Reviewerly/exHarmony huggingface dataset:
A Novel Method for Cardiac Risk Assessment using Neural Networks

Introduction: While metrics of cardiac risk factors abound, they largely suffer from a common issue - reductionism.In this abstract, we propose a naive big-data approach to risk factor estimation. Utilizing a linguistic neural network trained on massive health insurance dataset, we attempt to learn the hidden grammar of heart disease. This tool is intended as a hypothesis generator, and a risk-analysis tool for population health.Hypothesis: Human bodies generate a certain amount of chaos (entropy) during our lifetime, as reflected in the time-series data captured in EMR systems or insurance claim data. We hypothesize that, by studying this data stream, we can distinguish between (a) relatively healthy patients, (b) patients that have suffered an acute, but treatable event, (c) patients with a managed chronic disease, and (d) patients at the end-of-life stage. We further hypothesize that, by training machine learning algorithms on entropy time-series data, we can predict certain types of health events with a lead time allowing for intervention and prevention.Methods: Utilizing Medicare Public Release file, we have compiled patient trajectories for n=600,000 patients who have been admitted to hospice due to a cardiac event, or suffered a cardiac event (MI or stroke) in the span of 36 months. By combining diagnostic (ICD-10), procedure (HCPCS) and prescription codes, we have built a set of “patient trajectories” corresponding to all events leading up to the acute event or hospice admission. An LSTM neural network was trained on 80% of these trajectories, learning “latent semantic graph” of cardiac treatment, as well as learning to reject irrelevant data. The remaining 20% of patient trajectories were used as a test set, with the neural network attempting to predict time of hospice admission, stroke or MI. Results: The neural network predicts the likelihood of hospice admission within 90 days with the f-score of 0.87%, and likelihood of MI or stroke within 90 days with f-score of 0.72%. Conclusions: While the current f-scores are insufficient for clinical use, this method is capable of long-term forecasting of cardiac risk on population level, and will evolve over time to allow for clinical interventions. This is a “clinically naive” technology that is entirely driven by the data. This is both an advantage and a disadvantage. On one hand, this tool can potentially alert clinicians and researchers to previously undetected risk factors; on the other hand it has likely learned existing clinical biases and may perpetuate bad decisions. We believe that this technology has a lot of potential, as well as many potential pitfalls. We welcome your feedback as the ideas and tools behind medical AI evolve.
An Insight Into Random Numbers & Applications in the Context of Signal Processing Using [ Imageai +python+qrng Libraries ] – a Simple Technical Note & Suggestion on Using QRNG Services/qrng Related Devices Towards Heterogeneous [ Iot/hpc] Image Processing

An Insight into Random Numbers & Applications in the Context of Signal Processing Using [ IMAGEAI +Python+qrng libraries ] – A Simple Technical Note & Suggestion on Using QRNG Services/QRNG Related Devices towards Heterogeneous [ IoT/HPC] Image Processing Software R&D. [ Exploring (QRNG+IMAGEAI) based R&D of Signal & Image Processing Software Using Python ]
The Effects of Self-Regulated Strategy Development(SRSD) on Reading Comprehension and Writing Competence for Students with Mild Intellectual Disabilities

본 연구는 자기조절전략을 활용한 읽기 및 쓰기교수가 경도지적장애 학생의 읽기이해와 쓰기능력에 미치는 효과를 알아보고자 하였다. 연구 대상은 경기도 안산시에 위치한 S 방과후센터에 있는 3명의 경도지적장애 중학생이며, 연구 설계로 대상자간 중다간헐기초선설계를 사용하였다. 본 중재는 주 3∽4회, 회기 당 50분씩 20회기 실시하였다. 실험효과를 알아보기 위해 연구자가 개발한 설명글 읽기이해 검사지를 활용하여 대상학생의 설명글 읽기이해 수준의 변화를 알아보았고 쓰기 평가는 읽은 지문을 참고하여 중심문장 및 세부내용을 찾아 지문에 대한 개요를 쓰도록 하고 그 결과물로 양적인 평가(어절 수, 중심문장 개수)와 질적인 평가를 실시하였다. 연구 결과는 다음과 같다. 첫째, 자기조절전략을 활용한 읽기 및 쓰기교수는 경도지적장애 중학생의 설명글 읽기이해 향상에 효과가 있다. 둘째, 자기조절전략을 활용한 읽기 및 쓰기교수는 경도지적장애 중학생의 쓰기능력의 양적 및 질적 향상에 효과가 있다. 셋째, 자기조절전략을 활용한 읽기 및 쓰기교수는 중재 후에도 경도지적장애 학생의 읽기이해 및 쓰기능력 유지에 긍정적인 영향을 미쳤다. 마지막으로 본 연구의 한계 및 시사점을 논하였다.This study investigated the effects of SRSD(Self-Regulated Strategy Development) reading and writing instruction on reading comprehension and writing competence for students with mild intellectual disabilities. Participants were three middle school students with mild intellectual disabilities. The experiment used multiple probe design across subjects and was carried out in the order of baseline, intervention and maintenance. Reading evaluation was based on participants’ use of SRSD strategies in reading an expository passage and solving the reading comprehension questions. Writing evaluation was based on participants’ use of SRSD strategies in writing an outline of the reading passage measured both quantitatively and qualitatively. The key findings of the experiment are as follows: (1) reading instruction with SRSD implementation was effective in improving reading comprehension for middles school students with mild intellectual disabilities, (2) SRSD can improve writing abilities for middle school students with mild intellectual disabilities both quantitatively and qualitatively and (3) the effects of intervention were maintained during the maintenance period following two weeks after the intervention. The implications and limitations of the present study are further discussed.
Sistem Informasi Geografis Pariwisata Dengan Integerasi Global Positioning System dan Algoritma Dijkstra

Geographic information system is a geospatial information system development of diverse data sources and heterogeneous includes news, events, routes and tourist attractions. The system provides information about the geographic location with integrated global positioning system (GPS). GPS is a system to determine the location on the earth's surface with the aid of the alignment of the satellite signal. GPS coordinates deci-sive point will be more complete if coupled with the use dijkstra algorithm to determine the shortest path to get to the tourist sites. Many tourist sites and have a short distance away from tourist spots and the other one is a problem because these sights are not known by the tourists who recently visited. In a case like this is necessary to travel guides which can provide information about the location - nearby tourist sites and pathways that can be bypassed by tourists. A cross between GPS and dijktra algorithms can be developed into a geographic information system based on Android that is interesting and informative in the field of tourism as a guide for tourists.
Truncated SVD-based Feature Engineering for Short Video Understanding and Recommendation

Short video app, like TikTok, has received wide acclaim due to the prevalence of social media and the availability of recording devices such as mobile phones. Moreover, with the advent of the big data age, the use of historical user behaviors from multi-modal resources plays a pivotal role in the video recommendation system. In the ICME 2019 Short Video Understanding Challenge, participants are asked to predict whether a user will finish and like a specific short video along with its multi-modal features, i.e., the problem is formulated as a click-through rate prediction task. In this paper, we present an ensemble of unconventional models to the task, including tailored neural networks structure based on Compressed Interaction Network (CIN) and Gradient Boosting Decision Trees (GDBTs) using classic SVD-based features. We achieved a weighted AUC score of 0.8029 and 0.8037 on the Public and Private Leaderboard of track2, respectively, and ended up with the 3rd place in the competition.
Semantics Images Synthesis and Resolution Refinement Using Generative Adversarial Networks

In this paper, we proposed a method to synthesizing a super-resolution image with the given image and text descriptions. Our work contains two parts. Wasserstein GAN is used to generate low-level resolution image under the guidance of a novel loss function. Then, a convolution net is followed to refine the resolution. This is an end-to-end network architecture. We have validated our model on Caltech-200 bird dataset, Oxford-102 flower dataset, and BSD300 dataset. The experiments show that the generated images not only match the given descriptions well but also maintain detailed features of original images with a higher resolution.
데이터베이스 자산의 가치평가를 위한 가치동인 분석 연구

Data can be considered a core driver of the Fourth Industrial Revolution, and databases are needed to create value by efficiently obtaining, storing...
Learning Technologies As A MethodOf Organization Of Pupil's Educational Activity

The urgency of the article is determined by the need to change the position of the pupil in the educational process in the conditions of a postindustrial society. The article contains an analysis of psychological and pedagogical studies of the modern education paradigm, in the context of which the activity of the pupil is a priority. Тhe analysis showed, that the majority of researchers associate the pupils activity in the educational process using pedagogical technologies. However, the pupil as a subject of the learning has the ability to organize his educational activity, if he has the skills of self-analysis, self-determination, self-planning, self-organization, self-control, self-assessment and self-correction, which compose the bases of the learning technology. The aim of the study is to characterize the learning technologies and to analyze their effectiveness in organizing the pupils educational activity. The methodological basis of the research is the person-centered approach. On the basis of this approach we developed the learning technologies "I want to learn", "I can learn", "I have skills to learn", which represent different methods of organization of the pupils educational activities. The main result of this study is as follows: on a sample of 450 respondents, the ability of pupils of different levels of subjectivity to experiment with the learning technology was experimentally proven.
Cytomorphological spectrum of thyroid lesions using Bethesda system of reporting with Histopathological correlation: A study of 87 cases

Cytomorphological spectrum of thyroid lesions using Bethesda system of reporting with Histopathological correlation: A study of 87 cases - IJPO- Print ISSN No: - 2394-6784 Online ISSN No:- 2394-6792 Article DOI No:- 10.18231/j.ijpo.2019.087, Indian Journal of Pathology and Oncology-Indian J Pathol Oncol
A Study on the Sense of Burden and Body Ownership on Virtual Slope

This paper provides insight into the burden when people are walking up and down slopes in a virtual environment (VE) while actually walking on a flat floor in the real environment (RE). In RE, we feel a physical load during walking uphill or downhill. To reproduce such physical load in the VE, we provided visual stimuli to users by changing their step length. In order to investigate how the stimuli affect a sense of burden and body ownership, we performed a user study where participants walked on slopes in the VE. We found that changing the step length has a significant impact on a burden on the user and less correlation between body ownership and step length.
Local and Outsourced Simultaneous Verification of Pairing-based Signatures

Many server-side applications require verifying a large number of digital signatures. In this paper, a practical problem of simultaneous verification of a large number of signatures (up to one hundred thousands) based on pairing-based cryptography is analysed. Based on three exemplary signature schemes, the options for outsourcing computationally intensive operations are presented together with a proposal of verification algorithms that outsource bilinear pairings computation and elliptic curve scalar multiplications. The experimental results from different scenarios of simultaneous verification of a large number of signatures are presented. The test scenarios include verification using a local server, using batch verification, by outsourcing computations to a trusted cloud and by secure outsourcing to possibly dishonest (untrusted) clouds.
Learning Single-View 3D Reconstruction with Limited Pose Supervision

It is expensive to label images with 3D structure or precise camera pose. Yet, this is precisely the kind of annotation required to train single-view 3D reconstruction models. In contrast, unlabeled images or images with just category labels are easy to acquire, but few current models can use this weak supervision. We present a unified framework that can combine both types of supervision: a small amount of camera pose annotations are used to enforce pose-invariance and view-point consistency, and unlabeled images combined with an adversarial loss are used to enforce the realism of rendered, generated models. We use this unified framework to measure the impact of each form of supervision in three paradigms: semi-supervised, multi-task, and transfer learning. We show that with a combination of these ideas, we can train single-view reconstruction models that improve up to 7 points in performance (AP) when using only 1% pose annotated training data.
Text Mining with HathiTrust

This tutorial will introduce attendees to the HathiTrust Research Center and its tools and services for computational text analysis research. HTRC leverages the scope and scale of the HathiTrust Digital Library collection to create opportunities for researchers to perform text data mining on subsets of the corpus. Attendees will develop skills that will allow them to conduct basic text analysis research using HathiTrust data, learn techniques for engaging with scholars on their text data mining research, and explore strategies for computational access to large-scale digital collections.
The higher reading interest of pregnant mother improves the utilization of MCH book

The utilization of MCH book could be defined to be an activity when a mother and her close relatives actively participate in reading, learning, comprehending the contents of MCH book, and implementing it in daily routine. This research aims to discover the correlation between the reading interest of pregnant mothers and the use of MCH book. This study has been approved by the research ethics committee of Yogyakarta ‘Aisyiyah University. This cross-sectional research was performed at the Community Health Centre of Mlati II Sleman Yogyakarta. Seventy-four pregnant mothers who owned MCH book were derived from incidental sampling technic. The retrieval of primary data used questioner method. The result of chi-square test states that pregnant mothers with higher reading interest will be utilizing the MCH book four times more effective compared to those with lower interest in reading (p-value: 0,005 and 95% CI: 1,488-10,902).
Reading Aviation Wire Text in Natural Images under Assembly Workshop via Deeplearning

Abstract Reading aviation wire text is an important step for aircraft wire assembly and also a challenging problem. Aviation wire text reading means to detect and recognize the serial number printed on the surface of aviation wires or aircraft cables. The main challenges of this task lie on small sizes, low contrast and background noise in images. In this paper, we propose a new method for aviation wire text detection and recognition via deep neural networks. The detection network utilizes text border features to roughly locate text regions in images. Then the coordinates of bounding boxes of texts are regressed by pixel-level predicted offsets in adjacent regions. The recognition network is capable to recognize the text regions with diverse lengths after padding strategies. In addition, a dataset of aviation wires captured by machine vision camera in the assembly workshop is developed. A flexible method to generate synthetic images of text whose characters are arranged in styles of both head-to-tail and left-to-right is presented. On aviation wire datasets, the proposed method for text detection and recognition achieves comparable performance and significantly outperforms previous approaches.
CD10 expression by stromal cells in carcinoma of breast and its correlation with ER, PR, HER2neu and Ki67-A tissue microarray study in a tertiary care hospital

CD10 expression by stromal cells in carcinoma of breast and its correlation with ER, PR, HER2neu and Ki67-A tissue microarray study in a tertiary care hospital - IJPO- Print ISSN No: - 2394-6784 Online ISSN No:- 2394-6792 Article DOI No:- 10.18231/j.ijpo.2019.085, Indian Journal of Pathology and Oncology-Indian J Patho
Self-Confidence Measures of a Decision Support System Based on Bayesian Networks

A prominent formalism used in decision support is decision theory, which relies on probability theory to model uncertainty about unknown information. A decision support system relying on this theory produces conditional probability as a response. The quality of a decision support system's response depends on three key factors: the amount of data available to train the model, the amount of information about the case at hand, and the adequacy of the system's model to the case at hand.In this dissertation, I investigate different approaches to measuring the confidence of decision support systems based on Bayesian networks addressing the three key factors mentioned above. Some of such confidence measures of the system response have been already proposed. I propose and discuss other measures based on analysis of joint probability distribution encoded by a Bayesian network. The main contribution of this dissertation is the analysis of the discussed measures whether they provide useful information about the performance of a Bayesian network model. I start the analysis with an investigation of interactions among these measures. Then, I investigate whether confidence measures help us predict an erroneous response of a classifier based on Bayesian networks when applied to a particular case. The results suggest that the discussed measures may be considered as indicators for possible mistakes in classification. Further, I conduct an experiment to check how confidence measures perform in combining the models' output in the ensemble of classifiers by weighting.Based on the findings presented in this dissertation, I conclude that the confidence measures may enrich the decision support system's output to serve as indicators for applicability of the model and its advice to a given case.
A New Approach to Cross-Modal Retrieval

The semantic correlation matching algorithm, which attempts to integrate semantic information based on the unified space obtained by CCA learning, achieves an improvement in the effect of cross-modal retrieval. However, there is still a lot of room for the algorithm to make further progress in the generation of shared subspace. This paper proposes a semantic correlation matching algorithm based on the single-layer feed forward network. It generates features through neural networks to represent weight and bias. Meanwhile, it maximizes the correlation between different modalities by means of cross-coupling. The experimental results show that the algorithm proposed in this paper is superior to the compared method in terms of cross-modal retrieval task of image-text and has better cross-modal retrieval ability.
Impact of Factors Causing Internal Conflict on the Effectiveness of Managing and Handling Conflict in the Jordanian Banks

The purpose of this paper is to investigate whether the factors causing internal conflict affect the effectiveness of the methods used in handling these conflicts. The main instruments of research used in this study were the survey questionnaire. Data gathered from the (192) respondents were statistically treated and analyzed using arithmetic weighted mean, it was interpreted based on the Likert scale, in testing the hypotheses multiple regression was used. The results showed that the following causes of conflict, task interdependencies, different goals of subunits, bureaucratic factors, communication problems, competition for scarce resources, differences in the performance standards, and differentiation were noted to be linked to the effectiveness of the conflict handling methods. Management, departments, units, division's heads, and team leaders of the Jordanian commercial banks favor resolving conflicts. It was found that, regardless of what causes conflicts, they were able to contain and resolve conflicts, using all different styles and modes of conflict handling methods. The result of the multiple regression test revealed that there was a significant positive effect of the factors causing conflict on all different styles and techniques, used in handling conflicts. It was concluded that avoidance style, would not resolve the conflict, and it might negatively affect the behavior of individuals, so it should be averted. Using the collaborating mode would encourage positive interactions among members of the organization, resulting in a more cooperative working environment. The compromising method should be used in a way that will satisfy all conflicting parties, to ensure that, the focus would be on achieving goals of the entire organization.
Designing and developing an augmented reality book: A sample Malayu subject Nurulhusna Abdullatif*

This research was aimed to develop an Augmented Reality Book on the topic of Malayu Alphabets for primary students. It studies the students’ satisfaction towards augmented reality 3D book. The samples were 23 primary students. The research instruments were augmented reality 3D book on the topic Malayu alphabets and the satisfaction questionnaire of students towards the augmented reality 3D book.
One-Click to Content: What LibKey Means for the Future of Discovery Services, Libraries, and Users

In 2011, authors of the influential article “Shapes in the Cloud” wrote, “The barometer of user expectations for searching library resources has been defined by Google.” Today, user expectations have evolved so this sentiment can be restated as “The barometer of user expectations for access library resources has been defined by SciHub.”Users expect the process of accessing full text to be fast, seamless and intuitive. Over the past year, hundreds of libraries have integrated LibKey APIs from Third Iron into Primo, Summon and other discovery environments to deliver one-click access to millions of articles and more. This session reports on how these integrations are being used, what the data shows about their use and how simplifying access to content can be extended in new ways.
On Geometric Properties of the Textile Set and Strict Textile Set

The textile plot is a tool for data visualisation proposed by Kumasaka and Shibata (2008). The textile set is a geometric object constructed to understand the textile plot outputs. In this study, we find additional facts on a proper subset called the strict textile set. Furthermore, we investigate differential and analytical geometric properties of the textile set.