Reviewerly/exHarmony huggingface dataset:
Training Specialists for the Use of the Resources of the "Moscow Electronic School" in Their Professional Activities for Teaching Schoolchildren with special Educational Needs

The article is devoted to the issues of training specialists in the field of special education using modern digital educational technologies, in particular, using the innovative resources of the Moscow Electronic School.&#x0D; The prerequisites are the urgent need to train modern special teachers who are able to work with children with disabilities and who are proficient in modern digital educational technologies.&#x0D; The purpose of the study is to develop a technology for training specialists in higher education, who own the resources of the "Moscow Electronic School".&#x0D; Research methods: questioning of students, expert assessment of students by pedagogical workers in order to assess the skills and abilities of working in the MES environment.&#x0D; The results of the study indicate that the proposed technology for training specialists in higher education, who own the resources of the Moscow Electronic School, within the elective, has demonstrated rather high results. The study revealed a greater satisfaction of teachers with the preparation of students, namely, an increase in the creative potential, the emergence of students' involvement in practice was noted. In particular, this was noted in the students' independent approach to adapting their lessons to the special educational needs of mentally retarded schoolchildren, in the correct temporal alternation of the stages of working with the interactive panel. So, teachers, heads of practice noted a high level of ownership of MES resources in 2020 among 57% of students, which is almost twice as high as among students who underwent teaching practice in 2018 (31%). The average level in 2020 was registered in 28%, low - in 15%, which is almost two times less than in 2018 (27%). &#x0D; The introduction of such an elective is exclusive, since it is focused on students enrolled in the specialty (defectological) education and solves the following problems: firstly, equipping with the skills and abilities of working in a digital environment, namely the environment of the Moscow Electronic School, and secondly, equipping with specific knowledge and skills of developing electronic content for children with disabilities.
Topology Optimization and 3D Printing: Toward a Functionally Graded Solid Insulator

Topology optimization and three-dimensional (3D) printing have recently attracted attention as potential technologies for ensuring the application of a functionally graded material to solid insulators of high-voltage power apparatuses. Topology optimization is a mathematical method by which the permittivity or conductivity distribution of a functionally graded solid insulator in a complex insulation system can be optimized. 3D printing can be used to obtain a solid insulator with a controlled permittivity or conductivity distribution. This study introduces research topics on topology optimization and 3D printing to develop a functionally graded solid insulator in Nagoya University.
Comparative Study of Open Source Software Quality Models

Software Quality Models are a structured software product assessment method. With the rising fashion in software production, new applications are being designed and created daily. This inevitably gives an increase to the need to ensure that the product so constructed meets at least the intended expectations. The software engineering literature includes numerous quality models and each software quality model consisting of various quality attributes or factors or characteristics as some models call them. These quality attributes can be viewed from this feature's perspective to describe the software component or product's quality. A real challenge is to identify which Quality Models to use. The following quality models' content will be presented in this paper by the researcher: McCall's Quality Model, Dromey's Quality Model, Boehm's Quality Model, FURPS Quality Model, and ISO 9126.
Development of National University Asset Management Application Using the Android-Based Life Cycle Development System Method: Development of National University Asset Management Application Using the Android-Based Life Cycle Development System Method

The goal of developing an asset management system is expected to make it easier for users to manage assets. The results of the application development that is developed first is the admin login if it is logged in, will enter the home menu, then the admin adds assets in the added assets menu according to the user's needs, if the assets have been added, appears in the asset view menu, all available asset data can be seen in the asset view menu, if the user requests an asset there is a request to request an asset, then requests as an admin to accept the request for the asset the user needs, if it has been received by Asset admin will appear in the history menu.
Low Complexity Convolutional Neural Networks for Wireless Receiver Chain Optimization

Technologies for 5G and beyond open up new chances for enabling new applications, which leads to an increasing variety of requirements, possible scenarios, and possible engineering decisions for wireless systems. Thus, having dynamic and robust techniques that can adapt to this huge variety has become more important than ever. One of the challenging adaptations is to select the most appropriate receiver architecture i.e. the architecture that gives the required performance with the least possible complexity, while modifying it dynamically based on the effects of having an instantaneous mix of a data sequence, channel effects, noise, and transmitter/receiver chains imperfections and impairments. One of the most innovative techniques is using convolutional neural networks (CNNs) as an initial pre-process that is capable of predicting the best receiver architecture. The technique depends on using offline pre-trained CNNs that can classify every incoming packet dynamically and assign it to the most appropriate receiver architecture. The technique shows high performance and accuracy that leads to higher certainty of the required resources and processing time, and consequently, better scheduling for the processes in the available receiver architectures and processing elements. Despite that, the technique adds an extra complexity due to the added CNNs. Although CNNs operations as multiplications have lower complexity than the operations in the receiver blocks, the added complexity due to using the CNNs is so high that they lead to total higher complexity than just using a higher complexity receiver in many cases.Here we propose a low complexity approach that gives an equivalent performance of the state of the art technique. Our approach here depends on reducing the size of the used CNNs by introducing parts of the incoming packet to the input layers of the CNNs instead of introducing the whole packet as in literature state of art, which reduces the added complexity due to the CNNs while keeping the advantage of the pre-knowledge of the required resources and the corresponding processing time. We show different approaches of how to extract enough information from the packet without the need to use all of it as input for the CNN, and analyzing the performance for every approach; then showing the total complexity reduction due to our new proposal.
PERANCANGAN MODEL LAYANAN PUBLIK PEMERINTAH DAERAH BERBASIS ONE STOP SERVICE

In order to improve the public service quality, the local government of Nusa Tenggara Barat at the Municipality of Lombok Barat should have improved the public service management. The combination of information technology function with the governmental services in the public service forms, it is a goal of the application of e-government. Application of conventional public service model and unsuitable with the public user will affect to the quality of the service. It can be low service quality and appearance of the unsatisfaction public as a user. One-stop service is one of the public service models that is applied by the local government of Nusa Tenggara Barat to easy and fast process the public services. The one-stop service model possibles the public service user can access the public services without they come to the office. The aim of this research is the design of the public service model of the local government that based on one stop service. It possibles the public users can access the system by using information technology device as a development effort of the public service quality that more efficient and effective. The method implementation of this research conducts the survey activity into the public user and the public service officer. The survey result of being the reference in the design of the model that more suitable at the present condition. The testing of the model conducted to find the performance of the business process and evaluation of model functionality. The last result of this research is a form of public service model for the local government based on the one-stop service. It is able to give effective and efficient services and also easy to access the government system about the status of the application form and searching process of the public service without they come to the government office.
On the Usability of Authenticity Checks for Hardware Security Tokens

The ultimate responsibility to verify whether a newly purchased hardware security token (HST) is authentic and unmodified lies with the end user. However, recently reported attacks on such tokens suggest that users cannot take the security guarantees of their HSTs for granted - even despite widely deployed authenticity checks. We present the first comprehensive market review evaluating the effectiveness and usability of authenticity checks for the most commonly used HSTs. Furthermore, we conducted a survey (n=194) to examine users’ perceptions and usage of these checks.We found that due to a lack of transparency and information, users often do not carry out - or are not aware of - essential checks but rely on less meaningful methods. Moreover, our results confirm that currently deployed authenticity checks cannot mitigate all variants of distribution attacks. Furthermore, some authenticity concepts of different manufacturers contradict each other. To address these challenges, we suggest a combination of already deployed and novel authenticity checks as well as a user-centered transparent design.
Tripfly: Predicting Gene-gene Interaction of Drosophila Eye Development Using Triplet Loss

The reconstruction of gene regulatory network (GRN) is of significance in system biology. In recent years, benefiting from the advances of deep learning technologies, image-based gene expression data, which contains spatial expression patterns, has become a new resource in network inference. Most of the existing image-based GRN inference models are based on unsupervised models, due to the lack of labeled data. And a few methods employ supervised learning models, whose performance is limited by the scale of training data.In this study, in order to predict the gene regulatory network of the eye development of Drosophila embryos, we develop a weakly supervised learning method. We generate image triplets of genes according to their orientation and developing stage. Then we build a deep convolutional neural network, using triplet loss to train a siamese network and extract the relationship between genes. The new method achieves promising results in the prediction of gene regulatory relationship in the eye development of Drosophila with a total accuracy of over 72%.
Decision-Making Support Using Nonmonotonic Probabilistic Reasoning.

The goal of this paper is to introduce a decision-making support by rule-based nonmonotonic reasoning enhanced with probabilities. As a suitable rule-based tool, we analyze answer set programming (Asp) and explore its probabilistic extension that permits the use of probabilistic expressions of two types. The first type represents an externally given prior probability distribution on literals in an answer set program \(\varPi \). The second type represents a posterior distribution conditioned on individual decisions and choices made, together with their consequences represented by answer sets of \(\varPi \). The ability to compare aspects of both the prior and posterior probabilities in the language of the program \(\varPi \) has interesting uses in filtering solutions/decisions one is interested in. A formal characterization of this probabilistic extension to Asp is provided in addition to examples demonstrating its potential use. It is also shown that the proposed techniques do not increase the complexity of standard Asp-based reasoning.
Learning Based Approach For Hindi Text Sentiment Analysis Using Naive Bayes Classifier

Sentiment analysis can be briefly described as the process of analyzing the emotion and opinion a particular sentence carries using natural language processing techniques. With the increase in the amount of information being communicated via regional languages like Hindi, 4th commonly spoken language in the world and its high potential for knowledge discovery comes a promising opportunity to apply sentiment analysis on this information. Hindi, being morphologically rich and free order language when contrasted with English, adds intricacy while dealing with the user-generated content. Most of the work in this domain has been done in the English language. This paper attempts to classify the polarities of the reviews or opinions expressed in the Hindi language into positive or negative sentiments using a supervised machine learning algorithm called Naive Bayes Classifier and evaluate the overall model’s performance with respect to various parameters.
Drone identification and location tracking based on YOLOv3

In recent years, increasing incidents of drone intrusion have occurred, and the drone collisions have become common. As a result, accidents may occur in densely populated areas. Therefore, drone monitoring is an important research topic in the field of security. Although many types of drone monitoring programs exist, most of them are costly and difficult to implement. To solve this problem, in the 5G context, this study proposed a method of using a city’s existing monitoring network to acquire data based on a deep learning algorithm for drone target detection, constructing a recognizable drone, and tracking the unmanned aerial vehicle. The method used the improved YOLOv3 (You only look once) model to detect the presence of drones in video frames. The YOLOv3 algorithm is the third generation version of the YOLO series, belonging to the one-stage target detection algorithm. This algorithm has significant advantages over the two-stage type of algorithm in speed. YOLOv3 outputs the position information of the drone in the video frame. According to the position information, the PID (Proportion integration differentiation) algorithm was used to adjust the center of the camera to track the drone. Then, the parameters of the plurality of cameras were used to calculate the actual coordinates of the drone, thereby realizing the positioning. We built the dataset by taking photos of the drone's flight, searching and downloading drone pictures from the Internet, and labeling the drones in the image by using the labelImg tool. The dataset was classified according to the number of rotors of the drone. In the experiment, the detection model was trained by the dataset classified by the number of rotors. The trained model can achieve 83.24% accuracy and 88.15% recall rate on the test set, and speed of 20 frames per second on the computer equipped with NVIDIA GTX 1060 for real-time tracking.
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.

Deep learning models owe their success at large, to the availability of a large amount of annotated data. They try to extract features from the data that contain useful information needed to improve their performance on target applications. Most works focus on directly optimizing the target loss functions to improve the accuracy by allowing the model to implicitly learn representations from the data. There has not been much work on using background/noise data to estimate the statistics of in-domain data to improve the feature representation of deep neural networks. In this paper, we probe this direction by deriving a relationship between the estimation of unknown parameters of the probability density function (pdf) of input data and classification accuracy. Using this relationship, we show that having a better estimate of the unknown parameters using background and in-domain data provides better features which leads to better accuracy. Based on this result, we introduce a simple but effective detection booster training (DBT) method that applies a detection loss function on the early layers of a neural network to discriminate in-domain data points from noise/background data, to improve the classifier accuracy. The background/noise data comes from the same family of pdfs of input data but with different parameter sets (e.g., mean, variance). In addition, we also show that our proposed DBT method improves the accuracy even with limited labeled in-domain training samples as compared to normal training. We conduct experiments on face recognition, image classification, and speaker classification problems and show that our method achieves superior performance over strong baselines across various datasets and model architectures.
The accessible IT for the courses on the special education undergraduate program

Students can learn through printed teaching materials, audio and video programs, weblogs, and or other media based on ICT (Information and Communication Technology). Yet, educators still play an essenstial role in designing every learning activity. This research aims to produce a prototype of the product development of accessible IT teaching materials for Special Education undergarduate program students. This research applied the ADDEM design model (Analyze, Design, Develop, Deliver, Evaluate, and Maintenance). Results were gathered by examining the feasibility on aspectts of efficacy and practicality of the develped product. Data were analyzed quantitatively by looking at the results of the validity and practicality test. The results of the validity test from the validators showed that all of the assesment items got a good rating of 3.0, in contrast the results of the practicality test, which was done by undergraduate students showed that there was an improvement on the score of the end-of-term test compared to the score of the mid-term test. Keywords: learning materials, accessible learning.
A pandemic of prediction: On the circulation of contagion models between public health and public safety

Digital prediction tools increasingly complement or replace other practices of coping with an uncertain future The current COVID-19 pandemic, it seems, is further accelerating the spread of prediction The prediction of the pandemic yields a pandemic of prediction In this paper, we explore this dynamic, focusing on contagion models and their transmission back and forth between two domains of society: public health and public safety We connect this movement with a fundamental duality in the prevention of contagion risk concerning the two sides of being-at-risk and being-a-risk Both in the spread of a disease and in the spread of criminal behavior, a person at risk can be a risk to others and vice versa Based on key examples, from this perspective we observe and interpret a circular movement in three phases In the past, contagion models have moved from public health to public safety, as in the case of the Strategic Subject List used in the policing activity of the Chicago Police Department In the present COVID-19 pandemic, the analytic tools of policing wander to the domain of public health - exemplary of this movement is the cooperation between the data infrastructure firm Palantir and the UK government's public health system NHS The expectation that in the future the predictive capacities of digital contact tracing apps might spill over from public health to policing is currently shaping the development and use of tools such as the Corona-Warn-App in Germany In all these cases, the challenge of pandemic governance lies in managing the connections and the exchanges between the two areas of public health and public safety while at the same time keeping the autonomy of each Copyright © 2021 Maximilian Heimstadt, Simon Egbert, Elena Esposito
Behind The Cello

New Perspectives QuarterlyVolume 36, Issue 4 p. 60-65 Original Article Behind The Cello YO-YO MA, YO-YO MA Reflects on the role of arts, creativity and the edges of life. His comments are adapted from a conversation with NPQ.Search for more papers by this author YO-YO MA, YO-YO MA Reflects on the role of arts, creativity and the edges of life. His comments are adapted from a conversation with NPQ.Search for more papers by this author First published: 06 November 2019 https://doi.org/10.1111/npqu.12232AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume36, Issue4October 2019Pages 60-65 RelatedInformation
Snowstorm 'a huge setback' for paramedic response times

While clearing several inches of heavy, wet snow may have been troublesome for you, for paramedics, it can mean slower response times, but you can make their job a little easier.
ENGLISH LEARNING MODEL AT HOME AND ONLINE APPLICATIONS USED TO INCREASE HEALTH VOCABULARY

Effort to prevent the spread of the COVID-19 corona virus from becoming more massive, the President of Indonesia urged the public activities such as learning, working and doing worship should be carried out at home. Despite giving a statement not to leave the house, President advised the people not to be panic and remain productive even though working from home. Restrictions imposed by governmental agencies had an impact on the daily routine for millions of people, including students in Indonesia. Some schools and universities in certain affected areas had suspended face-to-face learning for a period of time to prevent the spread of COVID-19. What do the students do during the school closed? This study was applied survey method through used online application Learning Model. With internet technology and smart devices, teaching and learning activities can now also be done at home. This study was conducted to increase student’s health vocabularies.