Reviewerly/exHarmony huggingface dataset:
Patient survey superior to EMR extraction for eliciting positive symptoms at COVID-19 illness onset

Context: COVID-19 has ravaged nations around the world, with New York City (NYC) and the NYC suburbs being particular epicenters of COVID-19 infection. Suffolk County, NY, has reported over 200,000 cases and 3,000 deaths to date. Most initial COVID-19 research concerned hospitalized patients. Presenting symptomology in the outpatient setting was poorly characterized, as were the implications of specific presenting symptoms, beyond respiratory distress or hypoxia, for eventual disease severity. This made it difficult for primary care physicians to predict which patients would require hospitalization for COVID-19 disease or decompensate while being managed at home during a time when hospital and ICU beds were limited. Objective: To characterize presenting symptoms of COVID-19 infection in the outpatient setting and evaluate for correlation with severity, duration, and chronicity of disease. Study Design and Analysis: We collected survey data from both patient telephone interviews and electronic medical record (EMR) extraction. Patient characteristics were described using means and percentages when appropriate. Percentage of symptoms by severity level, symptom duration, COVID-19 testing and escalating medical care were calculated. To evaluate association of risk factors with positive testing, severity, duration and chronicity of symptoms, logistic regression was used. Patient characteristics, medications and repeat measures were evaluated as risk factors in logistic regression. Setting or Dataset: 107 patients with suspected and confirmed COVID-19 cases at the 3 primary care practices of Stony Brook University Hospital between March and December, 2020. Population Studied: adult, English speaking primary care patients with suspected or confirmed COVID-19 Intervention/Instrument: patient self report telephone survey, EMR data extraction survey Outcome Measures: symptom duration, symptom severity, persistence of symptoms at 3 month time point Significant Results: Patient self-report survey elicited nearly twice as many symptoms described at illness onset vs. those recorded in the EMR. Conclusions: Early in the setting of newly emerging infectious diseases, particularly those such as COVID-19 which involve multiple organ systems, patient self report of symptoms of illness rather than EMR extraction alone may be crucial both for identifying cases and in order to characterize pathophysiology of disease in real time.
Análise do perfil epidemiológico dos profissionais da enfermagem acometidos pela COVID-19: repercussões para assistência

The aim was to describe the epidemiological profile of nursing professionals affected by COVID-19 in Brazil. This is an integrative review with a qualitative approach, using secondary information from public sources available at the Nursing Observatory of the Federal Council of Nursing. Data collection was carried out in August 2021, on the Virtual Health Library Portal, in the databases: LILACS, in the electronic library SciELO, BDENF, MEDLINE. It was identified that, 58,121 reported cases of nursing professionals infected by COVID-19 in Brazil, the total number of deaths corresponds to 858 cases with a case fatality rate of 2.65%. The case and mortality rates of nursing professionals affected by the virus are high and there is a significant difference in the number of cases between the years 2019 and 2021, so protecting the health of professionals working in the area is of paramount importance, since that they have direct contact with the infected person.
Towards Hand-Over-Face Gesture Detection

Facial microexpressions are immediately appearing reactions on the face that indicate various details about people's mental and emotional states.Their most important property is that their interpretation is identical or very similar for people all over the world.At present, their identification requires a psychologist expert.Thus automating this task would enable a broader application.The goal of this research is the detection of microexpressions using hybrid expert algorithms.Our algorithms mainly rely on landmark point detectors.Based on their output, several expert algorithms are utilized to extract key features and changes appearing on the face of a subject.These algorithms usually include several steps of image processing and time series analysis algorithms.In this paper, a component responsible for detecting hand gestures and hand pose is introduced.This component helps other algorithms to eliminate false positive detections by detecting the hands over the face.In addition, the recognizability of handover-face gestures is investigated.Finally, the implemented face occlusion detector method is evaluated on videos.
Estimating Underlying Articulatory Targets of Thai Vowels by Using Deep Learning Based on Generating Synthetic Samples From a 3D Vocal Tract Model and Data Augmentation

Representation learning is one of the fundamental issues in modeling articulatory-based speech synthesis using target-driven models. This paper proposes a computational strategy for learning underlying articulatory targets from a 3D articulatory speech synthesis model using a bi-directional long short-term memory recurrent neural network based on a small set of representative seed samples. Using a seeding set from VocalTractLab, a larger training set was generated that provided richer contextual variations for the model to learn. The deep learning model for acoustic-to-target mapping was then trained to model the inverse relation of the articulation process. This method allows the trained model to map the given acoustic data onto the articulatory target parameters which can then be used to identify the distribution based on linguistic contexts. The model was evaluated based on its effectiveness in mapping acoustics to articulation, and the perceptual accuracy of speech reproduced from the articulation estimated from the recorded speech by native Thai speakers. The model achieved more than 80% phoneme classification accuracy in the listening test conducted with 25 native Thai speakers. The results indicate that the model can accurately imitate speech with a high degree of phonemic precision.
Cyclic Lattices, Ideal Lattices and Bounds for the Smoothing Parameter

In this article, we introduce the discrete subgroup in  n as preliminaries first.Then we provide some theories of cyclic lattices and ideal lattices.By regarding the cyclic lattices and ideal lattices as the correspondences of finitely generated R-modules, we prove our main theorem, i.e. the correspondence between cyclic lattices in  n and finitely generated R-modules is one-to-one.Finally, we give an explicit and countable upper bound for the smoothing parameter of cyclic lattices.
Education linguistic model in teaching English as a second language: a discourse analysis

This paper aimed to describe the use of Education Linguistic Model in teaching English as a Second Language using discourse analysis. The study focused to the four (4) ESL teachers’ process in teaching English as a second language to the different groups of ESL learners in Koronadal National Comprehensive High School for the school year 2017-2018. The participants were selected based from the inclusion criteria formulated by the researcher: a) A participant must be a graduate of Bachelor in Secondary Education major in English; b) A participant must have an at least fifteen (15) years of experience in teaching English as a second language; and c) A participant must be one of the top ten (10) performing teachers as based from the Individual Performance Commitment and Review Form (IPCRF). Furthermore, classroom observation method was used to record classroom proceedings through field notes and voice recorder. Also, qualitative discourse analysis was employed to interpret and understand on how the four (4) ESL teachers utilize the seven (7) ELM categories which are building rapport, acknowledging success, modelling, addressing challenging behaviors, scaffolding the learning, responding to the individual learner and self-monitoring the teaching. The findings entail that the manifestations of ELM categories are evident in the process of teaching as a second language as reflected by the four (4) ESL teachers, but the process of exhibiting the ELM categories is unique and different compared among the ESL teachers. However, some of the categories are not consistently used in every class proceeding. Thus, modelling is not manifested by Teacher C and scaffolding the learning is not manifested by Teacher D. Moreover, both Teacher C and Teacher D do not manifest self-monitoring the teaching. Therefore, the study suggests that self-monitoring the teaching should be observed by the ESL teachers to enhance the process of teaching English as a second language in an ESL classroom.
Adaptive two-step method for providing the desired visual quality for SPIHT

Lossy compression has been widely used in various applications due to its variable compression ratio. However, distortions are introduced unavoidably, and this decreases the image quality. Therefore, it is often required to control the quality of the compressed images. A two-step method has been proposed recently to provide the desired visual quality. The average rate-distortion curve was used to determine the proper parameter value that controls compression. However, its performance for the wavelet-based coder Set Partitioning in Hierarchical Trees (SPIHT) is insufficient because there are very wide limits of visual quality variation for different images for a given value of the compression control parameter (CCP). Additionally, previous work has demonstrated that the level of errors, which is the subject of our study relates to texture features of an image to be compressed, where texture presence is an inherent property of remote sensing images. In this paper, our goal is to develop an adaptive two-step method for SPIHT to improve accuracy. The following tasks were solved. First, a prediction of visual quality for a particular parameter value is conducted. The prediction scheme is based on the information extraction from a certain number of image blocks to perform a visual quality calculation of the image compressed for a given CCP value. A threshold is adopted as the complexity grouping; in this paper, images are divided into two groups: simple and complex images. Second, the results of the grouping determine the adaptive curve model adopted. Finally, a two-step compression method is applied according to this curve. The classical metric Peak signal-to-noise ratio (PSNR) is employed to evaluate the image quality. The research method is based on a validation experiment that is conducted for an image set covering different image complexity and texture features. The comparison results of four typical desired values prove that the accuracy has been generally improved, the variances of both the first and second steps have been reduced sufficiently, and the mean absolute error has also been improved. Conclusion: the improvement effects are significant, particularly in the low desired visual quality. A remote sensing image is taken as an example to analyze in detail; the quality of the decompressed images meets the user’s visual requirement, and the errors are acceptable.
Handwritten Devanagari Digits Recognition Using Residual Neural Network

Handwritten digit recognition is a highly evolved research domain of pattern recognition. It is used to classify pre-segmented handwritten digits. The Devanagari script is one of the writing systems of various Indian languages including Sanskrit and Hindi. In this paper, an efficient Handwritten Devanagari numeral digit recognition using ResNet is proposed. Deep learning is a recent research trend in this field. Architectures like Residual neural Networks (ResNet) are being used. ResNet is an architecture that is computationally expensive and normally used to provide high accuracy in classification problems. The structural design of the network consists of sacks of two convolutional (Conv2D) layers with Batch Normalization and an activation function called Relu. We evaluated our scheme on 16000 handwritten samples of Devanagari numerals from the UCI machine learning database and from the experiment we have achieved 99.40% recognition rate.
A Portable Posture Monitoring Device for Gymgoers

With unprecedented witness in physical exercises, gyms are high in demand with their infrastructure and best exercise equipment to provide proper fitness to its customers. But incorrect posture is one of the overlooked issues that result in acute or chronic injuries while doing improper exercises. Heavy loads and wrong postures during the exercise are not monitored mostly due to insufficient assessment tools or infrastructure. The gait analysis setup makes it impossible to monitor exercise routines in regular gyms due to the space and expenditure. In this study, a wearable device integrated with multiple accelerometers is designed to monitor posture. This portable device can also deliver proper data for routines like squats, bench press, and deadlifts. Using built-in Bluetooth and Wi-Fi, the data can be tracked in real time. The real-time posture data can provide reliable information to the gym coaches for immediate correction remedy.
On the recognition of the author of a text fragment based on the frequency of alphabetic bigrams

On the example of a model collection of Tajik literary works, the problem of the possibility of determining the authorship of a fragment of the text of the minimum size extracted from the collection is studied. A model collection of texts in the Tajik language composed of works of classical poetry and modern prose in Cyrillic graphics is considered. Each piece is associated with a digital portrait - the distribution of the frequencies of symbolic bigrams. To solve the problem of identifying the authors of texts, bigrams are quite acceptable quantitative characteristics. A γ-classifier is used as a tool for implementing the task, which allows the authors of textual information to be identified by the frequency of elements of alphabetic bigrams with a sufficiently high degree of efficiency. The mathematical model of the γ-classifier is represented as a triad. Its first component is a digital portrait (DP) of the text - the distribution of the frequency of bigrams in the text; the second component is formulas for calculating the distances between the DP texts and the third is a machine learning algorithm. The tuning of the algorithm using a table of paired distances between all products of the model collection consisted in determining an optimal value of the real parameter γ, for which the error of violation of the “homogeneity” hypothesis is minimized. It was also found that with the help of a γ-classifier by a digital portrait, it is possible to identify the authors of works in the Tajik language. By using the metric classifier and the method of the nearest (in terms of distance) neighbor, it was possible to identify the authors of decreasing sequences of text fragments from 7000 words (40,000 characters) up to 20 words (100 characters). The minimum volume of a sample of words or symbols for recognition of the author of a Tajik text has been determined. The results of experiments with a minimum sample size of words (characters) for recognizing the author of a text are described.
A novel approach to discover numerical association based on the coronavirus optimization algorithm

The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes.
"Major Challenges of Recommender System and Related Solutions"

Recommender system is a very young area of machine learning &amp; Deep Learning research. The basic goal of the recommender system is to create a relationship between items and consumers. The relationship provides recommendations based on user interest. content-based, collaborative, demographic, hybrid filtering, knowledgebased, utility-based, classification model are well-known recommender models. The model uses an item's specifications in content-based filtering to suggest other objects with similar features. Collaborative filtering takes into the user's previous activity which means the user has previously viewed or purchased, as well as ratings Provided by the user to those items and similar conclusions reached by other users' item lists. View user profile data such as age category, gender, education, and living area to detect commonalities with other profiles.[31] All three filtering techniques are used in hybrid filtering. In the process of recommendations, various challenges are faced by the system. So, this paper lists various solutions by researchers in recent days.
Diurnal Temperature Range as Index of Climate Change in Lafia, Nasarawa State, Nigeria

The study focused on determining the Diurnal temperature range trend as an index of climate change in Lafia, Nasarawa State, Nigeria. The study relied on remote sensing data- the ERA5 – Temperature, which is the fifth generation reanalysis from ECMWF. The data for the period of forty (40) years was extracted and used to analyze the annual range, diurnal temperature, trend analysis and daily maximum and minimum temperatures. The results shows a decrease in the trend analysis for the decadal and pentad which means that the difference between the daily maximum temperature and daily minimum temperature is no longer wide. However, the trend analysis carried out for the annual DTR shows a positive trend which shows that the Maximum daily temperature is higher than the daily minimum range in the study area. The study recommended for more intense study of DTR in order to stop over generalizing the index of climate change over a macro area.
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation

The Transformer architecture has led to significant gains in machine translation. However, most studies focus on only sentence-level translation without considering the context dependency within documents, leading to the inadequacy of document-level coherence. Some recent research tried to mitigate this issue by introducing an additional context encoder or translating with multiple sentences or even the entire document. Such methods may lose the information on the target side or have an increasing computational complexity as documents get longer. To address such problems, we introduce a recurrent memory unit to the vanilla Transformer, which supports the information exchange between the sentence and previous context. The memory unit is recurrently updated by acquiring information from sentences, and passing the aggregated knowledge back to subsequent sentence states. We follow a two-stage training strategy, in which the model is first trained at the sentence level and then finetuned for document-level translation. We conduct experiments on three popular datasets for document-level machine translation and our model has an average improvement of 0.91 s-BLEU over the sentence-level baseline. We also achieve state-of-the-art results on TED and News, outperforming the previous work by 0.36 s-BLEU and 1.49 d-BLEU on average.
Implicit n-grams Induced by Recurrence

Although self-attention based models such as Transformers have achieved remarkable successes on natural language processing (NLP) tasks, recent studies reveal that they have limitations on modeling sequential transformations (Hahn, 2020), which may prompt re-examinations of recurrent neural networks (RNNs) that demonstrated impressive results on handling sequential data.Despite many prior attempts to interpret RNNs, their internal mechanisms have not been fully understood, and the question on how exactly they capture sequential features remains largely unclear.In this work, we present a study that shows there actually exist some explainable components that reside within the hidden states, which are reminiscent of the classical n-grams features.We evaluated such extracted explainable features from trained RNNs on downstream sentiment analysis tasks and found they could be used to model interesting linguistic phenomena such as negation and intensification.Furthermore, we examined the efficacy of using such n-gram components alone as encoders on tasks such as sentiment analysis and language modeling, revealing they could be playing important roles in contributing to the overall performance of RNNs.We hope our findings could add interpretability to RNN architectures, and also provide inspirations for proposing new architectures for sequential data.
Processing profile for quantifiers in verb phrase ellipsis: Evidence for grammatical economy

Quantifier Raising leaves no overt marking to indicate movement has occurred, making the task of identifying when raising has occurred extremely difficult for the parser. Beyond this challenge, evidence from interpretation and judgement studies suggests that raising causes difficulty in processing. These two aspects taken together have led some to suggest that human sentence processor employs a strategy in which the construction of raised structures is avoided, commonly called processing scope economy. This contrasts to the traditional notion of grammatical scope economy, where Quantifier Raising is restricted in the grammar. In this paper we discuss the properties of these two theories. We conclude that the two approaches make different predictions about when raising should occur online, with processing scope economy predicting that the parser avoids raising whenever possible and grammatical scope economy predicting that the parser raises regularly and sometimes produces ungrammatical structures in the process. We then present an experiment which examines complex scope structures in verb phrase ellipsis to observe when penalties related to Quantifier Raising are observed online. We find that penalties appear in configurations where Quantifier Raising would be ungrammatical under grammatical scope economy, suggesting the parser attempts Quantifier Raising in these configurations. This evidence indicates that the parser’s behavior matches the predictions of grammatical scope economy rather than those of processing scope economy.