Reviewerly/exHarmony huggingface dataset:
Pemutakhiran Zona Musim (ZOM) Provinsi Aceh Menggunakan Data Blending Berbasis Non-Hirarki K-Means Clustering

Climatologically, Aceh is influenced by global, regional, and local phenomena strongly influenced by the hilly mountain topography. Aceh is also surrounded by Indian Ocean in the west, Malacca Strait in the east and Andaman Sea in the north that making climate conditions more varied. The old season zone is not compatible to explain this condition. This paper makes an updated analysis by combining the observational and satellite data for rainfall activity explanation. We use k-means clustering based on the analysis of blending data between observation data and satellite imaging that produces more specific and updated season zone (ZOM). The clusters obtained are newly season zones that provide more specific mapping for dry and rainy season information. The updated season zone of 15 ZOM clusters based on k-means can figure a clear variation that means the accuracy of difference rainfall value can be more precise. From this research, the updated season zone can provide effective planning about strengthened from various government sectors in the future that can give a better policy to the public community.
Cumulative readings of "every" and leaks

every surprisingly gives rise to cumulative readings (Schein 1993; Kratzer 2000). The distribution of these readings is governed by scope-related asymmetries (Champollion 2010; Haslinger &amp; Schmitt 2018). In this work, I notice a third property of these readings: cumulative readings of every receive weaker "leaky" truth-conditions under negation, previously thought to be unattested (Bayer 2013). Exploiting this third property, I build an event semantics to deliver these "leaky readings" by default. Within this semantics, it becomes possible to account for cumulative readings of every and their properties, keeping to standard assumption about the denotation for every. I also show how the same analysis predict the scope-related asymmetries and their less studied interaction with overt movement.
Artificial Intelligence Cases in China: Feilin v. Baidu and Tencent Shenzhen v. Shanghai Yingxin

In 2019, two court rulings in China on the issue of copyrightability of AI creations received international attention.It was reported that in Feilin v. Baidu, known as the first AI case, the Beijing Internet Court denied copyright of AI creations, whereas the Shenzhen Nanshan District People's Court acknowledged copyright of AI creations in the Tencent Dreamwriter case.The two cases, however, were quite similar, as they acknowledged copyright of AIassisted, not AI-generated, written works and recognized these works as a work of a legal entity.The difference between the two judgments is that the Beijing Internet Court regarded originality as an independent requirement and judged it according to the objective standard, whereas the Shenzhen Nanshan District People's Court regarded human creation as part of the requirement of originality.In this sense, it was the Beijing Internet Court that actually made the more favorable judgment on an AI-generated work.
Transliterating Nôm Scripts into Vietnamese National Scripts using Statistical Machine Translation

Nôm scripts were used as the Vietnamese writing system from the 10th century to the early 20th century.During this period, Nôm scripts were the means to record a broad range of historical events, literary works, medical knowledge, as well as wisdom of many other domains.Unfortunately, since hardly any native Vietnamese speaker can read Nôm scripts nowadays, these valuable documents have not been fully harnessed.To address this gap, it is necessary to build an automatic transliteration system that can support us in decoding the ancient scripts and gaining knowledge of our Vietnamese ancestors.This study focuses on categorizing and reviewing the current progress on the Statistical Machine Translation (SMT) approaches to transliterate Nôm scripts into Vietnamese national scripts.In this paper, we discuss the differences between Nôm scripts and Vietnamese national scripts, systematically compare SMT models in transliterating Nôm scripts into Vietnamese national scripts, as well as having a thorough outlook on several promising research directions.
Incremental Kernel Principal Components Subspace Inference With Nyström Approximation for Bayesian Deep Learning

As the state-of-the-art technology of Bayesian inference, based on low-dimensional principal components analysis (PCA) subspace inference methods can provide approximately accurate predictive distribution and well calibrated uncertainty. However, the main problem of PCA method is that it is a linear subspace feature extractor, and it cannot effectively represent the nonlinearly high-dimensional parameter space of deep neural networks (DNNs). Firstly, in this paper, in order to solve the main problem of the linear characteristics of PCA in high-dimensional space, we apply kernel PCA to extract higher-order statistical information in parameter space of DNNs. Secondly, to improve the efficiency of subsequent computation, we propose a strictly ordered incremental kernel PCA (InKPCA) subspace of parameter space within stochastic gradient descent (SGD) trajectories. In the proposed InKPCA subspace, we employ two approximation inference methods: elliptical slice sampling (ESS) and variational inference (VI). Finally, to further improve the memory efficiency of computing the kernel matrix, we apply Nyström approximation to determine the suitable size of subsets in the original datasets. The novelty of this paper is that it is the first time to apply the proposed InKPCA subspace with Nyström approximation for Bayesian inference in DNNs, and the results show that it can produce more accurate predictions and well-calibrated predictive uncertainty in regression and classification tasks of deep learning.
Analisis Bibliometrik Pada Scholarly Journals Proquest Dengan Kata Kunci “Tourism In Indonesia” Menggunakan Perangkat Lunak Vosviewer

AbstrakPenelitian ini bertujuan untuk mengetahui peta perkembangan penelitian yang berkaitan dengan “Tourism in Indonesia”. Penelitian ini dilakukan pada bulan September-Oktober 2020 dengan melakukan penelusuran melalui ProQuest. Peneliti menggunakan kata kunci “Tourism in Indonesia” untuk menghasilkan penelusuran agar lebih spesifik dengan kategori scholarly journals dan memiliki subject tourism. Pada penelitian ini, peneliti menggunakan metode deskriptif kuantitatif dengan analisis bibliometrika. Berdasarkan hasil penelusuran, peneliti memperoleh 56 hasil penelusuran karya ilmiah yang kemudian di export dalam format RIS dan diolah menggunakan VOSviewer. Berdasarkan hasil analisis, penelitian ini menunjukkan bahwa kata kunci (Co-Occurrence) terbagi dalam 3 cluster yang memiliki total 17 topik. Berdasarkan kolaborasi penulis (Co-Authorship) memiliki 1 cluster yang didalamnya memuat 23 penulis. Dari total 56 hasil penelusuran karya ilmiah, untuk penulis yang paling produktif adalah Jaelani, Aan dengan 3 dokumen hasil karya artikel jurnal. Sedangkan, penelitian dengan kata kunci “Tourism in Indonesia” mengalami perkembangan secara fluktuatif dan untuk terbitan terbanyak terjadi pada tahun 2019 dengan jumlah 11 terbitan. Kata Kunci : Tourism in Indonesia, VOSviewer, ProQuest Bibliometric Analysis On Scholarly Journals Proquest With Keywords "Tourism In Indonesia" Using Vosviewer SoftwareAbstractThis research aims to determine the map of research developments related to "Tourism in Indonesia". This research was conducted in September-October 2020 by searching through ProQuest. Researchers use the keyword "Tourism in Indonesia" to produce searches to be more specific in the category of scholarly journals and subject to tourism. In this research, researchers used quantitative descriptive methods with bibliometric analysis. Based on the search results, the researcher obtained 56 tracing results of scientific papers which were then exported in RIS format and processed using VOSviewer. Based on the results of the analysis, this research shows that the keywords (Co-Occurrence) are divided into 3 clusters which have a total of 17 topics. Based on co-authorship, it has 1 cluster which contains 23 authors. From a total of 56 scientific research results, the most productive authors were Jaelani, Aan with 3 documents from journal articles. Meanwhile, research with the keyword "Tourism in Indonesia" has experienced fluctuating developments and the largest number of publications occurred in 2019 with 11 publications.Keywords: Tourism in Indonesia, VOSviewer, ProQuest
A General Setting for Gradual Semantics Dealing with Similarity

The paper discusses theoretical foundations that describe principles and processes involved in defining semantics that deal with similarity between arguments. Such semantics compute the strength of an argument on the basis of the strengths of its attackers, similarities between those attackers, and an initial weight ascribed to the argument. We define a semantics by three functions: an adjustment function that updates the strengths of attackers on the basis of their similarities, an aggregation function that computes the strength of the group of attackers, and an influence function that evaluates the impact of the group on the argument's initial weight. We propose intuitive constraints for the three functions and key rationality principles for semantics, and show how the former lead to the satisfaction of the latter. Then, we propose a broad family of semantics whose instances satisfy the principles. Finally, we analyse the existing adjustment functions and show that they violate some properties, then we propose novel ones and use them for generalizing h-Categorizer.
Feature Engineering for Human Activity Recognition

Human activity recognition (HAR) techniques can significantly contribute to the enhancement of health and life care systems for elderly people.These techniques, which generally operate on data collected from wearable sensors or those embedded in most smart phones, have therefore attracted increasing interest recently.In this paper, a random forest-based classifier for human activity recognition is proposed.The classifier is trained using a set of time-domain features extracted from raw sensor data after being segmented into windows of 5 seconds duration.A detailed study of model parameter selection is presented using the statistical t-test.Several simulation experiments are conducted on the WHARF accelerometer benchmark dataset, to compare the performance of the proposed classifier to support vector machines (SVM) and Artificial Neural Network (ANN).The proposed model shows high recognition rates for different activities in the WHARF dataset compared to other classifiers using the same set of features.Furthermore, it achieves an overall average precision of 86.1% outperforming the recognition rate of 79.1% reported in the literature using Convolution Neural Networks (CNN) for the WHARF dataset.From a practical point of view, the proposed model is simple and efficient.Therefore, it is expected to be suitable for implementation in hand-held devices such as smart phones with their limited memory and computational resources.
Design and Implementation of a Strong and Secure Lightweight Cryptographic Hash Algorithm using Elliptic Curve Concept: SSLHA-160

Cryptographic hash function assumes a fundamental job in numerous pieces of cryptographic algorithm and conventions, particularly in authentication, non-repudiation and information trustworthiness administrations.A cryptographic hash work takes a commitment of optional tremendous size message and conveys a fixed little size hash code as a yield.In the proposed work SSLHA-160 (a strong and secure lightweight cryptographic hash algorithm), each 512-digit square of a message is first diminished to 256-bit.A cryptographic hash work takes a contribution of discretionary enormous size message and delivers a fixed little size hash code as a yield.In the proposed work SSLHA-160 (A strong and secure lightweight cryptographic hash algorithm), each 512-digit square of a message is first diminished to 256-bit and afterward partitioned into eight equivalent block of 32 pieces each and each 32-cycle block is additionally separated into two sub-block of 16-piece each.These two sub-blocks go about as two purposes of an elliptic curve, which are utilized for computing another point which is of 16 pieces.The new point esteems are thusly handled to produce message digest.SSLHA-160 is easy to develop, simple to actualize and displays solid torrential slide impact (avalanche), when contrasted with SHA1, RIPEMD160 and MD5.
A Study on Interaction Patterns in Language Learning Online Classes – Adaptation and Efficiency

The aim of our research entitled "A study on interaction patterns in language learning online classesadaptation and efficiency" is to elaborate that in an online ESL/EFL class, tremendous interaction patterns can be employed by the teacher in order to enhance the classroom atmosphere for students' better involvement and collaboration as well as alter the pace of a lesson.Similar to offline classes, there are several prevailing interaction patterns in internet-based language learning classes: teacher-centered and student-centered.Drawn on several frameworks on interaction, this study explores different types of interaction applied in two distinct language learning online courses with 45 participants, including both teachers and students at a primary international institution in Hanoi during the Covid-19 pandemic.Furthermore, the researchers compared the interaction patterns taking place in both classes to identify their similarities and differences.Findings show that the most dominant interaction adopted in the two classes was teacher-student, especially instructor to the whole class, instructor to each member in the classroom, and learners to instructor.There were no obvious distinctions of interactions used in the class by the two teachers but the teacher's preference in utilizing certain interaction forms based on the characteristics of every single class.Additional pedagogical recommendations for online English courses would then be put forth based on the data gathered.
Developing E-learning Module by Using Telegram Bot on ICT for ELT Course

Pre-service teachers who are taking undergraduate education after graduating will face generation z students, where students are already facing the 21st century and gadgets have become their playmates.Using gadgets that only for playing games and social media has been deemed less useful.It needs to facilitate teaching how to make learning media using smartphones to equip pre-service teachers to be proficient in utilizing technology, which is called MALL.The objective of this study is to produce a product of teaching materials based e-learning by telegram bot application.The materials are about ICT in English language teaching course.This course is for the seventh-semester students in the English education study program Universitas PGRI wiranegara pasuruan.The method is research and development (RnD).The steps are finding the problems.Collecting data, product design, design validation, design revision, product try out, product revision, utility testing, final product revision, and dissemination.the product has been not only validated materials and media but also testing usage.the materials validation expert shows that the average percentage is 94% and the media expert validation shows that the average percentage is 72%.The tryout result shows that the average percentage is 82%.The dissemination product shows that the average percentage is 87%.Based on the results shows that this e-learning materials product includes an invalid category.
Developing Markerless Augmented Reality for Furniture Mobile Application

In India, purchasing furniture online consistently faces a quandary regarding how one can comprehend whether the furniture is a solid match to purchase, regardless of whether it will fit in space without really contacting and looking at the furniture in detail.With such a dilemma there are chances for online stores to decline in their business.There is always a good certainty that technology can help to bridge the barrier and help one to overcome the dilemma.This study creates a unique light on how Augmented Reality can help to create and simulate buying furniture an online experience and enhancing customer experience provided uniqueness and mobility with the help of a mobile application.Our application allows users to simulate and experience furniture in 3D simulation using markerless augmented reality.Users can try a piece of furniture in their space supported by gestures and color furniture to try out diverse tints of the same.This research advances a new way of implementing markerless augmented reality for buying furniture products online provided by mobile applications and communicating with virtual objects in a real environment with an easy-touse user interface.
Improving Students English Vocabulary Using Kahoot!

Acquiring vocabulary is compulsory for students to apply them later in their university year when writing their thesis.The combination of traditional and interactive to enhance student's vocabulary can show a significant result.This article aims to see how Kahoot!contributes to students' vocabulary improvement in the Intermediate Reading and Writing class.
Completeness of the ZH-calculus

There are various gate sets used for describing quantum computation. A particularly popular one consists of Clifford gates and arbitrary single-qubit phase gates. Computations in this gate set can be elegantly described by the ZX-calculus, a graphical language for a class of string diagrams describing linear maps between qubits. The ZX-calculus has proven useful in a variety of areas of quantum information, but is less suitable for reasoning about operations outside its natural gate set such as multi-linear Boolean operations like the Toffoli gate. In this paper we study the ZH-calculus, an alternative graphical language of string diagrams that does allow straightforward encoding of Toffoli gates and other more complicated Boolean logic circuits. We find a set of simple rewrite rules for this calculus and show it is complete with respect to matrices over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mo stretchy="false">[</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:mo stretchy="false">]</mml:mo></mml:math>, which correspond to the approximately universal Toffoli+Hadamard gateset. Furthermore, we construct an extended version of the ZH-calculus that is complete with respect to matrices over any ring <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi></mml:math> where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:math> is not a zero-divisor.
Network analysis of social awareness of media education for primary school students studied through big data

One of the core competencies of students who want to improve in future education in the 21st century is the ability to utilize media. Primary school students entering the school age can be regarded as the right time for media education because of their high adaptability and capacity for media. The purpose of this study is to examine the social debate about media education in Korean society, how media education is being conducted in this important primary school period. For this study, big data was collected in the last 5 years (2014.08.07- 2019.08.07) from internet portal sites with keywords of ?primary school media education? and ?primary school media literacy?. The data collected with Textom and Ucinet 6.0 was utilized as a data analysis solution. Semantic network analysis, CONCOR analysis, and content analysis were used as data analysis methodology. As the result of CONCOR analysis of 'Primary school Media Education' in this study, 'Direction of future education in the era of the 4th industrial revolution', 'Preparation for future education', 'Various factors related to expansion of future education program', 'Expansion to Four factors were derived, including ?Application to advanced classes' factor. In the 4th industrial revolution, primary school students are using media and digital devices in class. In particular, it was confirmed that not only curriculum for academic subjects such as English and Mathematics, but also new curriculum for new subjects such as coding, big data education are actively being conducted in the education field. Accordingly, it is revealed that it is the right time to provide future education that can have a sound digital identity so that media education can be achieved in a media-friendly local community and educational environment.
An Hybrid Approach for Cost Effective Prediction of Software Defects

Identifying software defects during early stages of Software Development life cycle reduces the project effort and cost.Hence there is a lot of research done in finding defective proneness of a software module using machine learning approaches.The main problems with software defect data are cost effective and imbalance.Cost effective problem refers to predicting defective module as non defective induces high penalty compared to predicting non defective module as defective.In our work, we are proposing a hybrid approach to address cost effective problem in Software defect data.To address cost effective problem, we used bagging technique with Artificial Neuro Fuzzy Inference system as base classifier.In addition to that, we also addressed Class Imbalance & High dimensionality problems using Artificial Neuro Fuzzy inference system & principle component analysis respectively.We conducted experiments on software defect datasets, downloaded from NASA dataset repository using our proposed approach and compared with approaches mentioned in literature survey.We observed Area under ROC curve (AuC) for proposed approach was improved approximately 15% compared with highly efficient approach mentioned in literature survey.
GovdeTurk: A Novel Turkish Natural Language Processing Tool for Stemming, Morphological Labelling and Verb Negation

GovdeTurk is a tool for stemming, morphological labeling and verb negation for Turkish language. We designed comprehensive finite automata to represent Turkish grammar rules. Based on these automata, GovdeTurk finds the stem of the word by removing the inflectional suffixes in a longest match strategy. Levenshtein Distance is used to correct spelling errors that may occur during suffix removal. Morphological labeling identifies the functionality of a given token. Nine different dictionaries are constructed for each specific word type. These dictionaries are used in the stemming and morphological labeling. Verb negation module is developed for lexicon based sentiment analysis. GovdeTurk is tested on a dataset of one million words. The results are compared with Zemberek and Turkish Snowball Algorithm. While the closest competitor, Zemberek, in the stemming step has an accuracy of 80%, GovdeTurk gives 97.3% of accuracy. Morphological labeling accuracy of GovdeTurk is 93.6%. With outperforming results, our model becomes foremost among its competitors