Reviewerly/exHarmony huggingface dataset:
KORELASI KETERAMPILAN MEMBACA PEMAHAMAN TEKS BERITA DENGAN KETERAMPILAN MENULIS TEKS BERITA SISWA KELAS VIII SMP NEGERI 18 PADANG

ABSTRACT The purpose of this research there are three. First, it describes the skills of reading comprehension text news grade VIII SMP Negeri Padang 18. Second, the governing text news writing skills of students of class VIII SMP Negeri Padang 18. Third, to analyze the correlation between the skills of reading comprehension texts with news writing skills text news grade VIII SMP Negeri Padang 18. Type of this research is a descriptive method quantitative research. The research design used was the korelasional relationship of two variables. The population of this research grade VIII SMP Negeri Padang 18 listed on the 2018/2019 school year with a total of 168 students scattered in five classes. The sample in this study amounted to 40 people. Sample taken using a simple random technique with a precision of 15% using simple random sampling. Research instrument this is a test performance, namely, test write text news and objective tests the skills of reading comprehension text news. Based on the data analysis and discussion, obtained three of the following. First, the skills of reading comprehension text news grade VIII SMP Negeri Padang is at 18 qualifying well. It is inferred based on the average count of 82.56. Secondly, the text of the news writing skills of students of class VIII SMP Negeri 18 are on good qualification. This can be seen from the average earnings to calculate a test write text news of 79.17. Third, there is a significant relationship between the relationship skills reading comprehension texts with news writing skills text news have a relationship on a significant level 95% with dk = n-1. Based on test-t accepted hypothesis, because thitung &gt; t table i.e. 2,509 &gt; 1.68. Kata kunci: Membaca Pemahaman, Teks Berita
Prediction Model of Financial Income of Listed Companies Based on Grey Model

The traditional financial income prediction model of listed companies is not accurate enough for the medium and long-term prediction results, so a grey model based on the financial income prediction model of listed companies is designed. First of all, the risk prediction ability of this model is improved by accumulating the risk statistical model and learning the risk model. In the case application analysis, we use the designed prediction model and the traditional model to predict the financial income of a company. The experimental results show that the results of the designed prediction model in the long-term forecast are closer to the actual situation.
Information and Communication Technologies Usage among Agricultural Students of Tertiary Institutions in Southwest of Nigeria

The study examined information and communication technologies usage among agricultural students of tertiary institution in Southwest of Nigeria. Multistage sampling procedure was used to select 300 agricultural students from tertiary institutions. Structured questionnaire was employed to elicit information on students’ socioeconomic characteristics, availability of ICTs, accessibility and purposes of ICTs usage. Data were subjected to percentage, chi-square, the Pearson Product Moment Correlation and the Analysis of variance at p=0.05. Findings revealed that the most available ICTs among students were internet and mobile phone (95.0%) respectivelywhile they had access to mobile phone (Weighted Mean (WM)=0.58) and radio (WM=0.51), they used ICTs for information (WM=0.77), academics (WM=0.70) and entertainment (WM=0.70) purposes; with high level of availability (71.0%), access (56.7%) and ICTs usage purpose (51.3%) respectively. There were significant relationships between respondents’ monthly stipend (r=0.200), state of institution (χ2=8.000), religion (χ2=222.660), marital status (χ2=211.680), category of students’ programme (χ2= 320.4608), type of institution (χ2=56.000) and their purposes for using ICTs but with a significant difference across the states (F=23.821; p=0.00). Students used ICTs for information, academic and entertainment purposes based on their age and type of their institutions. Since ICTs are used for beneficial purposes, it is recommended that ICTs should be adapted, applied and incorporated into tertiary institutions’ curricula across the nation.&#x0D; Keywords: ICTs usage, undergraduate students, entertainment purposes, mobile phone, tertiary institutions.
CAPTCHA recognition based on a generative-adversarial network

CAPTCHA recognition is certainly not a new research topic. Over the past decade, researchers have demonstrated various ways to automatically recognize text-based CAPTCHAs. However, in such methods, the recognition setup requires a large participation of experts and carries a laborious process of collecting and marking data. This article presents a general, low-cost, but effective approach to automatically solving text-based CAPTCHAs based on deep learning. This approach is based on the architecture of a generative-competitive network, which will significantly reduce the number of real required CAPTCHAs.
Property Business Classification Model Based on Indonesia E-Commerce Data

Online property business or known as e-commerce is currently experiencing an increase in home sales. Indonesia's e-commerce property business has positive trending shown by the increasing sales of more than 500% from 2011 to 2015. A prediction of the property price is important to help investors or the public to have accurate information before buying property. One of the methods for prediction is a classification based on several distinctive property industry attributes, such as building size, land size, number of rooms, and location. Today, data is easily obtained, there are many open data from E-commerce sites. E-commerce contains information about homes and other properties advertised to sell. People also regularly visit the site to find the right property or to sell the property using price information which collectively available as open data. To predict the property sales, this research employed two different classification methods in Data Mining which are Decision Tree and k-NN classification. We compare which model classification is better to predict property price and their attributes. We use Indonesia's biggest property-based e-commerce site Rumah123.com as our open data source, and choose location Bandung in our experiment. The accuracy result of the decision tree is 75% and KNN is 71%, other than that k-NN can explore more data patterns than the Decision Tree.
Web based Human Resource Information System for Skill-set based Academic Training Process

Combat Vehicle Research & Development Establishment (CVRDE) is a centre for Design & Development of state -ofthe -art
Postmodernism Creates Innovative English Teaching Learning by Utilizing ICT

Through comprehensive literature review and an examination of both Postmodernism and ICT, it is proposed that the influences of ICT have acted and continued to act to promote Postmodernism. The development of ICT is utilized by education field for teaching learning process. Many teachers and students utilize the existing of ICT as model of teaching and learning especially English. This study discussed about teachers’ belief and students’ belief in using ICT to have innovative English teaching learning. Qualitative descriptive was used in this study. The data was collected by conducting the interview. The participants were five English teachers and five students that chose randomly. The result showed that both teachers’ and students’ belief in using ICT is to make English teaching learning easier, more interesting, and effective. Keywords:Postmodernism,ICT, teachers’ belief, students’ belief
Understanding the input-output relationship of neural networks in the time series forecasting radon levels at Canfranc Underground Laboratory

Underground physics experiments such as dark matter direct detection need to keep control of the background contribution. Hosting these experiments in underground facilities helps to minimize certain background sources such as the cosmic rays. One of the largest remaining background sources is the radon emanated from the rocks enclosing the research facility. The radon particles could be deposited inside the detectors when they are opened to perform the maintenance operations. Therefore, forecasting the radon levels is a crucial task in an attempt to schedule the maintenance operations when radon level is minimum. In the past, deep learning models have been implemented to forecast the radon time series at the Canfranc Underground Laboratory (LSC), in Spain, with satisfactory results. When forecasting time series, the past values of the time series are taken as input variables. The present work focuses on understanding the relative contribution of these input variables to the predictions generated by neural networks. The results allow us to understand how the predictions of the time series depend on the input variables. These results may be used to build better predictors in the future.
UserReg: A Simple but Strong Model for Rating Prediction

Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a variety of recommendation tasks, such as rating prediction and item ranking. These newly published models usually demonstrate their performance in comparison to baselines or existing models in terms of accuracy improvements. However, others have pointed out that many newly proposed models are not as strong as expected and are outperformed by very simple baselines. This paper proposes a simple linear model based on Matrix Factorization (MF), called UserReg, which regularizes users' latent representations with explicit feedback information for rating prediction. We compare the effectiveness of UserReg with three linear CF models that are widely-used as baselines, and with a set of recently proposed complex models that are based on deep learning or graph techniques. Experimental results show that UserReg achieves overall better performance than the fine-tuned baselines considered and is highly competitive when compared with other recently proposed models. We conclude that UserReg can be used as a strong baseline for future CF research.
DP-AP: Differential Privacy-Preserving Affinity Propagation Clustering

The affinity propagation (AP) clustering can accurately and effectively process complex data and has been widely used in various data clustering analysis fields, especially in computer vision and computational biology. However, the dataset usually contains sensitive information. To prevent the leakage of users' privacy during the clustering process, this paper proposes an affinity propagation clustering with differential privacy algorithm. Based on the affinity propagation clustering, we first adjust the corresponding preference value by calculating the density value of each sample and then add Laplace noise to the responsibility matrix to protect the data privacy. Experimental results show that the adjusted preference value with the sample density can effectively reduce the running time of the algorithm and the loss of data accuracy caused by differential privacy.
Unbalanaced Ranked Set Sampling and its Application in Medical Science

Rank set sampling is a sampling procedure that can be considerably more efficient than simple random sampling.Although ranking processes for continuous variables that are implemented through either subjective judgment or via the use of a concomitant variable have been studies extensively in the literature, the use of RSS in the case of binary variables has not been investigated thoroughly.We use a National Health and Nutrition Examination Survey III (NHIS) data set to investigate the application of unbalanced RSS to estimation of population proportion.Our results indicate that this use of logistic regression improve the accuracy of the preliminary ranking in unbalanced rank set sampling and leads to substantial gains in precision for estimation of a population proportion.Further, we illustrate how data from one source can be used to construct the necessary logistic regression equation, which can in turn be used to estimate the relevant properties.This research was conducted to find out whether unbalanced rank set sampling is better than the simple random sampling, balanced rank set sample and rank set sampling.We also find out whether risk factors of diabetes like age, pregnancies, pg concentration, diastolic BP, trifold thick, serum Ins.After using simple random sampling, than we rank the values, using balanced and unbalanced rank set sampling.
Implementasi Virtual Reality Berbasis Foto 360o Untuk Memvisualisasikan Fasilitas Perguruan Tinggi Surabaya

The college selection process is an important phase because this process affects the future achievement targets of prospective students. One of the factors that is considered in determining a university is the supporting facilities provided during the lecture process. prospective students will continue to search and at the same time consider universities despite the pandemic COVID-19. By implementing 3600 photo-based virtual reality (VR), prospective students or external parties can get information about university facilities anytime and anywhere because it can be accessed online. This study uses the Multimedia Development Life Cycle (MDLC) method in application development, then uses a quantitative approach to test the feasibility of the application. The results showed that 3600 photo-based virtual reality (VR) is an alternative media in conveying information related to the facilities and logistics owned by universities, the variables of smoothness and convenience of operating 3600 photo-based videos have a high enough influence, but on user motivation to use VR is worth less. This happens because users are not used to using this technology.&#x0D;
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse and mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognised until now. Finally, our design patterns extend and refine Kautz' earlier attempt at categorising neuro-symbolic architectures.
BHDA - A Blockchain-Based Hierarchical Data Access Model for Financial Services

Blockchain brings opportunities and challenges for financial data sharing services. The essential properties in a distributed multi-parties system are data access control and privacy control. This paper proposes a hierarchical data access model for financial services, which contains consent management and dynamic credits management. We implement fine-grained data access control through rating accredited data recipients (ADRs). By accessing corresponding blockchain service logs, ADRs' credits will dynamically be updated. The credits evaluation algorithm is responsible for calculating ADRs' credits based on their completion rate, business ethics rate, and feedback positive rate. Moreover, through applying smart contracts, the efficiency of consent management can be improved, and privacy policies can be managed elastically. Finally, we deploy smart contracts on the Ethereum Rinkeby testnet to evaluate the model feasibility. Furthermore, the theoretical analysis and experimental results indicate that the prototype is secure and efficient.
Rapid and Accurate Local Gaussian Noise Removal

In this paper, we propose a rapid and high-accuracy Gaussian noise removal method by applying the learning linear filter used in RAISR for super-resolution. Our algorithm is a rapid local method, yet produces comparable results to the accuracy of the non-local method known for its high accuracy. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The conventional local processing includes smoothing processing, and has a problem that high-frequency components of an original signal are lost while reducing the noise. In order to solve the problem, this method incorporates a super-resolution method that compensates for high-frequency components as post-processing. The super-resolution method utilizes a process that applies a learning linear filter according to the feature of patches in RAISR. Because the proposed method consists of local precessing, its operation is rapid compared to non local processing like BM3D.
GAME BATTLE ROYALE PLATFORMER VIRTUAL REALITY BASED

Virtual Reality is a technology that allows users to interact with a 3D environment that issimulated by a computer, this technology can make someone seem like they are entering avirtual world that can interact with real movements. Virtual reality can be implemented in thefields of Architecture, Military, and Entertainment such as games. This study purpose to makegames by genre battle royale platformer who apply the technology of virtual reality whichplayed by players that must avoid obstacles-obstacles that exist to reach the finish line. TheGame will be divided into two stages that is Gate Crash and Door Dash, by using Virtual Realityhope the players can feel the sensation is more fun when playing the game. This study uses sixstages of the Multimedia Development Life Cycle (MDLC) method, that is Concept, Design,Material Collecting, Assembly, Testing and Distribution. The application has been testedthrough Blackbox testing, with the results of all test indicators being declared valid.
The Effectiveness of Developmental Therapies (VAK Learning Style) with Cognitive Information Processing Approach in Treatment of Cognitive Dyslexia

The Effectiveness of Developmental Therapies (VAK Learning Style) with Cognitive Information Processing Approach in Treatment of Cognitive Dyslexia
Demystifying Learning of Unsupervised Neural Machine Translation

Unsupervised Neural Machine Translation or UNMT has received great attention in recent years. Though tremendous empirical improvements have been achieved, there still lacks theory-oriented investigation and thus some fundamental questions like \textit{why} certain training protocol can work or not under \textit{what} circumstances have not yet been well understood. This paper attempts to provide theoretical insights for the above questions. Specifically, following the methodology of comparative study, we leverage two perspectives, i) \textit{marginal likelihood maximization} and ii) \textit{mutual information} from information theory, to understand the different learning effects from the standard training protocol and its variants. Our detailed analyses reveal several critical conditions for the successful training of UNMT.
Association between Receptive Language Skill and Social Communication Skill among Preschool Children: Evidence from Surakarta, Central Java

ABSTRACT Background: Receptive language skills are crucial for the development of cognitive and social/emotional skills. Children with social communication problems are at risk of failure in the academic field, difficulty forming friendships, and social exclusion. This study aimed to examine the association between receptive language skill and social communication skill among preschool children. Subjects and Method: This was a cross sectional study conducted in Surakarta, Central Java, in January 2020. A sample of 200 pre-school children was selected by simple random sampling. The dependent variable was social communication. The independent variables were receptive language skill, parenting style, gender, and birth order. The data were collected by questionnaire and analyzed by a multiple linear regression. Results: Good social communication increased with good receptive language skill (OR= 3.21; 95% CI= 0.01 to 0.04; p= 0.002), first birth order (OR= 3.71; 95% CI= 0.79 to 1.47; p&lt;0.001), democratic parenting style (OR= 5.21; 95% CI= 0.09 to 0.20; p&lt;0.001), and female gender (OR= 5.23; 95% CI= 0.89 to 1.97; p&lt;0.001). Conclusion: Good social communication increases with good receptive language skill, first birth order, democratic parenting style, and female gender. Keywords: social communication, receptive language skill Correspondence: Nadya Susanti. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: nadyasusanti3@gmail.com. Mobile: 081568222014 DOI: https://doi.org/10.26911/the7thicph.03.101
PENGARUH PEMBERIAN PENGUATAN TERHADAP HASIL BELAJAR SISWA MATA PELAJARAN IPS PADA SISWA SEKOLAH DASAR DI KABUPATEN PESISIR BARAT

The purpose of this study was to determine whether there was an effect of strengthening the learning outcomes of fourth-grade elementary school students in social studies subjects. The method used in this research is quantitative pre-experimental design in the form of one group pretest-posttest design. This study is a population study with a sampling technique using all members of the population as the research sample. The sample of this study was 20 grade IVA students with the research location of SD Negeri 46 Krui, Pesisir Barat. The results showed that the provision of reinforcement had an effect on student learning outcomes in the social studies subject for class IVA even semester of the 2019/2020 academic year. The results of this study used a different method from the research conducted by Rohani (2019), the method used was a descriptive method with the form of research being a relationship study. The results of this study indicate that there is an effect of strengthening by the teacher on student learning outcomes in class VII PKN learning at SMP Negeri 4 Rasau Jaya, Kubu Raya Regency.
ANALISIS VARIABEL-VARIABEL YANG MEMPENGARUHI MINAT SISWA SMA MELANJUTKAN PENDIDIKAN KE PERGURUAN TINGGI

Education is one of the essential assets in the development of society and the nation. The low percentage of people who study in Indonesia still needs to be explored. There are many reasons why students do not pursue their education in college. The purpose of this study was to analyze the influence of parents' socioeconomic interactions, self-efficacy, motivation, and student achievement on their interest in continuing education to college. This study uses a quantitative approach. Data was taken from 68 respondents majoring in Social Sciences at SMAN 1 Mejayan. The causal proof analysis performed using multiple linear regression equations. The results of the research prove a positive change in the variables of parents' socioeconomic status, self-efficacy, motivation, and student achievement towards the interest in continuing education. The implications of the results of this study are essential for teachers and school management always to increase student motivation and self-efficacy for students of lower socioeconomic status. Keywords : Socioeconomic Status, Self Efficacy, Motivation, Academic Achievement, Interest .