Reviewerly/exHarmony huggingface dataset:
A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation

Background The fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences. Objective We propose a new Korean word pair reference set to verify embedding models. Methods From January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Attending physicians (n=16) participated in the verification of the developed set with 607 word pairs. Results The proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (ρ=0.70, P&lt;.001). The intraclass correlation coefficients to assess the interrater agreements of the word pair sets were 0.47 on the similarity task and 0.53 on the relatedness task. The final reference standard was 604 word pairs for the similarity task and 599 word pairs for relatedness, excluding word pairs with answers corresponding to outliers and word pairs that were answered by less than 50% of all the respondents. When FastText models were applied to the final reference standard word pair sets, the embedding models learning medical documents had a higher correlation between the calculated cosine similarity scores compared to human-judged similarity and relatedness scores (namu, ρ=0.12 vs with medical text for the similarity task, ρ=0.47; namu, ρ=0.02 vs with medical text for the relatedness task, ρ=0.30). Conclusions Korean medical word pair reference standard sets for semantic similarity and relatedness were developed based on medical documents from the past 10 years. It is expected that our word pair reference sets will be actively utilized in the development of medical and multilingual natural language processing technology in the future.
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation

We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech recognition as an auxiliary task to improve the performance. CTC's success on translation is counter-intuitive due to its monotonicity assumption, so we analyze its reordering capability. Kendall's tau distance is introduced as the quantitative metric, and gradient-based visualization provides an intuitive way to take a closer look into the model. Our analysis shows that transformer encoders have the ability to change the word order and points out the future research direction that worth being explored more on non-autoregressive speech translation.
Remote Learning Under the COVID-19 Social Distancing: Reflections and a Netnography Study

The purpose of this work is two-fold. First, it intends to discuss and reflect on the transition from face-to-face to remote learning in accounting education because of the COVID-19 pandemic. And second, it presents the results of a netnography study conducted at the beginning of the COVID-19 outbreak in Brazil (March and April of 2020). I also provide some personal experiences during these times. In section 2, I provide key technology resources I found helpful for accounting instructors and students. In section 3, I discuss the implications for instructors and students brought by the COVID-19. I especially appreciate the effors that both instructors and students are doing to keep their academic lives going, despite all the adversity. In section 4, I describe a netnography study in which I examined data from a graduate students' Facebook community. And in section 5, I report closing thoughts.
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks

In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Graph theory is a widely used framework for modeling such complex networks and to evaluate their robustness to disruptions. Particularly, identification of critical nodes/links in a graph can facilitate the enhancement of graph (system) robustness and characterize crucial factors of system performance. Most existing methods of critical node identification are based on an iterative approach that explores each node/link of a graph. These methods suffer from high computational complexity and the resulting analysis is network specific. Additionally, uncertainty associated with the underlying graphical model further limits the potential value of these traditional approaches. To overcome these challenges, we propose a Bayesian graph neural network based node classification framework that is computationally efficient and systematically incorporates uncertainties. Instead of utilizing the observed graph for training the model, a MAP estimate of the graph is computed based on the observed topology and node target labels. Further, a Monte-Carlo (MC) dropout algorithm is incorporated to account for the epistemic uncertainty. The fidelity and the gain in computational complexity offered by the Bayesian framework is illustrated using simulation results.
El futuro de la educación presencial en escuelas secundarias en México.

In “The future on in-person education in middle schools in Mexico” we analyze the shutting down of schools around the world because of the coronavirus pandemic, the institutional projects of virtual and blended learning, and the teacher’s challenges in this period of semi- in-person learning Through an anthropological approach we analyze the school community’s sociocultural situation in Mexico during distance learning. Therefore, with this article we will understand that in the foreseeable future it will be necessary to strength the state education in order to battle educational lagging and ensure blended learning to low-income students.
A new adaptive fuzzy hybrid unscented Kalman/H‐infinity filter for state estimating dynamical systems

State estimation and dynamical model identification from observed data has been an attractive research area with a wide range of applications such as communication, navigation, radar target tracking, and system control. A method of Adaptive Fuzzy Unscented Kalman/H∞ Filter (AFUKH∞) to estimate non-linear systems is presented using a combination of the Unscented Kalman Filter (UKF) and Unscented H∞ Filter (UH∞F). The proposed filter does not need linearisation and is based on a combination of gain, a priori state estimation, and a priori measurement estimation in each time step. The performance of the filter is adaptively adjustable. Thus, its efficiency is better than the other two filters. Two fuzzy logic systems are proposed that determine the weight of the UKF and UH∞F filters at each step. These two fuzzy systems are designed to be independent of the dynamics of the system (problem). The proposed filter is referred to as a hybrid AFUKH∞-II. In the proposed method, the state of the feedback is used as input that improves the efficiency of the filter. The challenge of reentry vehicle tracking and the state estimation of a magnetic motor as two non-linear high-order problems are used as benchmarks, and the results are compared with the UKF, UH∞F, and AFUKH∞ filters. The experiments show that an estimation of the proposed hybrid filter (AFUKH∞-II) is improved against state-of-the-art filters. Also, estimation error and variance values of the proposed filter in the presence of Gaussian noise is decreased by 270% and 370%, respectively, compared with the AFUKH∞ filter.
SeeHear: Signer Diarisation and a New Dataset

In this work, we propose a framework to collect a large-scale, diverse sign language dataset that can be used to train automatic sign language recognition models.The first contribution of this work is SDTrack, a generic method for signer tracking and diarisation in the wild. Our second contribution is SeeHear, a dataset of 90 hours of British Sign Language (BSL) content featuring more than 1000 signers, and including interviews, monologues and debates. Using SDTrack, the SeeHear dataset is annotated with 35K active signing tracks, with corresponding signer identities and subtitles, and 40K automatically localised sign labels. As a third contribution, we provide benchmarks for signer diarisation and sign recognition on SeeHear.
Investigation of Microcirculatory Effects of Experiencing Burning Hands in Augmented Reality

In this paper, we report on our experiment to investigate the extent to which the human body reacts to virtual thermal stimuli presented in Augmented Reality (AR). Our AR experience enables users to see and hear their own hands burning while looking through a Video SeeThrough Head-Mounted Display (VST-HMD). We hypothesized that this AR experience can lead to a change in Skin Blood Flow (SkBF). Our results show that the SkBF in the affected hand did change significantly on some participants. They experienced a change of blood flow similarly to real thermal stimulation.
Emojis in Indonesian Intergenerational Family WhatsApp Group

This study aims to investigate the use of emojis in an Indonesian Intergenerational family WhatsApp group to reveal the types and functions of emojis used in the setting where members from various gender and age groups communicate online.The participants were 26 male and 16 female WhatsApp users aged 21 to 66 years old in the group.The data were collected from five months of interaction in the WhatsApp group.The findings showed that both men and women from various age groups used the following emojis, namely 'folded hands', 'beaming face with smiling eyes', 'palms up together', 'thumbs up', and 'face with hand over mouth', in the WhatsApp group communication.In terms of function, both men and women use emojis for 1) attitude/emotion enhancer, 2) attitude/emotion signal, 3) backchanneling, 4) humour, and 5) illocutionary force.The findings contribute to research on the use of emojis relatively scarce in Indonesian contexts.The findings also suggest how to communicate using emojis in forums with members from various ages and genders.
Rethinking Digital Literacy Practices and Educational Agendas in Times of Covid-19 Uncertainty

ABSTRACT In this article, we problematize literacy issues in the current context of instability and uncertainties generated by the Covid-19 pandemic, from its social, political and educational impacts. Our discussion will focus on fictional vignettes that we composed based on real stories circulating in the media and social networks, and marked (but not restricted to) by the intense use of digital technologies. Our aim is to formulate questions about the relationship among society, literacy, technology and education, which have been of primary concern to us as teachers and researchers of languages and literacy. We do not intend to offer definitive answers or solutions, but only to suggest research paths for studies in literacy and Applied Linguistics, in light of concepts such as multiplicity, emotions, rhizome and nomadic thought.
Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition

The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models. To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases. However, this simple structure may cause serious information loss during the encoding of documents since the queries are agnostic. To address this problem, we design a method to mimic the queries on each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids). To boost the retrieval process using approximate nearest neighbor search library, we also optimize the matching function with a two-step score calculation procedure. Experimental results on several popular ranking and QA datasets show that our model can achieve state-of-the-art results.
Augmented reality for ESL/EFL and bilingual education: an international comparison

In recent decades, educational technologies have enabled new opportunitiesfor teaching and learning on a recurrent basis. In contemporary educationalcontexts, which are mainly oriented towards active methodologies and studentcentered approaches, educational technologies have led to a significantbreakthrough in education. Among emergent educational technologies, it isinteresting to specifically highlight the current importance of Augmented Reality(AR), which allows overlaying of virtual objects (that is, ‘augmented’ elements)into the real world. This paper, after exploring the potential of AR in education, isaimed at comparing pre-service English as a Second Language (ESL), English asa Foreign Language (EFL) and Bilingual Education (BE) teachers’ perceptions,uses and preferences (n = 53) from two universities (Texas Woman’s University,USA, and University of Córdoba, Spain) regarding the use of AR, and analyzewhether there are similarities and/or differences based on sociocultural context,approaches to education or teacher practices. The findings reveal the positiveattitudes towards integrating AR in ESL/EFL and bilingual contexts of theparticipants of the two institutions. The most noted advantages of AR are thatit enhances classroom engagement and its focus is different from traditionalteaching methods, increasing student motivation and facilitating their learningprocesses. Some reported challenges imposed by AR are that access to mobiledevices is limiting, the cost of technology, technical issues and the necessityfor specialized teacher training. We also found other challenges and numerousadvantages of AR usage in ESL/EFL and bilingual contexts, which are discussedthoroughly. Finally, current needs in the field are identified and suggestions areoffered for further research in AR for ESL/EFL and BE.
Webquest as a means of future English teachers’ universal and professional competences development

The article shows the necessity to use information and communication technology in education due to the changes which have happened in the society and in the system of education in the coronavirus pandemic period. The requirements that a future English teacher must meet according to the third-generation higher education federal state standards are analysed. The conclusion that it is reasonable to include the questions concerning the usage of technologies, methods and teaching tools which suppose students’ cognitive, research and project activity in Methodology course in order to improve pedagogic education quality is made. Webquest is considered as a structured by the teacher Internet project, which helps the educator manage students’ research activity in distance and face-to-face learning. An emphasis is made on the fairly rigid structure that is typical of webquests – an introduction, a final task enunciation, a sequence of research steps, leading to the final task execution, presentation, analysis and evaluation. It is stated that the sequence of steps may vary depending on the pedagogic conditions. The author describes a webquest on Foreign Language Methodology that was created on the basis of this research. At the end of the article the questionnaire results of students who participated in the webquest piloting are given; methodological recommendations how to design and employ webquests are presented.
A Systematic Literature Review on Forensic Accounting

The objective of this study is to systematically analyze the existing literature on forensic accounting and thereby identifying the existing gap in the forensic accounting literature. In the era of increasing accounting scandals, the importance of forensic accounting is also increasing. This study provides an overview of previous studies which have been conducted in the area of forensic accounting which can be used by professionals, regulators, academics, researchers, forensic accounting practitioners and etc. The current study systematically analyzes a sample of twenty-four (24) research articles which have been conducted in the broad area of forensic accounting. The sample has been selected from research articles published during the period starting from 2015 to 2020. The results of this study reveal the importance of incorporating forensic accounting education into undergraduate and graduate-level courses. More importantly, Forensic Accountants should have skills in multi-disciplinary areas. Similarly, knowledge of big data is essential to be a good Forensic Accountant. Further, this study suggests four (04) key areas for future researches which are; the importance of forensic accounting education, how forensic accounting could be used to reduce financial collapses practical application of forensic accounting, and the role of Forensic Accountant in the field of auditing.
A Deep Learning and Transfer Learning Approach for Vehicle Damage Detection

According to the U.S. Department of Transportation, there is an average of six million motor vehicle crashes every year in the United States. For insurance companies, it is very time-consuming and expensive to process claims for detecting and classifying vehicle damages; thus, deep learning techniques have been used to automate this process to reduce the time and the cost. In this paper, Mask R-CNN is used for image segmentation to identify and crop vehicles from images. Then a convolutional neural network (CNN) model is built to classify whether or not the vehicles have damages. In addition, transfer learning is utilized in both image segmentation and classification phases to help build the models for vehicle detection and damage detection, using the pre-trained weights from Microsoft COCO dataset and ImageNet, respectively. 864 images of damaged vehicles collected from public websites, such as Google Images, are used in this research. The experiment on the detection of bumper damages has achieved 87.5% accuracy.
Quantifying Lexical Ambiguity in Speech To and From English-Learning Children

Because words have multiple meanings, language users must often choose appropriate meanings according to the context of use. How this potential ambiguity affects first language learning, especially word learning, is unknown. Here, we present the first large-scale study of how children are exposed to, and themselves use, ambiguous words in their actual language learning environments. We tag 180,000 words in two longitudinal child language corpora with word senses from WordNet, focusing between 9 and 51 months and limiting to words from a popular parental vocabulary report. We then compare the diversity of sense usage in adult speech around children to that observed in a sample of adult-directed language, as well as the diversity of sense usage in children's own productions. To accomplish this we use a Bayesian model-based estimate of sense entropy, a measure of diversity that takes into account uncertainty inherent in small sample sizes. This reveals that sense diversity in caregivers' speech to children is similar to that observed in a sample of adult-directed written material, and that children' use of nouns --- but not verbs --- is similarly diverse to that of adults. Finally, we show that sense entropy is a significant predictor of vocabulary development: children begin to produce words with a higher diversity of adult sense usage at later ages. We discuss the implications of our findings for theories of word learning.
FACEBOOK USER REACTIONS AND EMOTION: AN ANALYSIS OF THEIR RELATIONSHIPS AMONG THE ONLINE DIABETES COMMUNITY

With the advent of Web 2.0 technologies such as social media, online text sources provide large scale data repositories out of which valuable knowledge about human emotions can be derived. This paper aims to (i) detect and classify emotions of the Facebook diabetes community, (ii) examine the relationship of emotion and Facebook reactions, and (iii) identify user reaction predictors for each of the emotion. A total of 15K posts were randomly selected from several official Facebook diabetes support groups. Pre-processing was administered, resulting in 2475 Facebook posts for further analysis in this study. Emotion detection was first administered using Indico API, with results revealing anger, sadness and fear to be the top most emotions experienced, whilst love and wow emerged as the highest-ranking reactions. Precision and recall indicate the performance of the emotion detection mechanism ranged between 65 – 82% for all the emotions, compared to the human annotation. The average F-score recorded was 78%. Both love and wow were found to significantly predict joy and fear, whereas angry was found to predict anger. The findings indicate that human emotions can be effectively detected based on users’ textual communication, and significant relationships exists between several reactions and emotions.
Image Caption Generator Using Neural Networks

In this paper, we focus on one of the visual recognition facets of computer vision, i.e. image captioning. This model’s goal is to come up with captions for an image. Using deep learning techniques, image captioning aims to generate captions for an image automatically. Initially, a Convolutional Neural Network is used to detect the objects in the image (InceptionV3). Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) with attention mechanism are used to generate a syntactically and semantically correct caption for the image based on the detected objects. In our project, we're working with a traffic sign dataset that has been captioned using the process described above. This model is extremely useful for visually impaired people who need to cross roads safely.