Reviewerly/exHarmony huggingface dataset:
Neural text normalization leveraging similarities of strings and sounds

We propose neural models that can normalize text by considering the similarities of word strings and sounds.We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline.Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters.So that the proposed models achieved higher F 1 scores than the baseline.
Memory-Gated Recurrent Networks

The essence of multivariate sequential learning is all about how to extract dependencies in data. These data sets, such as hourly medical records in intensive care units and multi-frequency phonetic time series, often time exhibit not only strong serial dependencies in the individual components (the "marginal" memory) but also non-negligible memories in the cross-sectional dependencies (the "joint" memory). Because of the multivariate complexity in the evolution of the joint distribution that underlies the data generating process, we take a data-driven approach and construct a novel recurrent network architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates explicitly regulating two distinct types of memories: the marginal memory and the joint memory. Through a combination of comprehensive simulation studies and empirical experiments on a range of public datasets, we show that our proposed mGRN architecture consistently outperforms state-of-the-art architectures targeting multivariate time series.
IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation

In this paper, we present our approach and the results of our participation in OffensEval 2020.There are three sub-tasks in OffensEval 2020, namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (subtask C).We participated in sub-task A of English OffensEval 2020.Our approach emphasizes on how the emoji affects offensive language identification.Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media.The best model obtained macro F1-score of 0.88428.
CONcreTEXT @ EVALITA2020: The Concreteness in Context Task

Focus of the CONcreTEXT task is conceptual concreteness: systems were solicited to compute a value expressing to what extent target concepts are concrete (i.e., more or less perceptually salient) within a given context of occurrence. To these ends, we have developed a new dataset which was annotated with concreteness ratings and used as gold standard in the evaluation of systems. Four teams participated in this first edition of the task, with a total of 15 runs submitted.Interestingly, these works extend information on conceptual concreteness available in existing (non contextual) norms derived from human judgments with new knowledge from recently developed neural architectures, in much the same multidisciplinary spirit whereby the CONcreTEXT task was organized.
Arabic Curriculum Analysis

Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes.In this paper, we present a system to analyze Arabic curricula and provide insights into their content.It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected.Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.
Single-sensor acquisition without data jitter: a comparative sensor study

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Face Authenticated Hand Gesture Based Human Computer Interaction for Desktops

Abstract Hand gesture detection and recognition is a cutting-edge technology that is getting progressively applicable in several applications, including the recent trends namely Virtual Reality and Augmented Reality. It is a key part of Human-Computer Interaction which gives an approach to two-way interaction between the computer and the user. Currently, this technology is limited to expensive and highly specialized equipment and gadgets such as Kinect and the Oculus Rift. In this paper, various technologies and methodologies of implementing a gesture detection and recognition system are discussed. The paper also includes the implementation of a face recognition module using the Viola-Jones Algorithm for authentication of the system followed by hand gesture recognition using CNN to perform basic operations on the laptop. Any type of user can use gesture control as an alternative and interesting way to control their laptop. Furthermore, this can be used as a prototype for future implementations in the field of virtual reality as well as augmented reality.
La competencia digital de los preadolescentes gallegos/as antes de la pandemia: ¿y ahora qué?

The lockdown suffered in spring 2020 involved, among other changes, the widespread application of online training at all educational levels, highlighting not only problems related to technological resources and connectivity but also to students' formative deficiencies, such as a level development of the digital competence (DC) too limited to fulfil the ongoing challenges. In this context, we present the results of a study carried out in Galicia during the 2018-2019 academic year in order to delve into 6th-grade students' digital competence; a mixed methods research composed of a first qualitative phase, a multiple-case study, and a second one, quantitative, in which an assessment test of the DC was applied. The results attained show an average level of 5.96 out of 10 in DC, with an important difference between the two components of this punctuation; namely, knowledge and skills (M=5.10) and attitude (M=8.04). The case studies also allow us to observe: information-search processes carried out unsystematically and whose results are not adequately contrasted; differences between genders in the communicative area; scarce problem-solving skills; too basic content-creation strategies, and differences between teenagers’ attitude towards security and their actual behaviours. The conclusions stress the role of school in the development of DC in a digitalised future.
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using Deep Neural Networks and Linear Baselines

Sentiment Analysis is a well-studied field of Natural Language Processing.However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and tools.One of these challenges is code-mixing, which means using different languages to convey thoughts in social media texts.Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-mixed tweet.We used different preprocessing techniques and proposed to use different methods that vary from NBSVM to more complicated deep neural network models.Our best performing method obtains an F1 score of 0.751 for the Spanish-English sub-task and 0.706 over the Hindi-English sub-task.
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model

This paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media.Given a sentence, we are asked to find out the most important words as the suggestion for automated design.We leverage the unsupervised pre-training model and finetune these models on our task.After our investigation, we found that the following models achieved an excellent performance in this task: ERNIE 2.0, XLM-ROBERTA, ROBERTA and ALBERT.We combine a pointwise regression loss and a pairwise ranking loss which is more close to the final M atch m metric to finetune our models.And we also find that additional feature engineering and data augmentation can help improve the performance.Our best model achieves the highest score of 0.823 and ranks first for all kinds of metrics.
The Influence of the Professional Orientation of Students of Different Gender on Their Ideas of Happiness

In the conditions of unstable political, economic and social development of society the problem of quality of life, well-being, satisfaction of each citizen of the country acquires special importance. Research has shown that happiness is determined by various factors (age, marital, social and financial status, physiological status, social relations, personal characteristics), its level changes throughout life and can be corrected by certain psychological methods of influence. The purpose of our study was to study the state of development of the problem of happiness and study the content of the ideas of young people about happiness, their characteristics depending on gender and professional orientation. In order to study the peculiarities of the young generation's ideas about happiness, we studied 250 third-year students aged 20 to 22 years of various specialties. The researched questionnaire was asked to answer the questions, the analysis of which helped to make generalized conclusions about their ideas about happiness. The most of students rate their level of happiness as high or medium. But their level of happiness is much higher than their level of success, which can lead to a contradiction between the desire for success and happiness. This state of affairs requires correction of the content of ideas about happiness in adolescence by providing educational information about awareness of success as a process of self-realization and a component of happiness, the desire for integrated satisfaction of both material and spiritual needs for full personal development.
Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning

In this paper, we present our approach for the 'Detection of Propaganda Techniques in News Articles' task as a part of the 2020 edition of International Workshop on Semantic Evaluation.The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used.We propose a multi-system deep learning framework that can be used to identify the presence of propaganda spans in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution.Our proposed final model gave an F1-score of 0.48 on the test dataset.
On the Realistic Worst-Case Analysis of Quantum Arithmetic Circuits

We provide evidence that commonly held intuitions when designing quantum circuits can be misleading. In particular, we show that 1) reducing the T-count can increase the total depth; 2) it may be beneficial to trade controlled NOTs for measurements in noisy intermediate-scale quantum (NISQ) circuits; 2) measurement-based uncomputation of relative phase Toffoli ancillae can make up to 30% of a circuit’s depth; and 4) area and volume cost metrics can misreport the resource analysis. Our findings assume that qubits are and will remain a very scarce resource. The results are applicable for both NISQ and quantum error-corrected protected circuits. Our method uses multiple ways of decomposing Toffoli gates into Clifford+T gates. We illustrate our method on addition and multiplication circuits using ripple-carry. As a byproduct result, we show systematically that for a practically significant range of circuit widths, ripple-carry addition circuits are more resource-efficient than the carry-lookahead addition ones. The methods and circuits were implemented in the open-source QUANTIFY software.
Applying and Facilitating Serious Location-Based Games

The popularity of location-based games continues unabated and is benefiting from the increasing use of mobile end devices and advantageous general conditions, such as the Internet of Things and the Smart City paradigm. This enormous potential of engagement should also be tapped for serious location-based games, i.e. the use of location-based games beyond the purpose of entertainment. The workshop “Applying and Facilitating Serious Location-based Games” aims to contribute to the development of this potential. In the article, the theoretical basis for this workshop is derived and corresponding frameworks are presented.
GP-based Feature Selection and Weighted KNN-based Instance Selection for Symbolic Regression with Incomplete Data

Data incompleteness is one of the serious challenges in symbolic regression particularly when learning from real-world data. To handle this situation, the imputation approach works by replacing the missing values with estimated predictions. One popular imputation method is based on K-nearest neighbour (KNN). However, this method requires the use of the training data in the application process of imputing unseen data. This requirement means more computation time, especially in the case of large-scale data sets. To address this issue, this work proposes a combination of genetic programming-based feature selection and KNN-based instance selection for imputing incomplete data efficiently while constructing effective symbolic regression models. The experimental work is conducted on real-world data sets with different missingness scenarios and the obtained results show the empirical soundness of the proposed method compared to the benchmark methods.
Breast Cancer Detection Using Supervised Machine Learning: A Comparative Analysis

The rapidly changing lifestyle of the people in the modern era has led to a significant increase in their health issues. This busy lifestyle can delay the diagnosis of the disease which may at time be able to prove fatal. Breast cancer is one of the most prominent causes of deaths among women. However, the recent advancements in the technology have been proved to provide promising solutions to address this issue. The application of intelligent techniques can help in diagnosing breast cancer in early stages thereby reducing the chances of fatality. This has motivated several researchers to apply machine learning methods for the detection of breast cancer and as such it has become the locus of most of the ongoing research in recent times. The work presented in this paper aims to analyse the performance of multiple machine learning classifiers in detecting breast cancer. The obtained results clearly indicate the efficacy of linear support vector classifier and gradient boosting over other classifiers. The findings of this research can be further utilized for developing more efficient ensemble models as well as optimizing the performance of existing models thereby increasing their prediction accuracy.
Selecting the UD v2 Morphological Features for Indonesian Dependency Treebank

The objectives of our work are to propose the relevant Universal Dependencies (UD) morphological features for Indonesian dependency treebank and to apply the proposed features to an existing treebank. We propose the use of 14 UD v2 features and the corresponding 27 feature-value tags. To evaluate the quality of the resulting treebank, we built models for lemmatization, POS tagging, morphological features analysis, and dependency parsing using UDPipe, a trainable pipeline for tokenization, tagging, lemmatization, and dependency parsing of CoNLL-U files. For lemmatization, POS tagging, and morphological features analysis tasks, the resulting models have F1-score of more than 93% that shows that the consistency of annotations for the columns LEMMA, UPOS, and FEATS in the treebank is already good. However, the accuracy of the Indonesian dependency parser built is still only 82.59% for UAS and 79.83% for LAS. The experiments also show that morphological features information has no or little impact on improving the quality of lemmatization, POS tagging, and dependency parsing models for Indonesian.
Towards Higher Sense of Presence: A 3D Virtual Environment Adaptable to Confusion and Engagement

Virtual Reality scenarios where emitters convey information to receptors can be used as a tool for distance learning and to enable virtual visits to company physical headquarters. However, immersive Virtual Reality setups usually require visualization interfaces such as Head-mounted Displays, Powerwalls or CAVE systems, supported by interaction devices (Microsoft Kinect, Wii Motion, among others), that foster natural interaction but are often inaccessible to users. We propose a virtual presentation scenario, supported by a framework, that provides emotion-driven interaction through ubiquitous devices. An experiment with 3 conditions was designed involving: a control condition; a less confusing text script based on its lexical, syntactical, and bigram features; and a third condition where an adaptive lighting system dynamically acted based on the user's engagement. Results show that users exposed to the less confusing script reported higher sense of presence, albeit without statistical significance. Users from the last condition reported lower sense of presence, which rejects our hypothesis without statistical significance. We theorize that, as the presentation was given orally and the adaptive lighting system impacts the visual channel, this conflict may have overloaded the users' cognitive capacity and thus reduced available resources to address the presentation content.
Provchastic: Understanding and Predicting Game Events Using Provenance

Game analytics became a very popular and strategic tool for business intelligence in the game industry. One of the many aspects of game analytics is predictive analytics, which generates predictive models using statistics derived from game sessions to predict future events. The generation of predictive models is of great interest in the context of game analytics with numerous applications for games, such as predicting player behavior, the sequence of future events, and win probabilities. Recently, a novel approach emerged for capturing and storing data from game sessions using provenance, which encodes cause and effect relationships together with the telemetry data. In this work, we propose a stochastic approach for game analytics based on that novel game provenance information. This approach unifies all gathered provenance data from different game sessions to create a probabilistic graph that determines the sequence of possible events using the commonly known stochastic model of Markov chains and also allows for understanding the reasons to reach a specific state. We integrated our solution with an existing open-source provenance visualization tool and provided a case study using real data for validation. We could observe that it is possible to create probabilistic models using provenance graphs for short and long predictions and to understand how to reach a specific state.
Adversarial Robustness by Design Through Analog Computing And Synthetic Gradients

We propose a new defense mechanism against adversarial at-tacks inspired by an optical co-processor, providing robustness without compromising natural accuracy in both white-box and black-box settings. This hardware co-processor performs a nonlinear fixed random transformation, where the parameters are unknown and impossible to retrieve with sufficient precision for large enough dimensions. In the white-box setting, our defense works by obfuscating the parameters of the random projection. Unlike other defenses relying on obfuscated gradients, we find we are unable to build a re-liable backward differentiable approximation for obfuscated parameters. Moreover, while our model reaches a good natural accuracy with a hybrid backpropagation - synthetic gradient method, the same approach is suboptimal if employed to generate adversarial examples. Finally, our hybrid training method builds robust features against black-box and transfer attacks. We demonstrate our approach on a VGG-like architecture, placing the defense on top of the convolutional features, on CIFAR-10 and CIFAR-100.
Influence of codebook patterns on writer recognition: An experimental study

Abstract Codebook‐based writer characterization is an effective technique that has been investigated in a number of recent studies on identification and verification of writers. These methods divide a set of writing samples into small units (fragments or graphemes) and cluster these patterns to produce a codebook. Writer of a handwritten sample is then characterized by the probability (distribution) of producing the codebook patterns. In most cases, a small subset of the database under study is employed to produce the codebook while the rest of the database is used in evaluations. This work aims to validate the hypothesis that the codebook simply serves as a representation space to compare different writings and, in most cases, the patterns in the codebook do not significantly influence the identification and verification performance. The hypothesis is validated by generating a number of codebooks using Greek, Arabic and Chinese handwritten samples. Moreover, codebooks using fragments of handwritten music scores, printed text and synthetic data are also investigated. Evaluations on three well‐known handwriting databases (CVL, BFL and IAM) validate the idea that, in general, the codebook patterns do not have a significant impact on characterizing writer from handwriting.
Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies.

Explicit mechanisms for copying have improved the performance of neural models for sequence-to-sequence tasks in the low-resource setting. However, they rely on an overlap between source and target vocabularies. Here, we propose a model that does not: a pointer-generator transformer for disjoint vocabularies. We apply our model to a low-resource version of the grapheme-to-phoneme conversion (G2P) task, and show that it outperforms a standard transformer by an average of 5.1 WER over 15 languages. While our model does not beat the the best performing baseline, we demonstrate that it provides complementary information to it: an oracle that combines the best outputs of the two models improves over the strongest baseline by 7.7 WER on average in the low-resource setting. In the high-resource setting, our model performs comparably to a standard transformer.
Transformer Models for Drug Adverse Effects Detection from Tweets

In this paper we present the drug adverse effects detection system developed during our participation in the Social Media Mining for Health Applications Shared Task 2020. We experimented with transfer learning approach for English and Russian, BERT and RoBERTa architectures and several strategies for regression head composition. Our final submissions in both languages overcome average F1 by several percents margin.