Reviewerly/exHarmony huggingface dataset:
Handling Secure Healthcare Data Streaming using R2E Algorithm

In the present era, the transfer of information has increased using the internet, which cannot be reliable because any unauthorized user can tamper the data or obtain the unintended information, especially in healthcare. With the rise in the number of patient's health record, there's an increase in the number of data breaches. Healthcare data is something which sometimes can't be shared with unauthorized people because it stores lots of important personal and sensitive data. However, technologies such as cloud storage stored at any remote location (cloud) which is vulnerable to many cyber-attacks pose an adverse impression on the secrecy and safety of patient's healthcare records and require lots of security for protecting the medical information. So, protecting the confidentiality of the patient's health information is of utmost importance. This work tries to provide a better and more efficient solution for securing all the health care data that are stored on the cloud which protect health care data from unauthorized access from an unknown source.
A Natural User Interface implementation for an Interactive Learning Environment

In this paper we present an interactive learning environment (ILE) called CRA y JUEGA, focused on the development of computational thinking skills using block-based visual language programing with a 3D physics simulation system. It includes a programmable interaction system based on hand movement and gesture recognition, that allows to easily create and use customized hand gestures for interaction with 3D objects in a virtual environment. By applying the TAM model to discover the motivation, enjoyment and behavioral intention to use, the results show that: (1) using NUI technologies in interactive learning environments improve the enjoyment and the perceived ease of use; (2) perceived enjoyment was a significant issue in the intention to use; (3) NUIs interfaces during exercises could provide motivation to learn for middle school students.
Analysis of Radiation Dose of Patients on CT Scan Examination using Si-INTAN Application

CT scan is widely used to diagnose the inside of the human body, so supervision is needed to ensure the health and safety of workers, patients and the public. One surveillance that can be done is to analyze the radiation dose of the patient on CT scan with the application of Si-INTAN. Data processing of the results of CT scan of the head, thorax and abdomen for ages 0-4 years, 5-14 years and ? 15 years using the Si-INTAN application. From the results of the data processing, the highest DRL DLP value and CTDIVOL values were obtained, for CT scan heads were 1732,8 mGycm and 31,92 mGy, the CT scan of the thorax was 2450,78 mGycm and 19,36 mGy, and for CT Abdominal Scans were 3968,85 mGycm and 19,35 mGy.
AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction

Click-through rate (CTR) prediction is an important topic in mobile recommendation systems and computational advertising. As previous research indicates, a key point to maximize CTR is feature conjunction for making the training data more informative. Despite great progress, existing methods still fail to choose suitable settings of feature conjunction for the given data. In particular, a linear model on the pair-wise feature conjunction may overfit the training set if the data set is highly sparse. For such data, a model based on low-rank latent matrices are shown to be more appropriate. Unfortunately, practitioners now face difficulties to decide when to use which. In this paper, we propose an adaptive framework to address the feature conjunction problem. Our proposed framework adaptively chooses effective models to do feature conjunction according to data properties. We offer a case for building feature conjunction based on feature-pair frequency. Efficient training, as well as parameter selection, are thoroughly investigated. We conduct comprehensive online and offline experiments to demonstrate the effectiveness of the adaptive model over existing models for CTR prediction.
AUTO_FIT: WORKOUT TRACKING USING POSE-ESTIMATION AND DNN

Lack of physical fitness increases the risk of adverse health conditions including coronary heart diseases, high blood pressure, stroke, metabolic syndrome, type 2 diabetes which leads to a decrease in the life expectancy of humans.In our work, we have introduced Auto_fit, an application that suggests the workouts and tracks it.Auto_fit uses Postnet for doing pose estimation to find 17 body keypoints followed by using the DNN classifier to identify the state of exercise and then counts the repetitions performed.We collected the videos of trained professionals performing the exercise and then used it to train Auto_fit.Auto_fit takes live video feed and counts the repetitions of exercise performed.It works on two common exercises and can also be run on low single-board computers like Raspberry pi.Auto_fit helps in improving physical fitness and thus enables a person to live a longer and healthier life.
Block Shuffle: A Method for High-Resolution Fast Style Transfer With Limited Memory

Fast Style Transfer is a series of Neural Style Transfer algorithms that use feed-forward neural networks to render input images. Because of the high dimension of the output layer, these networks require much memory for computation. Therefore, for high-resolution images, most mobile devices and personal computers cannot stylize them, which greatly limits the application scenarios of Fast Style Transfer. At present, the two existing solutions are purchasing more memory and using the feathering-based method, but the former requires additional cost, and the latter has poor image quality. To solve this problem, we propose a novel image synthesis method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">block shuffle</i> , which converts a single task with high memory consumption to multiple subtasks with low memory consumption. This method can act as a plug-in for Fast Style Transfer without any modification to the network architecture. We use the most popular Fast Style Transfer repository on GitHub as the baseline. Experiments show that the quality of high-resolution images generated by our method is better than that of the feathering-based method. Although our method is an order of magnitude slower than the baseline, it can stylize high-resolution images with limited memory, which is impossible with the baseline. The code, models, and Android demonstration application will be made available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/czczup/block-shuffle</uri> .
Blockchain Technology and its Applications - An Overview

Blockchain technology is the key for decentralized society and gathered a lot of attention in recent times. The technology of Blockchain offers an infrastructure of secure network that allows the users to perform any operation without middleman. It is expected to bring a revolution due to its characteristics of being transparent, secure and scalable. Blockchain technology will bring the considerable efficiency gains, transparency, cost savings and fraud detection while allowing the data to be shared in real-time between multiple parties in a trusted and transparent manner. This chapter elaborately discusses the basic building blocks of Blockchain technology such as public key cryptography, Digital signature, Elements and types of Blockchain and its applications in different sectors.
CREDIT CARD FRAUD IDENTIFICATION USING MACHINE LEARNING ALGORITHM

Credit card extortion occasions happen often time and then bring about gigantic monetary misfortunes. Culprits can take advantage of some advancement, for example, phishing or Trojan to take another individual's credit card data. Furthermore, strong fraud identification technique is important to recognize the fraud in time when criminals use stolen cards. One strategy is to utilize historical transaction data to obtain normal and fraudulent transactions under the behavioural features of machine learning strategies, and the use of this feature is to verify whether the transaction is valid transaction or invalid transaction. This paper considers four different machine learning algorithms that are Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbor (KNN), and Random Forest to discipline the behavior of ordinary transactions and fraud features. Confusion matrix is utilized for estimating the performance analysis of the algorithms. Results obtained from the processing of datasets provide an accuracy of about 99-100%.
An Improved Convolutional Neural Network with LSTM Approach for Texture Classification

Texture classification is a problem that has several applications, such as remote detection and recognition of forest species.Solutions tend to be customized for the dataset used, but are not generalized.Machine learning algorithms play an important role in the current Texture Classification.However, these algorithms are suffering with low accuracy and classification rate.Deep learning is another sophisticated technique to solve these challenges because texture classification performance is not strong in traditional machine learning systems.The Convolutional Neural Network (CNN) in combination with Long Short-Term Memory (LSTM) forms a robust selection between a powerful invariant feature extractor and an accurate classifier.This model should automatically determine the efficient properties of the feature samples so that the texture samples can be classified accurately.Expert fusion provides stability in classification rates between different data sets and the proposed model will significantly increases texture classification performance.From the experimental analysis, it is ascertained that CNN-LSTM is outperforms with existing state of the art of the algorithms SVM and CNN.
PENGEMBANGAN MEDIA KARTU PINTAR (KATAR) PADA MATA PELAJARAN MATEMATIKA MATERI PERKALIAN BAGI SISWA KELAS II SD

This study aims to determine the steps of the development and quality of smart card media (KATAR) in Mathematics subject matter multiplication.This research using ADDIE model with 5 stages: analyze, design, develop, implement, evaluate. The trial subjects in this study were 19 students in grade II and teachers in Sidomulyo 1 Salaman Elementary School. Data collection techniques in the form of interviews, observation and questionnaires. Data analysis used qualitative data analysis and quantitative data analysis. The result showed that the smart card media (KATAR) was appropriate to be used with a score of 95 (Good) from the media expert validator, 45 (Good) from the material expert validator, 43 (Very Good) from the learning expert validator. The results of student responses in small- scale trials get a score of 60 with a percentage of 100 (Very Good) and in large-scaletrials get a score of 130 with a percentage of 100(Very Good). Students respond that learning to use smart card media (KATAR) is fun andcan be learned while playing and the material learned is easy to understand.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN TOKO HANDPHONE TERBAIK DI KOTA PADANGSIDIMPUAN MENGGUNAKAN METODE ORESTE

This research will discuss about the selection of the best mobile shops in the city of Padangsidimpuan. This research was conducted to find out the best mobile shop in the city of Padangsidimpuangiven to consumers through a Questionnaire. This study uses a Decision Support System with the Oreste method. The Oreste Method is one of the Decision Support System methods supported by Besson Ranking, which is the priority scale of each criterion indicator. The data used as an alternative is the data of cellphone shops in Padangsidimpuanwith a sample of 5 stores with 5 criteria, namely Price, Quality on Cellphones, Services, Consumer Attraction and Store Locations. The mount phase that is carried out in selecting a cell phone shop starts with analyzing the system that is done at the cellphone shop, then from the results of the analysis carried out weighting criteria at the cellphone shop, then carried out alternative weighting at the cell phone shop. After that the process of ranking the weight of alternative handphone shops is carried out. Based on alternative data and criteria using the Oreste method, it can be concluded that the best mobile shop is the Happy Cellular Shop with a Preference Value of Distance Score Value of 5.3556. Smaller than the other 4 stores.
Detection of Dense, Overlapping, Geometric Objects

Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary.The data is a large set of black and white images of scatterplots, taken from journals reporting thermophysical property data of metal systems, whose plot points are represented primarily by circles, triangles, and squares.We built a highly accurate single class U-Net convolutional neural network model to identify 97 % of image objects in a defined set of test images, locating the centers of the objects to within a few pixels of the correct locations.We found an optimal way in which to mark our training data masks to achieve this level of accuracy.The optimal markings for object classification, however, required more information in the masks to identify particular types of geometries.We show a range of different patterns used to mark the training data masks, and how they help or hurt our dual goals of location and classification.Altering the annotations in the segmentation masks can increase both the accuracy of object classification and localization on the plots, more than other factors such as adding loss terms to the network calculations.However, localization of the plot points and classification of the geometric objects require different optimal training data.
The Needs and Tasks of University ESD and SDG"s Education

초록 · 키워드 목차 오류제보하기 The SDG"s, which had been planned as the behavioral canons and the ethical canons in the global environment era, include a lot of the precious guidelines regarding the administration on the global level and on the national dimension. However, when intending to utilize the SDG"s as the solution of the problems on the level of each country or city, the situation in which it is difficult to use them just the way they are takes place. Because of this, regarding the SDG"s keynote, the reinterpretation and the reorganization that consider the special situations, the conditions, the cultures, the capabilities, and the level of the preparations of the countries and the regions are needed. This research had been carried out on the level of triggering a discussion for the innovation of the liberal education of the universities. This research intends to propose the need for the ESD and SDG"s education at the universities and the communities, the effects and the implications that are given by the SDG"s education, and a task for the spreading of the university SDG"s. #Sustainable development goals (SDG's) #SDG's localization #sustainable development education (ESD) #the key competencies in sustainability #the whole-system approach 1. 서론2. 이론적 배경3. SDGs와 ESD 교육의 필요성4. 대학 SDGs 교육 정착을 위한 과제5. 결론 및 시사점참고문헌Abstract
On the Storage Cost of Private Information Retrieval

We consider the fundamental tradeoff between the storage cost and the download cost in private information retrieval systems, without any explicit structural restrictions on the storage codes, such as maximum distance separable codes or uncoded storage. Two novel outer bounds are provided, which have the following implications. When the messages are stored without any redundancy across the databases, the optimal PIR strategy is to download all the messages; on the other hand, for PIR capacity-achieving codes, each database can reduce the storage cost, from storing all the messages, by no more than one message on average. We then focus on the two-message two-database case, and show that a stronger outer bound can be derived through a novel pseudo-message technique.
Federated Routing Scheme for Large-scale Cross Domain Network

With the development of multi-network integration, how to ensure interconnections among multiple independent network domains is becoming a key problem. Traditional interdomain routing protocol such as BGP (Border Gateway Protocol) or SR (Segment Routing) fails due to the limitation of information island (data privacy), where each autonomous network domain does not share any specific intra-domain information. In this poster, we propose a federated routing scheme FRP, which realizes global routing without any intra-domain data. Each domain only needs to exchange the very lightweight cumulative gradients of overlapped parameters to build the federated routing model. With FRP, flows between any pair of nodes can get global optimal routing results no matter which domain the source and destination nodes locate.
The Twenty Third International Conference «LIBCOM–2019» – «Information technologies, computer systems and publications for libraries». (Review of events)

The Twenty Third International Conference “LIBCOM-2019”– “Information Technologies, Computer Systems and Publications for Libraries” (November 18-22, 2019, Suzdal, Russia) is reviewed. The main program events are discussed: the Conference opening ceremony and plenary meeting; the Central Discussion Site “Five Years of the Project “National and Centralized Subscription to International Indexed Databases and Full-text Resources”; open session Digital Libraries and Library and Information Resources: Trends, problems, prospects”, “Information Support of Science and Education” and “Innovations and New Functionality of Modern Library. New Software and Technological Solutions”; discussion site “Open Access, scientometrics and bibliometrics”; master class “Web of Science: New Opportunities for Russian Scholars”. It is highlighted that for the first time within the framework of the Conference the school discussion site “Ecological Education forRussia’s Sustainable Development”, and the discussion site “Kids. School. Library. Society: A Contemporary View” were held. The conclusions are made. The speakers at “LIBCOM–2019” final session indicated the special character of the Conference – interactivity of events (discussion sites, master classes, round tables), participants’ activity at discussions, discussion of vital issues.
A Home Security Camera System Based on Cloud and SNS

Security network cameras are widely deployed not only in business offices and public facilities but also in general households. Most of commercial network camera systems come with dedicated application software to monitor and control the camera system. This paper presents a home security camera system based on cloud and SNS. Unlike most of the commercial network camera systems, our security camera system does not require dedicated application software to monitor or control the camera system. Instead, we use twitter as the user interface. Also, we take advantage of the Amazon Web Service (AWS) cloud system for AI-based face authentication.
Overcoming Team Boundaries in Agile Software Development

The usage of agile software development methods is increasing and so is the need for enhancing the collaboration between the different stakeholders.Thus, we chose to investigate the communication tools and challenges across the different boundaries and consequently deduce implications for practitioners.This research addresses inter-team communication by exploring the practitioners' perception on the different communication tools and the challenges faced at the three different boundaries, inter-team, team and customers, and geographically separated teams.We aim to enhance the productivity of software development through enhancing the communication between the different stakeholders.In this research, we use grounded theory approach to gather data from semi-structured open-ended interviews with practitioners in a geographically separated software development company.The findings observed three main inter-team communication means (Slack, Trello, face-to-face) used by practitioners, and how the preference differs among practitioners.This study also focuses on the different challenges faced, such as absence of communication during user story dependency, unclear customer requirements, and cultural differences, when implementing agile across different geographical locations.Our study discovers how team boundaries are overcome when team members adapt to other teams' preferred communication tool and enhance the boundary spanners' role.Furthermore, the study highlights how novice and mature teams require a transition time to adjust to the agile methods.
ANALISIS STRUCTURAL EQUATION MODELING (SEM) DENGAN MULTIPLE GROUP MENGGUNAKAN R

Structural Equation Model (SEM) is a statistical technique with simultaneous processing involves measurement errors, indicator variables, and latent variables. SEM is used to test hypotheses that state the relationships between latent variables when latent variables have been assessed through each of the indicator variables. Multiple Group SEM is a basic model analysis that uses more than one sample. This analysis aims to determine whether the components or models of measurement and structural models are invariant for the two sample groups. In this study, the data generated by some requirements. First, the data generated with sample size n = 250. The first generated data is homogeneous data where the measurement model is the same as the structural model in group 1 and group 2, while the second data is non-homogeneous data where the measurement model and the structural model in group 1 and group 2 is not the same. The data was analyzed using the help of the lavaan package available in R to obtain SEM estimation results and Goodness of Fit Model from some data that was formed. From the results of the merger of the two groups, it shows that the invariant of the two models with the largest df (63) which is Fit Mean model states the simplest model. However, the smallest df (48) with Fit.configural model states the most complex model.&#x0D; Keywords: SEM, Multiple Group, R Program
Continuity in Education in The Implementation of The STEM Education for The Children of Preschool and Elementary School Age Modular Program

The article reveals the potential of introducing the STEM Education for the Children of Preschool and Elementary School Age authorial program as a means of managing the continuity process in the modern educational organization. The specific continuity content at the level of educational modules of the program is shown along with the results of monitoring the effectiveness of system implementation of the program at educational establishments. The study has been aimed at the theoretical justification and empirical verification of the effectiveness of the STEM Education for the Children of Preschool and Elementary School Age modular program as one of the means for ensuring the continuity of the preschool and general elementary education levels. During the study, the methods of analysis, theoretical modeling, pedagogical diagnostics, and mathematical statistics (U-Mann-Whitney test) have been used. Studying the effectiveness of the STEM Education for the Children of Preschool and Elementary School Age program based on three years of testing in preschool education has allowed detecting positive dynamics of the indicators in the experimental group. Given the identified level of the development of intellectual operations, imagination, and social intelligence of the children involved in the program at kindergartens, the STEM education program has been developed for the elementary school, the modules of which are shown in this article