Reviewerly/exHarmony huggingface dataset:
ITEM CHARACTERISTICS ANALYSIS USING THE RASCH MODEL IN THE DEVELOPMENT OF READING LITERACY INSTRUMENTS FOR ELEMENTARY SCHOOLS STUDENTS

Literacy is a basic reading skill in modern society. The development of reading literacy questions is needed to be useful as an assessment function. The development of reading literacy questions needs to pay attention to the principle of conformity with indicators and pay attention to the characteristics of the items. A total of 167 fifth grade elementary school students were sampled in the test instrument for the reading literacy test. Instrument testing was carried out in two stages; first, content validity testing by seven experts, secondly analysis of model fit and item difficulty to determine item characteristics. The results showed that based on the results of expert judgment, 17 items were valid and 1 item had moderate validity. Items with moderate validity are S18 items that are classified as evaluating and reflecting type questions at level 6. Further analysis using the Rasch model obtained 2 items in the very easy category, 14 items in the easy category, 1 item in the difficult category and 1 item in the difficult category. very difficult category. With a distribution of difficulty levels, 11% of very easy questions, 78% of easy questions, 6% of difficult questions, and 6% of very difficult questions.
An Adaptive Local Gravitation-based Optimized Weighted Consensus Clustering for Gene Expression Data Classification

The appropriate categorization of tumors from a vast quantity of Gene Expression Data (GED) is one of the most difficult processes in clinical diagnosis.To combat this challenge, a Weighted Consensus of Lion Optimized K-means Ensemble with Peak Density Clustering (WECLO K-means-PDC) algorithm has been developed that calculates the Symmetric Neighborhood (SN) correlation among Data Points (DPs) using the lion optimization.An SN Graph (SNG) was created to select the number of Cluster Centroids (ClustCenter) at every iteration of clustering.But, it was not suitable if the dataset was sparse, as well as the constant density threshold may influence the distinguishing DPs within the cluster boundaries.In this paper, an Adaptive Local resultant Force neighborhood PDC for WECR K-means (WECLO K-means-ALFPDC) algorithm is proposed, which considers additional measures to determine the symmetric neighborhood correlation among the DPs when the dataset is sparse.The major goal is to consider the distinct variances between the sizes and orientations of the DPs nearer to the ClustCenters and edges.To find such variations, two novel local measures called Centrality (CR) and Coordination (CO) are introduced instead of SNG to select the ClustCenters and obtain more precise clustering for classifying cancer from genomic data.Finally, the test results show that the WECLO K-means-ALFPDC algorithm attains 88.7%, 89.1%, 88.42%, 88.38% and 89.04% accuracy on leukemia, lymphoma, prostate cancer, SRBCT and breast cancer databases, respectively compared to the WECLO K-means-PDC algorithm.
A kernel-based incomplete data ensemble classification method

In the era of the Internet of everything, the problem of data loss is often encountered due to the limitation of data source access or data omission and misreading. Therefore, using existing data sets to process missing values, deleting samples with missing values or using some techniques to estimate missing values in the pre-processing of incomplete data will more or less lead to deviation of the original distribution of the data set. Therefore, in this paper, we adopt a new idea to deal with missing data, and use the idea of ensemble learning to compare the Precision, Recall, F1 and ACC indexes of the three different methods in the classification test of different data sets.The results show that the method proposed in this paper has a better overall effect.
A Estratégia da Posição do Antecedente em línguas românicas de sujeito nulo

This study investigates the interpretation of pronominal subjects in European Portuguese, Spanish and Italian. 30 native speakers of each language completed two multiple-choice tasks (speeded and untimed), which elicited their interpretation of null and overt subject pronouns in intrasentential contexts with the order matrix‑subordinate. The tasks had a 2x2 design, crossing the following variables: animacy of the matrix object (+ animate vs. - animate) and type of pronominal embedded subject (overt vs. null). Results indicate that the PAS is consistently followed in EP but not in Italian or Spanish. In these languages, this strategy is adopted in overt subject resolution only in contexts where the antecedents are [+ animate]. In the resolution of null subjects, Italian and Spanish exhibit optionality between subject and object antecedents. Hence, there is microvariation in the resolution of pronominal subjects in these languages. Possible reasons for this are discussed in detail.
Automatic Illumination of Flat-Colored Drawings by 3D Augmentation of 2D Silhouettes

In this paper, a new automatic method for the illumination of flat-colored drawings is proposed. First, we reconstruct a 3D augmentation of a 2D silhouette from the analysis of its skeleton. Then, we apply the Phong lighting model that relies on the estimated normal map to generate an illuminated drawing. This method compares favorably to recent state-of-the-art methods, e.g. those using convolutional neural networks.
SENTIMENT ANALYSIS OF INTERNET SERVICE PROVIDERS USING NAÏVE BAYES BASED ON PARTICLE SWARM OPTIMIZATION

Twitter is a social media application that is widely used. Where as many as 18.45 million users in Indonesia, Twitter users can send and read messages with a maximum of 280 characters displayed. Many opinions and reviews uploaded by users via tweets on social media are experienced in everyday life. Lately, comments about internet service providers in the covid-19 pandemic have been widely reviewed by Twitter users. Problems about internet providers through words often uploaded include internet provider complaints related to network quality, package prices, user satisfaction, and others. This study aims to classify Twitter users' tweets against internet service providers in Indonesia by analyzing the sentiments of 3 internet service providers, namely with the keywords Biznet, first media, and Indihome, using the Naïve Bayes algorithm and optimization with Particle Swarm Optimization. This research is also helpful in helping to become a measure where prospective new users will see the quality of an internet service provider in Indonesia through tweets and then divide the opinion into positive and negative. The results of Biznet's research using Naïve Bayes produce an accuracy of 77.94%, and after optimization, it becomes 81.62%. First media using Naïve Bayes produces 91.39% accuracy, and after optimization, it becomes 92.88%. Indihome using Naïve Bayes produces an accuracy of 85.78%, and after optimization, it becomes 87.48%. It can be concluded that the Naïve Bayes algorithm is a good algorithm for classification, and optimization using Particle Swarm Optimization has an effect on increasing accuracy results
ANALYSIS OF DYNAMIC TIME WARPING IN THE DEVELOPMENT OF GROSS REGIONAL DOMESTIC PRODUCT YOGYAKARTA

Poverty in Indonesia has become a common thing that is still difficult to handle due to the presence of the covid virus outbreak attacks are causing the inability to buy and sell transactions, export and import goods and services, then the level of inequality increases. The tool measures the inequality level in an area seen from the Gini Ratio value. The Gini Ratio notes that the DI Yogyakarta province had the highest index value in Indonesia of 0,437 in September 2020. So this study aims to minimize the inequality in the DI Yogyakarta province by using the clustering method and Dynamic Location Quotient (DLQ). The clustering method with a hierarchical algorithm using the Dynamic Time Warping (DTW) distance and the DLQ method to predict regional economic sectors. Based on the result of the clustering analysis, there were 2 clusters, and the DLQ analysis obtained as many as 11 essential and 6 NPN-base sectors. Cluster 1 has 10 GRDP sectors with two industries that will become non-base sectors in the future, while cluster 2 has 7 GRDP sectors with three sectors it will become base sectors in the future.
Introduction pages, Nova Geodesia 2(3), 2022

Nova Geodesia (https://novageodesia.ro), Issue 3, Volume 2, 2022: The papers published in this issue represent interesting novelties in different topics of geodetic science. Among the exciting articles, we invite readers to find news about: Identification and evaluation of the risk induced by landslides based on G.I.S. models of spatial analysis, a case study: Bicazu Ardelean, Romania; Correlation between land surface temperature (LST) and normalized difference vegetation index (NDVI) in Wardha Valley Coalfield, Maharashtra, Central India; Tectonic evolution of the Qinling Orogenic Belt, Central China – new evidence from geochemical, zircon (U-pb) geochronology and HF isotopes.
Exploiting and Guiding User Interaction in Interactive Machine Teaching

Humans are talented with the ability to perform diverse interactions in the teaching process. However, when humans want to teach AI, existing interactive systems only allow humans to perform repetitive labeling, causing an unsatisfactory teaching experience. My Ph.D. research studies Interactive Machine Teaching (IMT), an emerging field of HCI research that aims to enhance humans’ teaching experience in the AI creation process. My research builds IMT systems that exploit and guide user interaction and shows that such in-depth integration of human interaction can benefit both AI models and user experience.
Low-Rank Representation of Reinforcement Learning Policies

We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability and convergence guarantees. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly represented in a low-dimensional space while the embedded policy incurs almost no decrease in returns.
Autonomization and Antitrust

The chapter aims to serve as a conceptual sketch for the intricacies involved in autonomous algorithmic collusion, including the notion of concerted practices for cases that would otherwise elude the cartel prohibition. Stefan Thomas, a law scholar, starts by assessing how algorithms can influence competition in markets before dealing with the traditional criteria of distinction between explicit and tacit collusion, which might reveal a potential gap in the existing legal framework regarding algorithmic collusion. Finally, he analyses whether the existing cartel prohibition can be construed in a way that captures the phenomenon appropriately. This chapter shows how enforcement paradigms that hinge on descriptions of the inner sphere and conduct of human beings may collapse when applied to the effects precipitated by independent AI based computer agents.
Bridging the Visual Semantic Gap in VLN via Semantically Richer Instructions

The Visual-and-Language Navigation (VLN) task requires understanding a textual instruction to navigate a natural indoor environment using only visual information. While this is a trivial task for most humans, it is still an open problem for AI models. In this work, we hypothesize that poor use of the visual information available is at the core of the low performance of current models. To support this hypothesis, we provide experimental evidence showing that state-of-the-art models are not severely affected when they receive just limited or even no visual data, indicating a strong overfitting to the textual instructions. To encourage a more suitable use of the visual information, we propose a new data augmentation method that fosters the inclusion of more explicit visual information in the generation of textual navigational instructions. Our main intuition is that current VLN datasets include textual instructions that are intended to inform an expert navigator, such as a human, but not a beginner visual navigational agent, such as a randomly initialized DL model. Specifically, to bridge the visual semantic gap of current VLN datasets, we take advantage of metadata available for the Matterport3D dataset that, among others, includes information about object labels that are present in the scenes. Training a state-of-the-art model with the new set of instructions increase its performance by 8% in terms of success rate on unseen environments, demonstrating the advantages of the proposed data augmentation method.
The Effectiveness Of Application Of Flashcard In Mastering English Vocabulary For Fifth Grade Student At MI Rare Muchtary

This study aims to 1) analyze students' English vocabulary mastery (2) describe the process of applying flashcard media and analyze students' English vocabulary mastery after using flashcard media; and (3) find factors that support and hinder the application of flashcard media for fifth grade children at MI Rare Muchtary.Sources of data were obtained from 32 student of five grade in MI Rare Muchtary.The results showed that the students mastery of english vocabulary was quite good although in reality several things were found to be factors in the learning process, both factors from students and environmental factors.
Application of DTCWT Decomposition and Partial Differential Equation Denoising Methods in Remote Sensing Image Big Data Denoising and Reconstruction

The precision of the traditional satellite remote sensing image denoising model cannot deal well with some precise production scenes. To solve this problem, this research proposes an improved remote sensing image processing model, in which the dual tree complex wavelet transform (DTCWT) method is used to conduct multiscale decomposition of the impact, and the fourth-order differential equation is used to denoise the decomposed complex high-frequency subband information, and then the denoised subbands are reconstructed into the denoised image. Through these two advanced signal-processing methods, the quality of reconstructed signals is improved and the noise content of various types is greatly reduced. The experimental results show that the normalized root mean square error of the denoising model designed in this study after training convergence is 0.02. When the noise variance is 0.030, the structure similarity, peak signal to noise ratio, and normalized signal to noise ratio are 0.74, 25.3, and 0.76, respectively, which are better than all other comparison models. The experimental data prove that the satellite remote sensing image data denoising model designed in this study has better denoising performance, and has certain application potential in high-precision satellite remote sensing image big data processing.
An E-Quiz System: A Pathway to Improve English Learning for Preschoolers in Rural Areas

E-Quiz Systems is a system that replaces manual techniques, which are paperless methods often utilised in rural preschools. This paper presents an interactive web-based solution to assist students and teachers in rural regions with English language learning and instruction. This study attempts to motivate preschoolers in rural locations to learn English while providing instructors with interactive web-based methods to measure their students’ knowledge levels. A web-based application system is created to display e-quiz questions using the interactive multimedia principles of multiple media types and navigation with user input. Using interactive multimedia concepts, an E-Quiz System is designed to serve as an interactive quiz for English learning. ADDIE Model was the approach employed in this project. In addition, this project underwent testing for functionality and usability. As a contribution, it has been established that this study will give a pathway that piques children’s interest in learning English and makes it simpler for instructors to evaluate children’s growth using the E-Quiz System score.
Efektivitas Media Aplikasi Ruangguru terhadap Hasil Belajar Matematika Siswa Kelas V SDN 146 Bengo Kecamatan Bengo Kabupaten Bone

This research is a type of quantitative research with a pre-experimental research design. The purpose of this study was to determine students' mathematics learning outcomes and to find out the significant differences before and after using the Ruangguru application media. The research design used was One-Group Pretest-Posttest Design.. Data collection techniques are learning outcomes tests to measure student learning outcomes before and after using the Ruangguru application media. The data analysis used is descriptive statistical analysis which obtains an average pretest of 30.33 which shows student learning outcomes before learning using the Ruangguru application media are in the less category and the posttest average is 80.83 which indicates that student learning outcomes after learning using the Ruangguru application media was in the good category, and inferential statistical analysis obtained N-Gain data, namely 0.7301 which was included in the high category. The results of the study showed that there were significant differences before and after learning using the Ruangguru application media on the mathematics learning outcomes of fifth grade students at SDN 146 Bengo, Bone Regency.
DIGITAL INSTRUMENT FOR INFANT AND TODDLER MORTALITY REVIEW USING USER-CENTERED DESIGN METHOD

The Infant Mortality Rate and Toddler Mortality Rate are still relatively high in Indonesia. Data from the South Sumatra Health Office shows a relatively high number of infant and toddler mortality cases in Banyuasin and Musi Banyuasin Regency, with about 68 and 51 cases in 2017. Through the Program Kerja Sama (PKS) of the Family Health Directorate of the Health Ministry of the Republic of Indonesia and the Public Health Faculty of Sriwijaya University, it was found that the problems related to these were triggered by the absence of an instrument in digital form to conduct reviews about infant and toddler issues of mortality at Health Facilities, which were expected to assist in recording and reporting the review process runs more effectively and efficiently. This research uses the User-Centered Design (UCD) method because it optimizes the application prototype according to the needs and desires of the end-user, which in this case is the health worker in the Health Facilities in Banyuasin and Musi Banyuasin Regency. The UCD method phases include understanding the context of use, specifying the user requirements, designing the solutions, and evaluating against requirements. Based on the research that has been done, the results obtained that the average usability score is 94, which means that the prototype of this application has been made according to the needs and desires of the end-user, also the prototype of this application is feasible to be implemented.
Platform Politics and a World Beyond Catastrophe

In this chapter the authors set out some of the stakes of platform politics at the current conjuncture. Data governance issues concerning the social production of value, data rights in automated markets, data surveillance motivated by pervasive paranoia and a general ideological intolerance against off-message articulations of disaffection. These are just some of the prevailing discursive and governmental tendencies that define the horizon of our platform present. Yet there is more, and the authors write their way through crisis to find some bearing and orientation in a world of real-time updates and automated injunctions. The machinic signalling of pervasive despair is wrought by contagion, climate and a future at once forestalled and bearing down upon us.
Anxiety Disorder Caused by Learning Difficulties in Mathematics

One of the brain disorders causing great damage to those who suffer from it is dyscalculia. Dyscalculia often causes math anxiety. This circumstance is characterized by feelings of tension and fear that interfere with performance in tasks related to Mathematics. Mathematics anxiety is not always caused by a learning difficulty but in cases where this difficulty exists, if it is not diagnosed and treated properly, it is not possible to eliminate the anxiety. By using the BAEP technique, it is possible to determine the existence of a certain type of learning disorder, which allows attention to be focused on remedying the specific cause that produces the anxiety. Due to the existence of multiple factors that cause anxiety, the neuronal system that is activated is a network of interconnected networks whose controllability we need to study. In this work, we also study the controllability of multiagent neural networks by simulating possible brain networks.
Hybrid Recommendation Algorithm for User Satisfaction-oriented Privacy Model

Anonymization technology is an important technology for privacy protection in the process of data release.Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data.However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem.In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool.On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed.The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds.The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.
Innovative Approaches to the Organization of the Educational Process in the Higher Educational Institutions

Currently, under the conditions of the rapid development of information technologies, in all areas of activity, the need arises to change approaches to the organization of the working process. The purpose of the research lies in studying the efficiency, distribution and features of using innovative approaches in the organization of the educational process at HEIs. Results. In the course of the analysis, various types of classification of innovative research methods were studied and data on the effectiveness of approaches to organizing the educational process in higher educational institutions were obtained in the form of the survey results.