Reviewerly/exHarmony huggingface dataset:
A Novel Technique for Full-Field Deformation and Temperature Measurement by Ultraviolet Imaging: Experimental Design and Preliminary Results

Synchronous measurement of full-field temperature and deformation at high temperature especially more than 1273 K is of much significance especially for part applications of turbine structures and materials. Non-contact optical methods attract more and more attention, however, current methods all face different challenges, such as strong light reflection on the surface of the specimen, disturbing radiation from environment, complex equipment setup, limited measured temperature not higher than 900 ℃ and so on. In this work, we develop an innovative technique to overcome some current problems. The measurement system employing an ultraviolet (UV) imaging system is composed of a scientific complementary metal oxide semiconductor (sCMOS) camera, a lens and a UV bandpass filter. The UV bandpass filter was used for thermal radiation elimination to acquire high quality images at elevated temperatures for deformation field calculation suitable for digital image correlation (DIC) method. The UV sensitive sCMOS camera without using active illumination was employed to collect enough UV radiation energy and eliminate the interference of the external ambient light, which is applicable for high accuracy temperature field measurement. Our system can realize the synchronous capture of image and temperature acquisition with passive UV imaging system at temperature not lower than 1473K. The feasibility of the method was verified through heating molybdenum (Mo) and Ni-based superalloy IC21 materials. The temperature fields of Mo measured by the established imaging system up to 1835 K with error less than 0.25% showed the effectiveness for temperature measurement. The estimated deformation and temperature field of Ni-based superalloy IC21 up to 1473 K with measured temperature error less than 0.5% demonstrated well the great potential of the UV imaging system in simultaneous measurement of temperature and deformation fields at elevated temperatures.
A Framework for Objective Evaluation of Single Image De-Hazing Techniques

Real-world environment, where images are acquired with digital camera, may be subject to sever climatic conditions such as haze that may drastically reduce the quality performance of sophisticated computer vision algorithms used for various tasks, e.g., tracking, detection, classification etc.Even though several single image de-hazing techniques have been recently proposed with many deep-learning approaches among them, a general statistical framework that would permit an objective performance evaluation has not been independently introduced yet.In this manuscript, certain performance metrics that emphasize different aspects of image quality, output ranges and polarity, are identified and combined into a single performance indicator derived in an unbiased manner.A general methodology is thus introduced, as a framework for objective performance evaluation of current and future dehazing tasks, through an extensive comparison of 15 single image de-hazing techniques over a vast range of image data sets.The proposed unified framework shows several advantages in evaluating diverse and perceptually meaningful image features but also in elucidating future directions for improvement in image dehazing tasks.
Clustering of HIV Patients in Ethiopia

Background: Among the many worldwide health problems, HIV/AIDS has caused severe health problems in several countries.The problem is also widely seen in Ethiopia.The general objective of the study is to cluster HIV patients and to find out the factors that mostly affect the prevalence of HIV within a group (cluster) and between groups (clusters) of HIV patients.Methods: The study is made based on the 2016 Ethiopian Demographic Health Survey (EDHS) which was collected by the Central Statistical Agency (CSA) of Ethiopia, and the survey collected a total of 26,753 samples, of which 14,785 were women and 11,968 were men and the age group was between 15 and 49 years for both.Binary logistic regression, principal component analysis, cluster analysis, and ANOVA were applied to analyze the data. Results:The result from binary logistic regression reveals that 15 factors such as ever heard of AIDS, region, water not available for at least a day in the last 2 weeks, has a radio, family members wash their hands, location of the source of water, everything completed to water to make it harmless to drink, food cooked in the house/separate house/outside, has a mobile telephone, has a table, type of place of residence, highest education level attained, current marital status, sex of household members, and age of household members are all significant factors that affect HIV status.Conclusion: Using these significant variables, 12 principal components are identified which describe 78% of the variation in the data.The result of HIV patients are clustered into 3 clusters and determine the status of HIV levels.Mainly, cluster 2 accounts for 50% of HIV patients whereas cluster 3 and 1 accounts for 40% and 10%, respectively.
A Typology-Based Approach to Tracing-App Adoption During the COVID-19 Pandemic: The Case of the SwissCovid App

Contact tracing technology has been introduced in many countries as part of the COVID-19 containment strategy. However, research indicates that current adoption rates are too low for tracing apps to be effective. To address the complexity of app adoption, a typology-based approach is presented that differentiates between refusers who generally reject app adoption, ditherers who hesitate to adopt the app, adopters who have downloaded and activated the app, and de-adopters who had installed the app but later uninstalled it. A national online survey of N = 1,535 participants in Switzerland revealed significant differences among these groups of people regarding their perceptions about app-related benefits and costs, knowledge about data handling in technology, and app-related opinion leadership. The results indicate that communication strategies that aim to enhance the rate of app adoption should focus on ditherers and adopters; the former are the most promising target group for behavioral change efforts, and the latter seem to be the best means to reach out to this promising group.
Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

Face recognition is an identification system that uses the characteristics of a person's face for processing. There is a feature in the face image so that it can be distinguished between one face and another face. One way to recognize face images is to analyze the texture of the face image. Texture analysis generally requires a feature extraction process. In different images, the characteristics will also differ. This characteristic will be the basis for the recognition of facial images. However, existing face recognition methods experience efficiency problems and rely heavily on the extraction of the right features. This study aims to study the texture characteristics of the extraction results using the Local Binary Pattern (LBP) method which is applied to deal with the introduction of Probabilistic Linear Discriminant Analysis (PLDA). The data used in this study are human face images from the AR Faces database, consisting of 136 objects (76 men and 60 women), each of which has 7 types of images Based on the results of testing shows the LBP method can produce the highest accuracy with a value of 95.53% in the introduction of PLDA.
Perceived Learning Outcomes and Interaction Mode Matter: Students’ Experience of Taking Online EFL Courses During COVID-19

The recent health emergency has changed the teaching mode globally, with traditional classroom teaching shifting to online platforms. This created challenges for both foreign/second language teachers and learners. Some recent studies investigated the challenges brought by online teaching from the teachers&amp;rsquo; perspective; however, little is known about how students perceive the effectiveness of online language learning based on their experiences. Therefore, this study surveyed 252 Chinese students who took online EFL courses during the pandemic through an online survey platform. Survey questions include 75 statements regarding EFL learner&amp;rsquo;s perceived experience in taking online EFL courses, such as learning platforms, teaching and assessment methods, interaction mode, classroom management, and the effectiveness of online courses. Students rated each statement on a 5-point Likert Scale. Open-ended questions further targeted students&amp;rsquo; suggestions to improve the quality of online language courses. Results from analysis of variance, factor analysis, and multiple linear regression analysis showed that students perceived the interactional opportunity and learning outcome as the most important factors in their online EFL learning experiences. Students generally showed a positive attitude toward their online language learning experiences and a high level of participation in synchronized online EFL courses. They further suggested a mixture of traditional and online class as an ideal teaching model for EFL learning, especially in the face of public crisis. Findings from this study may shed light on language curriculum design, language teacher education, and educational technology.
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL

Several practical applications of reinforcement learning involve an agent learning from past data without the possibility of further exploration. Often these applications require us to 1) identify a near optimal policy or to 2) estimate the value of a target policy. For both tasks we derive \emph{exponential} information-theoretic lower bounds in discounted infinite horizon MDPs with a linear function representation for the action value function even if 1) \emph{realizability} holds, 2) the batch algorithm observes the exact reward and transition \emph{functions}, and 3) the batch algorithm is given the \emph{best} a priori data distribution for the problem class. Our work introduces a new `oracle + batch algorithm' framework to prove lower bounds that hold for every distribution. The work shows an exponential separation between batch and online reinforcement learning.
Multi-script handwritten digit recognition using multi-task learning

Handwritten digit recognition is one of the extensively studied areas in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourages the development of robust and multipurpose systems. Additionally, working on multi-script digit recognition enables multi-task learning. It is evident that multi-task learning improves model performance through inductive transfer using the information contained in related tasks. Therefore, in this study multi-script handwritten digit recognition using multi-task learning is proposed to be investigated. As a specific case of demonstrating the solution to the problem, Amharic handwritten character recognition is also experimentally tested. The handwritten digits of three scripts including Latin, Arabic, and Kannada are studied to show that multi-task models with a reformulation of the individual tasks have shown promising results. In this study, a novel approach of using the individual tasks predictions was proposed to help the classification performance. These research findings have outperformed the baseline and the conventional multi-task learning models. More importantly, it avoided the need for weighting the different losses of the tasks, which is one of the challenges in multi-task learning.
IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization

Although recent advances in abstractive summarization systems have achieved high scores on standard natural language metrics like ROUGE, their lack of factual consistency remains an open challenge for their use in sensitive real-world settings such as clinical practice.In this work, we propose a novel approach to improve factual correctness of a summarization system by re-ranking the candidate summaries based on a factual vector of the summary.We applied this process during our participation in MEDIQA 2021 Task 3: Radiology Report Summarization, where the task is to generate an impression summary of a radiology report, given findings and background as inputs.In our system, we first used a transformer-based encoder-decoder model to generate top N candidate impression summaries for a report, then trained another transformer-based model to predict a 14observations-vector of the impression based on the findings and background of the report, and finally, utilized this vector to re-rank the candidate summaries.We also employed a source-specific ensembling technique to accommodate for distinct writing styles from different radiology report sources.Our approach yielded 2nd place in the challenge.Base Model's Prediction Prediction after FAR Human-generated Impression No acute cardiothoracic process.R-1: 0 No acute cardiopulmonary process.Tiny right pleural effusion.R-1: 0.6 Tiny right pleural effusion.No acute cardiopulmonary process.R-1: 0.6 Normal chest radiograph.Mild cardiomegaly.R-1: 0.3 Mild cardiomegaly, new since ___.No acute cardiopulmonary process.
Morphological Segmentation for Seneca

This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario.The labeled data in our experiments comes from two sources: one digitized from a publicly available grammar book and the other collected from informal sources.We treat these two sources as distinct domains and investigate different evaluation designs for model selection.The first design abides by standard practices and evaluates models with the in-domain development set, while the second one carries out evaluation using a development domain, or the out-of-domain development set.Across a series of monolingual and cross-linguistic training settings, our results demonstrate the utility of neural encoderdecoder architecture when coupled with multitask learning.
A Cross-Sectional Review of Blockchain in Thailand: Research Literature, Education Courses, and Industry Projects

Blockchain technology, a decentralized database that encourages collaboration, transparency, and security, is popular in Thailand, as seen from many practical projects. Nevertheless, information about Thailand’s blockchain ecosystem is incomplete. We conducted literature reviews of research articles, education courses, and industry projects, to explore blockchain from the technology adoption flow including academic research, knowledge acquisition, and project implementation. Blockchain research articles were collected from the Scopus database, while projects and courses were retrieved from an internet search. After applying inclusion and exclusion criteria, a total of 13 research articles, 50 courses, and 54 industry projects, dated from 2016 to 2020, were included in our review. Based on an analysis with respect to trends, focuses, and characteristics in all three aspects, we found that blockchain technology in Thailand was in its infancy, but has been increasing in numbers and domain varieties. However, blockchain technology did not move towards the same direction for all aspects. Most research articles were found in government services, supply chain, and traceability domains; while most projects were in the financial domains. We offer possible explanations for this observation. This paper provides challenges and opportunities for the research community and involved parties in practical implementation.
Discovering Better Model Architectures for Medical Query Understanding

In developing an online question-answering system for the medical domains, natural language inference (NLI) models play a central role in question matching and intention detection.However, which models are best for our datasets?Manually selecting or tuning a model is time-consuming.Thus we experiment with automatically optimizing the model architectures on the task at hand via neural architecture search (NAS).First, we formulate a novel architecture search space based on the previous NAS literature, supporting crosssentence attention (cross-attn) modeling.Second, we propose to modify the ENAS method to accelerate and stabilize the search results.We conduct extensive experiments on our two medical NLI tasks.Results show that our system can easily outperform the classical baseline models.We compare different NAS methods and demonstrate that our approach provides the best results.
Development of Digital Competence in the Training of Future Teachers in Ukraine and China

The relevance of the issue of developing the digital competence of future teachers in higher education is obvious, as technological progress is developing at an extremely rapid pace, and digitalisation has already penetrated into all spheres of life, including the education system. The purpose of the study is to compare the approaches of state educational policy to the development of digital competence of future teachers in China and Ukraine. To obtain new results, the following theoretical methods were used in the study: analysis of pedagogical literature, regulations, online platforms, educational sites, systematisation, and generalisation of research, organisation of training courses for future teachers – to compare different approaches to digital competence of future teachers. It is noted that digital competence is key in lifelong learning, as well as one of the components of a teacher's professional competence, according to the Standard Standard for Primary School Teachers, General Secondary Education Teachersб and Primary Education Teachers. The concept of digital competence is defined and it is substantiated that its significance changes in accordance with the development of digital modern society. The national educational policy of Ukraine and the People's Republic of China is analysed, in particular the regulatory base, which regulates the development of digital competence in the countries. It has been found that the problem of digital competence is relevant in both countries, in particular in China, where its solution is more progressive, due to powerful resource opportunities. An example of the content of academic disciplines, namely “Digital Training Tools”, “Cybersecurity”, “Media Education” (Ukraine), “Fundamentals of Computer Use”, “Use of Multimedia Technology” (PRC), their place in the educational process, hours on study, form of control of educational and cognitive activities, etc. These disciplines are aimed at developing the digital competence of future teachers in higher education institutions of Ukraine and China. It is noted that further research will relate to the study and analysis of national educational policy of the European Union on the development of digital competence, in particular in the training of future teachers
Design and Evaluation of a Scalable Real-Time Online Digital Audio Workstation Collaboration Framework

Existing designs for collaborative online audio mixing and production, within a Digital Audio Workstation (DAW) context, require a balance between synchronous collaboration, scalability, and audio resolution.Synchronous multiparty collaboration models typically utilize compressed audio streams.Alternatively those that stream high-resolution audio do not scale to multiple collaborators or experience issues owing to network limitations.Asynchronous platforms allow collaboration using copies of DAW projects and high-resolution audio files.However they require participants to contribute in isolation and have their work auditioned using asynchronous communication, which is not ideal for collaboration.This paper presents an innovative online DAW collaboration framework for audio mixing that addresses these limitations.The framework allows collaborators to synchronously communicate while contributing to the control of a shared DAW project.Collaborators perform remote audio mixing with access to high-resolution audio and receive real-time updates of remote collaborators' actions.Participants share project and audio files before a collaboration session; however the framework transmits control data of remote mixing actions during the session.Implementation and evaluation have demonstrated the scalability of up to 30 collaborators on residential Internet bandwidth.The framework delivers an authentic studio mixing experience where highresolution audio projects are democratically auditioned and synchronously mixed by remotely located collaborators.
Exploring Factors Associated with the Social Discrimination Experience of Children from Multicultural Families in South Korea by using Stacking with Non-linear Algorithm

The number of children from multicultural families is increasing rapidly along with quickly increasing multicultural families.However, there are not enough surveys and basic researches for understanding the characteristics of multicultural children and issues such as social discrimination.This study discovered the machine learning model with the best performance for predicting the social discrimination experience of children from multicultural families by comparing the prediction performance (accuracy) of individual prediction models and stacking ensemble models.This study analyzed 19,431 adolescents (between 19 and 24 years old: 9,835 males and 9,596 females) among the children of marriage immigrants.This study used random forest (RF), rotation forest, artificial neural network (ANN), and support vector machine (SVM) for the base model.Logistic regression algorithm was applied for the meta model.Each machine learning model was built through 5-fold cross-validation.Root-mean-square-error (RMSE), index of agreement (IA), and variance of errors (Ev) were used to evaluate the prediction performance of the developed models.The results of this study indicated that the prediction performance of the rotation forest-logistic regression model had the best performance.The future studies need to explore stacking ensemble models with the best performance through combining a base model and a meta model by using various machine learning algorithms such as clustering and boosting.
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents

Turn-level user satisfaction is one of the most important performance metrics for conversational agents.It can be used to monitor the agent's performance and provide insights about defective user experiences.While endto-end deep learning has shown promising results, having access to a large number of reliable annotated samples required by these methods remains challenging.In a large-scale conversational system, there is a growing number of newly developed skills, making the traditional data collection, annotation, and modeling process impractical due to the required annotation costs and the turnaround times.In this paper, we suggest a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions.We show that the pre-trained models using the self-supervised objective are transferable to the user satisfaction prediction.In addition, we propose a novel few-shot transfer learning approach that ensures better transferability for very small sample sizes.The suggested few-shot method does not require any inner loop optimization process and is scalable to very large datasets and complex models.Based on our experiments using real data from a large-scale commercial system, the suggested approach is able to significantly reduce the required number of annotations, while improving the generalization on unseen skills.