Reviewerly/exHarmony huggingface dataset:
A preliminary Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow

The Covid-19 outbreak has changed to an unprecedented extent almost every aspect of human activity.At the same time, the pandemic has stimulated enormous amount of research by scientists across various disciplines, seeking to study the phenomenon itself, its epidemiological characteristics and ways to confront its consequences.Information Technology, and particularly Data Science, drive innovation in all related to Covid-19 biomedical fields.Acknowledging that software developers routinely resort to open 'question & answer' communities like Stack Overflow to seek advice on solving technical issues, we have performed an empirical study to investigate the extent, evolution and characteristics of Covid-19 related posts.Through the study of 464 Stack Overflow questions posted in February and March 2020 and leveraging the power of text mining, we attempt to shed light into the interest of developers in Covid-19 related topics and the most popular problems for which the users seek information.The findings reveal that indeed this global crisis sparked off an intense activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data, primarily using Python technologies.
Enhancing the digital capacity of EFL programs in the age of COVID-19: the ecological perspective in Japanese higher education

Purpose: The COVID-19 pandemic exposed the inability of the Japanese higher education system to adapt to widespread unexpected disruption The limited metrics available to assess Japanese higher education’s response in the wake of the pandemic indicate several areas where the system needs to be strengthened This paper aims to harness the ecological perspective to explore the procedures by which higher education in Japan can mitigate extant digital shortcomings Design/methodology/approach: Leveraging Zhao and Frank’s ecological perspective as its theoretical model, this paper proposes practical solutions to remedy deficiencies highlighted by the COVID-19 pandemic rooted in existing literature both within and outside of Japanese higher education research Findings: The paper suggests pragmatic ideas to embolden each of the three strata encompassing the educational “ecosystem”: institutions, faculty and students The paper identifies measures for strengthening institutions to become more adaptive and improve leadership capacity At the faculty level, meanwhile, an increase in professional development opportunities and the bolstering of support systems may function to bridge an intergenerational digital divide Finally, for students, the authors argue for mobile-assisted language learning in an effort to cultivate stronger learner outcomes, and prescribe how to integrate this method into formal IT platforms Originality/value: The current paper is among a select few that use the ecological perspective in the field of educational research in Japan The authors contend that the model, while effective, offers an incomplete view of education, suggesting that the ecological perspective must be expanded to include students as a distinct species © 2020, Emerald Publishing Limited
Refined UNet V2: End-to-End Patch-Wise Network for Noise-Free Cloud and Shadow Segmentation

Cloud and shadow detection is an essential prerequisite for further remote sensing processing, whereas edge-precise segmentation remains a challenging issue. In Refined UNet, we considered the aforementioned task and proposed a two-stage pipeline to achieve the edge-precise segmentation. The isolated segmentation regions in Refined UNet, however, bring inferior visualization and should be sufficiently eliminated. Moreover, an end-to-end model is also expected to jointly predict and refine the segmentation results. In this paper, we propose the end-to-end Refined UNet v2 to achieve joint prediction and refinement of cloud and shadow segmentation, which is capable of visually neutralizing redundant segmentation pixels or regions. To this end, we inherit the pipeline of Refine UNet, revisit the bilateral message passing in the inference of conditional random field (CRF), and then develop a novel bilateral strategy derived from the Guided Gaussian filter. Derived from a local linear model of denoising, our v2 can considerably remove isolated segmentation pixels or regions, which is able to yield “cleaner” results. Compared to the high-dimensional Gaussian filter, the Guided Gaussian filter-based message-passing strategy is quite straightforward and easy to implement so that a brute-force implementation can be easily given in GPU frameworks, which is potentially efficient and facilitates embedding. Moreover, we prove that Guided Gaussian filter-based message passing is highly relevant to the Gaussian bilateral term in Dense CRF. Experiments and results demonstrate that our v2 is quantitatively comparable to Refined UNet, but can visually outperform that from the noise-free segmentation perspective. The comparison of time consumption also supports the potential efficiency of our v2.
Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain.Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority class.On the other hand, missing values in data can induce complexity in the learning classifiers during data analysis.These two problems pose substantial challenges to the performance of learning algorithms in real-life water quality anomaly detection problems.Hence, the need for them to be carefully considered and addressed to achieve better performance.In this paper, the performance of a range of several combinations of techniques to deal with imbalanced classes in the context of binary-imbalanced water quality anomaly detection problem and the presence of missing values is extensively compare.The methods considered include seven missing data and eight resampling methods, on ten different learning state-of-the-art classifiers taking into account diversity in their learning philosophies.The different classifiers are evaluated using stratified 5-fold cross-validation, based on three performance evaluation metrics namely accuracy, ROC-AUC and F1-measure.Further experiments are carried out on seventeen variants of homogenous and heterogeneous ensemble techniques embedded with resampling and missing value strategies during their training phase as well as two optimized deep neural network models.The experimental results show an improvement in the performance of the learning classifiers, especially when dealing with the class imbalance problem (on the one hand) and the incomplete data problem (on the other hand).Furthermore, neural network models exhibit superior performance when dealing with both problems.
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?

Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning. Pre-trained on ImageNet, some self-supervised algorithms reported higher transfer learning performance compared to fully-supervised methods, seeming to deliver the message that human labels hardly contribute to learning transferrable visual features. In this paper, we defend the usefulness of semantic labels but point out that fully-supervised and self-supervised methods are pursuing different kinds of features. To alleviate this issue, we present a new algorithm named Supervised Contrastive Adjustment in Neighborhood (SCAN) that maximally prevents the semantic guidance from damaging the appearance feature embedding. In a series of downstream tasks, SCAN achieves superior performance compared to previous fully-supervised and self-supervised methods, and sometimes the gain is significant. More importantly, our study reveals that semantic labels are useful in assisting self-supervised methods, opening a new direction for the community.
SetConv: A New Approach for Learning from Imbalanced Data

For many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high.To address this problem, we propose a set convolution (SetConv) operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution.We prove that our proposed algorithm is permutation-invariant despite the order of inputs, and experiments on multiple large-scale benchmark text datasets show the superiority of our proposed framework when compared to other SOTA methods.
TeaForN: Teacher-Forcing with N-grams

Sequence generation models trained with teacher-forcing suffer from issues related to exposure bias and lack of differentiability across timesteps.Our proposed method, Teacher-Forcing with N-grams (TeaForN), addresses both these problems directly, through the use of a stack of N decoders trained to decode along a secondary time axis that allows modelparameter updates based on N prediction steps.TeaForN can be used with a wide class of decoder architectures and requires minimal modifications from a standard teacher-forcing setup.Empirically, we show that TeaForN boosts generation quality on one Machine Translation benchmark, WMT 2014 English-French, and two News Summarization benchmarks, CNN/Dailymail and Gigaword.
Self-supervised learning through the eyes of a child

Within months of birth, children develop meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms applied to sensory data, and how much of it requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is currently infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to improvements in data collecting technology and recent progress in deep learning. In this paper, our goal is precisely to achieve such progress by utilizing modern self-supervised deep learning methods and a recent longitudinal, egocentric video dataset recorded from the perspective of three young children (Sullivan et al., 2020). Our results demonstrate the emergence of powerful, high-level visual representations from developmentally realistic natural videos using generic self-supervised learning objectives.
TOP-Rank: A Novel Unsupervised Approach for Topic Prediction Using Keyphrase Extraction for Urdu Documents

In Natural Language Processing (NLP), topic modeling is the technique to extract abstract information from documents with huge amount of text. This abstract information leads towards the identification of the topics in the document. One way to retrieve topics from documents is keyphrase extraction. Keyphrases are a set of terms which represent high level description of a document. Different techniques of keyphrase extraction for topic prediction have been proposed for multiple languages i.e. English, Arabic, etc. However, this area needs to be explored for other languages e.g. Urdu. Therefore, in this paper, a novel unsupervised approach for topic prediction for Urdu language has been introduced which is able to extract more significant information from the documents. For this purpose, the proposed TOP-Rank system extracts keywords from the document and ranks them according to their position in a sentence. These keywords along with their ranking scores are utilized to generate keyphrases by applying syntactic rules to extracts more meaningful topics. These keyphrases are ranked according to the keywords scores and re-ranked with respect to their positions in the document. Finally, our proposed model identifies top-ranked keyphrases as topical significance and keyphrase with the highest score is selected as the topic of the document. Experiments are performed on two different datasets and performance of the proposed system is compared with existing state-of-the-art techniques. Results have shown that our proposed system outperforms existing techniques and holds the ability to produce more meaningful topics.
Improving the Effectiveness of Anti-Piracy Educational Deterrence Efforts: The Role of Message Frame, Issue Involvement, Risk Perception, and Message Evidence on Perceived Message Effectiveness

The objective of this study is to explore methods to improve the effectiveness of anti-piracy educational deterrence efforts. We studied the effects of message framing (positive vs. negative), issue involvement (high vs. low), risk perception (high vs. low), and message evidence (anecdotal vs. statistical) on the perceived effectiveness of an anti-piracy campaign message. Our experimental results suggest that message frame alone does not have an impact on perceived message effectiveness. However, the effect of message framing is moderated by issue involvement, risk perception, and message evidence. Specifically, a positively framed message is more effective for individuals with low issue involvement, high perceived piracy risk, and who are exposed to anecdotal evidence. In contrast, a negatively framed message is more effective for individuals with high involvement, low risk, and who are exposed to statistical evidence.
Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model

It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.
APSE: Attention-Aware Polarity-Sensitive Embedding for Emotion-Based Image Retrieval

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Pembuatan Aplikasi Mobile "Wonderful of Minangkabau" sebagai Gudang Informasi Pariwisata di Sumatera Barat Melalui Website Kodular

West Sumatra has unique traditions and culture and has become one of the attractions and destinations for both local and long-distance tourism. However, the lack of information and the lack of promotion carried out by the Government of West Sumatra has made the tourism sector run sluggishly, therefore a promotional tool that is capable of improving the tourism sector is needed. This paper reports the results and process of creating a tourism application called "Wonderful of Minangkabau". The research method used is descriptive research. Descriptive method is a method in examining the status of human groups, an object, a condition or a thought or tourism class in the present. The purpose of this descriptive research is to make descriptions, descriptions or paintings systematically, factually and accurately regarding facts, characteristics and relationships between phenomenon under investigation. This application is built using the Kodular website which is an application website that allows users to create applications based on the Android operating system using Block Programming so that users do not need to do coding (writing programming code). This application is expected to help tourists who want to get information about tourism in West Sumatra Province quickly and precisely.
Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification

Sexism, a form of oppression based on one’s sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, categorizing these recollections automatically can assist the fight against sexism, as it can facilitate effective analyses by gender studies researchers and government officials involved in policy making. In this paper, we investigate the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we work with considerably more categories of sexism than any published work through our 23-class problem formulation. Moreover, we propose a multi-task approach for fine-grained multi-label sexism classification that leverages several supporting tasks without incurring any manual labeling cost. Unlabeled accounts of sexism are utilized through unsupervised learning to help construct our multi-task setup. We also devise objective functions that exploit label correlations in the training data explicitly. Multiple proposed methods outperform the state-of-the-art for multi-label sexism classification on a recently released dataset across five standard metrics.
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts

While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc.Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes.Previous works have focused on classifying user posts into various forms of abusive behavior.But there has hardly been any focus on estimating the severity of abuse and the target.In this paper, we present a first of the kind dataset with 7,601 posts from Gab 1 which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior.We also propose a system to address these tasks, obtaining an accuracy of ∼80% for abuse presence, ∼82% for abuse target prediction, and ∼65% for abuse severity prediction.
COVID-19: Psychosocial Impact on School in Chile. Inequalities and Challenges for Latin America

The Coronavirus-19 (COVID-19) pandemic has exceeded five million cases worldwide, and its consequences have been devastating for health and society in general As part of the health protocols that seek to mitigate the pandemic's effects, school closures were implemented in most countries, where Chile was no exception In the framework of this article, we seek to give an account of the psychosocial impact of this initiative on Chilean schools and to broaden the reflection to the rest of Latin America In this sense, we analyze government decisions since the creation of the Aprendo en Linea [I Learn Online] program and propose a contextualization and a contrast with the measures adopted in other countries We then explore the conditions of the exercise of the teaching activity, its consequences in the family context, and the impact on the relationship between families and schools This allows us to visualize, once again, not only the structural inequality of the Chilean education system but also its maintenance and eventual deepening The discussions and measures to be implemented in the current situation pose challenges that, beyond the context of Chile, can be extended to other Latin American countries
MHT-X: offline multiple hypothesis tracking with algorithm X

An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for edge construction for maximum likelihood trajectories. The current version of the code was developed for applications in multiphase hydrodynamics, e.g. bubble and particle tracking, and is capable of resolving object motion, merges and splits. Feasible object associations and trajectory graph edge likelihoods are determined using weak mass and momentum conservation laws translated to statistical functions for object properties. The code is compatible with n-dimensional motion with arbitrarily many tracked object properties. This framework is easily extendable beyond the present application by replacing the currently used heuristics with ones more appropriate for the problem at hand. The code is open-source and will be continuously developed further.
Neural ODE Processes

Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-points, a fundamental requirement for real-time applications imposed by the natural direction of time. Second, time-series are often composed of a sparse set of measurements, which could be explained by many possible underlying dynamics. NODEs do not capture this uncertainty. To this end, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs. By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points. At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits.
Non-uniform low-light image enhancement via non-local similarity decomposition model

Many low-light image enhancement methods ignore the characteristic of non-uniform low-light images, thereby causing the enhanced results to lose their naturalness. In this paper, a non-local similarity decomposition model based on the Retinex theory is proposed to obtain high-quality enhanced results for non-uniform low-light images. For the reflectance layer, we explore a weighted L1-norm regularization according to the similarity measure of non-local patches. The similarity measure contains color, contrast and texture information, which are the intrinsic features of images. For the illumination layer, we introduce a weight matrix to constrain its gradient based on the edge information of non-uniform low-light images. By applying these constraints, it is more precise to estimate the reflectance and illumination layers simultaneously. Finally, the decomposed illumination is further adjusted. Furthermore, an alternating direction method of multipliers and preconditioned conjugate gradient are utilized to accelerate the solution of the proposed model. Experimental results demonstrate that our method is better for enhancing non-uniform low-light image both in subjective and objective assessments.