Reviewerly/exHarmony huggingface dataset:
Digitalization as a trend in library development

The purpose of this article is to introduce into the scientific circulation the results of the research work started in 2019 by the Russian State Library (RSL) with the aim to identify, analyze and characterize processes of digitalization of public libraries of the Russian Federation as well as to make the forecast of their further development. In the course of the research, there was done the analysis of normative documents, publications in the professional press and on the sites of Russian and foreign journals, other net actors, accumulating and testing scientific information about digitalization of librarianship; examination of libraries’ sites as points of access to distant library services. The summarization of the significant empirical base made it possible to identify and characterize the main trends in the process of digitalization of public libraries, as well as to conduct a comparative analysis of international and domestic experience in the introduction of digital technologies into library practice. During the first two stages of this scientific project, there was defined the concept of “library digitalization”, studied the content of the process of library digitalization, identified the main avenues and analyzed forms and areas of application of new information technologies in library practice. The results of the research have indicated that digitalization affects the development of all the main areas of library activities. The introduction of digital technologies leads to significant changes in the forms and methods of library work and library communications. There have been identified several main vectors of library digitalization development: digitization of library collections and creation of digital resources; development of the remote user services via the Internet, robotization of library processes and implementation of mobile communication technologies, augmented reality and radio frequency identification. The objectives of the introduction of digital technologies into library practice have been formulated as follows: liberation of library specialists from routine library work for to perform processes that require intellectual and creative abilities. The results of this research have important theoretical and practical significance.
Improving sum uncertainty relations with the quantum Fisher information

We show how preparation uncertainty relations that are formulated as sums of variances may be tightened by using the quantum Fisher information to quantify quantum fluctuations. We apply this to derive stronger angular momentum uncertainty relations, which in the case of spin-$1/2$ turn into equalities involving the purity. Using an analogy between pure-state decompositions in the Bloch sphere and the moment of inertia of rigid bodies, we identify optimal decompositions that achieve the convex- and concave-roof decomposition of the variance. Finally, we illustrate how these results may be used to identify the classical and quantum limits on phase estimation precision with an unknown rotation axis.
Weakly Supervised Segmentation of Small Buildings with Point Labels

Most supervised image segmentation methods require delicate and time-consuming pixel-level labeling of building or objects, especially for small objects. In this paper, we present a weakly supervised segmentation network for aerial/satellite images, separately considering small and large objects. First, we propose a simple point labeling method for small objects, while large objects are fully labeled. Then, we present a segmentation network trained with a small object mask to separate small and large objects in the loss function. During training, we employ a memory bank to cope with the limited number of point labels. Experiments results with three public datasets demonstrate the feasibility of our approach.
CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection With Low Complexity

Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: Concrete MAP Detection (CMD). Furthermore, extending CMD by the idea of deep unfolding into CMDNet, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMDNet based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today's communication systems. Numerical simulation results in MIMO systems reveal CMDNet to feature a promising accuracy complexity trade-off compared to State of the Art. Notably, we demonstrate CMDNet's soft outputs to be reliable for decoders.
Recognition of Fetal Facial Expressions Using Artificial Intelligence Deep Learning

Fetal facial expressions are useful parameters for assessing brain function and development in the latter half of pregnancy.Previous investigations have studied subjective assessment of fetal facial expressions using four-dimensional ultrasound.Artificial intelligence (AI) can enable the objective assessment of fetal facial expressions.Artificial intelligence recognition of fetal facial expressions may open the door to the new scientific field, such as "AI science of fetal brain", and fetal neurobehavioral science using AI is at the dawn of a new era.Our knowledge of fetal neurobehavior and neurodevelopment will be advanced through AI recognition of fetal facial expressions.Artificial intelligence may be an important modality in current and future research on fetal facial expressions and may assist in the evaluation of fetal brain function.
Education Continuation Strategies during COVID-19 in Malaysia

COVID-19 pandemic causes Malaysia to make an early decision of a Movement Control Order (MCO) to control the spread of the virus.The purpose of this study is to look on to the education continuation strategies that implemented by the Malaysian Ministry of Education (MoE) during schools' closure as a measure to control the spread of COVID-19.This article reviewed the measures and the implemented strategies that taken by the MoE to ensure the delivery of learning to all public schools' students in the country.The review revealed that, the MoE introduced four strategies to be implemented during COVID-19, which are (1) flexible class schedule and pedagogies, (2) synchronous and asynchronous online class, (3) collaboration with mass media, and (4) digital learning communities for professional development.The implemented strategies enabled the education system to overcome the school's sudden closing issue and deliver the necessary education to students during the pandemic.
Evaluation of structural and measurement models of student satisfaction in online learning

&lt;span&gt;This study aimed to evaluate structural models and measurement models of student satisfaction in online learning. This was a quantitative study using a survey research design. Structural model testing was done by examining the relationship between several variables. The variables in question were the learning management system (LMS), admin services, the performance of facilitator lecturers and student satisfaction in learning. The sample used in this study were 149 students. Data analysis was performed using the multivariate Structural Equation Modeling (SEM) technique. The findings of this study indicated that the facilitator lecturer performance is the variable that has the greatest effect in increasing student satisfaction in online learning. Admin service is another variable that has a positive effect on student satisfaction, both direct and indirect effects. The LMS variable does not show a direct effect on student satisfaction, but the LMS has an indirect effect on student satisfaction through the variable facilitator lecturer performance and admin services.&lt;/span&gt;
Neural Articulated Radiance Field

We present Neural Articulated Radiance Field (NARF), a novel deformable 3D representation for articulated objects learned from images. While recent advances in 3D implicit representation have made it possible to learn models of complex objects, learning pose-controllable representations of articulated objects remains a challenge, as current methods require 3D shape supervision and are unable to render appearance. In formulating an implicit representation of 3D articulated objects, our method considers only the rigid transformation of the most relevant object part in solving for the radiance field at each 3D location. In this way, the proposed method represents pose-dependent changes without significantly increasing the computational complexity. NARF is fully differentiable and can be trained from images with pose annotations. Moreover, through the use of an autoencoder, it can learn appearance variations over multiple instances of an object class. Experiments show that the proposed method is efficient and can generalize well to novel poses. We make the code, model and demo available for research purposes at this https URL
Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies

Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance.The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly-Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes.No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals.CRD42019130886.
Dark web advertising: the dark magic system on Tor hidden service search engines

Drawing on Raymond William's concept of the 'magic system,' this article argues that advertising on the Dark Web is a 'dark magic system'. The article first defines 'Dark Web' and then analyzes over 300 banner advertisements appearing on Tor onion service search engines. The advertisements are categorized into navigation, individual vendors, services, and markets. Next, the article traces the associations the advertisements make between advertised objects and values. The predominant values the advertisements invoke include navigation, OPSEC politics, and a justification to exploit the openness of others. The article then traces the limits of the dark magic system, including the system's inability to offer metrics, the problems of 'onion cloners,' and the constant threat of scams. The article concludes with an argument that the dark magic system is incapable of addressing the sort of anonymized political communication the Dark Web might afford.
Reassessing the computational advantage of quantum-controlled ordering of gates

Research on indefinite causal structures is a rapidly evolving field that has a potential not only to make a radical revision of the classical understanding of space-time but also to achieve enhanced functionalities of quantum information processing. For example, it is known that indefinite causal structures provide exponential advantage in communication complexity when compared to causal protocols. In quantum computation, such structures can decide whether two unitary gates commute or anticommute with a single call to each gate, which is impossible with conventional (causal) quantum algorithms. A generalization of this effect to $n$ unitary gates, originally introduced in Ara\'ujo et al. [Phys. Rev. Lett. 113, 250402 (2014)] and often called Fourier promise problem (FPP), can be solved with the quantum-$n$-switch and a single call to each gate, while the best known causal algorithm so far calls $O({n}^{2})$ gates. In this article, we show that this advantage is smaller than expected. In fact, we present a causal algorithm that solves the only known specific FPP with $O(nlog(n))$ queries and a causal algorithm that solves every FPP with $O(n\sqrt{n})$ queries. Besides the interest in such algorithms on their own, our results limit the expected advantage of indefinite causal structures for these problems.
Frequency Domain Transformer Networks for Video Prediction.

The task of video prediction is forecasting the next frames given some previous frames. Despite much recent progress, this task is still challenging mainly due to high nonlinearity in the spatial domain. To address this issue, we propose a novel architecture, Frequency Domain Transformer Network (FDTN), which is an end-to-end learnable model that estimates and uses the transformations of the signal in the frequency domain. Experimental evaluations show that this approach can outperform some widely used video prediction methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).
Multimodal Multitask Deep Learning for X-Ray Image Retrieval

Content-based image retrieval (CBIR) is of increasing interest for clinical applications spanning differential diagnosis, prognostication, and indexing of electronic radiology databases. However, meaningful CBIR for radiology applications requires capabilities to address the semantic gap and assess similarity based on fine-grained image features. We observe that images in radiology databases are often accompanied by free-text radiologist reports containing rich semantic information. Therefore, we propose a Multimodal Multitask Deep Learning (MMDL) approach for CBIR on radiology images. Our proposed approach employs multimodal database inputs for training, learns semantic feature representations for each modality, and maps these representations into a common subspace. During testing, we use representations from the common subspace to rank similarities between the query and database. To enhance our framework for fine-grained image retrieval, we provide extensions employing deep descriptors and ranking loss optimization. We performed extensive evaluations on the MIMIC Chest X-ray (MIMIC-CXR) dataset with images and reports from 227,835 studies. Our results demonstrate performance gains over a typical unimodal CBIR strategy. Further, we show that the performance gains of our approach are robust even in scenarios where only a subset of database images are paired with free-text radiologist reports. Our work has implications for next-generation medical image indexing and retrieval systems.
Enhancing decision-making in user-centered web development: a methodology for card-sorting analysis

Abstract The World Wide Web has become a common platform for interactive software development. Most web applications feature custom user interfaces used by millions of people every day. Information architecture addresses the structural design of information to build quality web applications with improved usability of content, navigation, and findability. One of the most frequently utilized information architecture methods is card sorting—an affordable, user-centered approach for eliciting and evaluating categories and navigable items. Card sorting facilitates decision-making during the development process based on users’ mental models of a given application domain. However, although the qualitative analysis of card sorts has become common practice in information architecture, the quantitative analysis of card sorting is less widely applied. The reason for this gap is that quantitative analysis often requires the use of customized techniques to extract meaningful information for decision-making. To facilitate this process and support the structuring of information, we propose a methodology for the quantitative analysis of card-sorting results in this paper. The suggested approach can be systematically applied to provide clues and support for decisions. These might significantly impact the design and, thus, the final quality of the web application. Therefore, the approach includes proper goodness values that enable comparisons among the results of the methods and techniques used and ensure the suitability of the analyses performed. Two publicly available datasets were used to demonstrate the key issues related to the interpretation of card sorting results and the overall suitability and validity of the proposed methodology.
Moments of quantum purity and biorthogonal polynomial recurrence

The Bures-Hall ensemble is a unique measure of density matrices that satisfies various distinguished properties in quantum information processing. In this work, we study the statistical behavior of entanglement over the Bures-Hall ensemble as measured by the simplest form of an entanglement entropy - the quantum purity. The main results of this work are the exact second and third moment expressions of quantum purity valid for any subsystem dimensions, where the corresponding results in the literature are limited to the scenario of equal subsystem dimensions. In obtaining the results, we have derived recurrence relations of the underlying integrals over the Cauchy-Laguerre biorthogonal polynomials that may be of independent interest.
Robust logistic zero-sum regression for microbiome compositional data

Abstract We introduce the Robust Logistic Zero-Sum Regression (RobLZS) estimator, which can be used for a two-class problem with high-dimensional compositional covariates. Since the log-contrast model is employed, the estimator is able to do feature selection among the compositional parts. The proposed method attains robustness by minimizing a trimmed sum of deviances. A comparison of the performance of the RobLZS estimator with a non-robust counterpart and with other sparse logistic regression estimators is conducted via Monte Carlo simulation studies. Two microbiome data applications are considered to investigate the stability of the estimators to the presence of outliers. Robust Logistic Zero-Sum Regression is available as an R package that can be downloaded at https://github.com/giannamonti/RobZS .
LG-Net: Lesion Gate Network for Multiple Sclerosis Lesion Inpainting

Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease that results in progressive damage to the brain and spinal cord. Volumetric analysis of the brain tissues with Magnetic Resonance Imaging (MRI) is essential to monitor the progression of the disease. However, the presence of focal brain pathology leads to tissue misclassifications, and has been traditionally addressed by “inpainting” MS lesions with voxel intensities sampled from surrounding normal-appearing white matter. Based on the characteristics of brain MRIs and MS lesions, we propose a Lesion Gate Network (LG-Net) for MS lesion inpainting with a learnable dynamic gate mask integrated with the convolution blocks to dynamically select the features for a lesion area defined by a noisy lesion mask. We also introduce a lesion gate consistency loss to support the training of the gated lesion convolution by minimizing the differences between the features selected from the brain with and without lesions. We evaluated the proposed model on both public and in-house data and our method demonstrated a faster and superior performance than the state-of-the-art inpainting techniques developed for MS lesion and general image inpainting tasks.
The effect of real-time pose recognition on badminton learning performance

Badminton is a very popular subject in Physical Education (PE). Many students enroll badminton courses in every semester which pose a tremendous teaching load to the instructors. The one-on-one guiding/feedback time provided by the instructor to each student is also greatly reduced. To overcome this challenge, some studies have tried to adopt pose recognition technique in teaching badminton. However, the lack of mobility and recognition accuracy problems hinder its applicability. To address this issue, a new pose recognition technique, OpenPose, was employed to develop a real-time pose recognition badminton teaching APP in this study. The APP was then installed on a mobile device to enhance badminton smash skill learning performance. The scores distribution of the experimental group shows majority of students achieved satisfactory performance, this implies the badminton teaching APP is helpful to all students no matter what their initial skill levels are.
Nonadiabatic nonlinear optics and quantum geometry — Application to the twisted Schwinger effect

We study the tunneling mechanism of nonlinear optical processes in solids induced by strong coherent laser fields. The theory is based on an extension of the Landau-Zener model with nonadiabatic geometric effects. In addition to the rectification effect known previously, we find two effects, namely perfect tunneling and counterdiabaticity at fast sweep speed. We apply this theory to the twisted Schwinger effect, i.e., nonadiabatic pair production of particles by rotating electric fields, and find a nonperturbative generation mechanism of the opto-valley polarization and photo-current in Dirac and Weyl fermions.
Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis

The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration.