Reviewerly/exHarmony huggingface dataset:
Identifying multiple changes for a functional data sequence with application to freeway traffic segmentation

Motivated by the study of road segmentation partitioned by shifts in traffic conditions along a freeway, we introduce a two-stage procedure, Dynamic Segmentation and Backward Elimination (DSBE), for identifying multiple changes in the mean functions for a sequence of functional data.The Dynamic Segmentation procedure searches for all possible changepoints using the derived global optimality criterion coupled with the local strategy of atmost-one-changepoint by dividing the entire sequence into individual subsequences that are recursively adjusted until convergence.Then, the Backward Elimination procedure verifies these changepoints by iteratively testing the unlikely changes to ensure their significance until no more changepoints can be removed.By combining the local strategy with the global optimal changepoint criterion, the DSBE algorithm is conceptually simple and easy to implement and performs better than the binary segmentation-based approach at detecting small multiple changes.The consistency property of the changepoint estimators and the convergence of the algorithm are proved.We apply DSBE to detect changes in traffic streams through real freeway traffic data.The practical performance of DSBE is also investigated through intensive simulation studies for various scenarios.
On extracting data from tables that are encoded using HTML

Tables are a common means to display data in human-friendly formats. Many authors have worked on proposals to extract those data back since this has many interesting applications. In this article, we summarise and compare many of the proposals to extract data from tables that are encoded using HTML and have been published between 2000 and 2018. We first present a vocabulary that homogenises the terminology used in this field; next, we use it to summarise the proposals; finally, we compare them side by side. Our analysis highlights several challenges to which no proposal provides a conclusive solution and a few more that have not been addressed sufficiently; simply put, no proposal provides a complete solution to the problem, which seems to suggest that this research field shall keep active in the near future. We have also realised that there is no consensus regarding the datasets and the methods used to evaluate the proposals, which hampers comparing the experimental results.
Unsupervised Domain Adaptation via Calibrating Uncertainties

Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset. Intuitively, a model trained on source domain normally produces higher uncertainties for unseen data. In this work, we build on this assumption and propose to adapt from source to target domain via calibrating their predictive uncertainties. The uncertainty is quantified as the Renyi entropy, from which we propose a general Renyi entropy regularization (RER) framework. We further employ variational Bayes learning for reliable uncertainty estimation. In addition, calibrating the sample variance of network parameters serves as a plug-in regularizer for training. We discuss the theoretical properties of the proposed method and demonstrate its effectiveness on three domain-adaptation tasks.
Comfortable Locomotion in VR: Teleportation is Not a Complete Solution

We compared two common techniques of controller-based locomotion (teleportation and steering locomotion) in virtual reality (VR) in terms of the cybersickness they produce. Participants had to continuously navigate a commercial VR application for 16 minutes using each technique, while standing and seated. While teleportation produced less cybersickness than steering locomotion on average, a number of participants reported teleportation to be more sickening. These ‘telesick’ participants were found to have greater medio/lateral positional variability in their spontaneous postural sway than ‘steersick’ participants prior to VR exposure. We conclude that different individuals may require unique techniques to comfortably locomote in VR.
Extracting relevant information from physician-patient dialogues for automated clinical note taking

We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. The system parses each dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant. We also classify the primary diagnosis for each conversation. In addition, we extract topic information and identify relevant utterances. This serves as a baseline for a system that extracts information from dialogues and automatically generates a patient note, which can be reviewed and edited by the clinician.
Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection

Owing to the ubiquity of computer software, software vulnerability detection (SVD) has become an important problem in the software industry and computer security. One of the most crucial issues in SVD is coping with the scarcity of labeled vulnerabilities in projects that require the laborious manual labeling of code by software security experts. One possible solution is to employ deep domain adaptation (DA) which has recently witnessed enormous success in transferring learning from structural labeled to unlabeled data sources. Generative adversarial network (GAN) is a technique that attempts to bridge the gap between source and target data in the joint space and emerges as a building block to develop deep DA approaches with state-of-the-art performance. However, deep DA approaches using the GAN principle to close the gap are subject to the mode collapsing problem that negatively impacts the predictive performance. Our aim in this paper is to propose Dual Generator-Discriminator Deep Code Domain Adaptation Network (Dual-GD-DDAN) for tackling the problem of transfer learning from labeled to unlabeled software projects in SVD to resolve the mode collapsing problem faced in previous approaches. The experimental results on real-world software projects show that our method outperforms state-of-the-art baselines by a wide margin.
Augmented Reality for Children in a Confirmation Task: Time, Fatigue, and Usability

The objective of this paper is to explore three different interaction methods in a confirmation task on a head-mounted Augmented Reality (AR) device with a population of children aged 9-11 years. The three interaction methods we look at are voice recognition, gesture recognition, and controller. We conducted a within-subjects study using a Fitts’ Law confirmation task performed by children with a Microsoft HoloLens. We measured elapsed time during the completion of the tasks. Also, we collected usability and fatigue measures using the System Usability Scale and the OMNI RPE (Ratings of Perceived Exertion) scale. We found significant differences between voice and controller for time, fatigue and usability. We also found significant differences between gesture and controller for time, fatigue and usability. We hope to apply the results of this study to improve augmented reality educational tools for children in the future.
HandPoseMenu

A system control technique (changing the interaction mode) is needed to provide various spatial interaction techniques for virtual, augmented and mixed reality (VR/AR/MR) applications. We propose HandPoseMenu, a hand posture-based virtual menus system in 3D space. HandPoseMenu recognizes the non-dominant hand's posture using a depth sensor that is attached to a wearable display and presents the virtual menu corresponding to the hand posture beside the non-dominant hand. When the user selects a desired function among the presented menu as a dominant hand, the interaction mode changes. The combination of the two system controls (graphical menu and hand posture) makes it possible to display the menu that the user wants to use in 3D space. This paper describes the implementation of HandPoseMenu and its application.
GAME TECHNIQUES AS A METHOD OF THE EDUCATIONAL PROCESS INTENSIFICATION IN TEACHING A FOREIGN LANGUAGE

Purpose of the study: The realities of the modern world order explain the increasing interest in learning a foreign language and require a search for ways and means of intensifying the educational process. The concept of modernization of the Russian higher education system requires the use of technologies that ensure the differentiation and individualization of education (the Ministry of Education).&#x0D; Methodology: Cultural approach. Basis: axiology - the study of values and value structure of the world. It is conditioned by the objective connection of a person with culture as a value system. The game is focused on group activity, which fully meets the needs of modern methods. It is also easily transformed into various forms of individual activity, giving every student an opportunity to try himself in a particular role to show individual abilities.&#x0D; Results: The goal of teaching foreign languages at the present stage is not a language system, but foreign language speech activity, and not by itself, but as a means of intercultural interaction. Using educational games allows you to apply the language material creatively, turn foreign language classes into a process of communication, discussion, research. The educational project, from the point of view of the student, is the possibility of maximizing your creative potential. This is an activity that will allow expressing oneself individually or in a group, trying one’s hand, applying one’s knowledge, benefit, and publicly showing the achieved result.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: In this research, the model of the Game Techniques as a Method of the Educational Process Intensification in Teaching a Foreign Language is presented in a comprehensive and complete manner.
Online Feature Selection (OFS) with Accelerated Bat Algorithm (ABA) and Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) for big data streams

Abstract Feature selection is mainly used to lessen the dispensation load of data mining models. To condense the time for processing voluminous data, parallel processing is carried out with MapReduce (MR) technique. However with the existing algorithms, the performance of the classifiers needs substantial improvement. MR method, which is recommended in this research work, will perform feature selection in parallel which progresses the performance. To enhance the efficacy of the classifier, this research work proposes an innovative Online Feature Selection (OFS)–Accelerated Bat Algorithm (ABA) and a framework for applications that streams the features in advance with indefinite knowledge of the feature space. The concrete OFS-ABA method is suggested to select significant and non-superfluous feature with MapReduce (MR) framework. Finally, Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) classifier is applied to classify the dataset samples. The outputs of homogeneous IDMLP classifiers were combined using the EIDMPL classifier. The projected feature selection method along with the classifier is evaluated expansively on three datasets of high dimensionality. In this research work, MR-OFS-ABA method has shown enhanced performance than the existing feature selection methods namely PSO, APSO and ASAMO (Accelerated Simulated Annealing and Mutation Operator). The result of the EIDMLP classifier is compared with other existing classifiers such as Naïve Bayes (NB), Hoeffding tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC)-KNN (K Nearest Neighbour). The methodology is applied to three datasets and results were compared with four classifiers and three state-of-the-art feature selection algorithms. The outcome of this research work has shown enhanced performance in accuracy and less processing time.
End-to-End Trainable Non-Collaborative Dialog System

End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative dialog tasks. Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. Our model utilizes intent and semantic slots as the intermediate sentence representation to guide the generation process. In addition, we design a filter to select appropriate responses based on whether these intermediate representations fit the designed task and conversation constraints. Our non-collaborative dialog model guides users to complete the task while simultaneously keeps them engaged. We test our approach on our newly proposed ANTISCAM dataset and an existing PERSUASIONFORGOOD dataset. Both automatic and human evaluations suggest that our model outperforms multiple baselines in these two non-collaborative tasks.
The Formation of Self-Organizational Skills of Student’s Academic Activity on the Basis of ‘Time Management’ Technology

This article is devoted to the study of self-organisational academic activity of future teachers of primary schools. The purpose of this study is to provide a theoretical basis and methodological support for the self-organisation of the educational activities of a future teacher of primary education based on the time management technology. The study describes theoretical and practical principles of self-organisation academic activity of bachelor students, examines the different approaches of scientists to the concept of ‘self-organisational academic activity’, given the characteristics of this phenomenon from the point of view of pedagogical science, including functions, components of this phenomenon; considered the pedagogical possibilities of technology ‘Time management’ in self-organisation of educational activity of future teachers. The ‘Time management’ technology is presented as the organisation of bachelor student’s life aimed for the most rational planning of the learning process and professional development. The theoretical studies are confirmed by experimental research related to the diagnostic and improvement skills of future elementary school teachers’ self-organisation. On the basis of theoretical, empirical research methods, as well as methods of statistical processing of research results, the results of a search study of students’ self-organisation are presented. The presented experimental results show the feasibility of using a variety of time-management technology tools in training sessions in the process of preparing specialists.
Fault-tolerant compass codes

We study a class of gauge fixings of the Bacon-Shor code at the circuit level, which includes a subfamily of generalized surface codes. We show that for these codes, fault tolerance can be achieved by direct measurements of the stabilizers. By simulating our fault-tolerant scheme under biased noise, we show the possibility of optimizing the performance of the surface code by stretching the bulk stabilizer geometry. To decode the syndrome efficiently and accurately, we generalize the union-find decoder to biased noise models. Our decoder obtains a $0.83\%$ threshold value for the surface code in quadratic time complexity.
Learning to Reuse: Adaptive Model Learning for Evolving Systems

Software systems undergo several changes along their life-cycle and hence, their models may become outdated. To tackle this issue, we propose an efficient algorithm for adaptive learning, called $$\mathtt {partial\text {-}Dynamic~L^*_M}$$ ($$\mathtt {\partial L^*_M}$$) that improves upon the state of the art by exploring observation tables on-the-fly to discard redundant prefixes and deprecated suffixes. Using 18 versions of the OpenSSL toolkit, we compare our proposed algorithm along with three adaptive algorithms. For the existing algorithms in the literature, our experiments indicate a strong positive correlation between number of membership queries and temporal distance between versions and; for our algorithm, we found a weak positive correlation between membership queries and temporal distance, as well, a significantly lower number of membership queries. These findings indicate that, compared to the state-of-the-art algorithms, our $$\mathtt {\partial L^*_M}$$ algorithm is less sensitive to software evolution and more efficient than the current approaches for adaptive learning.
The regulation of social media influencers: an introduction

Social media influencers have become increasingly important in recent years. Influencers, that is, individuals with a large number of followers on social media, filter information, advertise products and services, offer advice, and promote political opinions with a significant impact on a broad audience. Nevertheless, their actions have remained largely unregulated and overlooked in the literature. This chapter offers an exploratory analysis of this phenomenon and delves into the regulatory challenges resulting from the activity of social media influencers. It fills an important gap in the academic literature by providing an interdisciplinary analysis of a phenomenon with growing societal relevance. Given their large audiences, influencers are very effective not only at creating online engagement for the companies that employ their services, but also at manipulating their followers’ opinions and transactional behaviour, as the latter do not always distinguish between genuine and sponsored marketing advice. Although advertising regulations include endorsements made on Instagram or YouTube by ‘prosumers’, influencers appear to disregard these regulatory limits and national regulators have experienced difficulties in safeguarding their enforcement. In this context, average social media users are at an ever-greater risk of falling prey to inconspicuous commercial interests. This paper also introduces an interdisciplinary project that brings together insights from media studies, law, communication science and empirical legal studies, dealing with themes such as social media, influencer marketing, free speech, gig work, platform governance and consumer protection.
Kajian Kesulitan Belajar Membaca Menulis Permulaan (MMP) di Sekolah Dasar

The aims of this research are: 1) to investigate the type of learning difficulties in initial reading and writing faced by second grade students, 2)the factors that caused them having learning difficulties in initial reading and writing. The approach is descriptive qualitative research in form of case study. The subjects of research are second grade students of elementary school 01 Sumoroto, Ponorogo. The data collection techniques are obtained using documentations and interviews. The data obtained are non-statistic data. The data are analysed using interactive models, namely data reduction, data presentation, data conclusion and verification. The results of the study are (1) the type of learning difficulties, in initial reading and writing faced by second grade students of elementary school 01 Sumoroto, namely students that are not fluent in spelling, less clear pronunciation, lack of letters memorization, difficult to distinguish letters, incomplete of words writing, difficulties in composing sentences; (2) the factors that caused learning difficulties in reading and writing are adolescence, love to play rather than learning,like to be a trouble maker in class, study at home when the teacher gives homework, lack of attention, less motivation, and the teacher is less assertive.
Why Molière most likely did write his plays

Quantitative linguistics contradicts the much publicized theory naming Corneille as the author of Molière’s masterpieces.
A Meta-Analysis of Overfitting in Machine Learning

We conduct the first large meta-analysis of overfitting due to test set reuse in the machine learning community. Our analysis is based on over one hundred machine learning competitions hosted on the Kaggle platform over the course of several years. In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking available throughout the competition. Performance on a separate test set used only once determined the final ranking. By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition. Our study shows, somewhat surprisingly, little evidence of substantial overfitting. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts.
Towards Lingua Franca Named Entity Recognition with BERT

Information extraction is an important task in NLP, enabling the automatic extraction of data for relational database filling. Historically, research and data was produced for English text, followed in subsequent years by datasets in Arabic, Chinese (ACE/OntoNotes), Dutch, Spanish, German (CoNLL evaluations), and many others. The natural tendency has been to treat each language as a different dataset and build optimized models for each. In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language. To improve the initial model, we study the use of regularization strategies such as multitask learning and partial gradient updates. In addition to being a single model that can tackle multiple languages (including code switch), the model could be used to make zero-shot predictions on a new language, even ones for which training data is not available, out of the box. The results show that this model not only performs competitively with monolingual models, but it also achieves state-of-the-art results on the CoNLL02 Dutch and Spanish datasets, OntoNotes Arabic and Chinese datasets. Moreover, it performs reasonably well on unseen languages, achieving state-of-the-art for zero-shot on three CoNLL languages.
Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model.