Reviewerly/exHarmony huggingface dataset:
Emojis and Emoticons in Health Care and Dermatology Communication: Narrative Review

Background Emoticons and emojis have become staple additions to modern-day communication. These graphical icons are now embedded in daily society through the various forms of popular social media and through users’ personal electronic conversations. With ever-increasing use and inclusivity, exploration of the possible health care and dermatology applications of these tools is imperative. Objective The goal of this narrative review was to provide and evaluate an up-to-date literature survey examining the utility of emoticons and emojis in medicine. Special attention was paid to their existing and potential uses in the field of dermatology, especially during the COVID-19 pandemic. Methods A PubMed search of peer-reviewed publications was performed in mid-2021 to collect articles with emoticon or emoji keywords in combination with other health care–relevant or dermatology-relevant keywords. Screening of publications and described studies was performed by the authors with education and research experience in health care, dermatology, social media, and electronic communication trends. Selected articles were grouped based on common subjects for qualitative analysis and presentation for in-depth discussion. Results From this extensive search, researchers were able to identify a wide variety of publications detailing the use of emoticons and emojis in general health care, pediatric health care, public health, and dermatology. Key subject areas that emerged from the investigation included the ability of emoticons and emojis to improve communication within pediatric health care, enhance mood and psychological assessment or mental health screening in adults, develop interventions to improve patient medication adherence, complement novel means of public health and COVID-19 surveillance, and bolster dermatology-specific applications. Conclusions This review illuminated the repurposing of emojis and emoticons for a myriad of advantageous functions in health care and public health, with applications studied in many populations and situations. Dermatology-specific uses were relatively sparse in the literature, highlighting potential opportunities for growth in future studies and practices. The importance of diversity and inclusivity has extended to emojis, with the recent introduction of skin color customization and new emojis better representing the comprehensive spectrum of users’ experiences. A continuously evolving and technology-driven population creates a unique niche for emoticons and emojis to ease worldwide communication and understanding, transcending the barriers of age, language, and background. We encourage future studies and innovations to better understand and expand their utility.
Quality Diversity for Synthesizer Sound Matching

It is difficult to adjust the parameters of a complex synthesizer to create the desired sound. As such, sound matching, the estimation of synthesis parameters that can replicate a certain sound, is a task that has often been researched, utilizing optimization methods such as genetic algorithm (GA). In this paper, we introduce a novelty-based objective for GA-based sound matching. Our contribution is two-fold. First, we show that the novelty objective is able to improve the quality of sound matching by maintaining phenotypic diversity in the population. Second, we introduce a quality diversity approach to the problem of sound matching, aiming to find a diverse set of matching sounds. We show that the novelty objective is effective in producing high-performing solutions that are diverse in terms of specified audio features. This approach allows for a new way of discovering sounds and exploring the capabilities of a synthesizer.
Efficient Formulation of Collision Avoidance Constraints in optimization Based Trajectory Planning and Control

To be applicable to real world scenarios trajectory planning schemes for mobile autonomous systems must be able to efficiently deal with obstacles in the area of operation. In the context of optimization based trajectory planning and control a number of different approaches to formulate collision avoidance constraints can be found in the literature. This work presents a novel formulation building on constructive solid geometry (CSG) to describe collision avoidance constraints. It is highly efficient due to a very low number of nonlinear inequality constraints required for a given number of obstacles and sample points and in contrast to the original CSG formulation allows to consider the controlled system’s shape. To allow for a comparison, popular methods to represent obstacles from the literature are summarized and characterized, namely the simple ellipsoidal representation, the original CSG method as well as a direct and an indirect implementation of a signed distance based approach. A benchmark example shows the good performance of the proposed formulation. Here, optimal trajectory planning for marine surface vessels formulated as a nonlinear programming problem is used, where the scenario is designed based on the maritime test field in Kiel, Germany.
A cross-learning approach for cold-start forecasting of residential photovoltaic generation

This paper addresses the problem of forecasting, over a daily horizon, quarter hourly profiles of residential photovoltaic (PV) power production for sites with no historical data available. Typically, such forecasts are required for improving the local operation of low-voltage systems, where observability is still a practical challenge. In this context, we develop a cross-learning forecasting approach to predict unobserved PV sites, which exploits common patterns learned from neighboring monitored PV production profiles. Concretely, the proposed approach fits a single, generic forecasting function across the entire panel of monitored PV time series based only on series-specific features – i.e., the peak power installed, geographical position, orientation and inclination – and local numerical weather predictions. This allows to enlarge the dataset for training more complex data-driven techniques, while ensuring scalability for predicting each PV site. The proposed approach is evaluated using a k-nearest neighbors algorithm, different variants of neural networks and gradient boosted trees on five new residential PV sites. Outcomes highlight the ability of the cross-learning forecasting models to better generalize on new PV sites in comparison with a clear sky-based physical approach, without needing any adjustment of the models.
Multi-scale dehazing network via high-frequency feature fusion

Numerous learning-based methods have achieved significant improvements in haze removal. However, the dehazed results of these methods still suffer from the loss of edge details. To solve this problem, we propose a novel multi-scale dehazing network via high-frequency feature fusion (HFMDN). HFMDN is an end-to-end trainable network, which is mainly composed of four components: a base network (Backbone), a frequency branch network (FBN), a frequency attention module (FAM), and a refine block (RB). The Backbone is a multi-scale feature fusion architecture that can share useful information across different scales. For the training phase, we employ the Laplace Operator to obtain the image’s high-frequency (HF) information, which can specifically represent the details of the image (e.g., edges, textures). The FBN takes the HF derived from the original image as an additional prior and utilizes L1 norm loss to constrain the output of FBN to predict the HF of the haze-free image. We further design a frequency attention module (FAM), which automatically learns the weights map of the frequency features to enhance image recovery ability. Furthermore, a refine block (RB) is proposed to extract the features map by fusing the outputs of FBN and Backbone to produce the final haze-free image. The quantitative comparison of the ablation study shows that high-frequency information significantly improves dehazing performance. Extensive experiments also demonstrate that our proposed methods can generate more natural and realistic haze-free images, especially in the contours and details of hazy images. HFMDN performs favorably against the CNN-based state-of-the-art dehazing methods in terms of PSNR, SSIM, and visual effect.
PRNU-based Image Forgery Localization with Deep Multi-scale Fusion

Photo-response non-uniformity (PRNU), as a class of device fingerprint, plays a key role in the forgery detection/localization for visual media. The state-of-the-art PRNU-based forensics methods generally rely on the multi-scale trace analysis and result fusion, with Markov random field model. However, such hand-crafted strategies are difficult to provide satisfactory multi-scale decision, exhibiting a high false-positive rate. Motivated by this, we propose an end-to-end multi-scale decision fusion strategy, where a mapping from multi-scale forgery probabilities to binary decision is achieved by a supervised deep fully connected neural network. As the first time, the deep learning technology is employed in PRNU-based forensics for more flexible and reliable integration of multi-scale information. The benchmark experiments exhibit the state-of-the-art accuracy performance of our method in both pixel-level and image-level, especially for false positives. Additional robustness experiments also demonstrate the benefits of the proposed method in resisting noise and compression attacks.
CTTGAN: Traffic Data Synthesizing Scheme Based on Conditional GAN

Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset is imbalanced, which makes the algorithm have a low identification rate for small categories of malicious traffic samples. This paper presents a traffic sample synthesizing model named Conditional Tabular Traffic Generative Adversarial Network (CTTGAN), which uses a Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to expand the small category traffic samples and balance the dataset in order to improve the malicious traffic identification rate. The CTTGAN model expands and recognizes feature data, which meets the requirements of a machine learning algorithm for training and prediction data. The contributions of this paper are as follows: first, the small category samples are expanded and the traffic dataset is balanced; second, the storage cost and computational complexity are reduced compared to models using image data; third, discrete variables and continuous variables in traffic feature data are processed at the same time, and the data distribution is described well. The experimental results show that the recognition rate of the expanded samples is more than 0.99 in MLP, KNN and SVM algorithms. In addition, the recognition rate of the proposed CTTGAN model is better than the oversampling and undersampling schemes.
Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments

Sentiment analysis is one of the fields of affective computing, which detects and evaluates people's psychological states and sentiments through text analysis. It is an important application of text mining technology and is widely used to analyze comments. Bullet screen videos have become a popular way for people to interact and communicate while watching online videos. Existing studies have focused on the form, content, and function of bullet screen comments, but few have examined bullet screen comments using natural language processing. Bullet screen comments are short text messages of different lengths and ambiguous emotional information, which makes it extremely challenging in natural language processing. Hence, it is important to understand how we can use the characteristics of bullet screen comments and sentiment analysis to understand the sentiments expressed and trends in bullet screen comments. This study poses the following research question: how can one analyze the sentiments ex-pressed in bullet screen comments accurately and effectively? This study mainly proposes an ERNIE-BiLSTM approach for sentiment analysis on bullet screen comments, which provides effective and innovative thinking for the sentiment analysis of bullet screen comments. The experimental results show that the ERNIE-BiLSTM approach has a higher accuracy rate, precision rate, recall rate, and F1-score than other methods.
A Survey on NLP Resources, Tools, and Techniques for Marathi Language Processing

Natural Language Processing (NLP) has been in practice for the past couple of decades, and extensive work has been done for the Western languages, particularly the English language. The Eastern counterpart, especially the languages of the Indian subcontinent, needs attention as not much language processing work has been done on these languages. Western languages are rich in dictionaries, WordNet, and associated tools, while Indian languages are lagging behind in this segment. Marathi is the third most spoken language in India and the 15th most spoken language worldwide. Lack of resources, complex linguistic facts, and the inclusion of prevalent dialects of neighbors have resulted in limited work for Marathi. The aim of this study is to provide an insight into the various linguistic resources, tools, and state-of-the-art techniques applied to the processing of the Marathi language. Initially, morphological descriptions of the Marathi language are provided, followed by a discussion on the characteristics of the Marathi language. Thereafter, for Marathi language, the availability of corpus, tools, and techniques to be used to develop NLP tasks is reviewed. Finally, gap analysis is discussed in current research and future directions for this new and dynamic area of research are listed that will benefit the Marathi Language Processing research community.
Memorability: An Image-Computable Measure of Information Utility

The pixels in an image, and the objects, scenes, and actions that they compose, determine whether an image will be memorable or forgettable. While memorability varies by image, it is largely independent of an individual observer. Observer independence is what makes memorability an image-computable measure of information, and eligible for automatic prediction. In this chapter, we zoom into memorability with a computational lens, detailing the state-of-the-art algorithms that accurately predict image memorability relative to human behavioral data, using image features at different scales from raw pixels to semantic labels. We discuss the design of algorithms and visualizations for face, object, and scene memorability, as well as algorithms that generalize beyond static scenes to actions and videos. We cover the state-of-the-art deep learning approaches that are the current front runners in the memorability prediction space. Beyond prediction, we show how recent A.I. approaches can be used to create and modify visual memorability. Finally, we preview the computational applications that memorability can power, from filtering visual streams to enhancing augmented reality interfaces.
Factors Associated With Using the COVID-19 Mobile Contact-Tracing App Among Individuals Diagnosed With SARS-CoV-2 in Amsterdam, the Netherlands: Observational Study

Worldwide, efforts are being made to stop the COVID-19 pandemic caused by SARS-CoV-2. Contact tracing and quarantining are key in limiting SARS-CoV-2 transmission. Mathematical models have shown that the time between infection, isolation of cases, and quarantining of contacts are the most important components that determine whether the pandemic can be controlled. Mobile contact-tracing apps could accelerate the tracing and quarantining of contacts, including anonymous contacts. However, real-world observational data on the uptake and determinants of contact-tracing apps are limited.The aim of this paper is to assess the use of a national Dutch contact-tracing app among notified cases diagnosed with SARS-CoV-2 infection and investigate which characteristics are associated with the use of the app.Due to privacy regulations, data from the app could not be used. Instead, we used anonymized SARS-CoV-2 routine contact-tracing data collected between October 28, 2020, and February 26, 2021, in the region of Amsterdam, the Netherlands. Complete case logistic regression analysis was performed to identify which factors (age, gender, country of birth, municipality, number of close contacts, and employment in either health care or education) were associated with using the app. Age and number of close contacts were modelled as B-splines due to their nonlinear relationship.Of 29,766 SARS-CoV-2 positive cases, 4824 (16.2%) reported app use. Median age of cases was 41 (IQR 29-55) years, and 46.7% (n=13,898) were male. In multivariable analysis, males (adjusted odds ratio [AOR] 1.11, 95% CI 1.04-1.18) and residents of municipalities surrounding Amsterdam were more likely to use the app (Aalsmeer AOR 1.34, 95% CI 1.13-1.58; Ouder-Amstel AOR 1.96, 95% CI 1.54-2.50), while people born outside the Netherlands, particularly those born in non-Western countries (AOR 0.33, 95% CI 0.30-0.36), were less likely to use the app. Odds of app use increased with age until the age of 58 years and decreased sharply thereafter (P<.001). Odds of app use increased with number of contacts, peaked at 8 contacts, and then decreased (P<.001). Individuals working in day care, home care, and elderly nursing homes were less likely to use the app.Contact-tracing app use among people with confirmed SARS-CoV-2 infection was low in the region of Amsterdam. This diminishes the potential impact of the app by hampering the ability to warn contacts. Use was particularly low among older people, people born outside the Netherlands, and people with many contacts. Use of the app was also relatively low compared to those from some other European countries, some of which had additional features beyond contact tracing, making them potentially more appealing. For the Dutch contact-tracing app to have an impact, uptake needs to be higher; therefore, investing more into promotional efforts and additional features could be considered.
Boosting Video Representation Learning with Multi-Faceted Integration

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the problem as visual-semantic embedding learning, which explicitly maps video representation into a rich semantic embedding space, and jointly optimizes video representation from two perspectives. One is to capitalize on the intra-facet supervision between each video and its own label descriptions, and the second predicts the "semantic representation" of each video from the facets of other datasets as the inter-facet supervision. Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The prelearnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning.
ReverseORC: Reverse Engineering of Resizable User Interface Layouts with OR-Constraints

Reverse engineering (RE) of user interfaces (UIs) plays an important role in software evolution. However, the large diversity of UI technologies and the need for UIs to be resizable make this challenging. We propose ReverseORC, a novel RE approach able to discover diverse layout types and their dynamic resizing behaviours independently of their implementation, and to specify them by using OR constraints. Unlike previous RE approaches, ReverseORC infers flexible layout constraint specifications by sampling UIs at different sizes and analyzing the differences between them. It can create specifications that replicate even some non-standard layout managers with complex dynamic layout behaviours. We demonstrate that ReverseORC works across different platforms with very different layout approaches, e.g., for GUIs as well as for the Web. Furthermore, it can be used to detect and fix problems in legacy UIs, extend UIs with enhanced layout behaviours, and support the creation of flexible UI layouts.
Digital Literacy During COVID-19 Distance Education; Evaluation of Communication-Based Problems in Line With Student Opinions

DATA REPORT article Front. Psychol., 15 July 2022Sec. Educational Psychology https://doi.org/10.3389/fpsyg.2022.809171
Determinants of students’ adoption of virtual reality-based learning systems: An individual difference perspective

This study investigates the individual difference antecedents of students’ behavioural intention to use VR-based learning systems, especially from an integrated perspective of Big Five personality traits and perceived physiological vulnerability to IT use (PPVITU). Data collected from 149 respondents are tested against the research model using the partial least squares structural equation method (PLS-SEM). The results indicate that extraversion and neuroticism positively affect perceived usefulness, conscientiousness negatively influences usefulness, and agreeableness and neuroticism have positive effects on perceived enjoyment. Furthermore, this study finds that musculoskeletal discomfort: neck and shoulder back pain (one PPVITU component) positively influences perceived ease of use. The findings of this study provide several important theoretical and practical implications for promoting VR-based learning system use behaviour.