Reviewerly/exHarmony huggingface dataset:
Crossing the Uncanny Valley? Understanding Affinity, Trustworthiness, and Preference for More Realistic Virtual Humans in Immersive Environments

Developers have long strived to create virtual avatars that are more realistic because they are believed to be preferred over less realistic avatars; however, an "Uncanny Valley" exists in which avatars that are almost but not quite realistic trigger aversion.We used a field study to investigate whether users had different affinity, trustworthiness, and preferences for avatars with two levels of realism, one photo-realistic and one a cartoon caricature.We collected survey data and conducted one-on-one interviews with SIGGRAPH conference attendees who watched a live interview carried out utilizing two avatars, either on a large screen 2D video display or via 3D VR headsets.18 sessions were conducted over four days, with the same person animating the photo realistic avatar but with different individuals animating the caricature avatars.Participants rated the photo-realistic avatar more trustworthy, had more affinity for it, and preferred it as a virtual agent.Participants who observed the interview through VR headsets had even stronger affinity for the photorealistic avatar and stronger preferences for it as a virtual agent.Interviews further surprisingly suggested that our ability to cross the Uncanny Valley may depend on who controls the avatar, a human or a virtual agent.
Serious Refactoring Games

Software design issues can severely impede software development and maintenance.Thus, it is important for the success of software projects that developers are aware of bad smells in code artifacts and improve their skills to reduce these issues via refactoring.However, software refactoring is a complex activity and involves multiple tasks and aspects.Therefore, imparting competences for identifying bad smells and refactoring code efficiently is challenging for software engineering education and training.The approaches proposed for teaching software refactoring in recent years mostly concentrate on small and artificial tasks and fall short in terms of higher level competences, such as analysis and evaluation.In this paper, we investigate the possibilities and challenges of designing serious games for software refactoring on real-world code artifacts.In particular, we propose a game design, where students can compete either against a predefined benchmark (technical debt) or against each other.In addition, we describe a lightweight architecture as the technical foundation for the game design that integrates pre-existing analysis tools such as test frameworks and software-quality analyzers.Finally, we provide an exemplary game scenario to illustrate the application of serious games in a learning setting.
Catheter localization in 3D ultrasound using voxel-of-interest-based ConvNets for cardiac intervention

Efficient image-based catheter localization in 3D US during cardiac interventions is highly desired, since it facilitates the operation procedure, reduces the patient risk and improves the outcome. Current image-based catheter localization methods are not efficient or accurate enough for real clinical use.We propose a catheter localization method for 3D cardiac ultrasound (US). The catheter candidate voxels are first pre-selected by the Frangi vesselness filter with adaptive thresholding, after which a triplanar-based ConvNet is applied to classify the remaining voxels as catheter or not. We propose a Share-ConvNet for 3D US, which reduces the computation complexity by sharing a single ConvNet for all orthogonal slices. To boost the performance of ConvNet, we also employ two-stage training with weighted cross-entropy. Using the classified voxels, the catheter is localized by a model fitting algorithm.To validate our method, we have collected challenging ex vivo datasets. Extensive experiments show that the proposed method outperforms state-of-the-art methods and can localize the catheter with an average error of 2.1 mm in around 10 s per volume.Our method can automatically localize the cardiac catheter in challenging 3D cardiac US images. The efficiency and accuracy localization of the proposed method are considered promising for catheter detection and localization during clinical interventions.
Introduction: Populist constitutionalism: Varieties, complexities, and contradictions

Abstract The intense engagement of populists with constitutionalism—a phenomenon originally related to experiences in Latin America—is increasingly evident in some of the new European Union member states. But the populist phenomenon is clearly not confined to more recently established democracies. Populist constitutionalism stands for a number of distinctive tendencies in constitutional politics and practices which frequently are in tension with—and may even threaten—fundamental values, human rights, representative democracy, and the rule of law. The relation between populism and constitutionalism is, however, not necessarily one of anti-thesis, but rather manifests itself in distinctive ways, depending on specific contexts and variations. In this special issue, we argue that populist constitutionalism is best analyzed in a comparative, and historically and contextually attuned manner. The special issue wants to contribute to understandings of populist constitutionalism, which are both theoretically more robust and able to comparatively reflect on a diversity of “really existing” cases. The various contributions discuss central dimensions to the populist phenomenon. These pertain in particular to: (a) The varieties of populist engagement with constitutionalism; (b) a deeper understanding of the populist mindset; (c) the position-taking and reaction of constitutional scholars to populism; (d) the complex relation and overlap of populism with illiberalism and authoritarianism; and (e) the central nature of constituent power in populist projects.
Using Twitter to Infer User Satisfaction With Public Transport: The Case of Santiago, Chile

User satisfaction is an important aspect to consider in any public transport system, and as such, regular and sound measurements of its levels are fundamental. However, typical evaluation schemes involve costly and time-consuming surveys. As a consequence, their frequency is not enough to properly and timely characterize the satisfaction of the users. In this paper, we propose a methodology, based on Twitter data, to capture the satisfaction of a large mass of users of public transport, allowing us to improve the characterization and location of their satisfaction level. We analyzed a massive volume of tweets referring to the public transport system in Santiago, Chile (Transantiago) using text mining techniques, such as sentiment analysis and topic modeling, in order to capture and group bus users' expressions. Results show that, although the level of detail and variety of answers obtained from surveys are higher than the ones obtained by our method, the amount of bus stops and bus services covered by the proposed scheme is larger. Moreover, the proposed methodology can be effectively used to diagnose problems in a timely manner, as it is able to identify and locate trends, and issues related to bus operating firms, whereas surveys tend to produce average answers. Based on the consistency and logic of the results, we argue that the proposed methodology can be used as a valuable complement to surveys, as both present different, but compatible characteristics.
A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

This paper centers on a novel method for traffic sign recognition (TSR). The method comprises of two major steps: 1) make strong representations for TSR images, by extraction deep features with the deep convolutional generative adversarial networks (DCGANs) and 2) classifier defined by multilayer perceptron (MLP) neural networks trained with a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process is considered efficient in which it does not require the number of hidden layers specified nor does it need the setting of the learning control parameters. This results in the PILAE classifier attaining a better performance in terms of both accuracy and efficiency. Empirical results from the German TSR (GTSRB) and Belgium traffic sign classification (BTSC) have proved that TSR achieves excellent results with other algorithms and reasonably low complexity.
Using skeleton and Hough transform variant to correct skew in historical documents

As a main part of several document analysis systems, Skew estimation represents one of the major research challenges, particularly in case of historical documents exploration. In this paper, we propose an original skew angle detection and correction technique. Morphological Skeleton is introduced to considerably diminish the amount of data by eliminating the redundant pixels and preserving only the central curves of the image components. Next, the proposed method uses Progressive Probabilistic Hough Transform (PPHT) to find image lines. At the end, a specific procedure is applied in order to measure the global skew angle of the document image from these identified lines. Experimental results demonstrate the accuracy and the effectiveness of our approach on skew angle detection upon three popular datasets covering many types of documents of diverse linguistic writings (Chinese, Greek and English) and different styles (horizontal or vertical orientations, including figures and tables, multi-columns page layouts).
Can Genetic Programming Do Manifold Learning Too?

Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.
College English Teaching Status and Individualized Teaching Design in The Context of Mobile Learning

This paper aims to optimize the design of individualized classroom teaching for mobile English learning in colleges, and find a scientific and effective English teaching mode and strategy for mobile learning. For this purpose, the application and evolution of mobile learning in English teaching was investigated, and the concept and theoretical bases of mobile learning were introduced in details. Through literature review, questionnaire survey and face-to-face interview, the status of mobile English learning among college students was analysed qualitatively and quantitatively from three dimensions, namely, the mobile device, learning resource and learning attitude. Then, several strategies for individualized teaching reform were presented through status analysis. Finally, the application of individualized mobile English learning was demonstrated through case studies on public platforms like Weibo and WeChat. The research findings shed new light on the individualized English learning among college students and lay a scientific basis for the application of mobile learning in college English education.
Blockchain-Based System for Multiparty Electronic Registered Delivery Services

European Regulation (EU) 910/2014 lays down the rules for electronic identification and trust services for electronic transactions.Qualified Electronic Registered Delivery is one of the trust services included in the regulation, and it requires nonrepudiation of origin and reception together with the integrity of the data.This kind of services usually relies heavily on the use of trusted third parties.These entities are an obstacle to extend the use of protocols.In this paper, we use the blockchain-based technologies to provide, for the first time, two multiparty registered eDelivery schemes that reduce the involvement of trusted third parties compared with traditional approaches while satisfying the requirements of the norms created by the European Union for registered eDeliveries.Since confidentiality is not considered a compulsory property in the directive, we propose two protocols.The first one is well suited for those deliveries that do not require the confidentiality of the message or delivered data or even for those in which it is required that the message can be public and accessible to everybody.The second solution for multiparty registered eDelivery allows the message to be hidden to others than the receiver.We present the smart contracts for both proposals and also a complete analysis of their properties and performance.
Capsule Networks for Object Detection in UAV Imagery

Recent advances in Convolutional Neural Networks (CNNs) have attracted great attention in remote sensing due to their high capability to model high-level semantic content of Remote Sensing (RS) images. However, CNNs do not explicitly retain the relative position of objects in an image and, thus, the effectiveness of the obtained features is limited in the framework of the complex object detection problems. To address this problem, in this paper we introduce Capsule Networks (CapsNets) for object detection in Unmanned Aerial Vehicle-acquired images. Unlike CNNs, CapsNets extract and exploit the information content about objects’ relative position across several layers, which enables parsing crowded scenes with overlapping objects. Experimental results obtained on two datasets for car and solar panel detection problems show that CapsNets provide similar object detection accuracies when compared to state-of-the-art deep models with significantly reduced computational time. This is due to the fact that CapsNets emphasize dynamic routine instead of the depth.
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models

Neural networks are vulnerable to adversarial attacks - small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective method of defending against adversarial attacks is to use the methodology of adversarial training. We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers. Our analysis reveals that contrary to the input layer which is robust to adversarial attack, the latent layer of these robust models are highly susceptible to adversarial perturbations of small magnitude. Leveraging this information, we introduce a new technique Latent Adversarial Training (LAT) which comprises of fine-tuning the adversarially trained models to ensure the robustness at the feature layers. We also propose Latent Attack (LA), a novel algorithm for constructing adversarial examples. LAT results in a minor improvement in test accuracy and leads to a state-of-the-art adversarial accuracy against the universal first-order adversarial PGD attack which is shown for the MNIST, CIFAR-10, CIFAR-100, SVHN and Restricted ImageNet datasets.
<b>dataMaid</b>: Your Assistant for Documenting Supervised Data Quality Screening in <i>R</i>

Data cleaning and validation are important steps in any data analysis, as the validity of the conclusions from the analysis hinges on the quality of the input data.Mistakes in the data can arise for any number of reasons, including erroneous codings, malfunctioning measurement equipment, and inconsistent data generation manuals.Ideally, a human investigator should go through each variable in the dataset and look for potential errorsboth in input values and codings -but that process can be very time-consuming, expensive and error-prone in itself.We describe an R package, dataMaid, which implements an extensive and customizable suite of quality assessment aids that can be applied to a dataset in order to identify potential problems in its variables.The results are presented in an auto-generated, nontechnical, stand-alone overview document intended to be perused by an investigator with an understanding of the variables in the data, but not necessarily knowledge of R. Thereby, dataMaid aids the dialogue between data analysts and field experts, while also providing easy documentation of reproducible data quality screening.Moreover, the dataMaid solution changes the data screening process from the usual ad hoc approach to a systematic, well-documented endeavor.dataMaid also provides a suite of more typical R tools for interactive data quality assessment and screening, where the data inspections are executed directly in the R console.
Performance visualization spaces for classification with rejection option

The classification with reject option consists to train a classifier that rejects the examples when the confidence in its prediction is low. The objective is to improve the accuracy of the non-rejected examples and the reliability of the prediction. The performances of the reject classifiers depend on both the error rate and rejection rate. Since these two values are in opposition, we have to make a trade-off between them. This paper is focused on the visualization spaces the performances of the classifiers with rejection option. We analyze two common spaces, the ROC space and the error-rejection (ER) space, then we propose a new space: the cost-reject (CR) space. We show that the ROC space is the less convenient space to represent the performances of the reject classifier. However, it can be recommended for classification problems where the importance of the two classes is different. For the ER space, we point out that the linear interpolation that is commonly used to draw the error-reject curve is not correct and leads to an overestimation of the classifier performances. From the definition of the condition error and rejection rate, we propose a new interpolation of the error-rejection curve that is unbiased. We introduce a new visualization space called the cost reject space. The CR space plots the normalized classification cost in function on the normalized rejection cost. The performance of a classifier is represented in this space by a line. The three visualization spaces are compared on problems of classification algorithms comparison. The advantages and drawbacks of each spaces are discussed and some recommendations are provided in the conclusion.
Neural Machine Translation of Low-Resource and Similar Languages with Backtranslation

We present our contribution to the WMT19 Similar Language Translation shared task.We investigate the utility of neural machine translation on three low-resource, similar language pairs: Spanish -Portuguese, Czech -Polish, and Hindi -Nepali.Since state-of-theart neural machine translation systems still require large amounts of bitext, which we do not have for the pairs we consider, we focus primarily on incorporating monolingual data into our models with backtranslation.In our analysis, we found Transformer models to work best on Spanish -Portuguese and Czech -Polish translation, whereas LSTMs with global attention worked best on Hindi -Nepali translation.
A Sightseeing Support System Using Augmented Reality and Pictograms within Urban Tourist Areas in Japan

Though tourists can search for necessary information on the internet while sightseeing, it takes effort and is inconvenient to obtain available information related to specific sightseeing spots among the copious amount of information online. Targeting urban tourist areas in Japan, the present study aims to develop a system that can provide guidance and information concerning sightseeing spots by integrating location-based augmented reality (AR) and object-recognition AR and by using pictograms. The system enables users to efficiently obtain the directions to sightseeing spots and nearby facilities within urban tourist areas and sightseeing spot information. Additionally, the city of Chofu in the metropolis of Tokyo was selected as the operation target area. The operation of the system was conducted for 1 month, targeting those inside and outside the operation target area, and a web questionnaire survey was conducted with a total number of 50 users. From the evaluation results of the web questionnaire survey, the usefulness of the original functions of integrating location-based AR and object-recognition AR and by using pictograms, as well as of the entire system, was analyzed. From the results of the access analysis of users’ log data, it is expected that users will further utilize each function. Additionally, it is evident that location-based AR was used more often than was object-recognition AR.
AI surpasses humans at six-player poker

Self-learning Pluribus beats five humans in Texas hold'em showdown
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification

Cross-domain sentiment classification has drawn much attention in recent years.Most existing approaches focus on learning domaininvariant representations in both the source and target domains, while few of them pay attention to the domain-specific information.Despite the non-transferability of the domainspecific information, simultaneously learning domain-dependent representations can facilitate the learning of domain-invariant representations.In this paper, we focus on aspectlevel cross-domain sentiment classification, and propose to distill the domain-invariant sentiment features with the help of an orthogonal domain-dependent task, i.e. aspect detection, which is built on the aspects varying widely in different domains.We conduct extensive experiments on three public datasets and the experimental results demonstrate the effectiveness of our method.
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

Automatic news comment generation is a new testbed for techniques of natural language generation.In this paper, we propose a "readattend-comment" procedure for news comment generation and formalize the procedure with a reading network and a generation network.The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title.We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm.Experimental results on two datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.