Reviewerly/exHarmony huggingface dataset:
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills

Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning.In this work, we propose to leverage semi-structured tables, and automatically generate at scale questionparagraph pairs, where answering the question requires reasoning over multiple facts in the paragraph.We add a pre-training step over this synthetic data, which includes examples that require 16 different reasoning skills such as number comparison, conjunction, and fact composition.To improve data efficiency, we sample examples from reasoning skills where the model currently errs.We evaluate our approach on three reasoning-focused reading comprehension datasets, and show that our model, PReasM, substantially outperforms T5, a popular pre-trained encoder-decoder model.Moreover, sampling examples based on model errors leads to faster training and higher performance.
ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets

In this paper, we propose a novel R package, named ImbTreeAUC, for building binary and multiclass decision tree using the area under the receiver operating characteristic (ROC) curve. The package provides nonstandard measures to select an optimal split point for an attribute as well as the optimal attribute for splitting through the application of local, semiglobal and global AUC measures. Additionally, ImbTreeAUC can handle imbalanced data, which is a challenging issue in many practical applications. The package supports cost-sensitive learning by defining a misclassification cost matrix and weight-sensitive learning. It accepts all types of attributes, including continuous, ordered and nominal attributes. The package and its code are made freely available.
Targets and Aspects in Social Media Hate Speech

Mainstream research on hate speech focused so far predominantly on the task of classifying mainly social media posts with respect to predefined typologies of rather coarse-grained hate speech categories.This may be sufficient if the goal is to detect and delete abusive language posts.However, removal is not always possible due to the legislation of a country.Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives.For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post.As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain.The outcome of the experiments is promising and can serve as inspiration for further work on the task.
Explainable machine learning prediction of ICU mortality

There is a variety of mortality prediction models for patients in intensive care units (ICU) to guide appropriate clinical management. Advances in machine learning methodologies typically employ classifiers such as Neural Network and Random Forest which are often regarded by healthcare professionals as black boxes. These models often do not provide clear links between the input model features and output clinical event. We investigate whether features identified by Cox-Proportional Hazards (CPH) model can be used for ICU mortality prediction. We employ the PhysioNet Challenge 2012 dataset, a subset of MIMIC-II Clinical Database data of ICU patients admitted to Boston's Beth Israel Deaconess Medical Center from 2001 to 2008. The dataset is split into train set A, test set B and unseen set C, with 4000 patients each. Python is the programming language used alongside scikit-learn, and lifelines packages. Besides white-box feature selection methods (logistic regression and decision tree), we also explore using Cox-Proportional Hazards model for feature selection. We then trained the machine learning model using classifiers such as logistic regression and variants of decision tree. Extreme gradient boosted trees models performed better than other classifiers. The model is validated using 5-fold cross-validation and evaluated against unseen set C. The model performance is assessed using area under the precision-recall curve (AUC-PR) as the main metric. The data of about 12,000 patients is used, providing a high degree of generalizability. The number of statistically significant features identified by CPH (n = 16) is significantly smaller than logistic regression (n = 36), decision tree (n = 26) and all features (n = 42). With only 16 features used, the model achieves a performance of AUC-PR 0·438 on test set B, which is close to decision tree (AUC-PR 0·442) and logistic regression (AUC-PR 0·446) and all features (AUC-PR 0·446). The significantly fewer features identified by CPH allows the building of a model that is easily interpretable by clinicians whilst still achieving comparable results to other models. This finding allows clinicians to use CPH as an alternative method to determine and act on features that need to be closely monitored for ICU patients.
Code to Comment Translation: A Comparative Study on Model Effectiveness &amp; Errors

Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions.Most evaluations of such models are conducted using automatic reference-based metrics.However, given the relatively large semantic gap between programming languages and natural language, we argue that this line of research would benefit from a qualitative investigation into the various error modes of current stateof-the-art models.Therefore, in this work, we perform both a quantitative and qualitative comparison of three recently proposed source code summarization models.In our quantitative evaluation, we compare the models based on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics, and in our qualitative evaluation, we perform a manual open-coding of the most common errors committed by the models when compared to ground truth captions.Our investigation reveals new insights into the relationship between metric-based performance and model prediction errors grounded in an empirically derived error taxonomy that can be used to drive future research efforts.
Gaze Behavior and Positioning of Referee Teams during Three-Point Shots in Basketball

This study looks at the visual scan patterns of high-class basketball referees. Using mobile eye-tracking devices, referees’ gaze behavior was analyzed during the execution of three-point shots in the official pre-season games of Germany’s men’s professional basketball league. We evaluated the extent to which the referees fulfill the tasks assigned to them, where do they look, and to what extent does their gaze behavior overlap during a three-point shot. Results indicate that referees who are far away from the ball and are, therefore, not responsible for observing the actual shot, tend to comply with their areas of responsibility less often than referees standing nearer to the ball, i.e., they appear to observe the ball more than required (ball watching) at the expense of other areas that they are required to be observing at the beginning of the shooting process. However, referees spend a very small part of a three-point shot looking at the same areas of interest. This indicates that referee teams’ allocation of gaze is rather effective, remaining in line with FIBA recommendations and is presumably not the main cause for errors in officiating.
A Deep Support Vector Data Description Method for Anomaly Detection in Helicopters

Helicopters are high-value mechanical assets which has gained much attention from condition monitoring practitioners. Modern helicopter health management system leverages various sensors to collect in-flight signals. In order to trigger the alarm when an anomaly happens, signal processing methods are used to construct health indicators that require expert knowledge. On the other hand, classic features are always case-specific and may fail to discriminate anomalous in practical applications. Support Vector Data Description (SVDD) is a machine learning method used as a one-class classifier to serve anomaly detection tasks. It utilizes healthy samples to construct a hyper-sphere feature space as a detection threshold. In order to automate the anomaly detection pipeline, a deep SVDD model is proposed in this paper. A Convolution Neural Network (CNN) is used as the feature extractor, which provides smart features to an SVDD model. The SVDD model uses a soft-boundary hyper-sphere for decision-making. The optimization of the CNN and the SVDD is connected, which makes it an end-to-end process. The methodology is applied, tested and evaluated on a helicopter vibration dataset, which has been provided by Airbus SAS in the frames of AI Gym challenge. The experimental results reveal that the F1 score of the proposed Deep SVDD can reach 94%, showing its compelling efficacy for anomaly detection.
Asymptotically-Good Arithmetic Secret Sharing over $$\mathbb {Z}/p^{\ell }\mathbb {Z}$$ with Strong Multiplication and Its Applications to Efficient MPC

This paper studies information-theoretically secure multiparty computation (MPC) over rings $$\mathbb {Z}/p^{\ell }\mathbb {Z}$$ . In the work of [Abs+19a, TCC’19], a protocol based on the Shamir secret sharing over $$\mathbb {Z}/p^{\ell }\mathbb {Z}$$ was presented. As in the field case, its limitation is that the share size grows as the number of players increases. Then several MPC protocols were developed in [Abs+20, Asiacrypt’20] to overcome this limitation. However, (i) their offline multiplication gate has super-linear communication complexity in the number of players; (ii) the share size is doubled for the most important case, namely over $$\mathbb {Z}/2^{\ell }\mathbb {Z}$$ due to infeasible lifting of self-orthogonal codes from fields to rings; (iii) most importantly, the BGW model could not be applied via the secret sharing given in [Abs+20, Asiacrypt’20] due to lack of strong multiplication. In this paper we overcome all the drawbacks mentioned above. Of independent interest, we establish an arithmetic secret sharing with strong multiplication, which is the most important primitive in the BGW model. Incidentally, our solution to (i) has some advantages over the concurrent one of [PS21, EC’21], since it is direct, is only one-page long, and furthermore carries over $$\mathbb {Z}/p^{\ell }\mathbb {Z}$$ . Finally, we lift Reverse Multiplication Friendly Embeddings (RMFE) from fields to rings, with same (linear) complexity. Note that RMFE has become a standard technique for communication complexity in MPC in the regime over many instances of the same circuit, as in [Cas+18, Crypto’18] and [DLN19, Crypto’19]. We thus recover the same amortized complexity of MPC over $$\mathbb {Z}/2^{\ell }\mathbb {Z}$$ than over fields. To obtain our theoretical results, we use the existence of lifts of curves over rings, then use the known results stating that Riemann-Roch spaces are free modules. To make our scheme practical, we start from good algebraic geometry codes over finite fields obtained from existing computational techniques. Then we present, and implement, an efficient algorithm to Hensel-lift the generating matrix of the code, such that the multiplicative conditions are preserved over rings. On the other hand, a random lifting of codes over rings does not preserve multiplicativity in general. Finally we provide efficient methods for sharing and reconstruction over rings.
Perspective-Taking in Virtual Reality and Reduction of Biases against Minorities

This study examines the effect of perspective-taking via embodiment in virtual reality (VR) in improving biases against minorities. It tests theoretical arguments about the affective and cognitive routes underlying perspective-taking and examines the moderating role of self-presence in VR through experiments. In Study 1, participants embodied an ethnic minority avatar and experienced workplace microaggression from a first-person perspective in VR. They were randomly assigned to affective (focus on emotions) vs. cognitive (focus on thoughts) perspective-taking conditions. Results showed that ingroup bias improved comparably across both conditions and that this effect was driven by more negative perceptions of the majority instead of more positive perceptions of minorities. In Study 2, participants experienced the same VR scenario from the third-person perspective. Results replicated those from Study 1 and extended them by showing that the effect of condition on ingroup bias was moderated by self-presence. At high self-presence, participants in the affective condition reported higher ingroup bias than those in the cognitive condition. The study showed that in VR, the embodiment of an ethnic minority is somewhat effective in improving perceptions towards minority groups. It is difficult to clearly distinguish between the effect of affective and cognitive routes underlying the process of perspective-taking.
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis

This paper introduces RyanSpeech, a new speech corpus for research on automated text-to-speech (TTS) systems. Publicly available TTS corpora are often noisy, recorded with multiple speakers, or lack quality male speech data. In order to meet the need for a high quality, publicly available male speech corpus within the field of speech recognition, we have designed and created RyanSpeech which contains textual materials from real-world conversational settings. These materials contain over 10 hours of a professional male voice actor's speech recorded at 44.1 kHz. This corpus's design and pipeline make RyanSpeech ideal for developing TTS systems in real-world applications. To provide a baseline for future research, protocols, and benchmarks, we trained 4 state-of-the-art speech models and a vocoder on RyanSpeech. The results show 3.36 in mean opinion scores (MOS) in our best model. We have made both the corpus and trained models for public use.
Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot

A chatbot is a useful tool for communicating with users to extract necessary information.An intelligent chatbot requires an effective knowledge base as materials to adequately organize the knowledge domain and process many kinds of inputted queries as natural text.Ontology technology is effective for use in learning technology systems.In this paper, a model that uses ontology technology for relational knowledge to integrate a structure of scripts is presented.This integrated model, called the Rela-Scripts model, is used to organize the knowledge material of a chatbot to search for knowledge in education.Some problems in searching for knowledge by this chatbot are proposed and solved on the basis of the Rela-Scripts model.The proposed method is applied to build an intelligent chatbot for answering questions on contents of the Introduction to Programming course in a university.This chatbot acts as a tutor by communicating with students in Vietnamese and gives explanations that meet the requirements of students.Its instructions are useful for their self-learning to enhance their programming skills.
SHREC’21: Quantifying shape complexity

This paper presents the results of SHREC'21 track: Quantifying Shape Complexity. Our goal is to investigate how good the submitted shape complexity measures are (i.e. with respect to ground truth) and investigate the relationships between these complexity measures (i.e. with respect to correlations). The dataset consists of three collections: 1800 perturbed cube and sphere models classified into 4 categories, 50 shapes inspired from the fields of architecture and design classified into 2 categories, and the data from the Princeton Segmentation Benchmark, which consists of 19 natural object categories. We evaluate the performances of the methods by computing Kendall rank correlation coefficients both between the orders produced by each complexity measure and the ground truth and between the pair of orders produced by each pair of complexity measures. Our work, being a quantitative and reproducible analysis with justified ground truths, presents an improved means and methodology for the evaluation of shape complexity.
Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images

In the recent years, many objective image quality assessment methods have been proposed by different researchers, leading to a significant increase in their correlation with subjective quality evaluations. Although many recently proposed image quality assessment methods, particularly full-reference metrics, are in some cases highly correlated with the perception of individual distortions, there is still a need for their verification and adjustment for the case when images are affected by multiple distortions. Since one of the possible approaches is the application of combined metrics, their analysis and optimization are discussed in this paper. Two approaches to metrics’ combination have been analyzed that are based on the weighted product and the proposed weighted sum with additional exponential weights. The validation of the proposed approach, carried out using four currently available image datasets, containing multiply distorted images together with the gathered subjective quality scores, indicates a meaningful increase of correlations of the optimized combined metrics with subjective opinions for all datasets.
Vision Transformers for Dense Prediction

We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at this https URL.
Governing Fake News: The Regulation of Social Media and the Right to Freedom of Expression in the Era of Emergency

Governments around the world are strictly regulating information on social media in the interests of addressing fake news. There is, however, a risk that the uncontrolled spread of information could increase the adverse effects of the COVID-19 health emergency through the influence of false and misleading news. Yet governments may well use health emergency regulation as a pretext for implementing draconian restrictions on the right to freedom of expression, as well as increasing social media censorship (ie chilling effects). This article seeks to challenge the stringent legislative and administrative measures governments have recently put in place in order to analyse their negative implications for the right to freedom of expression and to suggest different regulatory approaches in the context of public law. These controversial government policies are discussed in order to clarify why freedom of expression cannot be allowed to be jeopardised in the process of trying to manage fake news. Firstly, an analysis of the legal definition of fake news in academia is presented in order to establish the essential characteristics of the phenomenon (Section II). Secondly, the legislative and administrative measures implemented by governments at both international (Section III) and European Union (EU) levels (Section IV) are assessed, showing how they may undermine a core human right by curtailing freedom of expression. Then, starting from the premise of social media as a “watchdog” of democracy and moving on to the contention that fake news is a phenomenon of “mature” democracy, the article argues that public law already protects freedom of expression and ensures its effectiveness at the international and EU levels through some fundamental rules (Section V). There follows a discussion of the key regulatory approaches, and, as alternatives to government intervention, self-regulation and especially empowering users are proposed as strategies to effectively manage fake news by mitigating the risks of undue interference by regulators in the right to freedom of expression (Section VI). The article concludes by offering some remarks on the proposed solution and in particular by recommending the implementation of reliability ratings on social media platforms (Section VII).
The Effects of COVID-19 in the Learning Process of Primary School Students: A Systematic Review

(1) Background: A variety of social and economic changes are happening worldwide due to the pandemic caused by COVID-19, which has produced new problems and challenges for the population as a whole. These events demand new investigations and key studies for their management. This review addresses the repercussions of COVID-19 at the educational level in the primary education stage, delving into the effects produced in teaching and different aspects related to it, such as the situation and challenges of teachers, family involvement, and the perceptions and repercussions of the learning and socio-educational development of students (especially in the case of students with Autism Spectrum Disorder). (2) Methods: The methodological design is a systematic review study, following the PRISMA guidelines, from a search carried out during the month of July 2021 in the Scopus, Dialnet, and WoS databases on the object of study. (3) Results: The selected studies were analyzed through a qualitative content analysis based on a population of 103 articles, with a final sample of 13, using the inclusion criteria: empirical studies or research in English or Spanish; free access through the Internet; categories restricted to “education/educational research” related to the proposed objectives; specific documents of the primary education stage and/or students with Autism Spectrum Disorder. (4) Conclusions: The research reveals the presence of an existing digital gap in certain sectors of the student population aggravated by the pandemic, as well as the scarcity of general teacher training in this type of situation, leading to different personal and professional problems that hinder teaching and emphasize the vulnerability of the right to education, which leads to further promoting the already existing social inequalities.