Reviewerly/exHarmony huggingface dataset:
Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution

Winograd schemas are a well-established tool for evaluating coreference resolution (CoR) and commonsense reasoning (CSR) capabilities of computational models.So far, schemas remained largely confined to English, limiting their utility in multilingual settings.This work presents Wino-X, a parallel dataset of German, French, and Russian schemas, aligned with their English counterparts.We use this resource to investigate whether neural machine translation (NMT) models can perform CoR that requires commonsense knowledge and whether multilingual language models (MLLMs) are capable of CSR across multiple languages.Our findings show Wino-X to be exceptionally challenging for NMT systems that are prone to undesireable biases and unable to detect disambiguating information.We quantify biases using established statistical methods and define ways to address both of these issues.We furthermore present evidence of active cross-lingual knowledge transfer in MLLMs, whereby fine-tuning models on English schemas yields CSR improvements in other languages. 1
Aesthetic of friction for exercising motivation

Despite an intention to exercise, it remains a challenge for many people to establish a workout routine over a period of time. Amongst identified barriers and enablers to exercise, getting dressed for a workout is considered as one of the tipping points of actually going. Implementing the Aesthetic of Friction in this specific context, could imply the right course of action for the user, while it also allows freedom and encourages meaning-making. In this Research-through-Design project, we designed an interactive shrinking hanger, that implements these key principles, to encourage exercise motivation. We followed an iterative process focusing on the aesthetics of the interaction to find out how a careful consideration of the look and feel of an interactive artefact influences the acceptance of the implemented friction. We document the design process of this aesthetics of friction exemplar, and reflect on how to implement friction in design.
Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc

During the Covid-19 pandemic, many people access information and even consult health problems online with the best doctors via smartphones. The Halodoc application is considered the most popular with 18 million users in 2020. So that many people have reviewed the application on the Google Play Store application provider. It may take a while to read the full review. However, if only a few comments are read, they are biased. For that, a platform is needed which can automatically identify positive or negative opinions. Sentiment analysis is a solution for the technique of classifying texts or sentiments into positive or negative opinion categories. The method used in this research is an experiment using the Naive Bayes algorithm, Support Vector Machine, and K-Nearest Neighbors. Evaluation is carried out using 10 Fold Cross-Validation. The results showed that K-Nearest Neighbors (KNN) had the best and most accurate performance in the sentiment classification because it produced the highest accuracy value of 95.00% and the largest AUC value of 0.985 compared to the Naive Bayes and Support Vector Machine algorithm.
Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching.

Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the lack of annotated data. The distinguishing case of low performance of multilingual models in CS is the intra-sentence mixing of languages leading to switch points. We first benchmark two sequence labeling tasks – POS and NER on 4 different language pairs with a suite of pretrained models to identify the problems and select the best performing char-BERT model among them (addressing (1)). We then propose a self training method to repurpose the existing pretrained models using a switch-point bias by leveraging unannotated data (addressing (2)). We finally demonstrate that our approach performs well on both tasks by reducing the gap between the switch point performance while retaining the overall performance on two distinct language pairs in both the tasks. We plan to release our models and the code for all our experiments.
Reduction Theory of Algebraic Modules and their Successive Minima

Lattices defined as modules over algebraic rings or orders have garnered interest recently, particularly in the fields of cryptography and coding theory. Whilst there exist many attempts to generalise the conditions for LLL reduction to such lattices, there do not seem to be any attempts so far to generalise stronger notions of reduction such as Minkowski, HKZ and BKZ reduction. Moreover, most lattice reduction methods for modules over algebraic rings involve applying traditional techniques to the embedding of the module into real space, which distorts the structure of the algebra. In this paper, we generalise some classical notions of reduction theory to that of free modules defined over an order. Moreover, we extend the definitions of Minkowski, HKZ and BKZ reduction to that of such modules and show that bases reduced in this manner have vector lengths that can be bounded above by the successive minima of the lattice multiplied by a constant that depends on the algebra and the dimension of the module. In particular, we show that HKZ reduced bases are polynomially close to the successive minima of the lattice in terms of the module dimension. None of our definitions require the module to be embedded and thus preserve the structure of the module.
Multi-view Contrastive Graph Clustering

With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attributed graph data. Specifically, inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method to learn a consensus graph since the original graph could be noisy or incomplete and is not directly applicable. Our method composes of two key steps: we first filter out the undesirable high-frequency noise while preserving the graph geometric features via graph filtering and obtain a smooth representation of nodes; we then learn a consensus graph regularized by graph contrastive loss. Results on several benchmark datasets show the superiority of our method with respect to state-of-the-art approaches. In particular, our simple approach outperforms existing deep learning-based methods.
9.C. Round table: Digital innovation to fight the pandemic, what is here to stay?

Abstract The COVID-19 pandemic has been a challenge to all Public Health and Healthcare systems around the world. Although strategies and results have varied, there has been a clear investment in digital health tools as part of the response in European Countries. From contact tracing mobile apps to dedicated track and trace information systems, the digital transformation of Public Health practice has been accelerated in this moment of crisis. As countries perfect their digital Public Health tools and reform their information systems, a question comes to mind: which digital transformations might be permanent? What apps, systems or other changes can become the new national norm in each country? How will this affect the European landscape on digital health? Those are the questions we intend to discuss with this workshop, learning from the experience of 3 representatives from 3 different countries and debating those perspectives with the audience. The ultimate goal of the workshop is to explore the digital approaches of European countries in the pandemic fight and to extract best case practices both for future crises and for day-to-day public health activity. There will be one speaker from each of the 3 countries: Malta, Portugal and Switzerland. Each one will have 10 minutes to contextualize the Digital Health Strategy of the country before the pandemic, showcase the digital Public Health tools that were developed during the pandemic and conclude with a prediction of what may permanently change in their respective Public Health systems. The second 30 minutes of the roundtable will be reserved for questions and a debate between the speakers and the audience. Speakers/Panelists Stefan Buttigieg Malta Association of Public Health Medicine, St. Paul's Bay, Malta Eduardo Freire Rodrigues ARSLVT ACES Amadora, Amadora, Portugal Marie-Annick Le Pogam Unisanté - Centre Universitaire de Médecine Générale et Santé Publique, Lausanne, Switzerland Key messages With “building back better” as a mantra, sharing national level experiences is key to empower informed decision making and better digital innovation. The challenges of the COVID-19 pandemic accelerated the digital transformation in a way that affected how public health information systems are implemented and scaled up.
A Questionnaire Research on Current Musical dissemination Status of Native Guangxi Bourau Chorus

This thesis utilize “Wenjuanxing”, a platform providing functions equivalent to Amazon Mechanical Turk, to conduct a questionnaire survey about music dissemination of native Bourau chorus among a total of 2241 people of college students and art high school candidates in Guangxi, in order to understand their interest on native Bourau chorus and the way for acknowledgement, analyzing the current attitude and strategic suggestion for music dissemination of native Bourau chorus by current young students, acquiring their recognition degree for performance. SPSS25.0 statistical software was used for reliability and validity data analysis. The Cronbach's Alpha test data is a=0.813>0.8, while KMO and Bartlertt test results are KMO=0.786>0.5, Bartlertt spherical test result is X=8622.153, Sig.=0.000 reached a relatively significant level, Therefore, it can be confirmed that the questionnaire has high reliability and validity and an important reference value. Showing that the primary data is available for the survey of the dissemination of native Bourau chorus.
Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge

In this paper, we study the possibility of almost unsupervised Multiple Choices Question Answering (MCQA). Starting from very basic knowledge, MCQA model knows that some choices have higher probabilities of being correct than the others. The information, though very noisy, guides the training of an MCQA model. The proposed method is shown to outperform the baseline approaches on RACE and even comparable with some supervised learning approaches on MC500.
The Educator’s Role in Lane’s Early Childhood Education Theory

The purpose of this study is to elucidate Lane’s theory of early childhood education and the role of educators in it. In order to achieve the purpose of this study, the background of Lane’s early childhood education theory was investigated. Observations were made upon the essence of his early childhood education theory, early childhood view, educational purpose, and educational method. Based on this, the role of the educator was suggested. The principal research method was literature study. The main analysis and discussion were focused on Lane’s book, ‘Talks to Parents and Teachers.’According to the study, the role of the educator in Lane’s early childhood education theory is to be a helper for infants who are self-educational beings, supporters who understand and encourage children, and creators of an educational atmosphere.In conclusion, Lane’s argument on early childhood education and the role of educators is persuasive in that it comes from practical experience in the educational field, and it is not mere conveyance of empty educational theories or educational ideologies. In this regard, it has great implications for early childhood education in Korea.
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training

Normalization techniques have become a basic component in modern convolutional neural networks (ConvNets). In particular, many recent works demonstrate that promoting the orthogonality of the weights helps train deep models and improve robustness. For ConvNets, most existing methods are based on penalizing or normalizing weight matrices derived from concatenating or flattening the convolutional kernels. These methods often destroy or ignore the benign convolutional structure of the kernels; therefore, they are often expensive or impractical for deep ConvNets. In contrast, we introduce a simple and efficient "Convolutional Normalization" (ConvNorm) method that can fully exploit the convolutional structure in the Fourier domain and serve as a simple plug-and-play module to be conveniently incorporated into any ConvNets. Our method is inspired by recent work on preconditioning methods for convolutional sparse coding and can effectively promote each layer's channel-wise isometry. Furthermore, we show that our ConvNorm can reduce the layerwise spectral norm of the weight matrices and hence improve the Lipschitzness of the network, leading to easier training and improved robustness for deep ConvNets. Applied to classification under noise corruptions and generative adversarial network (GAN), we show that the ConvNorm improves the robustness of common ConvNets such as ResNet and the performance of GAN. We verify our findings via numerical experiments on CIFAR and ImageNet.
A Robotic Software Framework for Autonomous Navigation in Unknown Environment

Autonomously navigating a robot in an unknown scene comprises tasks such as mapping and localization from the percepts via sensors, safe motion-planning under partial knowledge of the scene, and finally, plan execution using the available motion primitives of the robot. Though many software systems are available for these sub-tasks, there is a lack of integrated software that support autonomous navigation. In this paper, we present an integrated software framework comprising of simultaneous localization and mapping module using visual odometry, a motion-planning module based on constraint-solving, and a plan-execution module particularly designed for a lizard-inspired quadruped robot. To demonstrate its efficacy, we present a number of navigation tasks to the software framework in various indoor simulation settings together with a model of the lizard-inspired quadruped robot and show the results.
THE PRACTICE OF ENGLISH CLUB IN IMPROVING STUDENTS’ SPEAKING ABILITY AT SMAN 1 GONDANG TULUNGAGUNG

ABSTRACTOctaviyani, Anggi. 2020. Registered Number, 17203163108. The Practice of English Club in Improving Students’ Speaking Ability at SMAN 1 Gondang Tulungagung. Thesis. English Education Department. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Muh. Basuni, M. Pd.Keywords: English club, English club activities, contributons of English club, Speaking Improvement The students' achievement is related with learning strategy. In case, English club is one of strategy that used in improving English ability. The students who join English club have more English proficiency than students who didn’t join English club. Practically, the activities in English club offered many advantages towards students’ English ability. Especially speaking ability improvement. The formulation of the research problem were: 1) What are the activities of English club in improving students’ speaking ability at SMAN 1 Gondang Tulungagung? (2) How are the contributions of the of English club activities toward the improvement of students’ speaking ability? The purpose of this study were: 1) To describe the activities of English Club program SMAN 1 Gondang Tulungagung in improving students’ speaking ability. (2) To describe the contributions of the activities of English club toward the improvement of students’ speaking ability.The research design in this research is descriptive qualitative. The subjects of the study were the teacher and 5 students who join English club at SMAN 1 Gondang Tulungagung. The researcher use interviews as the instrument of the research. The data analysis in this research are data condensation, data display and the last is drawing and verifying conclusion. Data trustworthiness checking of the research was triangulation by data source and triangulation by method. The result showed that the activities of English club were speech, storytelling, group discussion, drilling, reading aloud, and communication game. The game were guessing words game, chain massage game, and scrabble game. Meanwhile in developing the activities of English club, the teacher used appropriate method in teaching and create a comfortable learning atmosphere so that students are calmer and enjoy in learning English. Furthermore, the set of activity gave the contribution to the students’ speaking ability in some aspect. Those are; building up the students’ self-confidence, increasing the grammar mastery, increasing the vocabularies mastery, increasing the pronunciation ability, increasing the fluency and organizing idea fast
Exploring Task Difficulty for Few-Shot Relation Extraction

Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRE tasks, where the relations are fine-grained and similar to each other. We argue this is largely because existing models do not distinguish hard tasks from easy ones in the learning process. In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information. We further design a method that allows the model to adaptively learn how to focus on hard tasks. Experiments on two standard datasets demonstrate the effectiveness of our method.
STUDENTS’ PERCEPTION OF ONLINE LEARNING ATENGLISH EDUCATION DEPARTEMENT FACULTY OFTARBIYAH AND TEACHER TRAINING OF IAINTULUNGAGUNG

ABSTRACTRahmawati, Putri Ayu Dewi. 2020. Registered Number, 17203163032. Students’Perception of Online Learning at English Education DepartementFaculty of Tarbiyah and Teacher Training of IAIN Tulungagung.Thesis. English Education Department. State Islamic Institute (IAIN)of Tulungagung. Advisor: Dr. Arina Shofia, M.pd.Keywords: Perception, Online Learning.Perceptions is a process that is preceded by the sensing process, which isthe process of receiving stimulus by individuals through the senssory devices oralso called sensory processes. Online learning is process delivered the materialused media. The Students’ perceptions is the argument of students’ about onlinelearning at English Education Department.This research aim to find out about students’ perceptions of onlinr learningin this Era and formula of the research problems was “ what are students’perceptions of online learning at English Educations Departement Faculty ofTarbiyah and teacher Training IAIN Tulungagung”.The researcher find the data by used quesionare, interview andobservation. And find out the types of applications used during online learningand to find out the supporting factors and inhibitory factors of running onlinelearning. This research was conducted by descriptive quantitatative.The sample inthis study is students’ on TBI (Tadris English Laguage) IAIN TulungagungSemester 3 to semester 7 with the number of respondents 145 students with thenumber of semester 3 as many as 50 students or 34.4% and semester 5 as many as46 students or 31.7% and semester 7 as many as 49 students or 33.8% who haveconducted online lectures. Data collection techniques using interviews andquestionnaires.The findings of this study are the first impression of students about onlinelearning as many as 85 students or 58.6% answer sometimes pleasant and 36students or 24.8% answer unpleasant and 10 students or 10.3%. The result of thisstudy is a lack of interest and passion towards online learning due to lack ofmaterial understanding and innovation of learning to teach in online learning.
Analisis Kualitas Air Hasil Pengolahan Air Hujan Dengan Metode Elektrolisis Menjadi Air Bersih

Water is all water that above or below the ground surface, including sea water that is on land. The need for clean water will increase, while clean water is scarce and must be paid handsomely. Meanwhile, the water source crisis is caused by the increasing need for water as a result of the growing population and land functions that can change the hydrological cycle. One way to meet human needs for clean water is by establishing the concept of rainwater harvesting, which is the concept of collecting rainwater that is stored in a reservoir and then the collected water can be used as an alternative source of water. The purpose of this study was to analyze the quality of rainwater before electrolysis and to determine the pH and TDS values of the permanent yield of rainwater and electrolysis. The results of this study are based on the results of laboratory tests showing that the overall quality of water in rainwater meets PerMenKes N0.416 of 1990. For the decreased pH and TDS values in the electrolysis process using a 1 ampere current adapter with a voltage of 300 volts has increased by 0 , 2-0.4 cm in acidic water. As well as for the area of titanium that affects the electrolysis process, the smaller the size of the titanium, the faster it will produce acid water. Keywords: water, rainwater, rainwater harvesting concept, electrolysis method, acid water.
Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Abstract Computational Fluid Dynamics (CFD) simulations are useful to the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the Super-Resolution Generative Adversarial Network (SRGAN) model to a multiphase turbulent fluid flow problem, specifically to reconstruct fluid phase fraction at a higher resolution. Two models were created in this work, one with a simple physics-constrained loss function and one without, and the results are discussed and analyzed. We found that both models were able to significantly outperform non-machine learning upsampling methods and can preserve an impressive amount of detail and nuance, showing the versatility of the SRGAN model for upsampling fluid simulations. However, the difference in accuracy between the two models is quite minimal. This indicates that, for these contexts studied here, the additional complexity of a physics-informed approach may not be justified.