Reviewerly/exHarmony huggingface dataset:
Public Information, Education, and Communication (IEC) of Health: Active Participation of Health Practitioners in Urban Radio in a Low Resource Setting

Introduction: Over the last two decades, Frequency Modulation (FM) radio has been established as the only form of sound broadcasting in Ghana. Radio is the most accessible of mass media. There are more than 40 operational radio stations in the Ashanti region of Ghana. Most stations are commercial, broadcasting in the local language (Asante-Twi). Many urban radio health slots discuss various diseases and their treatments mainly for the benefit of patients. Complementary and Alternative Medicine (CAM) practitioners who are able to pay for airtime dominate as ‘experts’ in most of these shows. Methods: We identified an IEC gap regarding policies governing healthcare delivery, healthcare financing, training, ethics and research, and environmental issues. In June 2015, orthodox medical practitioners collaborated with a private, local, English-speaking radio station to produce and host a weekly health show whose content was aimed at holistically discussing health from the viewpoint of practitioners, clients, policy makers, administrators, and financiers in a simplified language for the general public, including healthcare trainees. Discussion: The show dubbed “Staying Alive” first aired on Tuesday, July 7, 2015, at 20:00 GMT and continues to air to date, appealing to a wide range of active listeners. “Staying Alive” to the best of our knowledge remains the only show with a holistic approach to health. Over the last 23 months, we have experienced challenges in sustaining sponsorship to fund the cost of production and airtime for the show, and the cost of effectively assessing the public health impact of the show. Orthodox medical practitioners can employ mass radio as an effective tool for advocacy, information dissemination, and education of clients or health trainees in low or middle-income urban settings through effective collaboration with media stations.
Pembuatan Prototipe Aplikasi Distribusi Pangan Berbasis Sistem Informasi Geografis (SIG)

Food distribution and supply chain are the main problems to achieve food security in Indonesia. Geographical gaps, poor transportation systems and social differences in Indonesia induce a high complexity challenges to implement a perfect distribution system that can suitable with Indonesia characteristic. The purpose of this research is building a prototype for food distribution application using geographical information system (GIS) that can be implemented in Indonesia. GIS can visualize the entire data layer into a map which is easier to analyze. Indonesia consists of 34 provinces that spread over many island, each has specific geographical profile and transportation problems. This can be used as data layers with other instruments which related with food distribution in Indonesia. Building a food distribution application prototype is part of developing food distribution system which produce prototype of the system. Prototyping approach is being used to evaluate the designing result of GIS with analyzing features for food distribution in Indonesia. Final result for this research is an application that can be implemented into food distribution system that can create a stable food security in Indonesia, for all of the provinces.
UPAYA MENINGKATKAN HASIL BELAJAR BAHASA INDONESIA MELALUI MODEL PEMBELAJARAN KOOPERATIF TIPE TPS PADA PESERTA DIDIK KELAS III SDN 35 KAMPUNG SAWAH

ABSTRACT TPS (Think-Pair-Share) or (Thinking-Pairing-Sharing) is a type of cooperative learning that is designed to influence the patterns of interaction of students. TPS requires students to help each other in small groups (2-6 members) and are more detailed by cooperative rewards, rather than individual rewards (Ibrahim et al: 2000: 3). Problems to be studied in this study are: Is it through the use of learning models cooperative type of polling station can improve Indonesian language learning outcomes of Class III students at SDN 35 Kampung Sawah? The purpose of this action research is to find out the improvement of Indonesian language learning outcomes through the use of TPS cooperative learning models in student IV.B SDN 35 Kampung Sawah. This uses two rounds of classroom action research. Each round consists of four stages, namely: planning, implementation, observation, reflection. The target of this study was Class III students of SDN 35 Kampung Sawah. The data obtained are quantitative and qualitative data. From the results of the analysis of each cycle, namely pre cycle (48.72%), cycle I (76.92%), cycle II (97.44%) .The conclusion of this study is the TPS cooperative learning model has a positive influence , which can improve student learning outcomes as indicated by the average answers of students stating that students are interested and interested in the TPS cooperative learning model so that they become motivated to learn.
SISTEM DETEKSI PLAT KENDARAAN DENGAN MENGGUNAKAN METODE K-NEAREST NEGHBOUR (KNN)

The monitoring system and managemen of vehicle have developed. Identification and number plat recognition in the field of traffic one of them is like passing the stop line on the road so that is disturbs other drivers, this is the most and even often occurs in big city cities even regions. The purpose of this study is to extract and recognize license plates from the image of the vehicle, that infringes so that it can be used as a dataset in making reports on determining sanctions that are appropriate to the type of violation of the vehicle. This study uses an image processing extraction process so that method according to this method is K- Nearest Neighbour, using this method will facilitate the detection process because this method does not use looping, this process begins by preparing a training image and then performing the image recognition stage. Image recognition is a process of matching digital image data that has been converted into a matrix and then matched with existing datasets so that the result can be known in the form of reports
INFLUENCE OF SELECTED ECONOMIC FACTORS IMPACT ON THE RESPONSE OF COLLEGE STUDENTS TOWARDS VCT SERVICES

The researcher sought to find out whether the economic situation of the students has an impact on the way they respond to the VCT services. The researcher sought to find out their monthly income, place of residence, whether they have access to television, radio or newspapers as a source of information and if they have heard of any VCT awareness campaign. &#x0D; The VCT facilities have not however been fully utilized especially by the youth. Based on this realization the study sought to determine the influence of socio-demographic factors on the response of college students towards VCT services. Purposive sampling was used to select three colleges to be used in the study out of nine major colleges. This being a descriptive research, a stratified random sampling design was appropriate. More female students were aware of the VCT services 82.6% as opposed to 68.2% male while less than half of the population 45.7% were aware of the location of this facility. The study concludes that the percentage of college students who are tested for HIV is relatively low considering the effort that has been made by the government, non-governmental organizations and other stakeholders in creating awareness on HIV/AIDS campaigns. One of the recommendations by the authors is that college students should be helped to deal with stigmatisation, fear and discriminations.
A blind watermarking algorithm based on SIFT and Its application in mobile police system

Digital watermarking, as an important information security technology, can play a dual role of evidence tracing and encryption in Mobile Police System. In the process of uploading photographic pictures, the poor connectivity and stability of wireless network will greatly affect the robustness of watermarking algorithm. In this study, we proposed a blind watermarking algorithm based on SIFT feature points meanwhile with the idea of spread spectrum.The algorithm extracts sufficient sub-blocks of the host image using feature points to achieve high invisibility and extends multiple times of the original watermark to achieves high robustness and security. Experiment results show that the invisibility and robustness of watermark by using SIFT feature points are better than several other feature extractors and the algorithm meets the application requirements of Mobile Police System.
Analysis of cryptocurrencies market: current situation, tendencies of household investment and future forecasting

Purpose – the purpose of this study is to analyse the cryptocurrency market and to forecast household investments in it from a theoretical and practical point of view. Research methodology – in order to achieve the aim of the article, scientific literature and statistical data analysis, comparative analysis and SWOT analysis were done. Also, ARIMA forecasting model was adapted to forecast Household investment in cryptocurrencies. Findings – the first part of the study presents the theoretical aspects of cryptocurrencies. An overview of the cryptocurrencies and their market is provided, focusing on the concept of Bitcoins, its’ mining and technical principles of operation. The second part of this study is intended to analyse the cryptocurrencies market and its dynamics. In the last part of the research tendencies of household investments in cryptocurrencies are examined for the period of 2016–2019 according to the data from cryptocurrency exchange. Also, household investments in cryptocurrencies are analysed and major future investment trends and seasonality are identified as well. Household future investment in cryptocurrencies is also predicted. Research limitations – data used in the research only includes currency exchange information, that may be inaccurate when concluding the main trends of the market. Practical implications – the practical results of the study may be useful for households interested in investing, especially in risky investment alternatives. Results of the research justify the dynamics of the currency market and the fluctuation of cryptocurrencies’ prices that shows that investment in cryptocurrencies is very variable, unstable and risky. Originality/Value – cryptocurrencies market is particularly dynamic and fast-changing, so the newest scientific investigations are urgent. In this research, an attempt was made to forecast household investment in main cryptocurrencies
Hybrid intelligence model on the second generation neural network

In latest beyond, it has been visualised in various neural computing applications that the hybridisation of computational intelligence techniques yields better results over traditional methods. This proposed research introduces a new composite version (hybrid) computational model in both real and complex domain that is a unique fusion of revised evolutionary fuzzy clustering along with correlated neural networks. The whole methodology is based on two type of working framework as fuzzy assessment framework and neural categoriser framework. In first framework, suggest styles are disbursed as according to the quantity of clusters on the basis of learning strategy like revised evolutionary fuzzy clustering for generalisation of various evaluated cluster patterns that is totally responsible for selection process of network structure. The second framework of the proposed strategy is to fully based on the correlated neural network evolving the processes of training and subsequent generalisation. The existing real domain analysis is based on first generation neural network (RVNN) and for the analysis of proposed model in second generation neural network (CVNN), Hilbert transformation is used that is available in MATLAB for converting the real datasets in complex form.
MON-249 Algorithm-Driven Electronic Health Record Notification Enhances the Detection of Turner Syndrome

BACKGROUND: Turner syndrome (TS) results from a complete or partial loss of the second X chromosome and affects 25-50 per 100,000 females. TS is common in females with unexplained short stature, but the diagnosis is often not made until late childhood (8-9 years) if classic features are not present or recognized in infancy. This results in delayed medical intervention and screening for comorbid conditions. The aim of our study was to determine if an electronic health record (EHR) notification model improves the timing and rate of detection of TS. METHODS/RESULTS:&nbsp; A search of the EHR was performed to identify a cohort of females with idiopathic short stature (ISS) who were seen in the endocrine clinic from 2012-2017. Selection criteria included a height ≤ -2 SD, BMI > 5%-ile, absence of chronic illness, and presence of mid-parental height (MPH) data, which yielded 216 patients. Given that a height deflection from MPH %-ile is one of the better predictors of TS1, we focused on those ≥ 1 SD below MPH %-ile. Of these 189 patients, 72 (38%) hadn’t received prior genetic testing. This group formed our study population and microarray analysis was performed on available samples to assess for undiagnosed TS or other chromosomal abnormalities. A total of 39 patient samples were prospectively recruited or obtained from an IRB approved biobank (including 5 TS controls). Microarray data was generated for 37 samples, as 2 had an insufficient quantity of DNA, and data was manually analyzed by a cytogeneticist. We identified two cases of undiagnosed TS (6%) and one with a different chromosomal abnormality (3%). One of these patients returned to endocrine clinic prior to microarray analysis and had a karyotype of 45,X/46,XY. The two other chromosomal abnormalities were picked up by microarray. One had mosaic monosomy X (45,X/46,XX). The other had a 2.7 Mb deletion from chromosome 1 (1q25.3->1q31.1) and a 2.1 Mb duplication from 22q11.21, which has been associated with short stature. CONCLUSIONS: The EHR algorithm was effective at identifying patients at risk for TS that merit genetic testing. Of the ISS females ≥ 1 SD below their MPH %-ile, 38% never had a karyotype done and on further investigation two new cases of TS (6%) and one other chromosomal abnormality (3%) were found. Although not all patients had DNA available for microarray analysis, if we assume that all other cases were negative, the rate of undiagnosed TS would still approach 3%. Extrapolating this data to a larger scale suggests that the diagnosis of TS may be delayed for many females with ISS, even after evaluation in endocrine clinics. We recommend the implementation of additional tools in the clinic workflow, such as EHR alerts based on specific growth parameters, to increase clinical suspicion and testing for TS. REFERENCES: 1. Grote, FK, et al (2008). Developing evidence-based guidelines for referral for short stature.&nbsp;Archives of Disease in Childhood,93(3), 212-217.
Microsoft Kinect Sensor Used to Capture Data for Robotics Applications

The process of capturing digital images has greatly evolved since the initial appearance of photography in general.In recent years, this evolution has been greatly accelerated by the development of high resolution and specialized digital capture sensors which, in turn, has opened the door for research to develop new products and algorithms allowing imaging to be used as input for controlling different other devices or robots.Still, for there to be a real mapping between a digital image and physical word a lot of research has been done in the field of algorithms and sensors, which have lately resulted in the emergence of affordable and specialized devices on the market like Microsoft Kinect or Motion Leap.Initially, the Microsoft Kinect device was exclusively used for the gaming industry, but later captured the attention of the research community, who quickly noticed that the sensor could be used as a very affordable alternative in the three-dimensional mapping process of space.Soon, an SDK was developed by PrimeSense (OpenNI), which allowed the sensor to be used for any other purpose, not just in the field of games.One of these opportunities is the use of the sensor in the field of image analysis for which a product to capture the movement of a human was developed and is presented in this paper along with a proposal to use the capture mechanism to command and control an industrial robotic arm.
Correction to: Fast Methods for Reducing Dimensionality of Spectral Data for Their Visualization

The third author's name is incorrect. The name should read D. N. Nikol′skii.
Data Analytics Service as a Vector of Development of Modern Libraries

The aim of the article is to consider the data analytics service as a leading trend in the development of modern libraries.Research methodology. An analysis of recent research and publications that deal with the issues of system operation, data analytics services in libraries shows that data analytics activities are inherent in the latter, both in terms of fulfilling the task of maintaining readers, and solving problems of increasing the efficiency of managing a separate library and the library industry as a whole. Results. The modern paradigm of information and library service is based not only on the use of funds of a particular library. It also plans to apply fundamentally new opportunities for access to information and knowledge, regardless of the time and location of both the document and the reader. In order to meet the information, educational and cultural needs of its users, the library today makes available not only documented knowledge, information stored in its funds. It provides access to information resources belonging to other subjects of the information space, including those presented on the Internet. On the other hand, it creates electronic information resources that are behind its physical walls, provides virtual services for finding information and necessary knowledge. A modern library institution turns into a center that concentrates and distributes information flows. Novelty. The process of institutionalization of data analytics activities, which lasts for the last few decades, could not but affect the theory and practice of organizing data analytics activities in the library. In particular, it is about the creation of special data analytics services and units, consideration and solution at the library level of the relevant organizational, theoretical and methodological, technological and other issues.The practical significance. A qualitative result of the data analytics activi-ties in the system of library and information provision of the society will help to reduce the obligatory information load on all business spheres, including ad-ministrative ones. First of all, it relates to overcoming the contradiction between the huge amount of intellectual and information resources circulating and stored in information systems, and the possibilities for the processing and use of knowledge. To overcome such controversy will promote the establishment of cooperation between administrative structures with the data analytics units of libraries, distribution of functions related to the processing of intellectual and information resources.
Fuzzy Rule-based System for Corruption Control in Nigerian Police Force

This paper has attempted to develop an artificial intelligence model based on fuzzy logic for the control of corruption in Nigerian Police Force. The researcher employed fuzzy rule-base inference system methodology using four inputs variables; funding, logistics and operational equipment (FLOE) condition of service, remuneration and motivation (CSRM), recruitment, training and promotion (RTP), confidence and support by the community (CSC) were used to determine the corruption severity level. An output variable; corruption severity level (CSL) was adopted for the model development. The simulation was carried out using MATLAB 2015 for Windows. The results revealed that it is very obvious for Nigeria as a country to have a Police Force whose corruption severity level is low. Thus (i) Condition of service, remunerations and motivation has to be excellent; (ii) Funding, logistics and operational equipment has to be adequate; (iii) Recruitment, training and promotion has to be excellent, and finally (iv) Confidence and support by the community has to be very high.
AN ADAPTIVE VARIATIONAL MODEL FOR MEDICAL IMAGES RESTORATION

Abstract. Image denoising is one of the important tasks required by medical imaging analysis. In this work, we investigate an adaptive variation model for medical images restoration. In the proposed model, we have used the first-order total variation combined with Laplacian regularizer to eliminate the staircase effect in the first-order TV model while preserve edges of object in the piecewise constant image. We also propose an instance of Split Bregman method to solve the proposed denoising model as an optimization problem. Experimental results from mixed Poisson-Gaussian noise are given to demonstrate that our proposed approach outperforms the related methods.
PARADIGMATICS OF OBJECTIFICATION OF THE ART TEXT IN CULTURE. «LITERATUROCENTRIZM». CENSORSHIP

In article the phenomenon of objectification of the work of art in culture, in particular in the period of «literaturocentrizm» is investigated. «The art thing» in the sphere of political culture is analyzed. An attempt to consider censorship not only, and not so much in biographic or historical measurements how many to define it as a certain constant of culture which has procedural character is made. The phenomenon of censorship is studied as certain «influence» on art «thing», on the sphere of the author. This work was supported by Grant RFFI №17-03-00613 «The Phenomenon of object in the information culture».
Space plant image segmentation based on generative adversarial network

The study of plants in space plays an important role in serving astronauts. Automatic segmentation of space plant image (SPI) provides an effective method for studying plants, and many plant segmentation methods have been proposed. However, segmentation of SPI is still challenging. Because the number of SPI is small, which greatly increases the difficulty in model training (especially deep-network-based model). For dealing with this problem, we propose a plant segmentation method based on a generative adversarial network. Our method consists of a generative network (GN) and a discriminant network (DN). The GN firstly extracts features from an input image, and then generates a feature map by developing multiple convolution and deconvolution layers. The DN merges the feature map with an actual plant image, and then computes a segmentation result by a deep convolutional network. In DN, the addition of the feature map improves segmentation accuracy of DN, and reduces the requirements of training images during training DN. Several experiments are made, and the experimental results show that our method performs well when a small number of training images is provided for model training.
An Image Enhancement Based CDVS Matching Algorithm

Compact Descriptor for Visual Search (CDVS) provides a standardized bitstream syntax for mobile image retrieval and matching applications. Although the standard CDVS algorithm has significant advances in the images with the good lighting condition, this approach often fails on dealing with the low-light images. A CDVS matching algorithm based on image enhancement is proposed. Firstly, a histogram equalization method is used for improving the quality of the pictures on low-light conditions to increase the matching number of the key points. Then, a same image enhancement method, which is mainly according to the homomorphic filtering, is adopt as the preprocessing for enhancing the contrast of the image with suppressing the low frequency signal and highlighting the high frequency signal to better extract the descriptors. Experimental results show that the proposed algorithm performs favorably against the standard CDVS algorithm on low-light images. Furthermore, the histogram equalization based CDVS matching algorithm has the best performance comparing with the homomorphic filtering method and the standard CDVS matching algorithm.