Reviewerly/exHarmony huggingface dataset:
Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.
Remote Sensing Image Detection Based on YOLOv4 Improvements

Remote sensing image target object detection and recognition are widely used both in military and civil fields. There are many models proposed for this purpose, but their effectiveness on target object detection in remote sensing images is not ideal due to the influence of climate conditions, obstacles and confusing objects presented in images, image clarity, and associated problems with small-target and multi-target detection and recognition. Therefore, how to accurately detect target objects in images is an urgent problem to be solved. To this end, a novel model, called YOLOv4_CE, is proposed in this paper, based on the classical YOLOv4 model with added improvements, resulting from replacing the backbone feature-extraction network with a ConvNeXt network, replacing the Complete Intersection over Union (CIoU) loss with the Efficient Intersection over Union (EIoU) loss, and adding a coordinate attention mechanism to YOLOv4 as to improve the remote sensing image detection capabilities. The results, obtained through experiments conducted on two open data sets, demonstrate that the proposed YOLOv4_CE model outperforms, in this regard, both the original YOLOv4 model and four other state-of-the-art models, namely Faster R-CNN, Gliding Vertex, Oriented R-CNN, and EfficientDet, in terms of the mean average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1 score</i> , by achieving respective values of 95.03% and 0.933 on the NWPU VHR-10 data set, and 95.89% and 0.937 on the RSOD data set.
Implicit Extended Kalman Filter for Optical Terrain Relative Navigation Using Delayed Measurements

The exploration of celestial bodies such as the Moon, Mars, or even smaller ones such as comets and asteroids, is the next frontier of space exploration. One of the most interesting and attractive purposes from the scientific point of view in this field, is the capability for a spacecraft to land on such bodies. Monocular cameras are widely adopted to perform this task due to their low cost and system complexity. Nevertheless, image-based algorithms for motion estimation range across different scales of complexities and computational loads. In this paper, a method to perform relative (or local) terrain navigation using frame-to-frame features correspondences and altimeter measurements is presented. The proposed image-based approach relies on the implementation of the implicit extended Kalman filter, which works using nonlinear dynamic models and corrections from measurements that are implicit functions of the state variables. In particular, here, the epipolar constraint, which is a geometric relationship between the feature point position vectors and the camera translation vector, is employed as the implicit measurement fused with altimeter updates. In realistic applications, the image processing routines require a certain amount of time to be executed. For this reason, the presented navigation system entails a fast cycle using altimeter measurements and a slow cycle with image-based updates. Moreover, the intrinsic delay of the feature matching execution is taken into account using a modified extrapolation method.
SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (LSTM-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The LSTM-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution, different input features, different optimizations of the same models, or other user preferences.
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images Using a GAN

Due to privacy issues and limited amount of publicly available labeled datasets in the domain of medical imaging, we propose an image generation pipeline to synthesize 3D echocardiographic images with corresponding ground truth labels, to alleviate the need for data collection and for laborious and error-prone human labeling of images for subsequent Deep Learning (DL) tasks. The proposed method utilizes detailed anatomical segmentations of the heart as ground truth label sources. This initial dataset is combined with a second dataset made up of real 3D echocardiographic images to train a Generative Adversarial Network (GAN) to synthesize realistic 3D cardiovascular ultrasound images paired with ground truth labels. To generate the synthetic 3D dataset, the trained GAN uses high resolution anatomical models from Computed Tomography (CT) as input. A qualitative analysis of the synthesized images showed that the main structures of the heart are well delineated and closely follow the labels obtained from the anatomical models. To assess the usability of these synthetic images for DL tasks, segmentation algorithms were trained to delineate the left ventricle, left atrium, and myocardium. A quantitative analysis of the 3D segmentations given by the models trained with the synthetic images indicated the potential use of this GAN approach to generate 3D synthetic data, use the data to train DL models for different clinical tasks, and therefore tackle the problem of scarcity of 3D labeled echocardiography datasets.
Understanding Trust and Changes in Use After a Year With the NHS COVID-19 Contact Tracing App in the United Kingdom: Longitudinal Mixed Methods Study

Background Digital contact tracing (DCT) apps have been implemented as a response to the COVID-19 pandemic. Research has focused on understanding acceptance and adoption of these apps, but more work is needed to understand the factors that may contribute to their sustained use. This is key to public health because DCT apps require a high uptake rate to decrease the transmission of the virus within the general population. Objective This study aimed to understand changes in the use of the National Health Service Test &amp; Trace (T&amp;T) COVID-19 DCT app and explore how public trust in the app evolved over a 1-year period. Methods We conducted a longitudinal mixed methods study consisting of a digital survey in December 2020 followed by another digital survey and interview in November 2021, in which responses from 9 participants were explored in detail. Thematic analysis was used to analyze the interview transcripts. This paper focuses on the thematic analysis to unpack the reasoning behind participants’ answers. Results In this paper, 5 themes generated through thematic analysis are discussed: flaws in the T&amp;T app, usefulness and functionality affecting trust in the app, low trust in the UK government, varying degrees of trust in other stakeholders, and public consciousness and compliance dropping over time. Mistrust evolved from participants experiencing sociotechnical flaws in the app and led to concerns about the app’s usefulness. Similarly, mistrust in the government was linked to perceived poor pandemic handling and the creation and procurement of the app. However, more variability in trust in other stakeholders was highlighted depending on perceived competence and intentions. For example, Big Tech companies (ie, Apple and Google), large hospitality venues, and private contractors were seen as more capable, but participants mistrust their intentions, and small hospitality venues, local councils, and the National Health Service (ie, public health system) were seen as well-intentioned but there is mistrust in their ability to handle pandemic matters. Participants reported complying, or not, with T&amp;T and pandemic guidance to different degrees but, overall, observed a drop in compliance over time. Conclusions These findings contribute to the wider implications of changes in DCT app use over time for public health. Findings suggest that trust in the wider T&amp;T app ecosystem could be linked to changes in the use of the app; however, further empirical and theoretical work needs to be done to generalize the results because of the small, homogeneous sample. Initial novelty effects occurred with the app, which lessened over time as public concern and media representation of the pandemic decreased and normalization occurred. Trust in the sociotechnical capabilities of the app, stakeholders involved, and salience maintenance of the T&amp;T app in conjunction with other measures are needed for sustained use.
Semantic Feature Extraction and Deep Convolutional Neural Network-based Face Sentimental Analysis

Police and government agencies make use of facial recognition technology in order to determine the truth about the criminal. Though this might seem skeptical, it requires the support of the public in order to come into action. In this regard the media plays a major role in molding the public agenda and attracting sentiments and attitudes towards this topic. In this work, various perspectives are taken into consideration in order to determine the impact of social media on the public, police and government with the help of face recognition technology. A total of 443 videos have been analyzed and the outcome showed to be positive for this technology to be incorporated. Close examination of emotional language indicated several levels of anticipation and surprise along with fear and sadness. It is worth noting that trust is in emotion expressed in low levels only. Deep learning based CNN technique is used for categorization. Based on the information obtained and by incorporating new methodologies, conclusions are drawn, strategies are incorporated and recorded.
High Fidelity State Preparation and Measurement of Ion Hyperfine Qubits with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>I</mml:mi><mml:mo>&gt;</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math>

We present a method for achieving high fidelity state preparation and measurement (SPAM) using trapped ion hyperfine qubits with nuclear spins higher than I=1/2. The ground states of these higher nuclear spin isotopes do not afford a simple frequency-selective state preparation scheme. We circumvent this limitation by stroboscopically driving strong and weak transitions, blending fast optical pumping using dipole transitions, and narrow microwave or optical quadrupole transitions. We demonstrate this method with the I=3/2 isotope ^{137}Ba^{+} to achieve a SPAM infidelity of (9.0±1.3)×10^{-5} (-40.5±0.6 dB), facilitating the use of a wider range of ion isotopes with favorable wavelengths and masses for quantum computation.
ResNet-Based Fast CU Partition Decision Algorithm for VVC

H.266/VVC introduces new technologies based on previous video coding standards, including the QTMT partition structure and support for 65 angular intra-prediction modes.The introduced new technologies significantly improve the coding efficiency, but also lead to a great increase in coding complexity.This paper proposes a ResNet-based fast CU partition decision algorithm to reduce the coding complexity of VVC.First, we count and analyze the proportion of CU split modes to explore the appropriate CNN models.Then, the ResNet-based CNN models are designed to predict CU split mode, and the first convolutional layer of the CNN models combines symmetric and asymmetric convolutional kernels to extract features efficiently.We also introduce the RD cost into the loss function to improve the prediction accuracy of CNN models.Finally, two threshold schemes are used to achieve a compromise between coding complexity and coding performance.The experimental results show that the "Fast" scheme saves 55.12% of the encoding time with 1.83% BDBR increase and the "Moderate" scheme saves 47.03% of the encoding time with only 1.27% BDBR increase compared with VTM7.0.
Tight constraints on probabilistic convertibility of quantum states

We develop two general approaches to characterising the manipulation of quantum states by means of probabilistic protocols constrained by the limitations of some quantum resource theory. First, we give a general necessary condition for the existence of a physical transformation between quantum states, obtained using a recently introduced resource monotone based on the Hilbert projective metric. In all affine quantum resource theories (e.g. coherence, asymmetry, imaginarity) as well as in entanglement distillation, we show that the monotone provides a necessary and sufficient condition for one-shot resource convertibility under resource-non-generating operations, and hence no better restrictions on all probabilistic protocols are possible. We use the monotone to establish improved bounds on the performance of both one-shot and many-copy probabilistic resource distillation protocols. Complementing this approach, we introduce a general method for bounding achievable probabilities in resource transformations under resource-non-generating maps through a family of convex optimisation problems. We show it to tightly characterise single-shot probabilistic distillation in broad types of resource theories, allowing an exact analysis of the trade-offs between the probabilities and errors in distilling maximally resourceful states. We demonstrate the usefulness of both of our approaches in the study of quantum entanglement distillation.
An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

Sickle cell disease (SCD) is a genetic illness that affects red blood cells and can lead to major complications like Acute Chest Syndrome (ACS), Cerebrovascular Accident (CVA), and even death from a stroke.SCD is frequent in some places of the world, especially where consanguineous marriages are common, such as in Saudi Arabia.This research presents artificial intelligence (AI) models that were tested on hospital clinical data collected between 2008 to 2020 using five different classifiers: Naive Bayes, Neural Networks (NN), Support Vector Machine (SVM), J48, and PART.To select the optimum classification approach, we analyzed the models based on the accuracy kappa statistics and the classification time.We also compared performance criteria such as sensitivity, specificity, accuracy, F1 measure, and AUC.The naive Bayes classifier outperformed the other classifiers with 92.22% accuracy in our investigation, which was then utilized to determine key elements that are common between SCD patients' inheritance and demographic data.The findings of this study through AI are aimed to assist hospital doctors and practitioners understand the correlation between disease inheritance and other factors, allowing them to better manage the disease by increasing disease awareness in the community, particularly among mothers.In terms of accuracy, all classifiers obtained above 92%.J48, NN and SVM exhibited 92.99% accuracy, then PART exhibits accuracy as 92.92% followed by Naï ve Bayes at 92.22%.In terms of classification time, Naï ve Bayes was fastest (0.01 sec), then J48 (0.03 sec), then PART (0.24 sec) and SVM (0.28 sec) while NN was slowest at 22.43 sec.
Central Bank Digital Currency: Scenarios of Implementation and Potential Consequences for Monetary System

Abstract Computerization of social life enabled market participants to search for new forms of electronic payments. The share of cash in total payments is systematically decreasing, which poses the risk of being supplanted by other instruments, including cryptocurrencies. Central banks, therefore, faced the challenge of adjusting their issuing activities to the current social and market conditions, which resulted in the emergence of the concept and discussion of the introduction of digital cash. The purpose of this paper is to examine the abovementioned issues and indication of potential scenarios for the introduction of central bank digital currency with an indication of potential consequences for the banking sector. The thesis of this paper is the following statement: The emergence of digital money of the central bank may significantly change the nature of commercial banks’ activities - including potentially eliminating their role in issuing cashless money.
Can Segmentation Models Be Trained with Fully Synthetically Generated Data?

In order to achieve good performance and generalisability, medical image segmentation models should be trained on sizeable datasets with sufficient variability. Due to ethics and governance restrictions, and the costs associated with labelling data, scientific development is often stifled, with models trained and tested on limited data. Data augmentation is often used to artificially increase the variability in the data distribution and improve model generalisability. Recent works have explored deep generative models for image synthesis, as such an approach would enable the generation of an effectively infinite amount of varied data, addressing the generalisability and data access problems. However, many proposed solutions limit the user’s control over what is generated. In this work, we propose brainSPADE, a model which combines a synthetic diffusion-based label generator with a semantic image generator. Our model can produce fully synthetic brain labels on-demand, with or without pathology of interest, and then generate a corresponding MRI image of an arbitrary guided style. Experiments show that brainSPADE synthetic data can be used to train segmentation models with performance comparable to that of models trained on real data.
Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis

Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.
Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech

In this paper, we present our progress in pretraining Czech monolingual audio transformers from a large dataset containing more than 80 thousand hours of unlabeled speech, and subsequently fine-tuning the model on automatic speech recognition tasks using a combination of in-domain data and almost 6 thousand hours of out-of-domain transcribed speech.We are presenting a large palette of experiments with various fine-tuning setups evaluated on two public datasets (Common-Voice and VoxPopuli) and one extremely challenging dataset from the MALACH project.Our results show that monolingual Wav2Vec 2.0 models are robust ASR systems, which can take advantage of large labeled and unlabeled datasets and successfully compete with state-of-the-art LVCSR systems.Moreover, Wav2Vec models proved to be good zero-shot learners when no training data are available for the target ASR task.