Reviewerly/exHarmony huggingface dataset:
Error Analysis on Voicing Sounds Uttered by English Department Students at State Islamic Institute of Madura

English pronunciation is unique and said as badly spelled. The difference between what has been writtenwith what they should pronounce is a vital factor.This study is aimed at finding out the error in pronouncing the voiced and voiceless sounds made by the students of Tadris Bahasa Inggris IAIN Madura.The study uses a descriptive design with qualitative data.The population is second-semester students who are taking pronunciation subject in C class with 32 students.The result shows that of 10 minimal pairs tested to the students, the percentage students who can pronounce the words correctly is that 37% students for 1st minimal pairs, 81% for 2nd one, 31% for 3rd one, 50% for 4th one, for 5th one 31% in the word 'laugh' and 87% in the word 'love', 21% for 6th one, for the 7th one 6% in the word 'measure' and 18% in the word 'sure', for the 8th one 9% in the word 'stronger' and 84% in the word 'stranger', for the 9th one 21% in the word 'eyes' and 93% in the word 'rats' and 31% in the word 'buses', for the 10th one 31% in the word 'robbed' and 31% in the word 'washed' and 12% in the word 'needed'.It can be concluded that most of the students are not aware of voicing sounds in English pronunciation.It is suggested that giving drill of minimal pairs and introducing them to places and manners of articulation might be able to improve their awareness to produce correct pronunciation.
Phone Clustering Methods for Multilingual Language Identification

This paper proposes phoneme clustering methods for multilingual language identification (LID) on a mixed-language corpus. A one-pass multilingual automated speech recognition (ASR) system converts spoken utterances into occurrences of phone sequences. Hidden Markov models were employed to train multilingual acoustic models that handle multiple languages within an utterance. Two phoneme clustering methods were explored to derive the most appropriate phoneme similarities between the target languages. Ultimately a supervised machine learning technique was employed to learn the language transition of the phonotactic information and engage the support vector machine (SVM) models to classify phoneme occurrences. The system performance was evaluated on mixed-language speech corpus for two South African languages (Sepedi and English) using the phone error rate (PER) and LID classification accuracy separately. We show that multilingual ASR which fed directly to the LID system has a direct impact on LID accuracy. Our proposed system has achieved an acceptable phone recognition and classification accuracy in mixed-language speech and monolingual speech (i.e. either Sepedi or English). Data-driven, and knowledge-driven phoneme clustering methods improve ASR and LID for code-switched speech. The data-driven method obtained the PER of 5.1% and LID classification accuracy of 94.5% when the acoustic models are trained with 64 Gaussian mixtures per state.
A STUDY ON STUDENTS’ EXPERIENCE IN VIRTUAL CLASSROOM DURING COVID-19

Appears in: ICERI2020 Proceedings Publication year: 2020Pages: 6097-6106ISBN: 978-84-09-24232-0ISSN: 2340-1095doi: 10.21125/iceri.2020.1307Conference name: 13th annual International Conference of Education, Research and InnovationDates: 9-10 November, 2020Location: Online Conference
Context Flow Graphs: Situation Modeling for Rule-Based Proactive Context-Aware Systems

A proactive context-aware system automatically adapts its user interface to the user's situational needs. This is achieved by continuously capturing the environmental properties, reasoning upon the context, and detecting situations where unsolicited adjustments are helpful or notifications informative. If the characteristics of those situations are well known in advance, their occurrence can be detected at runtime by the rule-based processing of raw sensor data. However, rule-based context reasoning methods determine the user's situation mostly based on present sensor signals instead of considering the situation to be likewise the product of the past context. This article introduces a graph-based situation modeling formalism for the specification of system-relevant environmental circumstances as context flow graphs. A directed cyclic graph represents thereby the distinct contextual characteristics a user's situation is made of and the temporal order in which these appear and disappear during the evolution of the situation. Complex situations for rule-based proactive context-aware systems can then be expressed at a high level of abstraction and without the need to understand the underlying sensor-related signal processing mechanisms. The technical feasibility is demonstrated by a prototypical distributed proactive context-aware middleware that, in addition, comes up with a web-based user interface for the interactive graphical and logical modeling of situations as context flow graphs.
세계영어에 관한 국내 연구 동향 분석

This study examined research articles on World Englishes (WE) and English as a Lingua Franca (ELF) published in the Korean domestic journals. For the purpose of understanding the research trends in this academic field, a total of 158 articles were classified and coded in terms of the following areas: annual trend, type of journals, number of authors, research method, research type, research target, and research topic. The results showed that there existed an increasing trend in the number of articles in the past 15 years. A fair number of articles were published in journals specializing in English education, suggesting that WE is becoming a main concern among researchers in the field of English education. Singleauthored articles were far more than multiple-authored articles, and approximately 70% of the authors published only one article. In terms of research methods, the researchers conducted more empirical studies than theoretical studies, using quantitative methods more than qualitative methods or mixed methods. Among various research types, survey research and text (written or spoken) analysis research were the two most frequently used. As for the research target, undergraduate/graduate students were the most studied participants. Lastly, the most popular research topic was perceptions of and attitudes toward WE, followed by English teaching and learning. Based on the results, suggestions for future research are provided.
Detection of Illegal Parking Events Using Spatial-Temporal Features

In this work, we propose a novel deep learning framework, called Attention-Based 2-layer Bi-ConvLSTM (denoted as Att-2BiConvLSTM) model, to predict the number of illegal-parking events in urban spaces. We model the research as a "next frame" prediction problem, which aims to improve urban transportation conditions and enhance the security and right-of-way for pedestrians. Various features in the prediction model are considered: some of them (e.g., hourly weather, traffic volumes) are dynamic every hour, while others (e.g., road network, point-of-interests) are static. To boost the effectiveness of static features, we propose a dynamic training process to transform the static features into dynamics. After that, all features can vary with time so that they are capable of handling a real-time prediction scenario. Moreover, we propose an attention mechanism for enhancing our bi-directional ConvLSTM model. With experimental verifications, we find that our proposed Att-2BiConvLSTM model can outperform other state-of-art and baseline methods. Besides, our model is useful for combining all features to make an accurate prediction.
Usage of Cloud Computing and Big data for Internet of Things

The Internet of Things (IoT) has been very popular among researchers' study topics, especially for the past few years. Cloud Computing and Big data are two current issues that are increasingly attracted by almost everyone and that we frequently encounter in our daily lives. With these technologies, large amounts of data collected today can be stored in large-sized storage systems using information infrastructures and high-level calculations can be made. There are various requirements for objects to interact with stored data and communicate with other devices, which is an important opportunity in today's competitive environment. In this study, the relationship between the internet of things, Cloud computing, and Big data are examined. The aim of this study is to provide general information to researchers who will conduct data-based studies on Cloud systems in order to use the Internet of Things and related methods and technologies effectively.
Relationship between Mindfulness, Social Support, and Turnover Intention of Early Childhood Teachers

The purpose of this study is to grasp the relationship between early childhood teacher's mindfulness, social support, and turnover intention.The study was conducted on 115 infant teachers at OO city.As an analysis method, reliability analysis, frequency analysis, and correlation analysis were performed using the SPSS 22.0 program, and multiple regression analysis was performed on the causal relationship between variables.As a result of the study, first, it was found that the de-centered attention and present awareness factors, which are sub-factors of the mindfulness of early childhood teachers, had a significantly negative (-) effect on turnover intention.Second, early childhood teachers' mindfulness and social support had a significantly negative (-) effect on their turnover intentions.Third, the information support and material support factors, which are the sub-factors of social support for early childhood teachers, were found to have a significant negative (-) effect on turnover intention, and the emotional support and evaluation support factors had no significant effect.This means that the higher the degree of decentralization of mindfulness and present awareness perceived by early childhood teachers and the more informational support and material support of social support, the lower the turnover intention.The implications of this study are considered to be the basic data for the development of capacity building programs for the preparation of measures to reduce the intention of turning off early childhood teachers.
THE USE OF FREEZE FRAMING TECHNIQUE FOR TEACHING ENGLISH TO YOUNG LEARNERS AT TOWN FOR KIDS PRESCHOOL JAMBI

This research attempted to describe how the teacher used freeze framing technique engaged the students during the teaching and learning process. It also attempted to describe how the students responded toward the use of this technique. The participants of this research were Kindergarten 1 Pink students. Observation checklists, field note, video recorder and interview protocol were used as the instruments. This research was done as a classroom action research consisted of three cycles. Each cycles conducted in three meetings. In each meeting the researcher implemented the technique while she used video as the media. First, the researcher froze the video and then she asked several questions to the students. The questions were related to the video. She let the students answered freely without judging their answers. After that, she played the video again and let them watched the video to know whether their answers were correct or not. The result of this research showed that the students were engaged actively during the discussion. They were able to answer the teacher’s questions as well as share their ideas. Consequently, Freeze Framing Technique improved the teaching and learning process. The students became more active and involved during the classroom discussion. In conclusion, Freeze Framing Technique was helpful for the teacher in teaching English to young learners
A Metric to Compare Pixel-Wise Interpretation Methods for Neural Networks

There are various pixel-based interpretation methods such as saliency map, gradient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> input, DeepLIFT, integrated-gradient- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , etc. However, it is difficult to compare their performance as it involves human cognitive processes. We propose a metric that can quantify the distance from the importance scores of the interpretation methods to human intuition. We create a new dataset by adding a simple and small image, named as a stamp, to the original images. The importance scores for the deep neural networks to classify the stamped and regular images are calculated. Ideally, the pixel-based interpretation has to successfully select the stamps. Previous methods to compare different interpretation methods are useful only when the scale of the importance scores is the same. Whereas, we standardize the importance scores and define the measure to ideal scores. Our proposed method can quantitatively measure how the interpretation methods are close to human intuition.
String Diagrams for Regular Logic (Extended Abstract).

Regular logic can be regarded as the internal language of regular categories, but the logic itself is generally not given a categorical treatment. In this paper, we understand the syntax and proof rules of regular logic in terms of the free regular category FRg(T) on a set T. From this point of view, regular theories are certain monoidal 2-functors from a suitable 2-category of contexts -- the 2-category of relations in FRg(T) -- to that of posets. Such functors assign to each context the set of formulas in that context, ordered by entailment. We refer to such a 2-functor as a regular calculus because it naturally gives rise to a graphical string diagram calculus in the spirit of Joyal and Street. We shall show that every natural category has an associated regular calculus, and conversely from every regular calculus one can construct a regular category.
VRSurvival Runner: Aplikasi Exergaming Survival berbasis Virtual Reality

Exercising is not something that many people enjoy, and often a burden. In Indonesia, the prevalence of overweight and obesity is on a steady rise since 2013. One of the cause of these trend is the sedentary lifestyle. Lack of physical activities in this lifestyle has lead to poor cardiorespiratory fitness which proven to increase mortality rate. The purpose of this research is to create android game based on virtual reality and with the genre of exergaming. This research aims to create a new and variative ways to exercise without going out of your home dan to create a new atmosphere of exercising that is fun because of the survival horror genres. This research is using prototyping models and using Unity 3D as the game-engine that allow the application to run on Android Devices. The result of this research is VRSurvival Runner, an android game that allows the player to run, jump, to avoid zombies that are chasing the player, and also this game has a feature of counting the estimated burned calories while playing the games. A trial test on the app also has been done without any problem.
Recognizing Developer Activity Based on Joint Modeling of Code and Command Interactions

Recognizing the current activity of a software developer (e.g., debugging, reading, editing) can improve the effectiveness of recommendation systems that aim to reduce the cognitive load of information lookup during software development. Current recommendation systems based on developer activity detection focus primarily on a single dimension of developer behavior, e.g., both observing and recommending IDE commands or source code classes. In addition, the current state of the art techniques require that the number and type of activities exhibited by developers is pre-specified and that labeled interaction data is provided as input. In this article, we propose the use of an approach that eschews these requirements, leveraging an unsupervised statistical model that uses both IDE commands and source code accesses to discern latent developer activities. Our approach outperforms baseline supervised and unsupervised learning techniques on simulation-based evaluation of source code and command recommendations for most of the configurations we examined. We also show that our technique benefits from observing both commands and code accesses when identifying developer activity.
Feature extraction and identification of gas–liquid two-phase flow based on fractal theory

Due to the gas–liquid two-phase flow system with nonlinear characteristics, the fractal theory has a significant impact on nonlinear analysis, so the paper proposes applying the fractal theory to characterize the fractal characteristics of two-phase flow. Firstly, it performs the mathematical morphology fractal dimension (MMFD) to analyse the fractal dimension of typical signals, and the box-counting dimension is employed as a comparison. The results indicate that the MMFD has better accuracy in estimating the fractal dimension of the typical signals. The MMFD can reflect the complexity and nonlinear of a chaotic system; Finally, it applies the MMFD to extract features and analyse two-phase flow characteristics. The experimental results show that the MMFD can effectively identify signals of different flow patterns, especially the transitional flow pattern, and reflect the complexity of gas–liquid two phases.
Advanced Lecture Skill of FuzzyControl in Python

This paper aim is to provide advanced lecture skill of fuzzy control as intelligent control.Fuzzy set is a quite linguistic expression tool for good intelligent control.Currently, many are interested in deep learning or machining to recognize pattern and to classify figures, but it is not powerful as control tool in small size and lab-scale.However, fuzzy has a long history and many have been studying as linguistic expression tool for human being life.Therefore, it is an important to teach effectively and cooperate with Python because deep learning is using Python.This paper describes on how we can have a good lecture of fuzzy control for intelligent control.
Camera Pose Estimation Using Collaborative Databases and Single Building Image

Cities are in constant change and city managers aim to keep an updated digital model of the city for city governance.There are a lot of images uploaded daily on image sharing platforms (as "Flickr", "Twitter", etc.).These images feature a rough localization and no orientation information.Nevertheless, they can help to populate an active collaborative database of street images usable to maintain a city 3D model, but their localization and orientation need to be known.Based on these images, we propose the Data Gathering system for image Pose Estimation (DGPE) that helps to find the pose (position and orientation) of the camera used to shoot them with better accuracy than the sole GPS localization that may be embedded in the image header.DGPE uses both visual and semantic information, existing in a single image processed by a fully automatic chain composed of three main layers: Data retrieval and preprocessing layer, Features extraction layer, Decision Making layer.In this article, we present the whole system details and compare its detection results with a state of the art method.Finally, we show the obtained localization, and often orientation results, combining both semantic and visual information processing on 47 images.Our multilayer system succeeds in 26% of our test cases in finding a better localization and orientation of the original photo.This is achieved by using only the image content and associated metadata.The use of semantic information found on social media such as comments, hash tags, etc. has doubled the success rate to 59%.It has reduced the search area and thus made the visual search more accurate.
Estimating POS Annotation Consistency of Different Treebanks in a Language

We introduce a new symmetric measure (called θ pos ) that utilises the non-symmetric KL cpos 3 measure (Rosa and Žabokrtský, 2015) to allow us to compare the annotation consistency between different treebanks of a given language, annotated under the same guidelines.We can set a threshold for this new measure so that a pair of treebanks can be considered harmonious in their annotation if θ pos does not surpass the threshold.For the calculation of the threshold, we estimate the effects of (i) the size variation, and (ii) the genre variation in the considered pair of treebanks.The estimations are based on data from treebanks of distinct language families, making the threshold less dependent on the properties of individual languages.We demonstrate the utility of the proposed measure by listing the treebanks in Universal Dependencies version 2.5 (UDv2.5)(Zeman et al., 2019) data that are annotated consistently with other treebanks of the same language.However, the measure could be used to assess inter-treebank annotation consistency under other (non-UD) annotation guidelines as well.
AI and the

How might improvements in artificial intelligence (AI) technology affect trademark doctrine? This Article approaches the question by imagining trademark law in a world in which we can fully outsource our purchasing decisions to AIs that satisfy our preferences better than we can. Leaving aside whether the technology is possible, this thought experiment tells us something about today’s trademark doctrine and how it is responding to changing online technology and culture.A sufficiently sophisticated AI would render trademarks superfluous in many situations. Trademarks function by simplifying information. We use them to stand in for a broad range of (sometimes contradictory) data assembled from a variety of sources. Because human cognition is limited, the ability of trademarks to serve as a shortcut is valuable, but it is a second-best solution. With unlimited time or enhanced capacities, we would be better able to find optimal products without relying on the simple information signals offered by trademarks. As it is, sifting through all the available data is not a wise use of our limited attention. But the hypothetical AI is not similarly limited, and a sufficiently advanced AI “shopper” would exist to analyze the context that trademarks allow us to ignore. The role of trademarks in such a world is more limited—and consequently requires less protection—than what we see today.Though the hypothetical AI does not, and may never, exist, we can see its forerunners in web platforms like Amazon and Facebook. They are already changing trademark doctrine, and they illustrate why trademarks may be less important in the future. Though these technologies raise any number of troubling questions, they are not the sort that trademark law is well-equipped to address.