Reviewerly/exHarmony huggingface dataset:
Pedagogical communication as an acmeological problem

Currently, the writing team develops the research topic «Conceptual approaches to the creation of acmeologically oriented system of training and retraining of professional education». As noted by some researchers, the economy of Kazakhstan observed two processes with the ability to permanent development and improvement. We can talk about acmeologyzation of the personality of a professional and acmeological process of its preparation, which is possible only in the case of acmeologically-educated teacher of professional education and acmeologically-oriented system of training and retraining. There is no such a system today. Foregoing leads to: the need for scientific research of fundamental bases of creation of this system; a high degree of relevance to the problem of creating acmeologically -oriented system of preparation of teacher of professional education; high level of novelty of the study – expected results of the study justify new conceptual approaches to preparation of vocational teachers; study of the problems of pedagogic acmeology as a science of teacher achievement in mastership has been attracting the attention of scientists for a long time.
Image Description Generation based on Deep Learning

Abstract The paper firstly explains the importance and research significance of image description generation and makes a brief research summary on the generation of various algorithms for image description; it further introduces the principle and the algorithm process of the LSTM deep learning algorithm in details, specifically describes establishing steps and the model parameter selection process of the LSTM deep learning algorithm of the Microsoft COCO dataset, implements the deep learning models by employing Python, and finally applies the trained LSTM deep learning model to image description generation by using the Microsoft COCO dataset as an instance.
Regulations for Cybercrimes

The internet has made all types of information readily available, and this wealth of knowledge has opened up a whole new world of problems: cybercrimes. Despite the enactments of various legislation at both national and international level, cybercriminals are still mostly unpunished. The continued development of new technologies and mechanisms to protect anonymity on the Internet makes finding any response much harder. The lack of a common definition further impedes the finding of a global solution to eradicate the phenomenon. This creates an enforcement gap that allows cybercriminals to operate with near impunity. Over the years, the EU has taken steps to develop an adequate legal framework to strengthen the existing legislation. This chapter discussed that in EU, the adoption of The Cybersecurity Act 2019 would be enough to resolve some of the lingering issues of cybercrimes.
An Critical Analysis on Effect of Cyber Crime by E-Mail Pooling

The present study explore the field of cyber crimes and the specifically the concept of email pooling.The email pooling has been considered to be an important issue in current scenario. The study was performed in order to enlighten and to make aware about the concept of child pornography, in order to analyse the deeper side of the research. The aim of the study is to analyse the significant association between the gender and opinion regarding prevention of email pooling and also. To find out the significant association between email pooling and preventive measures. The purpose of study is to understand the concept of email pooling and the opinion of public regarding such issue. The study also depicts the effective measures to be taken in order to prevent email pooling. The current study is empirical in nature. The study was based on the survey taken from the public in order to know whether they are aware of the problem of email pooling. The sample size of the survey is 1460 Which was taken through random sampling. The study contain various tables and calculations in order to identify the number of percentage of people agree to the below mentioned questions. The research design is exploratory and experimental in nature. The analysis has been calculated on using the SPSS tool. The analysis has been carried out for variables like age,gender occupation and for testing the hypothesis the correlation and cross-tab has been used. The study also suggested with some preventive measures on considering this issue. The study has been concluded in a positive way as the alternative hypothesis has been proved. The research had been concluded in positive manner that the preventive measures has to be taken in order to prevent email pooling.
Unsupervised Single Image Super-Resolution Using Cycle Generative Adversarial Network

The current state-of-the-art deep learning based Single Image Super-Resolution (SISR) techniques employ supervised learning in the training process. In this learning, the Low-Resolution (LR) images are prepared by applying known degradation such as bicubic downsampling to the High-Resolution (HR) images. Unfortunately, bicubic down-sampling eliminates the natural image characteristics such as sensor noise, degradation due to built-in hardware, etc., and generates smooth images and hence generated images are different from the real-world data. When deep learning model is trained using such artificially generated LR-HR pairs, they often are prone to generate better SR results for real-world images. To circumvent this problem, we propose an SR framework that can train in an unsupervised manner using Generative Adversarial Networks (GANs). It contains mainly couple of networks called SR network and degradation network which work on an unpaired data of LR-HR images. The SR network learns to eliminate noise present in the LR image and super-resolve it. While, degradation network performs inverse of SR network (i.e. down-sampling and adding degradation from real-world images). We demonstrate the effectiveness of the proposed method by conducting extensive experiments on NTIRE-2020 Real-world SR challenge dataset where it demonstrates the superior performance over state-of-the-art methods in terms of both quantitative and qualitative assessments.
How to locate where a device is using a web application

The possibility of locating people is currently a highly demanded resource. In some cases it may be controversial, as the reasons for seeking this possibility may be malicious. However, there are also good reasons to want to locate people, from monitoring dependent people (especially children or minors) to controlling staff during work hours. In any case, there are some possibilities to achieve geolocation without cost and with currently available resources. The simplest and most effective way is through web browsers and this allows any modern device to allow using this possibility. Although this functionality is not new, since it has been available since the past decade, it is very useful and inexpensive and has allowed location through Web browser connections, based on Internet Protocol (IP) addresses. The World Wide Web Consortium (w3c) geolocation API specification defines a standard for accessing location services in the browser through JavaScript. The w3c Geolocation and the Device and Sensor Working Groups have addressed these demands with the specification of a Georeferencing API that is no longer maintained for the original version, but recently efforts have resumed. In this research work, geolocation resources have been used to locate college students who have been provided with University resources (cellphone and computer) so that they can receive teaching through a virtual platform teaching. The concern of the University is that the students are really where they have declared as their address and so the University wants to see that the equipment are there and are not elsewhere.
Hyper-parameter optimization with REINFORCE and Masked Attention Auto-regressive Density Estimators

Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS). In this paper, we demonstrate how its performance can be improved by using a simplified Transformer block to model the policy network. The simplified Transformer uses a 2-stream attention-based mechanism to model hyper-parameter dependencies while avoiding layer normalization and position encoding. We posit that this parsimonious design balances model complexity against expressiveness, making it suitable for discovering optimal architectures in high-dimensional search spaces with limited exploration budgets. We demonstrate how the algorithm's performance can be further improved by a) using an actor-critic style algorithm instead of plain vanilla policy gradient and b) ensembling Transformer blocks with shared parameters, each block conditioned on a different auto-regressive factorization order. Our algorithm works well as both a NAS and generic hyper-parameter optimization (HPO) algorithm: it outperformed most algorithms on NAS-Bench-101 [1], a public data-set for benchmarking NAS algorithms. In particular, it outperformed RL based methods that use alternate architectures to model the policy network, underlining the value of using attention-based networks in this setting. As a generic HPO algorithm, it outperformed Random Search in discovering more accurate multi-layer perceptron model architectures across 2 regression tasks. We have adhered to guidelines listed in Lindauer and Hutter [2] while designing experiments and reporting results.
Comments on: Recent advances in directional statistics

Abstract Inspired by this felicitous, highly concentrated and rather exhaustive review of a rapidly growing field, many larger research areas that warrant further investigation come to mind. In this comment, three areas are selected: fully satisfactory PCA on tori and polyspheres, harnessing linearity through Lie algebras underlying homogeneous spaces such as those for directional data, and statistical analysis based on critical points (e.g. mode and antimodes) of Fréchet $$L^p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>p</mml:mi> </mml:msup> </mml:math> -functions.
Construction of dual-axis sun tracker controlled by Arduino

Abstract In this work a dual-axis sun tracker was constructed using an Arduino to control two DC electric motors. The motors together with gears move a structure that tracks the sun. To achieve that purpose, an arrangement of four photoresistors was made to input the sun position into the Arduino in form of electric signal. The output signal from the Arduino is used in four relays with their respective optocouplers to transmit power into two DC electric motors to move the tracker. The Arduino uses an algorithm that processes the photoresistors signal and produce it an output signal that is transmitted to the relays. The sun tracker was applied in parabolic dish with good performance.
Improving learning step in artificial intelligence with categorization

The aim of this paper is to improve learning stage of machine learning. Input data are the main engine and first step in learning. Every small quality defect in the beginning can cause huge difference in result. There are two possible strategies to correct or filter. To correct data need to have some more knowledge about relations. For filtering we can calculate threshold and eliminate anomalies. It reduce amount of input data, what is also unwanted phenomenon. So we will mainly focus on correcting them and filling missing features in records. We expect more effective learning step which should result in better final (predicting) step of machine learning.
Methods Evaluation And Of Mechanism For Selecting Modern Managing Personnel In The Education System

Today, provision of the state institutions, including the education system with highly qualified personnel, is one of the priority directions of the country's policy. Personnel capacity is one of the key factors in improving the efficiency of the organization's goals and objectives, liberalization of all spheres, and implementation of key tasks before society and the state in the process of reforms. Therefore, a mechanism has been created to select the most suitable candidates for management in the education system
Comparison of Object Recognition Algorithms

Despite the increasing popularity of process automation, modern video surveillance systems still require constant human involvement to establish the fact of dangerous situations. But at present, systems are becoming more complex, this leads to an increase in threats and it is no longer possible for the operator to keep track of all emerging threats. In addition, in the field of video surveillance, tasks have been added that a person can no longer control just by watching video cameras. In this connection, you need to automate the process. Methods that provide maximum detection stability for small object movements, zoom changes, turning the object at a small angle, and changing lighting are based on describing the image at specific points. A special point is a point that has a number of key features that distinguish it from many other points in the image. Special points are the main characteristics of the object in the video surveillance system. The best object recognition algorithms based on this principle are the SURF and SIFT algorithms. These algorithms search for the direct occurrence of the reference image in relation to the observed one. The article discusses algorithms for detecting objects in an image based on the description of the image by special points. A comparison of SIFT and SURF algorithms, the analysis highlighted particular points in the recognition of each object, error analysis AI Node in identifying objects in the video stream.
Scrum Adoption Challenges in Higher Education in Indonesia: Case Study of Board of Information System, Universitas Islam Indonesia

Abstract The level of success in project development information systems or software in many countries is still low. A study of 110 information system projects in Indonesia found that most information system projects are problematics, and 18% were canceled. Even though it has been around for a while, Scrum as an approach is relatively new in Indonesia’s software development. Many efforts to adopt agile in various organizations are not running well. This research aims to identify the challenges in agile adoption in a higher education organization in Indonesia using the Scrum Adoption Challenges Detection Model (SACDM) theory. The study found that even though Scrum is performing well, there are still some works needed. Especially in improving the experiences and transforming the organizational culture and structure to make the scrum adoption more effective.
An Author Interest Discovery Model Armed with Authorship Credit Allocation Scheme

The author interest discovery can help personalized academic recommendation systems. However, many topic models for discovering author interest implicitly assume equal contribution from each coauthor to a target document. To loosen this limitation, a novel model, ATcredit, is proposed to strengthen the Author-Topic (AT) model with an authorship credit allocation scheme, and the collapsed Gibbs sampling is utilized to approximate the posterior and estimate the model parameters. In total, our model considers six counting schemes, including fixed and flexible versions, as well as equal contributors and hyper-authorship strategies.
Exploring Circular Hough Transforms for Detecting Hand Feature Points in Noisy Images from Ghost-Circle Patterns

Several applications involve the automatic analysis of hand images such as biometry, digit-ratio measurements, and gesture recognition. A key problem common to these applications is the separation of hands from the background. Color based approaches struggle to detect the boundaries of the hand and the background if these have similar colors. This paper thus describes work-in-progress with a spatial approach for finger feature point detection based on the circular Hough transforms. The main challenge is to interpret finger feature points in the patterns of circles amidst noise. The approach was implemented in java and tested on a set of images. The results were assessed using manual visual inspection. Such spatial approaches hold potential for more robust and flexible hand related image analysis application. Moreover, these approaches could also give faster algorithms as there is no need for image binarization and threshold optimization.
Digital Watermarking to Protect Deep Learning Model

There has been a significant progress in deep neural network. It is necessary to protect one’s model to prove his/her ownership. This can be achieved by embedding meaningful content or some irrelevant data or noise in the training data as watermark to protect deep neural network. In this paper, we embedded ‘WM’ character as a watermark to training images. To protect the rights of the shared trained models, we propose digital watermarking in this paper. The model was trained with both corona virus disease-19 (COVID-19) infected and non-infected peoples’ chest X-rays with a total of 2000 images. The model could achieve accuracy above 96%.
Spectrum Privacy Preserving for Social Networks: A Personalized Differential Privacy Approach

The characteristics of social networks has always been a hot topic of scientific research. In order to protect the privacy of users, the owner of the private data need to provide privacy protection when providing inquiries or publishing data. Local differential privacy (LDP) is difficult to construct a highly available social networks graph due to its independent perturbation process. Centralized differential privacy usually adds excessive noise due to the structural characteristics of social networks graphs. Higher security usually results in lower availability. Simply implementing any differential privacy mechanism will cause a large amount of data to be disturbed by noise. On the other hand, some spectral based privacy protection methods provide accurate spectrum, however ignore the disclosure of privacy data in spectrum query. Therefore, we propose a spectrum query algorithm based on personalized differential privacy. The algorithm effectively improves data availability by taking advantage of different privacy preferences of users in the social network and the characteristics of the spectrum. To verify the availability of these methods, experimental tests have been carried out in both model networks and actual networks, which shows that the algorithm improves the availability of data when it has the same security.
An Inclusive Finance Consortium Blockchain Platform for Secure Data Storage and Value Analysis for Small and Medium-Sized Enterprises

In the era of big data, people pay more and more attention to user privacy and data security. The market size of Small and Medium-sized Enterprises (SMEs) in China is sizable. Nevertheless, due to problems of data dispersion and lack of data features, it is very difficult to make use of the massive data of SMEs scattered in various institutions effectively, which leads to the inability to reflect the value of data. One of the problems is how to credit for SMEs better. Supported by federated learning for such scenarios, we present an inclusive Finance Consortium Blockchain platform in this paper. On the one hand, the platform combines decentralized identity and Blockchain as the underlying architecture, which guarantees authenticity of the data source and safe storage of user data on the chain. The smart contract mechanism provided by Blockchain can also assist secure storage and incentive mechanism of federated learning models effectively. On the other hand, we have innovatively introduced an asynchronous Federated Learning mode based on transfer learning, which encrypts the well-designed pre-training model and transfers it to each participant to guide training of the participant's local model. In the model reasoning stage, all the participants participate in a joint evaluation according to the local model and push the reasoning results on the Blockchain. The smart contract takes the weighted sum of the reasoning results provided by all the participants as the final result of shared model reasoning.
Study on Scaling and Rotation Invariance of IWSVR

Inspire by human vision, imaging with space-variant resolution (IWSVR) has the characteristic of invariance to rotation and scaling, but the match between the center of the field of view (FOV) and target centroid is very strict. To quantify this invariance in IWSVR, a novel evaluation function is proposed in this paper. The proposed function when compared with two existing methods of scale-invariant feature transform (SIFT) and biaxial projection similarity analysis, shows reduced error within a five-pixel range of eccentricity. The results motivate the application of rotation and scale invariance property of IWSVR in different applications.
Worksheet to Built Critical Thinking Skills for Prevalence Covid-19 in Indonesia on Limited Time: Curve Matching Modelling

Abstract Data on the spread of covid 19 in Indonesia is usually displayed in the form of a bar chart and a graph of the function of the number of people versus time for cases of increase, death and recovery. Display data in the form of graphs and diagrams is quite adequate for use in television news shows or breaking news. The Covid 19 case can be used as a source of authentic learning data in the Data Processing Workshop course investigation. Curve matching based data modelling can be developed in the form of scientific worksheets. The challenge of developing worksheets is at what stages must be done in order for the mathematical model to match the distribution of the data. The development of worksheets has met the academic requirements in terms of pedagogy and knowledge content. The validity and reliability of the worksheets have met the requirements according to the validator’s decision and evidence of empirical data analysis. Worksheets are developed based on excel, SPSS and Matlab with studies based on critical thinking skills. The scientific worksheets can improve critical thinking skills needed in the 21st century with several relevant indicators. The limitation of this study is that the prediction formula produced still does not meet the real data that occurs for cases of infection, recovery and death for a long time interval. The original research contained in the learning source data is authentic and continues to move during the pandemic. data is inputted into scientific worksheets that apply critical thinking skills in excel, spss and matlab presentation.
Solar Line-of-Sight Magnetograms Super-Resolution Using Deep Neural Networks

Image super-resolution is a branch of image processing that is concerned with enhancing the spatial resolution and quality of images by learning the intrinsic details and relations between the lower resolution input and the higher resolution output images. It is widely accepted as an ill-posed problem, which has seen tremendous advancements with deep learning based models. In this work, we present two magnetogram super resolution models, Sub-Pixel Convolutional Neural Network (CNN) and Enhanced Deep Residual Networks (ResNet), which can be used for improving the spatial resolution of solar magnetograms. While the ill-posed nature of problem is still a challenge, there are several application areas, including space weather prediction, which can greatly benefit from the improved spatial resolution of solar magnetograms. We show that through our experimental evaluation our models perform better than baselines and Sub-Pixel CNN super resolution model provides viable results for magnetogram super resolution.