Reviewerly/exHarmony huggingface dataset:
Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means

The quality of students in school has a lot of diversity, this makes students have different levels of understanding. This can be seen from the variety of student scores obtained on report card scores, this needs to be a concern for the school, especially teachers. One of them is by forming effective study groups so that every student has the opportunity to excel. So this research was carried out with the aim of helping schools, especially teachers, to map student study groups evenly based on student report cards obtained in Semester I to Semester IV. The method used was clustering with the K-Means algorithm on the report card scores of Class IX.C students at SMP Pembangunan Laboratorium UNP. The results in this study obtained 3 clusters of students, namely students with High Achievement, Achievement and Less Achievement. This research can be used as a guide for teaching teachers in making decisions about the formation of student study groups in Class IX.C.
Digital Humanities: humanities and media culture

The article examines the achievements and problems that exist in the digital research in the context of the development of media culture, today virtual reality has ceased to be a situational option. Media culture turns out to be a two-way phenomenon: on the one hand, it is a product of our civilization, on the other hand, it transforms our culture. Digital Humanities focus on different ways of processing and presenting information, at the same time, any data selection work presupposes the presence of an angle of view, ethical and ideological grounds for the selection itself and the context for the presentation of research results. Criticism of Digital Humanities can be viewed from the standpoint of the «remove culture» by R. Barth. The removal of culture presupposes the development of the postmodern idea of the need to reveal how much our ideas about the world are predetermined by certain cultural schemes, languages and genres, including how non-neutral media culture in general and digital research is. In the absence of criticism, lack of agreements on ethical norms, social responsibility, the risks are that we allow media culture and digital research tools in a sense to dictate to us the type of society in which we live
A Three Layer Super Learner Ensemble with Hyperparameter Optimization to Improve the Performance of Machine Learning Model

A combination of different machine learning models to form a super learner can definitely lead to improved predictions in any domain. The super learner ensemble discussed in this study collates several machine learning models and proposes to enhance the performance by considering the final meta- model accuracy and the prediction duration. An algorithm is proposed to rate the machine learning models derived by combining the base classifiers voted with different weights. The proposed algorithm is named as Log Loss Weighted Super Learner Model (LLWSL). Based on the voted weight, the optimal model is selected and the machine learning method derived is identified. The meta- learner of the super learner uses them by tuning their hyperparameters. The execution time and the model accuracies were evaluated using two separate datasets inside LMSSLIITD extracted from the educational industry by executing the LLWSL algorithm. According to the outcome of the evaluation process, it has been noticed that there exists a significant improvement in the proposed algorithm LLWSL for use in machine learning tasks for the achievement of better performances.
Composing Conversational Negation

Negation in natural language does not follow Boolean logic and is therefore inherently difficult to model.In particular, a broader understanding of what is being negated must be taken into account.In previous work, we proposed a framework for the negation of words that accounts for 'worldly context'.This paper extends that proposal to now account for the compositional structure inherent in language within the DisCoCirc framework.We compose the negations of single words to capture the negation of sentences.We also describe how to model the negation of words whose meanings evolve in the text.
The Impacts of Explicit and Implicit Instructions of English Connectors on EFL Students’ Reading Comprehension

There have been many studies on English grammar, but a dearth of studies has been conducted in the cross-sectional fields such as seeking how the field of grammar makes contributions to reading comprehension. Accordingly, the current study sought to find out the impacts of explicit and implicit instructions of English connectors on EFL students’ reading comprehension. 50 third semester students from an English department at a university in Bengkulu were incorporated as the samples. Adopting a quasi-experimental method, those 50 students were split into two classes, the so-called experimental and control classes. Students in the experimental class were taught English connectors explicitly, and those of the control class were taught English connectors implicitly. Before eight times of treatments in the form of the two ways of instructions, students of the two group were given a valid and reliable reading comprehension pre-test, and a similar construct of post-test was given after the eighth treatments ended. The data of the current study were analyzed by deploying paired sample t-test and independent sample t-test. The present study revealed that explicit and implicit instructions of English connectors had positive impacts on EFL students’ reading comprehension. However, the explicit instruction of English connectors enhanced EFL students’ reading comprehension more significantly and more effectively. It is recommended that further studies be conducted to replicate the present study in different contexts for the sake of providing rich data to confirm the current study’s results.
A new method for measuring the distribution consistency of mixed-attribute data sets

The measurement of data distribution consistency is a key problem in the process of generating random sample partition (RSP) of big data. How to measure the distribution consistency of mixed-attribute data sets reasonably and effectively is the focus of current research on RSP technology. This paper proposes a new method to measure the distribution consistency of mixed-attribute data sets based on deep encoding and maximum mean discrepancy (DE-MMD). Firstly, we conduct the one-hot encoding to transform the original data set with discrete attributes into the one-hot encoding data set. Then, we construct and train an autoencoder with single hidden layer based on the one-hot encoding data and thus we get the corresponding deep encoding data set by representing the original data set with hidden layer output. Finally, we measure the distribution consistency of mixed-attribute data sets based on the corresponding deep encoding data sets by using the maximum mean discrepancy index. On 4 benchmark mixed-attribute data sets, which are Adult、 Australian、 CRX and German, we compare the measure performances of DE-MMD method with those of the one-hot encoding-based MMD (OE-MMD) method and the binarization-based similarity measure (BSM) method. The experimental results show that the proposed method can measure the distribution consistency of mixed-attribute data sets more accurately and more effectively than OE-MMD and BSM methods.
INTERACTIVE VIDEOS AS GEOSPATIAL INTERFACES: A CASE STUDY FOR REGIONAL PROMOTION

Abstract. In this paper we discuss the potential of interactive video as an interface to geospatial information, and demonstrate our findings in a case study driven by goals of promoting a region for tourism and for preventing brain drain. Use of video is controversial due to various technical and human-centric debates; on the one hand, they offer unique qualitative insights as they enable a certain level of immersion via walk-throughs and fly-throughs and they are considered attractive by viewers, on the other hand, they are complex because they contain unfiltered (possibly too much) information that may be irrelevant to the goals of the users, and require careful consideration from computational and bandwidth concerns. We examine these arguments from the literature briefly, and demonstrate our case study in which we have embedded and overlain statistical and other local information interactively over drone footage, and measured levels of usability and user engagement using standardized scales. Our implementation and design choices are detailed in the article. Our small user experiment (n = 6) suggests high levels of usability, desirability and engagement by our participants, leading to the hypothesis that video is an attractive medium and indeed has strong potential for regional promotion, given that the computational and cognitive issues are taken into account.
Detecting COVID-19 Utilizing Probabilistic Graphical Models

Probabilistic graphical models are employed in a variety of areas such as artificial intelligence and machine learning to depict causal relations among sets of random variables.In this research, we employ probabilistic graphical models in the form of Bayesian network to detect coronavirus disease 2019 (denoted as COVID-19) disease.We propose two efficient Bayesian network models that are potent in encoding causal relations among random variable, i.e., COVID-19 symptoms.The first Bayesian network model, denoted as BN1, is built depending on the acquired knowledge from medical experts.We collect data from clinics and hospitals in Saudi Arabia for our research.We name this authentic dataset DS covid .The second Bayesian network model, denoted as BN2, is learned from the real dataset DS covid depending on Chow-Liu tree approach.We also implement our proposed Bayesian network models and present our experimental results.Our results show that the proposed approaches are capable of modeling the issue of making decisions in the context of COVID-19.Moreover, our experimental results show that the two Bayesian network models we propose in this work are effective for not only extracting casual relations but also reducing uncertainty and increasing the effectiveness of causal reasoning and prediction.
A Functional Abstraction of Typed Invocation Contexts

In their paper "A Functional Abstraction of Typed Contexts", Danvy and Filinski show how to derive a monomorphic type system of the shift and reset operators from a CPS semantics. In this paper, we show how this method scales to Felleisen's control and prompt operators. Compared to shift and reset, control and prompt exhibit a more dynamic behavior, in that they can manipulate a trail of contexts surrounding the invocation of previously captured continuations. Our key observation is that, by adopting a functional representation of trails in the CPS semantics, we can derive a type system that encodes all and only constraints imposed by the CPS semantics.
The Application of Excretion System E-Module to Improve Science Literacy Skills

The research aims to analyze the application of e-modules in improving scientific literacy skills. This research employed the quasi-experimental with pretest-posttest control design group. The population of this study was eleventh-grade students of SMA Negeri 1 Anjatan which consisted of 15 classes, specifically seven science classes and eight social studies classes. The research sample was taken using random sampling. The researchers collected the research data using observations and tests. Furthermore, the data was analyzed using ANES software and SPSS v.25. The analyzed data were the prerequisite tests data and the t-test data. The results obtained from students' learning activities show the highest learning activities occurred at the data collection stage. There science literacy skills improvement between the control class and the experimental class was different. The experimental class' N-gain value was 0.30 and the control class' N-gain value was 0.2. Furthermore, the result of the sig statistical tests was 0.000, which was lower than 0.655. Therefore, Ho was rejected and Ha was accepted. It can be concluded that there was an improvement difference between the experimental class and the control class regarding scientific literacy skills.
Analisis Model Pembelajaran Kooperatif Tipe STAD Dengan Media Pembelajaran Ular Tangga Terhadap Minat Belajar

The purpose of this study was to analyze the effect of snake and ladder learning media using the STAD type cooperative learning model in increasing interest in learning. This study uses primary data sources in the form of reference studies that are used as the main source of research references, the primary sources used are journals related to the research title, namely the Snakes and Ladders learning media journal model using the STAD Type Cooperative Learning Model in increasing interest in learning and other related references. Based on the results of the study that the author conducted on 10 previous research journals, it was found that the use of the snake and ladder educational game tool through the STAD type learning model can make students more active so as to increase interest and learning achievement. The conclusion from the research is that the activeness of students during learning shows a very active category. Of the 10 research journals studied that student learning outcomes were significantly different between before and before treatment. Based on the results of the validity of data from several previous studies regarding the use of snakes and ladders media through the STAD type learning model can increase student interest and learning outcomes by 90%.
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems, such as autonomous vehicles, during their decision making processes.Unfortunately, the annotation process for this task is very expensive.To overcome this, it is key to find models that generalize well or adapt to additional domains where labeled data is limited.This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models.We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space.Besides, we present a learningbased module to align the distribution of the semantic classes of the target domain to the source domain.Our approach achieves new state-of-the-art results on three different public datasets, which showcase adaptation to three different domains.
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings

This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task.Our system is a graph-based parser with the technique of Automated Concatenation of Embeddings (ACE).Because recent work found that better word representations can be obtained by concatenating different types of embeddings, we use ACE to automatically find the better concatenation of embeddings for the task of enhanced universal dependencies.According to official results averaged on 17 languages, our system ranks 2nd over 9 teams.
New Sublinear Algorithms and Lower Bounds for LIS Estimation.

Estimating the length of the longest increasing subsequence (LIS) in an array is a problem of fundamental importance. Despite the significance of the LIS estimation problem and the amount of attention it has received, there are important aspects of the problem that are not yet fully understood. There are no better lower bounds for LIS estimation than the obvious bounds implied by testing monotonicity (for adaptive or nonadaptive algorithms). In this paper, we give the first nontrivial lower bound on the complexity of LIS estimation, and also provide novel algorithms that complement our lower bound.Specifically, we show that for every e ∈ (0,1), every nonadaptive algorithm that outputs an estimate of the LIS length in an array of length n to within an additive error of e n has to make log^{Ω(log (1/e))} n queries. Next, we design nonadaptive LIS estimation algorithms whose complexity decreases as the number of distinct values, r, in the array decreases. We first present a simple algorithm that makes O(r/e³) queries and approximates the LIS length with an additive error bounded by e n. This algorithm has better complexity than the best previously known adaptive algorithm (Saks and Seshadhri; 2017) for the same problem when r ≪ polylog (n). We use our algorithm to construct a nonadaptive algorithm with query complexity O(√r⋅ poly(1/λ)) that, when the LIS is of length at least λ n, outputs a multiplicative Ω(λ)-approximation to the LIS length. Our algorithm improves upon the state of the art nonadaptive LIS estimation algorithm (Rubinstein, Seddighin, Song, and Sun; 2019) in terms of the approximation guarantee.Finally, we present a O(log n)-query nonadaptive erasure-resilient tester for monotonicity. Our result implies that lower bounds on erasure-resilient testing of monotonicity does not give good lower bounds for LIS estimation. It also implies that nonadaptive tolerant testing is strictly harder than nonadaptive erasure-resilient testing for the natural property of monotonicity.
Language Relatedness and Lexical Closeness can help Improve Multilingual NMT: IITBombay@MultiIndicNMT WAT2021

Multilingual Neural Machine Translation has achieved remarkable performance by training a single translation model for multiple languages.This paper describes our submission (Team ID: CFILT-IITB) for the MultiIndicMT: An Indic Language Multilingual Task at WAT 2021.We train multilingual NMT systems by sharing encoder and decoder parameters with language embedding associated with each token in both encoder and decoder.Furthermore, we demonstrate the use of transliteration (script conversion) for Indic languages in reducing the lexical gap for training a multilingual NMT system.Further, we show improvement in performance by training a multilingual NMT system using languages of the same family, i.e., related languages.
Automatic Sentence Simplification in Low Resource Settings for Urdu

To build automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification systems. We present a lexical and syntactically simplified Urdu simplification corpus with a detailed analysis of the various simplification operations and human evaluation of corpus quality. We further analyze our corpora using text readability measures and present a comparison of the original, lexical simplified and syntactically simplified corpora. In addition, we compare our corpus with other existing simplification corpora by building simplification systems and evaluating these systems using BLEU and SARI scores. Our system achieves the highest BLEU score and comparable SARI score in comparison to other systems. We release our simplification corpora for the benefit of the research community.
On Knowledge Distillation for Translating Erroneous Speech Transcriptions

Recent studies argue that knowledge distillation is promising for speech translation (ST) using end-to-end models.In this work, we investigate the effect of knowledge distillation with a cascade ST using automatic speech recognition (ASR) and machine translation (MT) models.We distill knowledge from a teacher model based on human transcripts to a student model based on erroneous transcriptions.Our experimental results demonstrated that knowledge distillation is beneficial for a cascade ST.Further investigation that combined knowledge distillation and fine-tuning revealed that the combination consistently improved two language pairs: English-Italian and Spanish-English.
Two Efficient Algorithms for Orthogonal Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a popular method for the multivariate analysis of nonnegative data. It involves decomposing a data matrix into a product of two factor matrices with all entries restricted to being nonnegative. Orthogonal nonnegative matrix factorization (ONMF) has been introduced recently. This method has demonstrated remarkable performance in clustering tasks, such as gene expression classification. In this study, we introduce two convergence methods for solving ONMF. First, we design a convergent orthogonal algorithm based on the Lagrange multiplier method. Second, we propose an approach that is based on the alternating direction method. Finally, we demonstrate that the two proposed approaches tend to deliver higher-quality solutions and perform better in clustering tasks compared with a state-of-the-art ONMF.
An Analysis of “nyx” (2017), a Computer Music Work by Kerry Hagan

Abstract Kerry Hagan composed “nyx,” a real-time computer music work, in 2017. The key inspiration of “nyx” is noise, which Hagan achieves through chaos theory. In Greek mythology, Nyx was born from Chaos, and she is the deity of the night. In the same way, two key materials of the work are chaotic synthesis and, at a higher level, the use of several random algorithms. To analyze “nyx,” I apply a specific methodology that considers both the sound and the computer code. In fact, I consider code as a medium through which Hagan has realized her musical ideas, that is to say, seeing code as a support for musical ideas, and not only as a computing object. This article has two main sections: The first describes Hagan's techniques through Pure Data code, and in the second the music is analyzed in its canonical form, describing the structure of the work. Finally, I argue that “nyx” involves many levels of noise, from the sound design, to the use of random algorithms, and lastly to the inspiration from Greek mythology to structure the work.
A LIFETIME PORTRAIT OF TSAR IVAN THE TERRIBLE: THE VISUALIZATION OF A FADED MONUMENT BY NATURAL SCIENCE METHODS

The collections of the State Historical Museum keep a single offering copy of the first-printed Apostle 1564 by Ivan Fedorov with the image of the tsar Ivan the Terrible. This portrait was made in the embossing technique on the cover of the binding and is now almost completely extinct. This extinguished image was investigated using multispectral macro photography and subsequent computer processing. It has allowed visualizing the tsar portrait. This image is the only lifetime portrait of the most famous ruler of Russia, which survived to our time. Embossing specifics on the cover of the binding suggests the use of engraving on metal for the manufacture of this image. This is one of the first example of this technique in Russia. The features of the portrait and the embossing of both covers of the binding corresponded to the Western European standards for the design of an elite book, which came to Russia along with typography. The portrait of the king on the chest of a heraldic eagle accompanied by his titles served as a representation of the royal status of Ivan IV, that had been recognized by the Oriental patriarchs for three years before. A review of the existing portraits of Ivan the Terrible led to the conclusion that the resulting image was the only lifetime portrait of the king, which has close correspondences with the reconstruction of his face on the skull, made by M. M. Gerasimov. The history of the existence of a royal copy of Apostol of 1564 from the moment of its creation to the present day is reconstructed in the article.