Reviewerly/exHarmony huggingface dataset:
Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models

While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood.We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed.Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73-0.78]) and validation (0.64 [interquartile range 0.60-0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59-0.90]) than distantly related cohorts (0.59 [interquartile range 0.43-0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation.There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making.
Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.
COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle

Abstract This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics.
Image Regularizations Based on the Sparsity of Corner Points

Many analysis-based regularizations proposed so far employ a common prior information, i.e., edges in an image are sparse. However, in local edge regions and texture regions, this prior may not hold. As a result, the performance of regularizations based on the edge sparsity may be unsatisfactory in such regions for image-related inverse problems. These regularizations tend to smooth out the edges while eliminating the noise. In other words, these regularizations’ abilities of preserving edges are limited. In this paper, a new prior that the corner points in a natural image are sparse was proposed to construct regularizations. Intuitively, even in local edge regions and texture regions, the sparsity of corner points may still exist, and hence, the regularizations based on it can achieve better performance than those based on the edge sparsity. As an example, by utilizing the sparsity of corner points, we proposed a new regularization based on Noble’s corner measure function. Our experiments demonstrated the excellent performance of the proposed regularization for both image denoising and deblurring problems, especially in local edge regions and texture regions.
Complex Unitary Recurrent Neural Networks Using Scaled Cayley Transform

Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN) which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training and a hyperparameter is introduced to tune the matrix for each particular task. In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We also provide an analysis of a potential issue of the modReLU activiation function which is used in our work and several other unitary RNNs. In the experiments conducted, the scaled Cayley unitary recurrent neural network (scuRNN) achieves comparable or better results than scoRNN and other unitary RNNs without fixing the scaling matrix.
Evaluating the usability of the information architecture of academic library websites

Purpose The purpose of this paper is to provide an integrated list of heuristics and an information architecture (IA) framework for the heuristic evaluation of the IA of academic library websites as well as an evaluation framework with practical steps on how to conduct the evaluation. Design/methodology/approach A set of 14 heuristics resulted from an integration of existing usability principles from authorities in the field of usability. A review of IA literature resulted in a framework for dividing academic library websites into six dialogue elements. The resulting heuristics were made applicable to academic library websites through the addition of recommendations based on a review of 20 related studies. Findings This study provides heuristics, a framework and workflow guidelines that can be used by the various evaluators of academic library websites, i.e. library staff, web developers and usability experts, to provide recommendations for improving its usability. Research limitations/implications The focus of the usability principles is the evaluation of the IA aspects of websites and therefore does not provide insights into accessibility or visual design aspects. Originality/value The main problem that is addressed by this study is that there are no clear guidelines on how to apply existing usability principles for the evaluation of the IA of academic library websites.
Full-body ownership illusion elicited by visuo-vestibular integration.

Vestibular signals allow us to maintain balance and orient ourselves in space. However, the possible contribution of the vestibular sense to the perception of the body as one's own (body ownership) remains poorly understood. The aim of the present study was to investigate how vestibular information contributes to the experience of body ownership using multisensory integration. We conducted 3 studies using a "full-body ownership illusion" induced by virtual reality technology and galvanic vestibular stimulation (GVS); the latter is a technique that allows for the selective stimulation of vestibular afferents. Participants wearing head-mounted displays saw a mannequin's body that was performing a slow swinging movement from a first-person perspective. At the same time, participants were exposed to GVS that elicited vestibular sensations of swinging whole-body movements in the corresponding direction. Perceived ownership of the seen body was measured using questionnaire ratings and skin-conductance responses to a knife threat toward the mannequin. We demonstrated that when participants were exposed to congruent visuo-vestibular information, they perceived a stronger ownership of the mannequin's body compared with when they were exposed to unimodal visual and vestibular conditions or an incongruent visuo-vestibular condition. The findings show that visuo-vestibular congruency is sufficient to increase the feeling of illusory body ownership of a mannequin's body. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fuzzy Linguistic Protoforms to Summarize Heart Rate Streams of Patients with Ischemic Heart Disease

Cardiac rehabilitation is a key program which significantly decreases mortality rates in high-risk patients with ischemic heart disease. Due to the huge lack of accessibility to such programs at Health Centers, outdoor-based programs for cardiac rehabilitation have been proposed as an excellent tool to improve accessibility for patients at Health Centers. These outdoor-based programs make use of wrist-worn devices for real-time monitoring of rehabilitation sessions based on clinical guidelines. In this way, a greater number of patients can fortunately gain access to the rehabilitation program. However, this advantage also means that the cardiac rehabilitation team has to monitor a greater number of sessions due to the increase of the number of benefited patients, so the team members spend a lot of time analyzing each patient’s sessions. In this paper, we present a methodology to evaluate heart rate streams of patients with ischemic heart disease using a linguistic approach. This innovative methodology manages relevant linguistic descriptions (protoforms) for the cardiac rehabilitation team to identify sessions with interest indicators by means of linguistic summaries. Therefore, the analysis process is automated in a comprehensible way, offering short linguistic descriptions to the cardiac rehabilitation team, who promptly provide feedback to their patients. In order to show the great efficiency and effectiveness of the proposed methodology, we have used and applied this methodology to real data provided by patients of an outdoor cardiac rehabilitation program run by the Health Council of the Andalusian Health Service (Spain).
Scalar potential reconstruction method of axisymmetric 3D refractive index fields with background-oriented schlieren

Deflection angles of light rays passing through a refractive index field can be measured by the background-oriented schlieren (BOS) technique. Assuming that the deflection angle is sufficiently small and the paraxial approximation can apply to the light rays, a vector consisting of deflection angles in two orthogonal directions is shown to be derived from a gradient of a scalar potential. The scalar potential can be written as an integration of the refractive index field over the light ray path. Thus, a method to reconstruct an axisymmetric 3D refractive index field with the scalar potential is proposed here. An arbitrary measured deflection angle vector, however, is generally written not only with a scalar potential but with a vector potential. Thus, the Poisson's equation is derived to extract a scalar potential from a measured deflection angle vector. The axisymmetric 3D refractive index field is able to be reconstructed using the Abel transformation [1] of the scalar potential derived by applying the 2D Fourier transformation to the Poisson's equation. The scalar potential reconstruction method is validated by reconstructing a spherically symmetric refractive index field where a deflection angle vector field is able to be calculated accurately.
Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling

Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits. The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes. In particular, knowledge-driven approaches (fuzzy logic) use a conceptual mineral systems model from which data proxies represent the critical components. These typically vary based on the scale of study and the type of mineral system being predicted. Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model. In the case study presented, the prospectivity for remobilised nickel sulphide (NiS) in the west Kimberley, Western Australia, is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios. Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered. The first technique grades the strength of structural interpretations on an individual line-segment basis. Gradings are obtained from an objective measure of feature evidence, which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure. Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence. This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty. The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems. In this approach, multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition. With a suite of prospectivity maps, the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite. This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model, robust modelling of prospectivity and significantly reducing the exploration search space for Ni.
A Comprehensive Survey on Extractive and Abstractive Techniques for Text Summarization

Over the years as the technology advanced, the amount of data generated during the simulations and processing has been constantly increasing. Techniques for creating synopses of this massively generated data have been in the forefront of the research in the recent times. Text Summarization was one such aspect of the research which focused on representing the idea of the context in a short representation. Efforts were put to create a system which was able to generate effective summaries providing an overview of all the ideas represented by the article. Text Summarization techniques can be broadly classified into Extractive and Abstractive Text Summarization techniques. The paper compares all the prevailing systems, their shortcomings, and a combination of technologies used to achieve improved results. The paper also draws attention towards the state-of-the-art standardized datasets used in developing the summarization systems. The paper also focuses on testing parameters and techniques used to test the efficiency of the summarizing systems.
3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed

Structured light 3D shape metrology has become a very important technique and one of the hot research topics in 3D face recognition. However, it is still very challenging to use the digital light projector (DLP) in a 3D scanner and achieve high-speed, low-cost, small-size, and infrared-illuminated measurements. Instead of using a DLP, this paper proposes to use a galvanometer scanner to project phase-shifted fringes with a projection speed of infrared fringes up to 500 fps. Moreover, the measurement accuracy of multi-frequency (hierarchical) and multi-wavelength (heterodyne) temporal phase unwrapping approaches implemented in this system is analyzed. The measurement accuracy of the two methods is better than 0.2 mm. Comparisons are made between this method and the classical DLP approach. This method can achieve a similar accuracy and repeatability compared to the classical DLP method when a face mask is measured. The experiments on real human face indicate that this proposed method can improve the field of 3D scanning applications at a lower cost.
Monitoring meaningful activities using small low-cost devices in a smart home

A challenge associated with an ageing population is increased demand on health and social care, creating a greater need to enable persons to live independently in their own homes. Ambient assistant living technology aims to address this by monitoring occupants’ ‘activities of daily living’ using smart home sensors to alert caregivers to abnormalities in routine tasks and deteriorations in a person’s ability to care for themselves. However, there has been less focus on using sensing technology to monitor a broader scope of so-called ‘meaningful activities’, which promote a person’s emotional, creative, intellectual, and spiritual needs. In this paper, we describe the development of a toolkit comprised of off-the-shelf, affordable sensors to allow persons with dementia and Parkinson’s disease to monitor meaningful activities as well as activities of daily living in order to self-manage their life and well-being. We describe two evaluations of the toolkit, firstly a lab-based study to test the installation of the system including the acuity and placement of sensors and secondly, an in-the-wild study where subjects who were not target users of the toolkit, but who identified as technology enthusiasts evaluated the feasibility of the toolkit to monitor activities in and around real homes. Subjects from the in-the-wild study reported minimal obstructions to installation and were able to carry out and enjoy activities without obstruction from the sensors, revealing that meaningful activities may be monitored remotely using affordable, passive sensors. We propose that our toolkit may enhance assistive living systems by monitoring a wider range of activities than activities of daily living.
Preliminary Analysis of Short-term Solar Irradiance Forecasting by using Total-sky Imager and Convolutional Neural Network

The installation of photovoltaic system (PV) is increasing rapidly across the world. However, the fluctuation of PV output causes serious challenges in the power grid operation. Among the fluctuation, a quick part of fluctuation is mainly caused by the change of cloud coverage. A total-sky imager (TSI), measuring device to take sky and cloud, could be useful for short-term solar irradiance forecasting. In this paper, a convolutional neural network (CNN) is applied to forecasting model (called CNN model) to forecast 5-20 min ahead of global horizontal irradiance (GHI) using total-sky images and lagged GHI. To verify the effectiveness of CNN, three forecasting models are compared. They are the persistence model, the CNN model using only total-sky images, and the CNN model using both total-sky images and lagged GHI. From the computation, the proposed CNN model using both total-sky images and lagged GHI performs root-mean-square error (RMSE) of 49-177W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 93-146W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 71-118W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in sunny day, partly cloudy day and overcast day, respectively. From these results, the proposed method is shown to be suitable for short-term solar irradiance forecasting.
Smart University Development Evaluation Models

The contemporary approach to the education sphere’s formation and development is characterized by the knowledge intellectualization and the ability to quickly reorganize the work of the team that carries out functions of teaching and upbringing, in compliance with the outside environment conditions. Today, these are new intellectual and digital technologies generally called SMART. Consequently, while considering the main conditions and tendencies of a contemporary university development, one cannot but consider the concept of a smart university and the specific features of managing it. At that the self-learning property is established as a basic constitutive feature of a smart university. So, we shall distinguish two notions: the notion of “smart university” and the notion of “self-learning university”. In present paper, we contemplate the work of the performers’ team that is aimed at implementing, promoting and carrying out innovational approaches and basic ideas of self-learning organizations. The personnel knowledge transfer inside the performers’ team is one of the conditions of a smart university efficient functioning. The goal of this article is to validate mathematically the criteria necessary for a self-learning university to be classified as a “smart university”.
Joint CNN and Variational Model for Fully-Automatic Image Colorization

This paper aims to couple the powerful prediction of the convolutional neural network (CNN) to the accuracy at pixel scale of the variational methods. In this work, the limitations of the CNN-based image colorization approaches are described. We then focus on a CNN which is able to compute a statistical distribution of the colors for each pixel of the image based on a learning stage on a large color image database. After describing its limitation, the variational method of [17] is briefly recalled. This method is able to select a color candidate among a given set while performing regularization of the result. By combining this approach with a CNN, we designed a fully automatic image colorization framework with an improved accuracy in comparison with CNN alone. Some numerical experiments demonstrate the increased accuracy reached by our method.