Reviewerly/exHarmony huggingface dataset:
Research on Image Motion Deblurring Based on Generative Adversarial Networks

As the carrier of information, images can be used to obtain all kinds of important information. With the gradual progress of science and technology, people have higher and higher demand for image quality. However, affected by various reasons, the image will show varying degrees of information loss, resulting in image degradation. Therefore, this paper has done the following research on the motion blur of the image. This research takes the Generative Adversarial Networks as the main framework. First, a shallow feature extraction is carried out on the blurred image, and the Adaptive Residual Block is used as our encoder. The Adaptive Residual Block combines the Deformable Convolution Module, which can automatically adjust the size of the convolution kernel according to the deformable information of the motion blurred image, greatly improving the reconstruction ability of the image. Finally, the image is decoded by Transposed Convolution, and finally the blurred image is generated. In this study, PatchGAN network is used as our discriminator, which has good discriminating ability compared with other discriminator networks. The results of comparative tests show that the PSNR and SSIM of this algorithm are improved by 4.4% and 3.4% compared with other algorithms.
Study on the comprehensive evaluation of regional financial risk

Since 2011, the Chinese government began to pay attention to the prevention of regional financial risk in the financial field.The effective assessment of regional financial risk is an indispensable link in risk prevention, which is of great significance to maintain economic operation.This paper selects 5 first-level indicators and 15 second-level indicators to build a regional financial risk evaluation system.Based on the economic data of Jiangsu from 2015 to 2020, AHP and entropy weight method are used to calculate the corresponding weights of indicators at all levels.The multi -level fuzzy evaluation model is used to evaluate the financial risk in Jiangsu province.The results show that the membership degree of financial risk to the "general security" level is 0. 5050 at most, among which the proportion of macro environment is 0. 3932 at most, and the degree of external environment is 0. 0625 at least.It can be seen that the economic operation of Jiangsu is relatively stable and the financial risk is small.
HASIL BELAJAR MATERI KOMPUTER DAN JARINGAN DASAR: SEBUAH EFEK DARI MINAT BELAJAR DAN FASILITAS BELAJAR

The main objective of this study is to identify the factors that have the greatest impact on the overall quality of education received by students. Interest in learning and the availability of educational facilities are two characteristics that are considered to affect the results. Multiple Linear Regression was used to understand this finding. Information was collected using questionnaires and test results, and then given to students, with 26 of them representing the entire population of grade 10 SMK Al-Falah. The findings collected include the fact that 1) there is an influence of interest in learning on learning achievement / learning outcomes 2) Learning facilities do not have a substantial impact on the results of the learning process. 3) The level of interest in learning and the facilities available for learning both have a major influence on the results of the learning process. The most significant component in improving basic computer and network learning outcomes is maintaining interest in the material being studied.&#x0D; &#x0D;
FOV Recognizer: Telling the Field of View of Movie Shots

In film art, the usage of various fields of view in a movie reflects the shooting style of the director or artist. Analyzing which field of view was used by the shot can help us achieve the semi-automatic design and generation of the storyboard. Before achieving the generation of movie shots, the corresponding field of view recognition method and field of view dataset need to help the machine select a suitable field of view. However, although the traditional field of view recognition consumes many resources and time, no one establishes a relevant field of view dataset successfully. To solve this problem, we propose a method for automatically recognizing the field of view in a movie shot. In addition, we create a new field of view dataset by extracting shot images from available movies, including 10041 pictures. Experiments show that the proposed method can accurately achieve the automatic field of view recognition work.
Research on Distributed Energy Trading Mode and Mechanism Based on Blockchain

Traditional distributed energy transactions mainly provide trading contracts by using centralized trading platforms, and provide power supply and transmission by cooperating with power grid. There are problems such as high service cost, low transaction efficiency, and lack of user privacy security. This paper puts forward a kind of suitable for distributed energy trading considered Internet fee and risk appetite of bilateral auction trading rules, and consider the risk appetite can avoid the user power purchase price is too low or distributed sell electricity price is too high can’t auction success so as to reach a deal, to ensure efficient distribution of resources and maximize the interests of both sides, Considering that the network fee can make the distributed energy as near as possible to trade, reduce the loss of energy in the transmission process, reduce waste. At the same time, this paper designs a system software architecture suitable for distributed energy transaction, and gives the corresponding complete transaction flow.
Detection Algorithm Based on Improved YOLOv4 for Pedestrian

In this paper, the application of the target pedestrian detection algorithm is studied based on the YOLOv4 network. The simulative results show that the improved YOLOv4 algorithm, which integrates the CBAM attention mechanism and Focal Loss function, shows high detection accuracy in target pedestrian detection. By CBAM, The new feature map will get the attention weight of the channel and space dimension, practical features of the target. Besides, focal loss enhanced the training of difficult-to-classify samples. Based on the improved YOLOv4 algorithm, which will have great potential, the problems of low detection accuracy and seriously missed pedestrian detection in realistic, complex visual scenes are solved.
Mechanical characterization of aluminum 7075 using digital image correlation and finite element method

Numerous empirical and numerical studies have been carried out on the mechanical characterization of ductile materials. The application of digital image correlation (DIC) equipped with three-dimensional measurement analysis was used to examine the mechanical characteristics of aluminum alloy (AL 7075). Then, the DIC outcome with finite element simulation results will be compared with the experimental results to measure the performance of the DIC. A tensile specimen of AL 7075 is tested under constant loading with a 2 mm/min displacement rate until fracture. Strain gauge and extensometer are used along with DIC monitoring technique to capture the structural deformation of the specimens. The specimen and test are simulated in ABAQUS software, in which the results are compared with experiment and DIC data, indicating a good correlation between the results. Limited data in the form of average deformation and strain of the experiments are not applicable to the plastic and necking process. In this respect, the 3D DIC results are used to analyze the strain field throughout the test, including specimen necking, and predict fracture location. Moreover, the outcome of DIC and finite element simulation are employed to examine the characteristics of AL 7075 about the large plastic deformation and mechanical behavior. The proposed 3D DIC methodology in providing insight into the characteristic mechanical behavior of ductile materials is highlighted in this paper.
The use of VR tools in the context of gamification and the introduction of game mechanics in the educational field

AR and VR tools have become firmly established in the practice of teaching in foreign educational organizations. As one of the trends of the educational environment, gamification and the use of game mechanics are highlighted, which contribute to more effective involvement of students in the learning process. The form of the game retains its appeal, the ability to satisfy the needs for self-realization, competition and success. The purpose of the study is to determine the subject areas of application of AR and VR tools in modern Russian higher education based on world experience and practice. The analysis of international experience in the use of gamification and virtual and augmented reality programs, as well as an assessment of the problems of implementing these tools in Russian education is carried out. Conclusions. World experience shows that virtual and augmented reality technologies show significant effectiveness in the gamification of education. The introduction of technologies contributes to more effective student-teacher and student-student interaction, increasing students' motivation and mastering additional material. However, the main obstacles to adapting this experience are the low level of innovation activity and the willingness of teachers to use innovative approaches and techniques. The main conclusion: Modern technologies in education are currently not limited only to the availability of equipment (computers, projectors, etc.) and the Internet. Today's world experience shows that virtual and augmented reality technologies show significant effectiveness in the gamification of education. The introduction of technologies contributes to more effective student-teacher and student-student interaction, increasing students' motivation and mastering additional material. However, the main obstacles to adapting this experience are the low level of innovation activity and the willingness of teachers to use innovative approaches and techniques.
Motion Planning for HyTAQs: A Topology-guided Unified NMPC Approach

In this study, a topology-guided unified nonlinear model predictive control (NMPC) approach is proposed for autonomous navigation of a class of Hybrid Terrestrial and Aerial Quadrotors (HyTAQs) in unknown environments. The approach can fully exploit the hybrid terrestrial-aerial locomotion of the vehicle and as such ensure a high navigation efficiency. A unified terrestrial-aerial NMPC is first formulated with a type of complementarity constraints involving the hybrid dynamics, together with the collision avoidance constraints for safety. Further, a topological roadmap with both terrestrial and aerial paths is leveraged to guide the kinodynamic path searching and thus the unified NMPC. Then, a complete and distinctive navigation framework is established and validated on our self-developed HyTAQ. Compared with the existing unified terrestrial-aerial planning methods, ours takes the vehicle dynamics into account for the first attempt and achieves a more reasonable decision of modes switching. Experimental results are presented to demonstrate the effectiveness and superiority of the proposed approach.
Light but Tight: Lightweight Composition of Serialized S-Boxes with Diffusion Layers for Strong Ciphers

The widespread advent of the Internet-of-Things has motivated new design strategies for lightweight block ciphers. In particular, security against traditional cryptanalysis should ideally be complemented by resistance to side-channel attacks, while adhering to low area and power requirements. In FSE 2018, Ghoshal et al. proposed a dedicated design strategy based upon Cellular Automata (CA) for S-Boxes that are amenable to side-channel secure threshold implementations. However, CA-based S-Boxes have some limitations concerning the absence of BOGI properties and low branch numbers making them vulnerable to classical cryptanalysis attacks. In this paper, we address the vulnerabilities of these weak S-Boxes by complementing them with an ultra-lightweight linear layer and subsequently building (Light but Tight) $$\textsf {LbT} $$ - the area-efficient and side-channel resilient family of block ciphers. This super-optimal cellular automata (CA)-rule-based S-Box layer is appropriately complemented with a linear layer consisting of shuffle cells and matrix multiplication with an ultra-lightweight almost-MDS matrix with only 6-XOR gates. This ensures high diffusion at the cost of a minimal area overhead. Hence, we show that these vulnerable S-Boxes are not weak but when complemented appropriately with proper linear layer can lead to cryptographically strong as well as lightweight cipher design. Overall, the TI-protected circuit of $$\textsf {LbT} $$ requires an area footprint of only 3063 GE, which is $$12\%$$ lower than any first-order side-channel protected implementation among all of the existing lightweight block ciphers. Finally, we illustrate that LbT-64-128 obtains a reasonable throughput when compared to other lightweight block ciphers.
Self-diagnostic of the competence of scientific-pedagogical worker: aspect of educational work

The article deals with the problem of self-diagnostic of the competence of a scientific-pedagogical worker: aspect of educational work. Based on the analysis of approaches to monitoring in education and diagnostic in education, it was found that the interest in the issues of quality of higher education, objective assessment of the activities of subjects and structural units of higher education, as well as innovative approaches to the management of quality of education, in particular, through the management of quality of activities of scientific and teaching staff is growing. On the basis of scientific generalizations and the results of empirical studies, significant signs of monitoring and diagnostics in higher education system are highlighted and it is determined that educational monitoring and diagnostic in higher education should be considered as a kind of regular observation process using certain signs, indicators, criteria, factors and scales. On view of the analysis of information sources, it was found that pedagogical diagnostic is used in such objects of educational environment as: training sessions; individual work of students; teaching methods; curricula and programs; forms of education; technology of organization of the educational process; schedule of classes; qualimetry of teachers; study of advanced teaching experience; methodical work; practical training, etc. Namely here it gives a tangible increase in efficiency. The essence of the concept of “self-diagnostic of educational work of a scientific-pedagogical worker” is formulated. Taking into account the worked out sources of information and own pedagogical experience, as well as interviews with students, the key directions of self-diagnostic of the educational work of a scientific and pedagogical worker are determined. Four key organizational and pedagogical conditions for self-diagnostic of a scientific-pedagogical worker in educational work are stated. The methodology of self-diagnostic of a scientific-pedagogical worker for educational work is substantiated. A questionary of an anonymous survey of students on self-diagnostic of the state of educational work by a scientific-pedagogical worker is presented. The main ways of improving the skill of a scientific-pedagogical worker in educational work are determined on the basis of the results of anonymous questioning of students.
Performance Enhancement and Detection of Non Rapid Eye Movement using CNN Comparison over Recurrent Neural Network

Aim:The main aim of this work is to detect novel non rapid eye movements using Convolution Neural Networks (CNN) and comparing them with Recurrent Neural Networks (RNN).Materials and Methods: Two groups, namely Convolution Neural Network and Recurrent Neural Network algorithm were used to find the accuracy of Novel Non rapid eye movement detection with 15 samples each to evaluate, a total of 30 samples were taken for this study.Sample size was calculated using G power with pretest power at 80%, with the error rate of 0.05.Data models were trained using these Neural networks algorithms where the Novel Non rapid eye movement adapts the system effectively.Results : The accuracy of the Convolution Neural Network is significantly (0.013; p<0.05) improved with 95.65% than the RNN with 92.1%.Conclusion: CNN algorithm has obtained more accuracy which significantly shows differences when compared to the RNN algorithm.
Legal Risks and Regulation of Virtual Currency

Virtual Currency is a new thing active in the currency field in recent years.Because of its decentralized characteristics and the convenience and anonymity caused by the unique block chain technology, it is sought after by the majority of believers.However, everything has its two sides.Anonymity, as a significant advantage of Virtual Currency, also brings a series of problems: for example, difficulties in legal supervision and facilitating economic crimes.With the rapid development of Virtual Currency, the exposed legal problems and market risks are increasing day by day.Without effective regulation, the crisis contained in Virtual Currency will be very huge.In legislation, we should clearly base on reality and clarify the principle of responsibility distribution.Also, we should strengthen the regulation of economic crimes, improve the electronic evidence system in combination with the characteristics of Virtual Currency.
Peningkatan Keterampilan Berbicara dengan Metode Teams Games Tournaments (TGT) pada Mahasiswa PBSI Universitas Muhammadiyah Kupang

This Classroom Action Research aimed at describing the process and the results implementation of learning speaking skill using Teams Games Tournaments (TGT) method for the first semester students of Indonesia Language and Literature Education, Muhammadiyah University of Kupang 2019/2020 academic year. The research was conducted in two cycles with the data collection technique through observation, test, and documentation. The data obtained then were analyzed descriptive quantitatively and qualitatively. The study showed that there was an improvement in the learning process and the students’ score after applying Teams Games Tournaments method. Students were motivated and enthusiastic in learning activities. There was an active interaction between students and lecturer during the learning process. In addition, there was also an improvement in the students’ speaking skill score. In the first cycle, the whole average of students’ score was 66.13 became 75,33 in the second cycle.
A Comparative Study of the "Double Creation" Education System in the "Double High" Construction between China and the United States

At present, the construction of "double high" is in full swing, and vocational education has more practical advantages.How to further improve the quality of innovation and entrepreneurship education has become the key.This paper makes a comparative analysis of China and the United States in terms of curriculum setting, motivation purpose, teacher source and participants in innovation and entrepreneurship education, and tries to provide corresponding theoretical basis for the development of innovation and entrepreneurship in the future.
A Novel Marketplace Perspective Promoting Customized Low Energy Computing and IoT: The SMART4ALL Approach

The rapid evolution of cloud services as well as the widespread use of microservices has led to the proliferation of data that has to be optimally shared with many different users. In recent years, marketplaces for software resources have emerged mainly supporting a specific data type. In this paper we propose a novel marketplace approach supporting that can on one hand support multiple data types and artefacts mainly in the field of Customized Low Energy Computing (CLEC) such as Embedded Systems, IoT, hardware design etc. and on the other hand promote collaborations among partners of these fields. The design is based on cutting edge opensource cloud technologies delivering the required elasticity and scalability towards the sustainability of the system. It is also supported by an innovative user driven service enabling a highly configurable matchmaking of a specific user profile to all types of offered artefacts using data modeling and AI techniques. The efficiency of such services and the Marketplace as a whole is tested on a rapidly expanding network of 840 members from 44 countries comprising the network of H2020 SMART4ALL project in the context of which, it is developed. It has been in public operation for 6 months and the results are quite impressive as it has gathered 184 artefacts, 4483 views and 213 unique users singed up to have access to the SMART4ALL Marketplace resources and services.
Essential Mathematics for Artificial Intelligence (AI) and Machine Learning (ML)

Scientist have been attempting to find out "how we think" and the rationale underlying our thinking, comprehension, and predictions for thousands of years.The field of artificial intelligence advances by attempting to implement that intelligence in machines.It is a current discipline of research and engineering that was founded in 1956 by cognitive scientist Marvin Minsky, where the idea of AI was realised (the future of technology).Machine Learning is a subset of AI that was developed via mathematical modelling of neural networks.This article discusses the mathematics that lies beneath today's AI and ML, as well as the common milestones it has reached thus far.
Active SLAM: Place Classification and Viewpoint Planning from Domain-Invariant Scene Graphs

Training a domain-invariant viewpoint planner (VP) is an important task in visual place recognition tasks for autonomous mobile robots. robots. The objective of this problem is to acquire effective viewpoint shifts (e.g., landmark observation behavior) for place recognition based on visual experience under past domains (e.g., season, weather, illumination, etc.). Existing VP techniques assume that the domain is almost invariant, and none of them has ever dealt with essential cross-domains. In order to train domain-invariant VPs, we need domain-invariant scene representations. Inspired by the recent emergence of techniques for training graph classifiers (e.g., graph convolutional networks), we focus on research and development of new domain-invariant scene representations based on semantic scene graphs.
Broad View of Computational Statistics

The emergence of computational statistics could be identified as the second statistical revolution.From this point, the development of statistics depends on the advancement of computer science.This paper tried to summarise computational statistics from four sides: computational statistics drivers and motivations, the difference between computational statistics and traditional statistics characteristics, essential computational statistical techniques, and application and use cases.In particular, the critical computational statistical techniques section discussed Monte Carlo, bootstrap, graphical, and randomization methods.
Modeling a large crawler crane on Gazebo

In recent years, the shortage of successors at construction sites has become more serious. At the same time, autonomous driving technology for automobiles at lower prices for LiDAR is progressing. At this background, this research aims to realizing an automating crawler crane. However, its verification test is not easily carried out with an actual machine due to cost, difficulty in ensuring safety, and human and mechanical loads. Instead, we build a crawler crane simulator model on Gazebo. This paper introduces our crane model on the Gazebo simulator, hoping to perform SLAM with the crane model.