Reviewerly/exHarmony huggingface dataset:
How to Return to Normalcy: Fast and Comprehensive Contact Tracing of  COVID-19 through Proximity Sensing Using Mobile Devices

We outline a contact-tracing strategy based on proximity sensing using mobile devices. We discuss what an ideal system should look like and what it can do. We show that, when adopted sufficiently broadly, such a contact-tracing strategy can bring COVID-19 under complete control, end the need of social distancing, and return the society to full normalcy. We also review some of the challenges faced by the current generation of proximity-sensing technologies, including Bluetooth Low Energy used by phones, and consider both interim and longer-term solutions. Our main contribution is that we reason through why such a contact-tracing strategy is likely to achieve the stated goal of returning to full normalcy. Using probabilistic models, we show that universal adoption is not necessary to achieve the stated goal, thus there is some room for exceptions; however, the adoption rate needs to be very high, e.g., above $95\%$ depending on the disease parameters. With more vigilance in disease surveillance to detect mild cases earlier, the number may be brought down to about $90\%$. The results call for deployment effort to be led by public authorities at the state or federal level so that the required adoption rate can be reached and the tracing coverage is wide enough to be relevant for disease control.
On the variety of Euclidean point sets

We construct a continuum of non-homeomorphic compact subspaces of the real line R without singleton components. Thus from the purely topological point of view the real line contains not only more closed sets than open sets but also more closures of open sets than open sets. On the other hand, we show that this discrepancy vanishes either if the topological point of view is sharpened in the metrical or in the order-theoretical direction, or if R is replaced with R^n for dimension n>1. Furthermore, we track down a continuum of topological types of closed and totally disconnected subsets of R. In doing so we also track down a continuum of metrical types of infinite, discrete subsets of the unit interval [0,1]. (As a consequence, any countably infinite discrete space has a continuum of non-homeomorphic metrizable compactifications.)
Parallelization Techniques for Verifying Neural Networks

Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification. We introduce an algorithm based on partitioning the verification problem in an iterative manner and explore two partitioning strategies, that work by partitioning the input space or by case splitting on the phases of the neuron activations, respectively. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems. An extensive experimental evaluation shows the benefit of these techniques on both existing benchmarks and new benchmarks from the aviation domain. A preliminary experiment with ultra-scaling our algorithm using a large distributed cloud-based platform also shows promising results.
DeepFakes Evolution: Analysis of Facial Regions and Fake Detection  Performance

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1st generation such as UADFV and FaceForensics++ up to the latest databases of the 2nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1st and 2nd DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2nd generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors.
Natural language processing for word sense disambiguation and  information extraction

This research work deals with Natural Language Processing (NLP) and extraction of essential information in an explicit form. The most common among the information management strategies is Document Retrieval (DR) and Information Filtering. DR systems may work as combine harvesters, which bring back useful material from the vast fields of raw material. With large amount of potentially useful information in hand, an Information Extraction (IE) system can then transform the raw material by refining and reducing it to a germ of original text. A Document Retrieval system collects the relevant documents carrying the required information, from the repository of texts. An IE system then transforms them into information that is more readily digested and analyzed. It isolates relevant text fragments, extracts relevant information from the fragments, and then arranges together the targeted information in a coherent framework. The thesis presents a new approach for Word Sense Disambiguation using thesaurus. The illustrative examples supports the effectiveness of this approach for speedy and effective disambiguation. A Document Retrieval method, based on Fuzzy Logic has been described and its application is illustrated. A question-answering system describes the operation of information extraction from the retrieved text documents. The process of information extraction for answering a query is considerably simplified by using a Structured Description Language (SDL) which is based on cardinals of queries in the form of who, what, when, where and why. The thesis concludes with the presentation of a novel strategy based on Dempster-Shafer theory of evidential reasoning, for document retrieval and information extraction. This strategy permits relaxation of many limitations, which are inherent in Bayesian probabilistic approach.
Distributed Bayesian clustering using finite mixture of mixtures

In many modern applications, there is interest in analyzing enormous data sets that cannot be easily moved across computers or loaded into memory on a single computer. In such settings, it is very common to be interested in clustering. Existing distributed clustering algorithms are mostly distance or density based without a likelihood specification, precluding the possibility of formal statistical inference. Model-based clustering allows statistical inference, yet research on distributed inference has emphasized nonparametric Bayesian mixture models over finite mixture models. To fill this gap, we introduce a nearly embarrassingly parallel algorithm for clustering under a Bayesian overfitted finite mixture of Gaussian mixtures, which we term distributed Bayesian clustering (DIB-C). DIB-C can flexibly accommodate data sets with various shapes (e.g. skewed or multi-modal). With data randomly partitioned and distributed, we first run Markov chain Monte Carlo in an embarrassingly parallel manner to obtain local clustering draws and then refine across workers for a final clustering estimate based on any loss function on the space of partitions. DIB-C can also estimate cluster densities, quickly classify new subjects and provide a posterior predictive distribution. Both simulation studies and real data applications show superior performance of DIB-C in terms of robustness and computational efficiency.
MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality

With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community. Eye gaze estimation is a crucial component in XR -- enabling energy efficient rendering, multi-focal displays, and effective interaction with content. In head-mounted XR devices, the eyes are imaged off-axis to avoid blocking the field of view. This leads to increased challenges in inferring eye related quantities and simultaneously provides an opportunity to develop accurate and robust learning based approaches. To this end, we present MagicEyes, the first large scale eye dataset collected using real MR devices with comprehensive ground truth labeling. MagicEyes includes $587$ subjects with $80,000$ images of human-labeled ground truth and over $800,000$ images with gaze target labels. We evaluate several state-of-the-art methods on MagicEyes and also propose a new multi-task EyeNet model designed for detecting the cornea, glints and pupil along with eye segmentation in a single forward pass.
Industrial Scale Privacy Preserving Deep Neural Network

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become a serious problem currently, i.e., different parties cannot share data with each other. To solve this issue, most research leverages cryptographic techniques to train secure DNN models for multi-parties without compromising their private data. Although such methods have strong security guarantee, they are difficult to scale to deep networks and large datasets due to its high communication and computation complexities. To solve the scalability of the existing secure Deep Neural Network (DNN) in data isolation scenarios, in this paper, we propose an industrial scale privacy preserving neural network learning paradigm, which is secure against semi-honest adversaries. Our main idea is to split the computation graph of DNN into two parts, i.e., the computations related to private data are performed by each party using cryptographic techniques, and the rest computations are done by a neutral server with high computation ability. We also present a defender mechanism for further privacy protection. We conduct experiments on real-world fraud detection dataset and financial distress prediction dataset, the encouraging results demonstrate the practicalness of our proposal.
THE EFFECT OF USING FLASHCARDS MEDIA TO INCREASE VOCABULARY SIZE FOR THE SECOND-GRADE STUDENTS AT MTs MADANI ALAUDDIN

This research aims to find out the effect of Flashcards media to increase vocabulary size for the second grade students at Mts Madani Alauddin. This research was conducted at Mts Madani Alauddin Regency in academic year 2021/2022. The researcher used the instrument to collect data Vocabulary Size Test and Vocabulary Level Test (VLT). This research used quantitative data applied a pre-test and post-test group design, in this research the population were 145 students of the second grade at Mts Madani Alauddin. The sample were 20 students of Class VIII A as an experimental class using the pre-experimental design method, type one group pretest-posttest (single group initial test). The research findings showed that the second grade of students at Mts Madani Alauddin obtained scores on the pre-test with a mean score of 50.5, which was classified as very poor. After giving treatment, they obtained a score with a mean score of 65.5, which was classified as fair. It means that there was a significant increase in students’ vocabulary size using Flashcard media it was supported by the results of the one-sample T-Test where sig. (2-tailed) 0.001 &lt; 0.05 has an effect in increasing students’ vocabulary. Therefore Ho is rejected and H1 is accepted, apart from that the results of the vocabulary level test showed that the average student got a fair score. The result of the data analysis indicated that there was the improvement in the students’ vocabulary size after being taught using Flashcard media.
JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts

In this study, we examine the ability of contextualized representations of pretrained language model to distinguish whether sequences from instructional articles are plausible or implausible.Towards this end, we compare the BERT, RoBERTa, and DeBERTa models using simple classifiers based on the sentence representations of the [CLS] tokens and perform a detailed analysis by visualizing the representations of the [CLS] tokens of the models.In the experimental results of Subtask A: Multi-Class Classification, DeBERTa exhibits the best performance and produces a more distinguishable representation across different labels.Submitting an ensemble of 10 DeBERTa-based models, our final system achieves an accuracy of 61.4% and is ranked fifth out of models submitted by eight teams.Further in-depth results suggest that the abilities of pretrained language models for the plausibility detection task are more strongly affected by their model structures or attention designs than by their model sizes.
Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity Classifier

We present our contribution to the SemEval 22 Shared Task 8: Multilingual news article similarity.The approach is lightweight and language-agnostic, it is based on the computation of several lexicographic and embeddingbased features, and the use of a simple ML approach: random forests.In a notable departure from the task formulation, which is a ranking task, we tackled this task as a classification one.We present a detailed analysis of the behaviour of our system under different settings.
Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models

We propose a novel Thai grapheme-tophoneme conversion method based on a neural regression model that is trained using neural networks to predict the similarity between a candidate and the correct pronunciation.After generating a set of candidates for an input word or phrase using the orthography rules, this model selects the best-similarity pronunciation from the candidates.This method can be applied to languages other than Thai simply by preparing enough orthography rules, and can reduce the mistakes that neural network models often make.We show that the accuracy of the proposed method is .931,which is comparable to that of encoder-decoder sequence models.We also demonstrate that the proposed method is superior in terms of the difference between correct and predicted pronunciations because incorrect, strange output sometimes occurs when using encoder-decoder sequence models but the error is within the expected range when using the proposed method.
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript

Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining.A key step in this process is punctuation restoration which restores fundamental text structures such as phrase and sentence boundaries from the video transcripts.This work presents a new human-annotated corpus, called BehancePR, for punctuation restoration in livestreaming video transcripts.Our experiments on Behan-cePR demonstrate the challenges of punctuation restoration for this domain.Furthermore, we show that popular natural language processing toolkits like Stanford Stanza, Spacy, and Trankit underperform on detecting sentence boundary on non-punctuated transcripts of livestreaming videos.The dataset is publicly accessible at http://github.com/ nlp-uoregon/behancepr.
Overconfidence in the Face of Ambiguity with Adversarial Data

Adversarial data collection has shown promise as a method for building models which are more robust to the spurious correlations that generally appear in naturalistic data.However, adversarially-collected data may itself be subject to biases, particularly with regard to ambiguous or arguable labeling judgments.Searching for examples where an annotator disagrees with a model might over-sample ambiguous inputs, and filtering the results for high inter-annotator agreement may under-sample them.In either case, training a model on such data may produce predictable and unwanted biases.In this work, we investigate whether models trained on adversarially-collected data are miscalibrated with respect to the ambiguity of their inputs.Using Natural Language Inference models as a testbed, we find no clear difference in accuracy between naturalistically and adversarially trained models, but our model trained only on adversarially-sourced data is considerably more overconfident of its predictions and demonstrates worse calibration, especially on ambiguous inputs.This effect is mitigated, however, when naturalistic and adversarial training data are combined.
What Do Users Care About? Detecting Actionable Insights from User Feedback

Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line.Detecting actionable insights can be challenging owing to large amounts of data as well as the absence of labels in real-world scenarios.In this work, we present an aggregation and graphbased ranking strategy for unsupervised detection of these insights from real-world, noisy, user-generated feedback.Our proposed approach significantly outperforms strong baselines on two real-world user feedback datasets and one academic dataset.
IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News Similarity

The task of multilingual news article similarity entails determining the degree of similarity of a given pair of news articles in a language-agnostic setting.This task aims to determine the extent to which the articles deal with the entities and events in question without much consideration of the subjective aspects of the discourse.Considering the superior representations being given by these models as validated on other tasks in NLP across an array of high and low-resource languages and this task not having any restricted set of languages to focus on, we adopted using the encoder representations from these models as our choice throughout our experiments.For modeling the similarity task by using the representations given by these models, a Siamese architecture was used as the underlying architecture.In experimentation, we investigated on several fronts including features passed to the encoder model, data augmentation and ensembling among our major experiments.We found data augmentation to be the most effective working strategy among our experiments.
CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach

Recognizing complex and ambiguous named entities (NEs) is one of the formidable tasks in the NLP domain.However, the diversity of linguistic constituents, syntactic structure, semantic ambiguity as well as differences from traditional NEs make it challenging to identify the complex NEs.To address these challenges, SemEval-2022 Task 11 introduced a shared task MultiCoNER focusing on complex named entity recognition in multilingual settings.This paper presents our participation in this task where we propose two different approaches including a BiLSTM-CRF model with stackedembedding strategy and a transformer-based approach.Our proposed method achieved competitive performance among the participants' methods in a few languages.
Automating Human Evaluation of Dialogue Systems

Automated metrics to evaluate dialogue systems like BLEU, METEOR, etc., weakly correlate with human judgments.Thus, human evaluation is often used to supplement these metrics for system evaluation.However, human evaluation is time-consuming as well as expensive.This paper provides an alternative approach to human evaluation with respect to three aspects: naturalness, informativeness, and quality in dialogue systems.I propose an approach based on fine-tuning the BERT model with three prediction heads, to predict whether the system-generated output is natural, fluent and informative.I observe that the proposed model achieves an average accuracy of around 77% over these 3 labels.I also design a baseline approach that uses three different BERT models to make the predictions.Based on experimental analysis, I find that using a shared model to compute the three labels performs better than three separate models.
Deep Learning-Based Morphological Segmentation for Indigenous Languages: A Study Case on Innu-Aimun

Recent advances in the field of deep learning have led to a growing interest in the development of NLP approaches for low-resource and endangered languages.Nevertheless, relatively little research, related to NLP, has been conducted on indigenous languages.These languages are considered to be filled with complexities and challenges that make their study incredibly difficult in the NLP and AI fields.This paper focuses on the morphological segmentation of indigenous languages, an extremely challenging task because of polysynthesis, dialectal variations with rich morpho-phonemics, misspellings and resource-limited scenario issues.The proposed approach, towards a morphological segmentation of Innu-Aimun, an extremely low-resource indigenous language of Canada, is based on deep learning.Experiments and evaluations have shown promising results, compared to state-of-the-art rule-based and unsupervised approaches.
Transfer Learning and Masked Generation for Answer Verbalization

Structured Knowledge has recently emerged as an essential component to support fine-grained Question Answering (QA).In general, QA systems query a Knowledge Base (KB) to detect and extract the raw answers as final prediction.However, as lacking of context, language generation can offer a much informative and complete response.In this paper, we propose to combine the power of transfer learning and the advantage of entity placeholders to produce high-quality verbalization of extracted answers from a structured KB.We claim that such approach is especially well-suited for answer generation.Our experiments show 44.25%, 3.26% and 29.10% relative gain in BLEU over the state-of-the-art on the VQuAnDA, ParaQA and VANiLLa datasets, respectively.We additionally provide minor hallucinations corrections in VANiLLa standing for 5% of each of the training and testing set.We witness a median absolute gain of 0.81 SacreBLEU.This strengthens the importance of data quality when using automated evaluation.