Reviewerly/exHarmony huggingface dataset:
SRUNet: Stacked Reversed U-Shape Network for Lightweight Single Image Super-Resolution

In recent years, for relieving the heavy computational cost, lightweight models have been successfully applied to single image super-resolution (SISR) task.However, most lightweight models adopt low-resolution images as input and apply transposed convolution or subpixel convolution only in the tail of the model to reconstruct the super-resolution image which neglect to fully utilize the multi-scale features.To resolve this problem, we propose a stacked reversed U-shape network (SRUNet) to further extract and utilize the multi-scale features at different resolutions.In detail, SRUNet consists of shallow feature extraction (SFE), stacked reversed U-shape module (SRUM), multi-scale backward fusion module (MSBFM) and feature refinement module (FRM).Instead of upsampling the feature map at the tail of the model, we perform the upsampling and downsampling operation progressively and iteratively by the stacked reversed U-shape module to extract richer multi-scale features.Furthermore, for archiving better use of multi-scale features, a scale-wise dense connection with residual channel-wise attention and multi-scale backward fusion is added to the network.The fused super-resolved features are refined by the feature refinement module and reconstructed to the image.Extensive experiments demonstrate that our model can achieve competitive performance compared with the state-of-the-art methods.When scaling factor is 4, SRUNet achieves the highest SSIM performance in all benchmarks and it takes 29.2ms to process per image in Urban100 dataset.
Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense

We study how to enhance text representation via textual commonsense.We point out that commonsense has the nature of domain discrepancy.Namely, commonsense has different data formats and is domain-independent from the downstream task.This nature brings challenges to introducing commonsense in general text understanding tasks.A typical method of introducing textual knowledge is continuing pre-training over the commonsense corpus.However, it will cause catastrophic forgetting to the downstream task due to the domain discrepancy.In addition, previous methods of directly using textual descriptions as extra input information cannot apply to large-scale commonsense.In this paper, we propose to use large-scale out-of-domain commonsense to enhance text representation.In order to effectively incorporate the commonsense, we proposed OK-Transformer (Out-of-domain Knowledge enhanced Transformer).OK-Transformer effectively integrates commonsense descriptions and enhances them to the target text representation.In addition, OK-Transformer can adapt to the Transformer-based language models (e.g.BERT, RoBERTa) for free, without pre-training on large-scale unsupervised corpora.We have verified the effectiveness of OK-Transformer in multiple applications such as commonsense reasoning, general text classification, and low-resource commonsense settings. 1
Controlling Translation Formality Using Pre-trained Multilingual Language Models

This paper describes the University of Maryland's submission to the Special Task on Formality Control for Spoken Language Translation at IWSLT, which evaluates translation from English into 6 languages with diverse grammatical formality markers.We investigate to what extent this problem can be addressed with a single multilingual model, simultaneously controlling its output for target language and formality.Results show that this strategy can approach the translation quality and formality control achieved by dedicated translation models.However, the nature of the underlying pre-trained language model and of the finetuning samples greatly impact results.
Content-based Stock Recommendation Using Smartphone Data

The number of investors holding risky assets in Japan is much lower than that in western countries even though it is an effective way for building investor assets. Although Japanese investment companies offer a service to invest in points through coalition loyalty programs instead of actual currency to address this situation, the problem still persists. One reason for this is that novice investors do not know in which stocks to invest. One possible solution is recommending stocks; however, we still face the cold-start problem because there is no transaction history for novice investors. In this study, we propose a novel content-based recommendation approach that utilizes touchpoint information, e.g., payment and app usage data, on smartphones in daily life. This approach employs user-weighted recency, frequency, and monetary, called UW-RFM and a complementary module to comply with Japanese guidelines that prohibit presenting only a small number of companies and establishing a minimum number of companies to be presented. We conduct an online evaluation to validate the effectiveness of the proposed approach in an actual investment service. The evaluation results show that the proposed approach motivates users to invest more, i.e., 0.352 more clicks on the recommendation area and 3, 016 points (yen), than the baseline method that does not consider touchpoint information.
DMix: Adaptive Distance-aware Interpolative Mixup

Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities.We extend Mixup and propose DMIX, an adaptive distanceaware interpolative Mixup that selects samples based on their diversity in the embedding space.DMIX leverages the hyperbolic space as a similarity measure among input samples for a richer encoded representation.DMIX achieves state-of-the-art results on sentence classification over existing data augmentation methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations.We probe the effectiveness of DMIX in conjunction with various similarity measures and qualitatively analyze the different components.DMIX being generalizable, can be applied to various tasks, models and modalities.
Transformed Grid Distance Loss for Supervised Image Registration

Many deep learning image registration tasks, such as volume-to-volume registration, frame-to-volume registration, and frame-to-volume reconstruction, rely on six transformation parameters or quaternions to supervise the learning-based methods. However, these parameters can be very abstract for neural networks to comprehend. During the optimization process, ill-considered representations of rotation may even trap the objective function at local minima. This paper aims to expose these issues and propose the Transformed Grid Distance loss as a solution. The proposed method not only solves the problem of rotation representation but unites the gap between translation and rotation. We test our methods both with synthetic and clinically relevant medical image datasets. We demonstrate superior performance in comparison with conventional losses while requiring no alteration to the network input, output, or network structure at all.
Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations

It is commonly claimed that inter-annotator agreement (IAA) is the ceiling of machine learning (ML) performance, i.e., that the agreement between an ML system's predictions and an annotator can not be higher than the agreement between two annotators.Although Boguslav and Cohen (2017) showed that this claim is falsified by many real-world ML systems, the claim has persisted.As a complement to this real-world evidence, we conducted a comprehensive set of simulations, and show that an ML model can outperform IAA even if (and especially if) annotators are noisy and differ in their underlying classification functions, as long as the ML model is reasonably well-specified.Although the latter condition has long been elusive, leading ML models to underperform IAA, we anticipate that this condition will be increasingly met in the era of big data and deep learning.Our work has implications for (1) maximizing the value of machine learning, (2) adherence to ethical standards in computing, and (3) economical use of annotated resources, which is paramount in settings where annotation is especially expensive, like biomedical natural language processing.
Measuring the Students’ Perception towards Changed Knowledge Sharing System during the Pandemic: A Case on Public Universities of Bangladesh

The COVID-19 outbreak has disrupted the education of more than 300 million students worldwide.Bangladesh was not in exception.Various educational institutions in Bangladesh have suspended classes for more than two years, and some public and private university teachers have begun to take classes and take homework online.But most students at public universities come from poor or lower-middle-class families, and many of them lack access to mobile devices and the internet, hampering the online teaching system.In this regard, the purpose of this study was to measure students' perceptions of changes in the knowledge sharing system during the pandemic.Using quantitative methods, 200 responses (students) from Bangladeshi public universities were collected through an online questionnaire between May 2021 and July 2021, according to the study purpose.Interesting findings from the study suggest that most students are comfortable with the changing knowledge-sharing system during the pandemic.It also found that online knowledge-sharing systems during the pandemic have increased students' technological efficiency.Furthermore, the study found that overall institutional efforts to transform knowledge-sharing systems during the pandemic have been sufficient.However, changing knowledge-sharing systems have negatively impacted mental health.Therefore, there is no evidence that changing the knowledge sharing system during the pandemic has had a positive impact on the overall learning environment.The findings of this study will help policymakers and educational institutions to make the changed knowledge sharing system more inclusive, practical and effective to arrange educational institutions better in future.
Assessment of Massively Multilingual Sentiment Classifiers

Models are increasing in size and complexity in the hunt for SOTA.But what if those 2% increase in performance does not make a difference in a production use case?Maybe benefits from a smaller, faster model outweigh those slight performance gains.Also, equally good performance across languages in multilingual tasks is more important than SOTA results on a single one.We present the biggest, unified, multilingual collection of sentiment analysis datasets.We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included results on the internally annotated datasets.We deeply evaluate multiple setups, including fine-tuning transformer-based models for measuring performance.We compare results in numerous dimensions addressing the imbalance in both languages coverage and dataset sizes.Finally, we present some best practices for working with such a massive collection of datasets and models from a multilingual perspective.
Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces

Abstract Automated driving is widely seen as one of the areas, where key innovations are driven by the application of deep learning. The development of safe and robust deep neural network (DNN) functions requires new validation methods. A core insufficiency of DNNs is the lack of generalization for out-of-distribution datasets. One path to overcome this insufficiency is through the analysis and comparison of the domains of training and test datasets. This is important because otherwise, deep learning cannot advance automated driving. Variational autoencoders (VAEs) are able to extract meaningful encodings from datasets in their latent space. This chapter examines various methods based on these encodings and presents a broad evaluation on different automotive datasets and potential domain shifts, such as weather changes or new locations. The used methods are based on the distance to the nearest neighbors between datasets and leverage several network architectures and metrics. Several experiments with different domain shifts on different datasets are conducted and compared with a reconstruction-based method. The results show that the presented methods can be a promising alternative to the reconstruction error for detecting automotive-relevant domain shifts between different datasets. It is also shown that VAE loss variants that focus on a disentangled latent space can improve the stability of the domain shift detection quality. Best results were achieved with nearest neighbor methods using VAE and JointVAE, a VAE variant with a discrete and a continuous latent space, in combination with a metric based on the well-known z-score, and with the NVAE, a VAE variant with optimizations regarding reconstruction quality, in combination with the deterministic reconstruction error.
Digital competence of the educational process parties

The rapid development of new technologies observed in recent years and the active digital transformation of society impose special requirements for a future specialist and his digital competence. The authors should note that the concept of digital competence has become very popular in recent years, and now there are many studies, which in one way or another reveal its various aspects. However, typically, plenty of different definitions lead to a blurring of boundaries of the concept and the absence of a unified designation of its essential and semantic characteristics. As a result, this concept is considered the ability to use information and communication technologies, a part of social competence, and a way of communication. To identify the initial level of digital competencies, the authors carried out a diagnostic study involving the students of Mari State University (Yoshkar-Ola, Russia) in different training programs. The paper presents a detailed overview of the results of this study and identifies three levels of students’ digital competencies: high, medium, and low. The study shows that students with a high level of digital competence have a positive attitude towards the digital transformation of education emphasizing that it provides new opportunities to use digital technologies in learning and increases the effectiveness of the learning process. These students support digital learning, online consultations, and providing information for lectures and seminars in electronic form, as opposed to the students with a low level of digital competence, who have a negative attitude towards the digital transformation of education, noting only its weaknesses. These students support digital learning, online consultations, and providing information for lectures and seminars in electronic form, as opposed to the students with a low level of digital competence, who have a negative attitude towards the digital transformation of education, noting only its weaknesses.
Composing Structure-Aware Batches for Pairwise Sentence Classification

Identifying the relation between two sentences requires datasets with pairwise annotations.In many cases, these datasets contain instances that are annotated multiple times as part of different pairs.They constitute a structure that contains additional helpful information about the inter-relatedness of the text instances based on the annotations.This paper investigates how this kind of structural dataset information can be exploited during training.We propose three batch composition strategies to incorporate such information and measure their performance over 14 heterogeneous pairwise sentence classification tasks.Our results show statistically significant improvements (up to 3.9%) -independent of the pre-trained language model -for most tasks compared to baselines that follow a standard training procedure.Further, we see that even this baseline procedure can profit from having such structural information in a low-resource setting. 1
Digital Sociotechnical Systems of Mutual Aid: How Communities Connected, Adapted, and Innovated During the COVID-19 Pandemic in New York City

New York City was one of the hardest-hit areas in the first wave of the COVID-19 pandemic. At the time, knowledge about SARS-CoV-2 and strategies to prevent and respond to outbreaks were incipient. Social distancing created additional challenges. As such, people turned to digital technologies to find creative ways, not only to keep in touch with their loved ones, but also to find help and to assist strangers in need. In this essay, we describe how individuals and organizations in New York City used digital technologies to monitor and share information on COVID-19, to provide support for vulnerable people, and to get medical devices to those in need. Using the concepts of citizen science, mutual aid, and digital sociotechnical systems, we make three arguments. First, digital sociotechnical systems have a unique capacity to enroll and connect people—including strangers—over long distances, therefore enabling participation in mutual aid initiatives despite strict social-distancing limitations. Second, pre-existing mutual aid initiatives supported by digital sociotechnical systems demonstrated high adaptability and were quickly repurposed for COVID-19 mutual aid. Lastly, mutual aid initiatives, confronted with certain limitations of digital sociotechnical systems, engendered innovations and calls for transformations toward more inclusive systems. Time will tell whether these emerging transformations outlast the disaster itself and enhance long-term community resilience.
Systematic Map of Data Mining for Gynecologic Oncology

Gynecologic cancers are a significant cause of morbidity and mortality among women both in developed and low- middle- income countries. To alleviate the burden, the application of Data Mining (DM) in gynecologic oncology is needed in clinical environments. This study presents a systematic mapping to explore in detail the breadth of the available literature on the use of DM in gynecologic oncology. The mapping questions and the PICO framework served to determine the search string of this systematic map. The resultant was conducted on five well-known databases, PubMed, IEEE Xplore, ScienceDirect, Springer Link, and Google Scholar, to catch relevant articles published between 2011, and mid of 2021. Of the 2,807 potential records, 169 studies fulfilled the inclusion/exclusion criteria and were in-depth analyzed. The findings revealed that DM efforts peaked considerably from 2019 in terms of cervical cancer screening and diagnosis. Further studies are needed to investigate a wider range of research questions as gynecologic oncology is a very rich field with a collection of distinct features cancers which, in turn, allow Machine Learning (ML) opportunities.
Analysis of Fisher Effects between Nominal Interests and Inflation

The interest rate is the borrowing price in the capital market, while the real interest rate is the interest rate level after deducting the price factor from the nominal interest rate.The real interest rate not only affects the savings, consumption and investment decisions of micro subjects, but also has important reference value for asset pricing in the capital market and the formulation of fiscal and monetary policies in the macro economy.This study investigates correlation between nominal rate of interest and rate of inflation and tests the fisher effect in China based on data from 1978 to 2020 [9].Because the equation of fisher effect is a simple linear equation, linear regression is used to fit data.After that, time series stationarity test is used to judge whether the regression is meaningful, followed by cointegration test and Granger causal relation test against each macroeconomic variable respectively.We consulted the research results of other people and found a large number of empirical studies failed to reach a consistent conclusion.In fact, the phenomenon is known as "Fisher Effect Paradox".Although it's hard to reach consistent conclusion, fisher effect does explain the relationship between interest rate and inflation to some degree.It's still a good theory to analyse macroeconomic.On the one hand, the rise of nominal interest rate is caused by the rise of inflation rate; on the other hand, it is the result of the central bank's loose monetary policy.Therefore, the rise and fall of nominal interest rate is not exactly the same as loose or tight monetary policy.
Uncertainty-aware Personalized Readability Assessment Framework for Second Language Learners

Assessing whether an ungraded second language learner can read a given text quickly is important for supporting learners of diverse backgrounds. Second language acquisition (SLA) studies have tackled such assessment tasks wherein only a single short vocabulary test result is available to assess a learner. Such studies have shown that the text-coverage or namely the percentage of words the learner knows in the text, is the key assessment measure. Currently, count-based percentages are used, in which each word in the given text is classified as being known/unknown to the learner, and the words classified as known are then simply counted. When each word is classified, we can also obtain an uncertainty value as to how likely each word is known to the learner. However, how to leverage these informative values to guarantee their use as an assessment measure that is comparable to that of the previous values remains unclear. We propose a novel framework that allows assessment methods to be uncertainty-aware while guaranteeing comparability to the text-coverage threshold. Such methods involve a computationally complex problem for which we also propose a practical algorithm. In our evaluation using newly created crowdsourcing-based dataset, our best method under our framework outperformed conventional methods.
Voxel-informed Language Grounding

Natural language applied to natural 2D images describes a fundamentally 3D world.We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model.We show that VLG significantly improves grounding accuracy on SNARE (Thomason et al., 2021), an object reference game task.At the time of writing, VLG holds the top place on the SNARE leaderboard, 1 achieving SOTA results with a 2.0% absolute improvement.
Tracing Origins: Coreference-aware Machine Reading Comprehension

Machine reading comprehension is a heavilystudied research and test field for evaluating new pre-trained language models (PrLMs) and fine-tuning strategies, and recent studies have enriched the pre-trained language models with syntactic, semantic and other linguistic information to improve the performance of the models.In this paper, we imitate the human reading process in connecting the anaphoric expressions and explicitly leverage the coreference information of the entities to enhance the word embeddings from the pretrained language model, in order to highlight the coreference mentions of the entities that must be identified for coreference-intensive question answering in QUOREF, a relatively new dataset that is specifically designed to evaluate the coreference-related performance of a model.We use two strategies to finetune a pre-trained language model, namely, placing an additional encoder layer after a pre-trained language model to focus on the coreference mentions or constructing a relational graph convolutional network to model the coreference relations.We demonstrate that the explicit incorporation of coreference information in the fine-tuning stage performs better than the incorporation of the coreference information in pre-training a language model.