed to construct EUD graphs from UD trees are present in the UD trees. Coupled with a standard UD parser, the method applies to the official test data and yields and ELAS score of 67.85 and a EULAS score is 80.18.
Augmented reality using Natural Feature Tracking (NFT) method for learning media of makharijul huruf

Abstract Makharijul huruf is the place of the letter hijaiyah (Arabic letters) from alif to ya . If someone mistakes in reciting a letter on al-Qur’an then the meaning of the word or sentence may be different from the meaning of the original word. Few learning media that provides information about makharijul huruf such as school academic books, books of tajwid al-Qur’an , etc. However, to make terms of visual and audio clearer we can use augmented reality technology as a learning media. Augmented reality for makharijul huruf requires a marker of hijaiyah in printing form. Tracking marker in this research using Natural Feature Tracking (NFT) method. In the application of learning this makharijul huruf will produce a 3D object visualization of the oral organs and audio sounds hijaiyah letters and explanations about makharijul huruf . The results of black box testing and beta testing states that this application can be accepted by users by obtaining a percentage of 91%. It can be concluded that applications built functionally have produced results as expected.
Using Electronic Design Automation and Guided Inquiry Learning Model in Higher Engineering Education

Electronic design automation software is a part of software learning.This study uses digital electronic circuit design software as learning media, namely, Electronic Design Automation (EDA), in the form of simulations and a guided inquiry learning model in higher engineering education.The device is a type of multimedia, because it involves graphics, animation, and text as external data.The software facilitates the learning process of cadets using the guided inquiry learning model.Given its accessibility, mobile learning has its own advantages.This study uses quantitative data with a type of quasi-experimental research.The sample consists of 100 cadets in the engineering study program.Results show that the model can improve the learning process of cadets in the air digital engineering program subject, in which the implementation of the model has been integrated into a special theme from the test scores and results of cadets.An effect is observed on the use of the EDA and guided inquiry model to the learning outcome and motivation of cadets in engineering education.The researchers concluded that the implementation of EDA and the guided inquiry model are effective.In addition, it provides benefits for practitioners and educators in improving the learning outcomes and motivation of cadets in engineering education.
Healthcare Monitoring and Analytic System Based Internet of Thing

In this work, a healthcare monitoring system-based Internet of Medical Things (IoMT) is proposed, implemented, analyze it by artificial intelligence using fuzzy logic. Atmega microcontroller was used to achieve the function of the proposed work and provide the area for monitoring and Analytic(decision) to the caretakers or doctors through putting the results in the platform. In this paper, the heart rate pulse sensor and infrared temperature sensor are chosen, which give skin temperature and room temperature to provide their results to the caretaker. The decision that gives the patient is in a normal state, or the fuzzy logic does an abnormal state or risk state. The fuzzy logic is used for it accurate and fast in processing data and gives a result very closer to the reality in smart health services. IoMT enables the doctors and caretakers to monitor the patient easily at any time and any place by using their intelligent laptops, tablets, and phones. Finally, the proposed system can contribute to the construction of a wide healthcare monitoring system in the unit or in the department that follows on for the hospital. Therefore, Doctors can improve the accuracy of the diagnosis, as they receive all the patient data necessary.
The Shortest Path Finder for Tsunami Evacuation Strategy using Dijkstra Algorithm

Abstract Having a tsunami early warning system during the tsunami disaster is important. The tsunami early warning system that is widely applied in Indonesia using loudspeakers and information board as a medium to convey information directly to the community during the disaster. The information provided is only about the status of the disaster and what needs to be done. People who panic, tend to choose the evacuation route through the main roads without thinking that the path will become very crowded resulting in total congestion. This paper discusses a tsunami evacuation strategy by finding the shortest path of evacuation route using dijkstra algorithm. This system contains information about the shortest evacuation routes that can be taken by the community towards a safe location, either vertically or horizontally. So, people can evacuate vertically or horizontally based on the geographical location of their residence. This system will be implemented in Cilacap districts. Shortest evacuation route is determined using the djikstra algorithm. Disaster-prone locations is the starting vertex and safe locations is finish vertex.
Shareholders' Social Network, Environmental Uncertainty, and Companies' M&amp;A Decisions: Evidence from Companies in Coastal Regions of China

Li, C. and Zhang, Q., 2020. Shareholders' social network, environmental uncertainty, and companies' M&A decisions: Evidence from companies in coastal regions of China. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 285–289. Coconut Creek (Florida), ISSN 0749-0208.China's merger and acquisition (M&A) activities are increasingly emerging in recent years, especially in coastal regions, which are the most economically active regions. Shareholders are the final decision makers of M&A, and their social network has more and more influence on M&A decisions. This article selected China's A-share listed companies in coastal regions from 2011 to 2018 as research samples and empirically tested the influence of shareholders' social network on companies' M&A decisions. We found that shareholders' social network can significantly enhance companies to make M&A decisions. Specifically, with stronger centrality, companies have more tendencies to make M&A decisions. In addition, we examined the moderating effect of environmental uncertainty on the relationship between them. We found that environmental uncertainty can positively moderate the relationship between shareholders' social network and companies' M&A decisions. Therefore, it is necessary for companies to explore and optimize their positions in the shareholders' social network, as well as take full account of the possible impact of environmental uncertainty to make reasonable M&A decisions.
Adaptive Gaussian Mixture Model-Based Statistical Feature Extraction for Computer-Aided Diagnosis of Micro-Calcification Clusters in Mammograms

In mammography, detection and categorization of micro-calcification clusters (MCCs) using computer-aided diagnosis (CAD) systems are very important tasks because MCCs are important signs at an early stage of breast cancer. However, the conventional methods of CAD only classify MCCs into benign and malignant types, and no method has been developed for a medical requirement to classify the MCCs into more detailed categories according to the spatial distribution of MCCs. To provide a cogent second opinion, we specifically focus on analyzing MCCs&apos; spatial distribution and propose an adaptive Gaussian mixture model-based method to extract the statistical features of the spatial distribution in this study. By mimicking the radiologists&apos; workflow, the proposed method used the main feature of each spatial distributions to classify the MCCs and then provide a cogent second opinion to increase the confidence level of diagnosis decisions. The experiments have been performed on 100 mammographic images with MCCs from a clinical dataset. The experimental results showed that the proposed method was able to detect the MCCs and classify the spatial distribution of the MCCs effectively.
Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks

This research aims to analyze the effects of different parameter estimation on the recognition performance of satellite modulation signals based on deep learning (DL) under low signal to noise ratio (SNR) or channel non-ideal conditions. In this study, first, the common characteristics of broadband satellite modulation signal and the commonly used signal feature extraction algorithm are introduced. Then, the broadband satellite modulation signal pattern recognition model based on deformable convolutional neural networks (DCNN) is built, and the broadband satellite signal simulation is conducted based on Matlab software. Next, the signal characteristics of binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), 8 phase shift keying (PSK), 16 quadratic amplitude modulation (QAM), 64QAM, and 32 absolute phase shift keying (APSK) are extracted by the constellation map, and the ratio changes of T1 and T2 with SNR are compared. When SNR is given, it is compared with VGG model, AlexNet model, and ResNe model. The results show that the constellation points of satellite signals with different modulations are evenly distributed. T1 of PSK modulation signals increases significantly with the increase of SNR. When SNR is greater than 10, PSK modulation signals can be identified. When T2 is set and SNR is greater than 15dB, 16QAM and 32APSK signals can be distinguished. In the model, the Relu activation function, mini-batch gradient descent (MBGD) algorithm, and Softmax classifier have the best recognition accuracy. PSK modulation signals have the best recognition rate when the SNR is 0dB, and the recognition accuracy of different modulation signals at 20dB is over 98%. When the data length reaches 4000, the recognition accuracy of different modulation signals is higher than 97%. Compared with other algorithms, this algorithm has the highest recognition accuracy (99.83%) and shorter training time (3960s). In conclusion, the broadband satellite modulation signal pattern recognition algorithm of DCNN constructed in this study can effectively identify the patterns of different modulation signals.
Research on the integration development of sports intangible cultural heritage and national fitness

Abstract This paper explored the integration and development of intangible cultural heritage sports and national fitness from the perspective of organization. The research shows that the intangible cultural heritage of sports based on folk custom and tradition can enrich people’s spiritual and cultural life and the content system of national fitness. The construction of national fitness system with Chinese characteristics is helpful to protect the intangible cultural heritage of sports. Conclusion: We should strengthen the construction of grass-roots sports organizations, improve the degree of organization, explore the sports methods in line with national conditions, and organically integrate the protection of sports intangible cultural heritage with the national fitness system.
Application of Blended Teaching in Public Administration Courses under the Background of Artificial Intelligence

Abstract The deep integration of artificial intelligence and education has become an inevitable trend in the development of higher education in the near future. Taking public administration courses for example, this paper constructed a blended teaching mode in the teaching and learning process, which was proved to be more suitable and helpful for teachers to play a guiding role in public administration professionals. By using big data in the teaching platform, teachers could generate appropriate teaching plans to improve students’ learning efficiency and subjective initiatives according to the knowledge and learning habits of each student. Meanwhile, the assessment system in the platform could also improve teaching quality by helping teachers acquire the learning outcome of their students. The blended teaching mode could largely promote the reform of the traditional teaching mode of the course in colleges and universities in China.
Lightweight Security Protocols for Blockchain Technology

Blockchain is defined as a distributed database that gives a secure, yet transparent way to make, record, and validate any kind of transaction record. Blockchain helps in eliminating the necessity of a centralized control and the transactions are digitally signed with the help of an asset owner public/private key pair. In addition, once the transactions are recorded, data in a block cannot be modified/altered/deleted retrospectively. In this chapter, we first discuss blockchain technology design space and its various consensus mechanisms. We then discuss various practical applications related to blockchain. Next, we also discuss various advantages and disadvantages of blockchain including limitations. After that, we emphasize on authentication protocols that are needed in blockchain to provide stronger security as compared to traditional authentication mechanisms and perform a comparative study on various existing lightweight authentication protocols that are applicable in blockchain technology. Finally, we discuss various testbeds that are deployed and implemented for blockchain technology.
Low-dose CT Image Denoising Using Classification Densely Connected Residual Network

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT.Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts.Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning.On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images.On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images.The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive).Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost.In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method.Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.
Proof by Experimentation? Towards Better IR Research

The current fight against the COVID-19 pandemic illustrates the importance of proper scientific methods: Besides fake news lacking any factual evidence, reports on clinical trials with various drugs often yield contradicting results; here, only a closer look at the underlying empirical methodology can help in forming a clearer picture.
A Reverse Planning Method of Autonomous Parking Path

The traditional parking path planning method based on geometric curve has the characteristics of simple calculation and high real-time performance, but it needs high requirements for initial parking position. In practical application, when the vehicle deviates from the path, the automatic parking task will be terminated in order to ensure safety and prevent collision. In this paper, an improved method is proposed. Firstly, according to the location of the target point of the parking space, the reverse driving method is used; according to the collision constraints and the minimum parking space constraints, the parking starting area is generated, which solves the problem of high requirements for the parking initial position based on the planning method. Then, the parking area is sampled to generate a smooth parking area based on the method of state space sampling, which can realize the dynamic adjustment of the parking track and solve the problem that the vehicle cannot complete the parking when it deviates from the planned path. Finally, the simulation software is applied to verify the results. The experimental results show that the method is effective.
Modelling Methodology as the Basis for Implementation of an Interdisciplinary Approach in the Training of Students of Pedagogical Specialties

Introduction. Nowadays, according to the tendencies of education modernisation, an interdisciplinary approach is taking a leading role, based on deepening the links between education and science, which is the most acute problem in the conditions of large amounts of scientific information. The implementation of the interdisciplinary approach is based on the methodology of modelling using information technologies, as a methodology of a new post-industrial stage of scientific culture of research in the digital age, which is of fundamental importance in modern teacher training. The aim of the present article is to investigate the aspects of modelling methodology using information technologies in the implementation of an interdisciplinary approach in the training of students of pedagogical specialties. Methodology and research methods. In the course of the research, a modelling methodology, a systematic approach, the analysis and generalisation of the results of academic works on the implementation of interdisciplinary, cultural, and meta-subject approaches in education were employed. Results and scientific novelty. The authors conducted the analysis of interdisciplinary approach (interdisciplinary integration) as a leading trend in post-industrial education. It is justified that interdisciplinary training, implemented on the basis of modelling methodology, is of fundamental importance in the formation of general-cultural scientific ideas among students and in their awareness of science as the ideal scientific knowledge. Various aspects of modernisation of teacher training based of modelling methodology are investigated. The main peculiarities of implementation of cultural and meta-subject approaches in the selection of the content of profile training of future teachers are identified. It is revealed that interdisciplinary courses play an important role in the implementation of the interdisciplinary approach. The didactic and methodological principles for the development of model-theoretical interdisciplinary courses for future teachers are established. It is demonstrated that in accordance with the modelling methodology, the main didactic goal of such courses is to teach the implementation of stages for investigating solutions of research problems (objects of processes or phenomena) using information technologies. Practical significance. The materials of the article contribute to the implementation of the interdisciplinary approach in the content of training of students of pedagogical specialties. Also, the research findings might be useful for both theorists of education and for teachers, who are engaged in professional training of students of pedagogical specialties, and all those who are interested in the further development of educational system.
Visual Image Processing of Humanoid Go Game Robot Based on OPENCV

The visual system is a key part of the research on humanoid robots. Aiming at the chessboard information extraction of Go robots, a python-based chessboard recognition method is proposed. Projection transformation is used to correct the image, and binarization is used to enhance the image. Image processing techniques such as color space transformation, threshold segmentation, high-pass filtering, and Huff transform are used to detect, locate, and segment chess pieces in a chess game. Experiments show that the method can locate and segment chess pieces accurately and has a high recognition rate.