Reviewerly/exHarmony huggingface dataset:
Enhancing Students’ Literacy Competence Through Writing Bilingual Folklore Book by IT: Process Approach

Writing bilingual storybooks based on local wisdom is a product of the Prose Appreciation course. The results of the evaluation so far indicate that students' low literacy skills in developing literary works such as short stories, novels, and bilingual folklore books with IT-based ISBN in 8 ethnic groups in North Sumatra. There is also a phenomenon of the influence of globalization from the use of the internet which removes boundaries between countries resulting in the spread of globalization itself which has led to increasing interest among teenagers or students towards modern literary works originating from outside Indonesia such as America, Europe, and from Asian countries. For this reason, this study will be developed to improve st udent literacy competencies through 6 types of Tasks KKNI in making IT-based ISBN-based folklore books for 8 ethnic groups in North Sumatra. Indirectly, this research explores the elements of the existing culture and is also a measure to preserve local culture. This study uses the Classroom Action Research Method (PTK). This activity is carried out through several stages of the process are a. Training in writing folklore literary works based on local wisdom in Taman Budaya. b. Review process by internal reviewers (students (peer review), and lecturers). c.Validation by internal and external expert teams. d. Publishes ISBN books of bilingual folk tales. e. Create a blog about bilingual folklore books.
A Hyperbolic Arnold's Cat Map-Based System for Multimedia Data Encryption

Cryptography is one of the most used techniques to secure data since antiquity. It has been largely improved by introducing several mathematical concepts. This paper proposes a new asymmetric cryptography approach using combined Arnold's cat map with hyperbolic function and Chebyshev chaotic map for audio and image encryption. The proposed scheme uses Chebyshev map for public and secrete keys generation and the same equation with Arnold's cat map for encryption and decryption. Hyperbolic functions are also introduced replacing regular integer values in Arnold's map. The results show a good and promising efficiency as well as the theoretical discussion. Several future possible improvements are presented in the conclusion.
University Students’ Learning Behavior, Online Learning Satisfaction, University Satisfaction, and Emotional Difficulties according to ADHD Tendencies and Gender in Covid-19 CRISIS

Purpose: The purpose of this study was to examine the learning and emotional difficulties that students with ADHD tendencies experienced at a local university where its’ learning environment has changed dramatically due to the crisis of COVID-19. The differences in learning behaviors, online learning satisfaction, and university satisfaction according to university students’ ADHD tendencies, gender, and the level of emotional difficulties such as depression and anger were investigated. The effects of the coexistence of ADHD tendencies and emo-tional difficulties on those differences were also examined. Method: A total of 1,032 students in the K university at Daegu participated in this study. The learning behav-iors, online learning satisfaction, university satisfaction, and the level of emotional difficulties including depres-sion and anger were surveyed. The collected data were analyzed, first, using t-test in order to examine the dif-ferences according to ADHD tendencies, gender, and the level of emotional difficulties. Next, the participants were divided into four groups according to their ADHD tendencies/depression level and ADHD tendencies/anger level, and then t-test and ANOVA were conducted. Finally, two-way ANOVA was implemented to investigate whether the differences among those four groups differed by gender. Results: First, university students with ADHD tendencies tended to be more passive and neglected in their learning than those without ADHD tendencies. And, their online learning satisfaction and university satisfaction were also significantly lower than those of students without ADHD tendencies. Second, while female students were more active in learning and their level of online learning satisfaction and university satisfaction were higher than those of male students, they also showed higher levels of emotional difficulties such as depression and anger than male students. Third, the learning behavior, online learning satisfaction, and university satisfac-tion of the students, who had the coexistence of ADHD tendencies and emotional difficulties such as depression or anger, were significantly lower than those of students who had not, regardless of their gender. Conclusion: This study shows that there are differences in learning behavior, online learning satisfaction, university satisfaction, and emotional difficulties of university students according to their ADHD tendencies and gender. As COVID-19 shows no signs of subsidence, universities in Korea are more likely to offer online classes in 2021 again. At the same time, the problem of depression and anger among university students is likely to become even more serious due to prolonged COVID-19. This study indicates that university students ADHD tendencies, gender, and emotional difficulties need to be considered in order to effectively implement online learning in Korean universities in the COVID-19 crisis.
OFFLangOne@DravidianLangTech-EACL2021: Transformers with the Class Balanced Loss for Offensive Language Identification in Dravidian Code-Mixed text.

The intensity of online abuse has increased in recent years. Automated tools are being developed to prevent the use of hate speech and offensive content. Most of the technologies use natural language and machine learning tools to identify offensive text. In a multilingual society, where code-mixing is a norm, the hate content would be delivered in a code-mixed form in social media, which makes the offensive content identification, further challenging. In this work, we participated in the EACL task to detect offensive content in the code-mixed social media scenario. The methodology uses a transformer model with transliteration and class balancing loss for offensive content identification. In this task, our model has been ranked 2nd in Malayalam-English and 4th in Tamil-English code-mixed languages.
Persian SemCor: A Bag of Word Sense Annotated Corpus for the Persian Language

Supervised approaches usually achieve the best performance in the Word Sense Disambiguation problem. However, the unavailability of large sense annotated corpora for many low-resource languages make these approaches inapplicable for them in practice. In this paper, we mitigate this issue for the Persian language by proposing a fully automatic approach for obtaining Persian SemCor (PerSemCor), as a Persian Bag-of-Word (BoW) sense-annotated corpus. We evaluated PerSemCor both intrinsically and extrinsically and showed that it can be effectively used as training sets for Persian supervised WSD systems. To encourage future research on Persian Word Sense Disambiguation, we release the PerSemCor in http://nlp.sbu.ac.ir.
EdiSensor: Facilitating Food Electricity for Eating Habits Analysis

Eating is an essential task and has a significant impact on our emotional and physical conditions. Monitoring an eating behavior such as biting activity could optimize the eating habit. In this paper, we explore a new method to monitor eating behavior by facilitating the electrical conductivity of food material and turning such normal food into a biting detection sensor. To verify the potential of our method, we studied the changes in resistance of a food sensor when biting with different amounts of food's surface area. Our results show that the resistance changed when the participant bit the food sensor and increased linearly afterward. We believe that our proposed method could potentially be used as the biting activity monitoring tool. For example, our food sensor when integrated with smart devices could simultaneously estimate the eating speed, alert the person when they eat too fast or record the eating behavior without requires additional hardware.
Home sleep monitoring based on wrist movement data processing

In this paper, two original sleep monitoring algorithms, including threshold and k-means clustering algorithms are presented. All the proposed algorithms use only acceleration data acquired from the non-dominant wrist with a 3-axis accelerometer, allowing the detection of falling asleep and waking up and a classification into 4-sleep stages (“awake”, “light sleep”, “deep sleep” and “REM”). We validate the proposed methods by comparing them to the results of “Fitbit Charge 2” and “Withings Sleep Analyzer”. Based on wrist movement data collected during 10 nights of sleep of a volunteer, we can show that the algorithms obtain promising results that allow us to consider a new non-intrusive method for users and medical staff to follow the trend of sleep quality through long term monitoring. This longitudinal monitoring can help to detect abnormal changes in sleep that are usually a sign of a change in health status.
Soft Bigram distance for names matching

Background Bi-gram distance (BI-DIST) is a recent approach to measure the distance between two strings that have an important role in a wide range of applications in various areas. The importance of BI-DIST is due to its representational and computational efficiency, which has led to extensive research to further enhance its efficiency. However, developing an algorithm that can measure the distance of strings accurately and efficiently has posed a major challenge to many developers. Consequently, this research aims to design an algorithm that can match the names accurately. BI-DIST distance is considered the best orthographic measure for names identification; nevertheless, it lacks a distance scale between the name bigrams. Methods In this research, the Soft Bigram Distance (Soft-Bidist) measure is proposed. It is an extension of BI-DIST by softening the scale of comparison among the name Bigrams for improving the name matching. Different datasets are used to demonstrate the efficiency of the proposed method. Results The results show that Soft-Bidist outperforms the compared algorithms using different name matching datasets.
AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets

This paper presents our strategy to tackle the EACL WANLP-2021 Shared Task 2: Sarcasm and Sentiment Detection. One of the subtasks aims at developing a system that identifies whether a given Arabic tweet is sarcastic in nature or not, while the other aims to identify the sentiment of the Arabic tweet. We approach the task in two steps. The first step involves pre processing the provided dataset by performing insertions, deletions and segmentation operations on various parts of the text. The second step involves experimenting with multiple variants of two transformer based models, AraELECTRA and AraBERT. Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.
Discovering closed and maximal embedded patterns from large tree data

Many current applications and systems produce large tree datasets and export, exchange, and represent data in tree-structured form. Extracting informative patterns from large data trees is an important research direction with multiple applications in practice. Pattern mining research initially focused on mining induced patterns and gradually evolved into mining embedded patterns. A well-known problem of frequent pattern mining is the huge number of patterns it produces. This affects not only the efficiency but also the effectiveness of mining. A typical solution to this problem is to summarize frequent patterns through closed and maximal patterns. No previous work addresses the problem of mining closed and/or maximal embedded tree patterns, not even in the framework of mining multiple small trees. We address the problem of summarizing embedded tree patterns extracted from large data trees, by defining and mining closed and maximal embedded unordered tree patterns. We design an embedded frequent pattern mining algorithm extended with a local closedness checking technique. This algorithm is called closedEmbTM-eager as it eagerly eliminates non-closed patterns. To mitigate the generation of intermediate patterns, we devise pattern search space pruning rules to proactively detect and prune branches in the pattern search space which do not correspond to closed patterns. The pruning rules are accommodated into the extended embedded pattern miner to produce a new algorithm, called closedEmbTM-prune, for mining all the closed and maximal embedded frequent patterns. Our extensive experiments on synthetic and real large-tree datasets demonstrate that, on dense datasets, closedEmbTM-prune not only generates a complete closed and maximal pattern set which is substantially smaller than that generated by the embedded pattern miner, but also runs much faster with negligible overhead on pattern pruning.
DATAtourist

Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.
Generative Adversarial Network

Generative Adversarial Network has great potential and a lot of applications. They can be used in nearly every possible field and production sector. The rapid research developments had provided hope for developing a lot of creative applications using GAN. Each variation of GAN provides new features. These variations are enveloping various kinds of neural networks and techniques that help gather complex features and generate more relatable and realistic images as discussed. The vast collection of GAN implies the variety of its applications. The impact of GAN had been on every aspect of creative tasks. Every form of natural and synthetic data has been manipulated using GAN with proper research work. These works on GAN takes us closer and closer to the possibility of creating automated machines that are creative.
PENGEMBANGAN MEDIA POP-UP BOOK PADA PEMBELAJARAN IPA DI SD

Abstract: Development of Pop-Up Book media on science learning in elementary schools. This studyaims to determine the validity level of pop-up book media in grade 1 SDS Syalom. The type ofresearch used is development research (R&D). Researchers used the ADDIE development model. TheADDIE model development method consists of 5 development stages including: (1) Analysis(analysis stage), (2) Design (initial product design stage), (3) Development (product developmentstage), (4) Implementation (product implementation stage), (5) Evaluation (product evaluationstage). Due to the limitations of researchers and the occurrence of the Covid19 pandemic during thestudy, the steps used in media validation were carried out online. Based on the validation resultsfrom material experts, media experts, design experts, it can be concluded that the quality of the popupbookmediadevelopedisintheappropriatecategoryorcanbeusedforgrade1elementaryschoolstudents.Student responses to pop-up book media in the field implementation test got a goodresponse.
Minat Investasi di Kalangan Mahasiswa Generasi Milenial di Surakarta

This research aims to see how the influence of capital market knowledge and returns on student investment interest. This research was conducted using quantitative methods with sample size of 120 respondents. The analysis used is multiple linear regression analysis. The results of this research indicated that capital knowledge has a significant effect on student investment interest, When student understand about capital market knowledge deeply, it willl increase the student interest on invetsment. Return also also has a significant effect on student investment interest. The level of profit offered by the capital market is the basis for student interest in investing
Assessing Grammatical Correctness in Language Learning

We present experiments on assessing the grammatical correctness of learners’ answers in a language-learning System (references to the System, and the links to the released data and code are withheld for anonymity). In particular, we explore the problem of detecting alternative-correct answers: when more than one inflected form of a lemma fits syntactically and semantically in a given context. We approach the problem with the methods for grammatical error detection (GED), since we hypothesize that models for detecting grammatical mistakes can assess the correctness of potential alternative answers in a learning setting. Due to the paucity of training data, we explore the ability of pre-trained BERT to detect grammatical errors and then fine-tune it using synthetic training data. In this work, we focus on errors in inflection. Our experiments show a. that pre-trained BERT performs worse at detecting grammatical irregularities for Russian than for English; b. that fine-tuned BERT yields promising results on assessing the correctness of grammatical exercises; and c. establish a new benchmark for Russian. To further investigate its performance, we compare fine-tuned BERT with one of the state-of-the-art models for GED (Bell et al., 2019) on our dataset and RULEC-GEC (Rozovskaya and Roth, 2019). We release the manually annotated learner dataset, used for testing, for general use.
Social Network Extraction Unsupervised

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.
Pola Sebutan Monoftong Bahasa Jerman oleh Penutur Bahasa Melayu

This study investigates the pronunciation of German monophthongs (L3) by Malay speakers. It also compares the way the L3 monophthongs are produced with the monophthongs produced in Standard Malay (L1) and English (L2) to examine if there are any similarities in the way that equivalent vowels are produced. A total of 10 female Malay speakers who were learning German were recorded reading aloud carrier sentences containing the target vowels in a /bVd/ and a /bVt/ context in all three languages. All these speakers also speak English as a second language. Based on the formant frequency model, the first (L1) and second (L2) formants of the target monophthongs were measured. The duration of the vowels was also measured. Scatter plots of the vowels were also generated to enable comparison among similar sets of vowels within and among the three languages within the context of L3 acquisition. The findings suggest that speakers tend to produce equivalent vowels with similar acoustic properties, whilst tending to collapse â€œnewâ€ ones in the L3 to neighbouring L1 or L2 vowels. The findings from this study contribute to the growing area of L3 acquisition research.
Certifying optimality for convex quantum channel optimization problems

We identify necessary and sufficient conditions for a quantum channel to be optimal for any convex optimization problem in which the optimization is taken over the set of all quantum channels of a fixed size. Optimality conditions for convex optimization problems over the set of all quantum measurements of a given system having a fixed number of measurement outcomes are obtained as a special case. In the case of linear objective functions for measurement optimization problems, our conditions reduce to the well-known Holevo-Yuen-Kennedy-Lax measurement optimality conditions. We illustrate how our conditions can be applied to various state transformation problems having non-linear objective functions based on the fidelity, trace distance, and quantum relative entropy.
UnCanny: Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video

Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang-Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal "false positive" noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.
Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations

Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-crafted frameworks for robotic manipulation. Surprisingly, an extension of these methods to the multiview domain is relatively unexplored. A successful multiview policy could be deployed on a mobile manipulation platform, allowing the robot to complete a task regardless of its view of the scene. In this work, we demonstrate that a multiview policy can be found through imitation learning by collecting data from a variety of viewpoints. We illustrate the general applicability of the method by learning to complete several challenging multi-stage and contact-rich tasks, from numerous viewpoints, both in a simulated environment and on a real mobile manipulation platform. Furthermore, we analyze our policies to determine the benefits of learning from multiview data compared to learning with data collected from a fixed perspective. We show that learning from multiview data results in little, if any, penalty to performance for a fixed-view task compared to learning with an equivalent amount of fixed-view data. Finally, we examine the visual features learned by the multiview and fixed-view policies. Our results indicate that multiview policies implicitly learn to identify spatially correlated features.
Location Trace Privacy Under Conditional Priors.

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a Renyi differentially private framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for every user location in a trace.