Reviewerly/exHarmony huggingface dataset:
A systematic mapping study on usability and user eXperience evaluation of multi-touch systems

A significant increase in multi-touch interactions is notable with the expanded use of smartphones, tablets, and tabletops. Multi-touch systems are software that uses the user's fingers to perform some action. These systems need evaluation before being available in the market to improve the interaction and general user experience. In this context, User eXperience (UX) and Usability are relevant criteria related to multi-touch systems quality. Therefore, we performed a Systematic Mapping Study (SMS) seeking to identify evaluation technologies used by professionals and researchers to evaluate the aspects of Usability and UX in multi-touch systems. After the selection process, 65 out of 622 identified papers were selected for data extraction. As a result, 29 different evaluation technologies were identified. Besides, we also obtained characteristics of these technologies, empirical studies, and the multi-touch systems presented in the papers. Our study shows a lack of evaluation technologies focused on the multi-touch context; most do not evaluate Usability and UX concepts jointly; none go under an empirical evaluation since researchers tend to create their technologies to assess their aspects. This SMS presents data about the evaluation technologies extracted and the gaps found over the researched topic, contributing to the scientific and industrial community that evaluates the Usability and UX of multi-touch systems.
Extending the ontology, metacommunication and communicability of semiotic engineering to the emerging paradigm of human-computer integration (HInt)

Human-Computer Integration (HInt) is an emerging paradigm in the Human-Computer Interaction (HCI) area, which characterizes the relationship between humans and technologies as a partnership. This paradigm offers new challenges for the HCI area, including demands for reviewing and proposing theories and methods to consolidate and evolve HInt within HCI. Thus, this paper presents an analysis of the applicability and a proposal for extending the Semiotic Engineering ontology and concepts to characterize HInt. As contributions, our results are useful to: (1) advance in the consolidation and evolution of HInt as a new HCI paradigm; and (2) stimulate other initiatives and contributions grounded in Semiotic Engineering for HCI with a lens on HInt.
Participatory design with teenagers for cyberbullying prevention and mitigation

Despite being a serious problem among teenagers, brought by the wide use of social media platforms, little has been explored about the development and use of technological tools to support the mitigation of cyberbullying in schools. Particularities that involve the occurrence of cyberbullying in online social networks can explain this scarcity. In this context, our study applies Participatory Design techniques to two groups of high school students. During four design sessions carried out remotely, participants shared their experiences with cyberbullying and designed potential solutions to prevent and mitigate it through three low-fidelity prototypes. The discussions around these prototypes suggest that schools still have a long way to go in dealing with this type of violence. However, it is possible to reach victims, aggressors, and bystanders through technological resources, constituting an essential step toward preventing and combating cyberbullying among teenagers.
Investigating whether people identify how suitable a data visualization is for answering specific analysis questions

Choosing the type of chart to represent data may be challenging. Various books and websites provide catalogs of data visualizations to help choose the correct chart, usually taking into account data types and general tasks. However, they often do not consider the analyst's goal, as expressed by specific questions. In this work, we studied how participants assess the suitability of certain data visualizations for answering specific analysis questions before and after being exposed to related guides that mimic what is often found in online catalogs. We also investigated whether they could assess suggestions (both textual and visual) to change the charts to better answer the questions or recommend suitable alternative modifications to improve them. We discovered that, for basic charts, the participants could identify whether the visualization is suitable to answer the posed question. However, when they found it unsuitable, they could not always recommend changes to improve them. When presented with alternative visualizations, the participants seemed to provide assessments in line with the literature more often than when presented with textual descriptions of recommended changes, but the difference was not significant. Our results showed that guidelines, either in a textual or visual format, may not help novices effectively relate analysis questions to specific chart properties, and may even confuse them and lead them to make mistakes they might not have made otherwise.
Analysis of transcription tools for Brazilian Portuguese with focus on disfluency detection

Advancements and easier access to technology has led to a greater demand for applications whose interaction is performed through voice recognition, since multimedia content has been a valuable source for computational analysis. In this sense, vocal representations are extracted for various purposes in applications in several areas such as convenience, accessibility, security and sentiment analysis. The main challenge of speech recognition lies in the variability of speakers, environments, devices and the presence of disfluencies during spoken speech. These aspects influence transcription tools, essential when the user requires interaction through voice, aiming at producing texts from this interaction. In particular, detection of disfluencies can help to identify aspects related to the emotional status of the speaker. This work presents an analysis of text transcription tools, with focus in disfluency detection, encompassing the metrics most used for evaluation and databases used in evaluations in the context of Brazilian Portuguese. An experiment was conducted to evaluate the performance of three tools (IBM Watson, Google Speech and Vosk). The Google Speech tool achieved the best performance with average Word Error Rate of 9.69% for fluent sentences and 17.15% for disfluent sentences, followed by IBM Watson with 11.86% and 23.44% and Vosk with 14.39 % and 22.56% respectively.
Graphical User Interface Programming with Python

Event-driven (or visual) programming is the process during which one or more of the properties/attributes of a widget/object changes state or value. This is done through the use of specific methods and is triggered when through interactions between the user and the widget/object, caught by the associated event. The focus of this chapter is to introduce the concept of event-driven (or visual) programming by presenting some of the most popular widgets and the associated methods and properties/attributes/options, and the most commonly used events for the creation of a Graphical User Interface (GUI) experience.
More refined superbag: Distantly supervised relation extraction with deep clustering

Distant supervision (DS) can automatically generate annotated data for relation extraction (RE) with knowledge bases and corpora. The existing DS methods that train on bags selected by attention mechanism are susceptible to noisy bags and neglect useful information in noisy bags. In this paper, we propose DCSR, a novel DS method which utilizes deep clustering to obtain refined superbag representations for solving the wrong labeling problem. we substitute deep clustering for selective attention to construct superbags, capturing helpful information between spatially-close bags, including noisy bags. Moreover, we implement data augmentation on the input sentences to handle the long-tail problem. Experiments on the NYT2010 and NYT-H datasets show that our method can effectively improve RE and significantly outperforms state-of-the-art methods.
Graded labellings for abstract argumentation

The labelling-based semantics is one kind of the main semantics of abstract argumentation frameworks (AAFs), which has some computational advantages. This paper aims to consider graded labelling-based semantics for AAFs. Following the standard labelling approach for AAFs, five criteria called ℓ-Cf, m-Rein, η-Rej, m-RI and η-RO are introduced, which are graded variants of their counterparts in non-graded situations. Based on these criteria, a family of graded labelling-based semantics are defined. Moreover, by introducing the transition functions between graded labellings and extensions, the relationships between them are explored and a number of representation theorems are given, which provides the theoretical foundations for solving computational problems (e.g., the acceptance decision and extension enumeration problems) related to graded extensions based on labellings.
Image interpolation by direct cross downsampling

Direct downsampling (DD) based interpolation system can be used as an encoder-decoder framework for secure data transmission, while DD is often used as a data compression way and interpolation is used for decompression. In this paper, we propose a new direct cross downsampling (DCD) algorithm, which has the same function as DD. Compared to DD, the DCD has three main differences from the angle of image interpolation: 1) DCD can use the nearest neighboring pixels for image interpolation to effectively reduce the interpolation error; 2) More pixels can be interpolated in the first step in those two-step interpolation methods with the help of the DCD; 3) The DCD preserves the information of each column or each row in the original high-resolution (HR) image while DD discards the even columns and rows. On the basis of the proposed DCD algorithm, we further propose four DCD-based interpolation methods: DCD-based bilinear interpolation, DCD-based bicubic interpolation, DCD-based content-adaptive interpolation (CAI) and DCD-based edge-oriented interpolation (EOI). By comparing the proposed four DCD-based image interpolation ways with those corresponding DD-based interpolation strategies in both experimental and analytical ways, we have proved the superiority of the DCD algorithm. Furthermore, we compared the proposed DCD-based EOI with three previous excellent DD-based interpolation works and experimental results demonstrate that the proposed DCD-based EOI has the best performance on PSNR and SSIM.
RECONNAISSANCE OF DAMAGE TO THE REPAIRED SCHOOL GYMNASIUM AFTER THE 2011 TOHOKU EARTHQUAKES

A reconnaissance of steel school gymnasium damaged by the earthquake off the coast of Fukushima Prefecture on February 13, 2021 has been conducted. These gymnasiums were also damaged in the 2011 Tohoku earthquake, and have been repaired or strengthened. In the two gymnasiums, severe damage to non-structural components occurred again. It means that restoration to the original condition would have caused the damage to recur. In the other one, all of the additional braces for strengthening were fractured. It has the complicated structural system, indicates that how to repair or strengthen should be discussed more.
Dual Space Coupling Model Guided Overlap-Free Scatterplot

The overdraw problem of scatterplots seriously interferes with the visual tasks. Existing methods, such as data sampling, node dispersion, subspace mapping, and visual abstraction, cannot guarantee the correspondence and consistency between the data points that reflect the intrinsic original data distribution and the corresponding visual units that reveal the presented data distribution, thus failing to obtain an overlap-free scatterplot with unbiased and lossless data distribution. A dual space coupling model is proposed in this paper to represent the complex bilateral relationship between data space and visual space theoretically and analytically. Under the guidance of the model, an overlap-free scatterplot method is developed through integration of the following: a geometry-based data transformation algorithm, namely DistributionTranscriptor; an efficient spatial mutual exclusion guided view transformation algorithm, namely PolarPacking; an overlap-free oriented visual encoding configuration model and a radius adjustment tool, namely frdraw. Our method can ensure complete and accurate information transfer between the two spaces, maintaining consistency between the newly created scatterplot and the original data distribution on global and local features. Quantitative evaluation proves our remarkable progress on computational efficiency compared with the state-of-the-art methods. Three applications involving pattern enhancement, interaction improvement, and overdraw mitigation of trajectory visualization demonstrate the broad prospects of our method.
GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis

Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks. Recent self-supervised learning methods are dominated by noise-contrastive estimation (NCE, also known as contrastive learning), which aims to learn invariant visual representations by contrasting one homogeneous image pair with a large number of heterogeneous image pairs in each training step. Nonetheless, NCE-based approaches still suffer from one major problem that is one homogeneous pair is not enough to extract robust and invariant semantic information. Inspired by the archetypical triplet loss, we propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images, to group homogeneous dermatology images while separating heterogeneous ones. In addition, a hardness-aware attention is introduced and incorporated to address the importance of homogeneous image views with similar appearance instead of those dissimilar homogeneous ones. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks, sometimes by 5 percents under extremely limited supervision. More importantly, when equipped with the pre-trained weights provided by GraVIS, a single model could achieve better results than winners that heavily rely on ensemble strategies in the well-known ISIC 2017 challenge. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://bit.ly/3xiFyjx</uri> .
Learning to Optimize on Riemannian Manifolds

Many learning tasks are modeled as optimization problems with nonlinear constraints, such as principal component analysis and fitting a Gaussian mixture model. A popular way to solve such problems is resorting to Riemannian optimization algorithms, which yet heavily rely on both human involvement and expert knowledge about Riemannian manifolds. In this paper, we propose a Riemannian meta-optimization method to automatically learn a Riemannian optimizer. We parameterize the Riemannian optimizer by a novel recurrent network and utilize Riemannian operations to ensure that our method is faithful to the geometry of manifolds. The proposed method explores the distribution of the underlying data by minimizing the objective of updated parameters, and hence is capable of learning task-specific optimizations. We introduce a Riemannian implicit differentiation training scheme to achieve efficient training in terms of numerical stability and computational cost. Unlike conventional meta-optimization training schemes that need to differentiate through the whole optimization trajectory, our training scheme is only related to the final two optimization steps. In this way, our training scheme avoids the exploding gradient problem, and significantly reduces the computational load and memory footprint. We discuss experimental results across various constrained problems, including principal component analysis on Grassmann manifolds, face recognition, person re-identification, and texture image classification on Stiefel manifolds, clustering and similarity learning on symmetric positive definite manifolds, and few-shot learning on hyperbolic manifolds.
24-GAN: Portrait Generation with Composite Attributes

We present a portrait-generation framework that can control composite attributes. Our target attribute covers the three global attributes of age, sex, and race. We built control vectors for the garget attributes and attached them to the latent vector that produced portrait images. Our generator was devised using the StyleGAN generator, and our discriminator has a dual structure for qualities and attributes. Our framework successfully generated 24 faces with the same identity while varying the three attributes. We evaluated our results from three aspects. The identity of the generated faces was estimated using Frechet inception distance, and the attributes of the generated faces were validated using a facial-attribute recognition model. We also performed a user study for further evaluation.
Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network

Sign language aids in overcoming the communication barrier between hearing-impaired individuals and those with normal hearing. However, not all individuals with normal hearing are skilled at using sign language. Consequently, deaf and hearing-impaired individuals generally encounter the problem of limited communication while interacting with individuals with normal hearing. In this study, a sign language recognition method based on a movement-in-a-video detection scheme is proposed. The proposed scheme is applied to extract unique spatial and temporal features from each gesture. The extracted features are subsequently used to train a neural network to classify the gestures. The proposed movement-in-a-video detection scheme is applied to sign language videos featuring short, medium, and long gestures. The proposed method achieved an accuracy of 90.33% and 40% in classifying short and medium gestures, respectively, compared with 69% and 43.7% achieved using other methods. In addition, the improved accuracies were achieved with less computational complexity and cost. It is anticipated that improvements in the proposed method, for it to achieve high accuracy for long gestures, can enable hearing-impaired individuals to communicate with normal-hearing people who do not have knowledge of sign language.
Outlier Detection of Gravity Dam Deformation Monitoring Data Based on the Multiple Local Outlier Coefficient Method

In the deformation monitoring data of gravity dams, a few outliers are often found, which will have adverse effects on the monitoring of model building and other data analysis work. In this study, the multiple local outlier coefficient method was proposed to quickly detect the outlier data in real time and to provide high-quality data for subsequent data analysis. This method was based on the idea of distance in the outlier detection algorithm, aiming at laws and characteristics of gravity dam deformation monitoring data. First, the basic principle, calculation steps, and basic features of the multiple local outlier coefficient method were studied. Then, for the two important parameters of the algorithm, the appropriate window length was selected using autocorrelation and partial autocorrelation analysis, and the appropriate threshold values were selected using the 3σ criterion, maximum method, and empirical method. Finally, an engineering example was used to verify that the algorithm could accurately detect the outliers in the gravity dam deformation monitoring data, and the deviation degree and meaning of the outliers were understood according to the calculated outlier coefficient. The multiple local outlier coefficient method has the advantages of a simple calculation principle, fast calculation speed, real-time detection, and a clear meaning of calculation results. By selecting the appropriate parameters, the method could satisfy the outlier detection of different types of data, offering an advantage in adapting to the computing demand of massive monitoring data and improving the intelligence and real-time monitoring of dam safety.
Three-channel Retinex decomposition for low-light image enhancement

Low-Light Image Enhancement (LLIE) is generally regarded as a challenging task that aims to brighten the low-light images and reduce the noise. Many studies have contributed to the community by applying different technologies, including Retinex-based ones. However, most Retinex-based methods dealt with the LLIE issue in mono-channel decomposition way, leading to the imperfect results. In this work, a novel two-stage convolutional neural network (CNN)-based framework composed of the three-channel Retinex decomposition module and the illumination enhancement module is proposed to deal with the LLIE issue. Inspired by the Retinex theory, the former is optimized by introducing the reflectance consistency loss and the well-designed illumination regularization loss, while the latter is supervised by the parameter loss and the enhancement loss. It has proven more effective and flexible, when comparing with the prevalent mono-channel decomposition approaches, due to the capabilities of illumination adjustment, global optimization and shot noise suppression. Both the quantitative and qualitative experiment results on the Low-Light (LOL) dataset demonstrate the better performance of the proposed three-channel Retinex decomposition network in comparison with other works.
Deontic Paradoxes in Mīmāṃsā Logics: There and Back Again

Abstract Centered around the analysis of the prescriptive portion of the Vedas, the Sanskrit philosophical school of Mīmāṃsā provides a treasure trove of normative investigations. We focus on the leading Mīmāṃsā authors Prabhākara, Kumārila and Maṇḍana, and discuss three modal logics that formalize their deontic theories. In the first part of this paper, we use logic to analyze, compare and clarify the various solutions to the śyena controversy, a two-thousand-year-old problem arising from seemingly conflicting commands in the Vedas. In the second part, the formalized Mīmāṃsā theories are analyzed and employed to provide alternative perspectives on well-known paradoxes from the contemporary field of deontic logic. Thus, we go from logic to Mīmāṃsā and back again.