Reviewerly/exHarmony huggingface dataset:
A Robust Blockchain Framework for Healthcare Information System

Nearly every organization needs solution to handle sensitive data. Though there are different systems to store and transmit electronic records, users often deal with missing or tampered information when they distribute it in a public network. When the data is distributed, the traceability and governance of it throughout the globe is impractical. The evolving world of Data Security demands healthcare industry to have an infrastructure in place to deal with patient records. Regardless of the nature of present and future security risks, Blockchain Technology will play a vital role to maintain the originality of the digital data. Blockchain stores data in a decentralized, distributed ledger for achieving immutability and transparency. The system analyses a case study which aims at maintaining Healthcare records in Blockchain ledger to facilitate secure storage of the patient’s details. For efficient prediction of diabetes the Blockchain and machine learning algorithms are integrated. The various other applications of combining Blockchain technology with machine learning algorithms are Smart city applications, Forensic information storage systems, Energy data management systems, banking and finance management systems.
Impact of Distance Learning on Reading and Writing Ability in Elementary School Students

Online learning is a new challenge for teachers and students, there must be many positive and negative impacts that occur, especially on the ability to read and write. This study aims to describe the impact of the implementation of online learning on the literacy skills of elementary school students based on previous studies and direct surveys of elementary school students to find out what impacts occur on students' literacy skills when learning is carried out online, both it's a positive or negative impact. This study uses a qualitative method with a descriptive design, while the research results are taken in the form of a one-group pre-test and post-test design. The results of this study showed an increase after being given material in the form of learning videos, but the role of the teacher during this online learning period was very important to continue to provide guidance and direct students so that their reading and writing skills remained good.
A Gibbs sampler for learning DAG: a unification for discrete and Gaussian domains

One of the major challenges in modern day statistics is to formulate models and develop inferential procedures to understand the complex multivariate relationships present in high-dimensional datasets. In this paper, we address the issue of model determination for DAGs, with respect to a given ordering of the variables, together with the corresponding parameter estimation. For this, we use a hierarchical mixture prior and develop a Gibbs sampling algorithm to carry out the posterior computations. We first focus on the Gaussian DAG models and calculate the posterior probability of being the edge between two nodes. We then extend our idea to construct a DAG for discrete data under the assumption that the data generated by discretization of the marginal distributions of a latent multivariate Gaussian distribution via a set of predetermined threshold values. Results show that the proposed method has high accuracy. The source code is available at http://bs.ipm.ac.ir/softwares/Gibbs/code.rar
Gender Prediction Based on Morphometry of Eyes Using Deep Learning Models

In the modern days, the growth of online social networking websites and social media leads to an increasing adoption of computer-aided image recognition systems that automatically recognize and classify human subjects. One such familiar one is the anthropometric analysis of the human face that performs craniofacial plastic and reconstructive surgeries. To analyze the impact on facial anthropometrics, it is also essential to consider various factors such as age, gender, ethnicity, socioeconomic status, environment, and region. The repair and reconstruction of facial deformities to find the anatomical dimensions of the facial structures as used by plastic surgeons for their surgeries result from the physical or facial appearance of an individual. Gender classification plays an important role of identifying the person as either male or female using biometric images. The main goal is to interact with the system, so that gender differences are produced effectively and accurately. Hence, it is essential to select the features optimally to achieve better accuracy. Data mining or machine learning techniques can be useful to infer properties such as the gender or age of the people involved to analyze the human activities. Towards this end, the proposed work focuses on gender recognition thereby building a model to scan the eye image of a patient and determine if the gender of the patient is either male or female by applying deep learning methods. It is identified from the work that deep learning network yields a better performance for gender based classification based on the morphometry of eyes.
Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition

One of the foremost challenges of using Deep Neural Network-based methods for a fully automated segmentation in clinics is the lack of performance guarantee. In the foreseeable future, a feasible and promising way is that radiologists sign off the machine’s segmentation results and make corrections if needed. As a result, the human effort for image segmentation that we try to minimize will be dominated by segmentation correction. While such effort can be reduced by the advance of segmentation models, for ultrasound a novel direction can be explored: optimizing the data acquisition. We observe a substantial variation of segmentation quality among repetitive scans of the same subject even if they all have high visual quality. Based on this observation, we propose a framework to help sonographers obtain ultrasound videos that not only meet the existing quality standard but also result in better segmentation results. The promising result demonstrates the feasibility of optimizing the data acquisition for efficient human-machine collaboration.
Cyber-security Risks Assessments by External Auditors

The rise in cybercriminal activities in recent years has sparked concern about the costs of technological advancement and the growing reliance of humans on technology.The seriousness of this situation in the business world is indeed more noteworthy and more prominent than other areas, prompting many people to wonder how external auditorswho are responsible for identifying any accounting flawswill respond to cybersecurity-affected businessesthe ones which can make an honest effort to mask and conceal their difficulties and challenges from their investors and stakeholders.Consequently, the aim of this study is to search whether external auditors focus harder on cybersecurity-attacked firms and businesses by charging higher audit fees.The study found a positive correlation between audit fees and breach employing a sample of 100 global small-, medium-sized, and large businesses.This indicates that external auditors find more risks and spend more effort while auditing cybersecurity-attacked businesses.
Youth Unemployment and Productivity-Pay in the GCC Countries

Although the total youth unemployment rate in the Gulf Cooperation Council countries is less than in other high-income countries, the female youth unemployment rate is more than quadruple the male youth unemployment rate compared to equal rates in high-income countries. The gender bias in youth unemployment is attributable to the generous social contract GCC nationals enjoy as well as the largely conservative GCC culture that perceives a more important role of women in the household than in the job market. The generous social contract is also a key factor in the duality of the GCC labor markets with one segment for national labor and the other for foreign labor. Foreign labor constitutes most of the labor force, and the link between pay and productivity is strong in the foreign labor segment indicating labor market efficiency. This chapter investigates whether the presence of strong pay-productivity links in both labor market segments reduces the national youth unemployment rates in the GCC countries. Empirical evidence shows that linking pay to productivity robustly reduces the total and female youth unemployment rates. The influence is strongest on the female youth unemployment rate, however. Productivity-pay helps reduce youth unemployment and the associated gender bias.
A Method of Infrared Image Pedestrian Detection with Improved YOLOv3 Algorithm

The principle of infrared image is thermal imaging technology.Infrared pedestrian detection technology can be applied to the safety monitoring of the elderly, which can not only protect personal privacy, but also realize pedestrian identification at night, which has strong application value and social significance.A method of infrared image pedestrian detection with improved YOLOv3 algorithm is proposed to increase the detection accuracy and solve the problem of low detection accuracy caused by infrared pedestrian target edge blurring.And according to the characteristics of infrared pedestrian, a complex sample data set is established which is applied to infrared pedestrian detection.The infrared image enhancement method with WDSR-B is adopted to improve the clarity of the data set.In addition, based on YOLOv3 algorithm, the output of the 4-time down-sampling layer is added to obtain richer context information for small targets and improve the detection performance of the network for small-target pedestrians.And the improved YOLOv3 network is trained by the enhanced infrared data set.Experimental results show that the scheme precision of pedestrian detection is higher than that of YOLOv3 algorithm.Therefore, this method can be applied to the detection of pedestrians at night and the safety monitoring of the elderly.
PENGARUH PENGGUNAAN MEDIA FOCUSKY TERHADAP HASIL BELAJAR SISWA PADA SUBMATERI PEMISAHAN CAMPURAN

This research is based on the problems experienced in schools, namely the problems of the learning media used. This research uses a quasi-experimental form, namely a quasi-experimental design research and the design used is a non-equivalent control group design. This study was to determine the comparison of learning outcomes between classes using Focusky media and classes using PowerPoint media at SMP Negeri 21 Pontianak in Mixed Separation SubMaterial and to know the impact of using Focusky Media on the learning outcomes of seventh grade students at SMP Negeri 21 Pontianak. The research sample was 57 students consisting of 28 students in class VII B, namely the control class and 29 students in class D, the experimental class. Based on the results obtained, the value of the experimental class is greater than the control class. For statistical analysis, the pretest data used a t-test and showed that there were still differences in the pretest results in the two classes with a sig. (2- tailed) value of 0.001 so that the N-Gain test was carried out, obtained a value of 0.48 and was in the medium category. The effect size value shows a value of 46, 78, the results are categorized as moderate so that Focusky media-assisted learning is efficient in increasing student learning outcomes.
Power-Expected-Posterior Priors as Mixtures of g-Priors in Normal Linear Models

One of the main approaches used to construct prior distributions for objective Bayes methods is the concept of random imaginary observations. Under this setup, the expected-posterior prior (EPP) offers several advantages, among which it has a nice and simple interpretation and provides an effective way to establish compatibility of priors among models. In this paper, we study the power-expected-posterior prior as a generalization to the EPP in objective Bayesian model selection under normal linear models. We prove that it can be represented as a mixture of g-prior, like a wide range of prior distributions under normal linear models, and thus posterior distributions and Bayes factors are derived in closed form, keeping therefore its computational tractability. Following this result, we can naturally prove that desiderata (criteria for objective Bayesian model comparison) hold for the PEP prior. Comparisons with other mixtures of g-prior are made and results are presented in simulated and real-life datasets.
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data

Recently, there is great interest to investigate the application of deep learning models for the prediction of clinical events using electronic health records (EHR) data. In EHR data, a patient's history is often represented as a sequence of visits, and each visit contains multiple events. As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models. While a large variety of RNN models were proposed in the literature, it is unclear if complex architecture innovations will offer superior predictive performance. In order to move this field forward, a rigorous evaluation of various methods is needed. In this study, we conducted a thorough benchmark of RNN architectures in modeling EHR data. We used two prediction tasks: the risk for developing heart failure and the risk of early readmission for inpatient hospitalization. We found that simple gated RNN models, including GRUs and LSTMs, often offer competitive results when properly tuned with Bayesian Optimization, which is in line with similar to findings in the natural language processing (NLP) domain. For reproducibility, Our codebase is shared at https://github.com/ZhiGroup/pytorch_ehr.
Learning the Markov Decision Process in the Sparse Gaussian Elimination

We propose a learning-based approach for the sparse Gaussian Elimination. There are many hard combinatorial optimization problems in modern sparse solver. These NP-hard problems could be handled in the framework of Markov Decision Process, especially the Q-Learning technique. We proposed some Q-Learning algorithms for the main modules of sparse solver: minimum degree ordering, task scheduling and adaptive pivoting. Finally, we recast the sparse solver into the framework of Q-Learning. Our study is the first step to connect these two classical mathematical models: Gaussian Elimination and Markov Decision Process. Our learning-based algorithm could help improve the performance of sparse solver, which has been verified in some numerical experiments.
PENGGUNAAN MEDIA PEMBELAJARAN DALAM PENGAJARAN BAHASA INDONESIA

The purpose of this study is to describe "Use of Learning Media in Teaching Acepte Indonesian". This type of research is conceptual research. Based on the discussion, it can be concluded 5 things, namely (1) learning media are tools, methods, and techniques used to assist the learning process; (2) the benefits of learning media are to simplify and clarify and make learning more interesting; (3) the function of learning media is as a tool for both teachers and students to achieve targeted learning outcomes; (4) the types of learning media are divided into three, namely media in terms of their nature, in terms of their reach, and in terms of their use; (5) The use of media in teaching Indonesian is related to the material and learning objectives to be achieved, also associated with the availability of facilities and infrastructure in schools or those that can be used by teachers to support an effective and targeted learning process.
A STUDY ON THE TEACHING AND LEARNING PROCESS OF READING SKILL AT MATHAYUM ISLAM BACHOK WITTAYA SCHOOL THAILAND

The aims of this study are (1) to find out how reading instruction is prepared by the English teachers of Mathayum Islam Bachok Wittaya School, (2) to find out how the reading instruction is applied by the English teachers of Mathayum Islam Bachok Wittaya School, and (3) to find out how the English teachers Mathayum Islam Bachok Wittaya School evaluate the instruction of reading. This study belongs to qualitative research because only describes the situation and condition of applying reading instruction at the school. Since the number of population of this study is only two teachers so it should be taken as sample and it is named data source. The instruments that are used to collect data are observation, interview, and documentation. The data gotten was analyzed by narration procedure and discussed. The result of study shows the teachers usually apply cooperative learning “The Students Team Achievement Divisions” (STAD) in reading instruction (teaching learning process of reading). Using various methods or techniques aim to make the students not bored. The instructional system of reading skill that the English teachers of Mathayum Islam Bachok Wittaya School apply is effective. Based on the result of study, the English teachers of Mathayum Islam Bachok Wittaya School are suggested to take the students out class in learning reading because this way makes the students relax.
Argus: A centralized control system for preserving privacy during digital forensics investigations

Abstract In criminal investigation, the ability to verify the identity of an individual (e.g., suspect or witness) is crucial. However, evidence including personal data can potentially infringe the individual's privacy. To avoid privacy infringement, data collectors in investigative agencies are required to have approval to access personal data from data subjects, but it is hard to check personal data policies of various data subjects. It is necessary to establish a centralized system to control personal data policies. In this paper, we propose the personal data control center to avoid to infringe privacy for data collectors during investigation. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation
Generic Event Boundary Detection: A Benchmark for Event Segmentation

This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. Conventional work in temporal video segmentation and action detection focuses on localizing pre-defined action categories and thus does not scale to generic videos. Cognitive Science has known since last century that humans consistently segment videos into meaningful temporal chunks. This segmentation happens naturally, without pre-defined event categories and without being explicitly asked to do so. Here, we repeat these cognitive experiments on mainstream CV datasets; with our novel annotation guideline which addresses the complexities of taxonomy-free event boundary annotation, we introduce the task of Generic Event Boundary Detection (GEBD) and the new benchmark Kinetics-GEBD. Our Kinetics-GEBD has the largest number of boundaries (e.g. 32x of ActivityNet, 8x of EPIC-Kitchens-100) which are in-the-wild, open-vocabulary, cover generic event change, and respect human perception diversity. We view GEBD as an important stepping stone towards understanding the video as a whole, and believe it has been previously neglected due to a lack of proper task definition and annotations. Through experiment and human study we demonstrate the value of the annotations. Further, we benchmark supervised and un-supervised GEBD approaches on the TAPOS dataset and our Kinetics-GEBD, together with method design explorations that suggest future directions. We release our annotations and baseline codes at CVPR'21 LOVEU Challenge: this https URL
Modal context restriction for multiagent BDI logics

Abstract We present and discuss a novel language restriction for modal logics for multiagent systems, called modal context restriction, that reduces the complexity of the satisfiability problem from EXPTIME complete to NPTIME complete. We focus on BDI multimodal logics that contain fix-point modalities like common beliefs and mutual intentions together with realism and introspection axioms. We show how this combination of modalities and axioms affects complexity of the satisfiability problem and how it can be reduced by restricting the modal context of formulas.
THE EFFECT OF ANXIETY TOWARD STUDENTS’ LEARNING MOTIVATION OF THE ELEVENTH GRADE AT SMAN 4 PALU

The objective of this research was to investigate the effect of anxiety toward students’ learning motivation of the eleventh grade at SMAN 4 Palu in the academic year 2019- 2020. This research used as a survey quantitative research method. The population of this research was conducted for the eleventh-grade students of SMAN 4 Palu with consisted of 50 students, who used purposive sampling with took 2 classes as the samples to encourage representative data. In the procedure of collecting data, the researcher used one of instrument is questionnaire. The questionnaire used to encourage the effect of anxiety and the motivation in learning English of the students. The researcher was giving students a questionnaire using WhatsApp. The questionnaire in Google form, so students just open and checklist their answer and send. The result of the questionnaire has shown there was no significant effect of anxiety toward students’ learning motivation. This result of Pearson Product Moment table, which has shown were scores of Sigs between effect Anxiety and Motivation was 0,450 or more than 0,05. It means there was no effect of anxiety toward motivation. The researcher concluding the effect of anxiety toward students' learning motivation was normal.
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation

Deep neural networks (DNNs) for the semantic segmentation of images are usually trained to operate on a predefined closed set of object classes. This is in contrast to the "open world" setting where DNNs are envisioned to be deployed to. From a functional safety point of view, the ability to detect so-called "out-of-distribution" (OoD) samples, i.e., objects outside of a DNN's semantic space, is crucial for many applications such as automated driving. A natural baseline approach to OoD detection is to threshold on the pixel-wise softmax entropy. We present a two-step procedure that significantly improves that approach. Firstly, we utilize samples from the COCO dataset as OoD proxy and introduce a second training objective to maximize the softmax entropy on these samples. Starting from pretrained semantic segmentation networks we re-train a number of DNNs on different in-distribution datasets and consistently observe improved OoD detection performance when evaluating on completely disjoint OoD datasets. Secondly, we perform a transparent post-processing step to discard false positive OoD samples by so-called "meta classification". To this end, we apply linear models to a set of hand-crafted metrics derived from the DNN's softmax probabilities. In our experiments we consistently observe a clear additional gain in OoD detection performance, cutting down the number of detection errors by up to 52% when comparing the best baseline with our results. We achieve this improvement sacrificing only marginally in original segmentation performance. Therefore, our method contributes to safer DNNs with more reliable overall system performance.