Reviewerly/exHarmony huggingface dataset:
L1-Norm Robust Regularized Extreme Learning Machine with Asymmetric C-Loss for Regression

Extreme learning machines (ELMs) have recently attracted significant attention due to their fast training speeds and good prediction effect. However, ELMs ignore the inherent distribution of the original samples, and they are prone to overfitting, which fails at achieving good generalization performance. In this paper, based on expectile penalty and correntropy, an asymmetric C-loss function (called AC-loss) is proposed, which is non-convex, bounded, and relatively insensitive to noise. Further, a novel extreme learning machine called L1 norm robust regularized extreme learning machine with asymmetric C-loss (L1-ACELM) is presented to handle the overfitting problem. The proposed algorithm benefits from L1 norm and replaces the square loss function with the AC-loss function. The L1-ACELM can generate a more compact network with fewer hidden nodes and reduce the impact of noise. To evaluate the effectiveness of the proposed algorithm on noisy datasets, different levels of noise are added in numerical experiments. The results for different types of artificial and benchmark datasets demonstrate that L1-ACELM achieves better generalization performance compared to other state-of-the-art algorithms, especially when noise exists in the datasets.
Fast and High-Quality Monocular Depth Estimation with Optical Flow for Autonomous Drones

Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based on Convolutional Neural Networks (CNNs) to estimate the distance to obstacles. Such applications are implemented onboard embedded systems. In order to precisely estimate the depth, such CNN models are in general so complex to extract many features that the computational complexity increases, requiring long inference time. In order to solve the issue, we employ optical flow to aid in-depth estimation. In addition, we propose a new attention structure that makes maximum use of optical flow without complicating the network. Furthermore, we achieve improved performance without modifying the depth estimator by adding a perceptual discriminator in training. The proposed model is evaluated through accuracy, error, and inference time on the KITTI dataset. In the experiments, we have demonstrated the proposed method achieves better performance by up to 34% accuracy, 55% error reduction and 66% faster inference time on Jetson nano compared to previous methods. The proposed method is also evaluated through a collision avoidance in simulated drone flight and achieves the lowest collision rate of all estimation methods. These experimental results show the potential of proposed method to be used in real-world autonomous drone flight applications.
PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both their brain similarity and economic advantages. Medical image processing is a crucial part of research, as it allows researchers to monitor their experiments and understand disease development. By pairing four reinforcement learning models and five deep learning UNet segmentation models with existing algorithms, we developed PigSNIPE, a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. PigSNIPE allows for image registration, AC-PC alignment, detection of 19 anatomical landmarks, skull stripping, brainmask and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images, which can significantly reduce the time and resources needed for analyzing minipig neuroimages.
Key Quality Factors in Digital Competence Assessment: A Validation Study from Teachers’ Perspective

The progressive shift from a transmissive to a competence-based approach emphasises the need to design new models and tools for competence assessment. In order to address this need, this study, firstly, introduces the assessment model for digital competence in primary and secondary schools developed within the H2020 CRISS project. The Competence Assessment Model (CAM) (1) embraces the integration pedagogy approach in which competences are embedded within the curriculum and (2) provides competence assessment scenarios (CAS). Secondly, the study presents the results of the evaluation of its quality from the teachers’ perspective through an instrument designed on the basis of nine competence assessment quality criteria. The CAM was tested in a large-scale pilot study conducted in 535 primary and secondary schools across Europe. The outcomes show that the CAM mainly fulfills two out of the nine quality criteria: meaningfulness and authenticity. However, the exploratory factor analysis also reveals that all these quality criteria can be summarized into four key quality factors that more accurately describe how CAM is experienced in school teaching. Out of these, the most relevant is the (1) efficiency over time, student monitoring, support and evaluation. The three secondary factors identified are (2) fairness and cognitive complexity, (3) meaningfulness and authenticity and (4) reproducibility and transparency. The study provides a conceptual and methodological approach for the evaluation of Digital Competence in the school context. Most of all, it helps highlight four factors that, according to teaching staff, describe the quality of the proposed model when applied to daily teaching practices.
Examining the Antecedents of Blockchain Usage Intention: An Integrated Research Framework

Blockchain is considered one of the key technologies that can accelerate the Industrial Revolution 4.0. The intention to use Blockchain can still be improved in several ways, and how users perceive Blockchain is likely to be influenced by how well they understand the underlying theory. This study examines several important factors, namely government regulation, social influence, perceived security, and Blockchain functional benefits, to measure trust and satisfaction with relationship quality, which may influence the intention to use Blockchain. A sample of 460 people participated in the online questionnaire survey, which was then evaluated with SmartPLS 3. The findings reveal that the social influence and Blockchain functional benefits have a substantial impact on relationship quality, which further results in a positive impact on Blockchain usage intention as well. This study can serve as a reference for companies that need to consider the factors discussed in this study when implementing Blockchain technology to achieve marketing goals and generate sustainable Blockchain usage intentions.
Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Human activity recognition (HAR) has been applied to several advanced applications, especially when individuals may need to be monitored closely. This work focuses on HAR using wearable sensors attached to various locations of the user body. The data from each sensor may provide unequally discriminative information and, then, an effective fusion method is needed. In order to address this issue, inspired by the squeeze-and-excitation (SE) mechanism, we propose the merging-squeeze-excitation (MSE) feature fusion which emphasizes informative feature maps and suppresses ambiguous feature maps during fusion. The MSE feature fusion consists of three steps: pre-merging, squeeze-and-excitation, and post-merging. Unlike the SE mechanism, the set of feature maps from each branch will be recalibrated by using the channel weights also computed from the pre-merged feature maps. The calibrated feature maps from all branches are merged to obtain a set of channel-weighted and merged feature maps which will be used in the classification process. Additionally, a set of MSE feature fusion extensions is presented. In these proposed methods, three deep-learning models (LeNet5, AlexNet, and VGG16) are used as feature extractors and four merging methods (addition, maximum, minimum, and average) are applied as merging operations. The performances of the proposed methods are evaluated by classifying popular public datasets.
Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar

With the popularity of virtual reality (VR) games and devices, demand is increasing for estimating and displaying user motion in VR applications. Most pose estimation methods for VR avatars exploit inverse kinematics (IK) and online motion capture methods. In contrast to existing approaches, we aim for a stable process with less computation, usable in a small space. Therefore, our strategy has minimum latency for VR device users, from high-performance to low-performance, in multi-user applications over the network. In this study, we estimate the upper body pose of a VR user in real time using a deep learning method. We propose a novel method inspired by a classical regression model and trained with 3D motion capture data. Thus, our design uses a convolutional neural network (CNN)-based architecture from the joint information of motion capture data and modifies the network input and output to obtain input from a head and both hands. After feeding the model with properly normalized inputs, a head-mounted display (HMD), and two controllers, we render the user’s corresponding avatar in VR applications. We used our proposed pose estimation method to build single-user and multi-user applications, measure their performance, conduct a user study, and compare the results with previous methods for VR avatars.
Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19

Recently, research is emerging highlighting the potential of cannabinoids' beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of cannabinoid-based medicines since COVID-19 was declared a pandemic. The objective of this research is 3 fold: i) to evaluate the relationship of the clinical delivery of cannabinoid-based medicine for anxiety, depression and sleep scores by utilizing machine learning specifically rough set methods; ii) to discover patterns based on patient features such as specific cannabinoid recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (CAT) scores over a period of time; and iii) to predict whether new patients could potentially experience either an increase or decrease in CAT scores. The dataset for this study was derived from patient visits to Ekosi Health Centres, Canada over a 2 year period including the COVID timeline. Extensive pre-processing and feature engineering was performed. A class feature indicative of their progress or lack thereof due to the treatment received was introduced. Six Rough/Fuzzy-Rough classifiers as well as Random Forest and RIPPER classifiers were trained on the patient dataset using a 10-fold stratified CV method. The highest overall accuracy, sensitivity and specificity measures of over 99% was obtained using the rule-based rough-set learning model. In this study, we have identified rough-set based machine learning model with high accuracy that could be utilized for future studies regarding cannabinoids and precision medicine.
An analysis of the preschool education system’s development in the Chechen Republic for the period 2001–2021

Subject. This article discusses the development of a competitive education system in the Chechen Republic as a necessary condition for the integration of the Republic into a single socioeconomic space of Russia. Objectives. The article aims to develop proposals for improving the quality of educational services in preschool educational institutions of the Chechen Republic. Methods. For the study, we used the statistical analysis method. Results. The article tells of the fast-moving improvement of the main system features of preschool education in the Chechen Republic for the period from 2001 to 2021 due to the implementation of programmes for the restoration and development of the social sphere of the region. Conclusions. The issue of training teachers for preschool educational institutions in the Chechen Republic is of utmost importance.
INAM-Based Image-Adaptive 3D LUTs for Underwater Image Enhancement

To the best of our knowledge, applying adaptive three-dimensional lookup tables (3D LUTs) to underwater image enhancement is an unprecedented attempt. It can achieve excellent enhancement results compared to some other methods. However, in the image weight prediction process, the model uses the normalization method of Instance Normalization, which will significantly reduce the standard deviation of the features, thus degrading the performance of the network. To address this issue, we propose an Instance Normalization Adaptive Modulator (INAM) that amplifies the pixel bias by adaptively predicting modulation factors and introduce the INAM into the learning image-adaptive 3D LUTs for underwater image enhancement. The bias amplification strategy in INAM makes the edge information in the features more distinguishable. Therefore, the adaptive 3D LUTs with INAM can substantially improve the performance on underwater image enhancement. Extensive experiments are undertaken to demonstrate the effectiveness of the proposed method.
Bayesian logical neural networks for human-centered applications in medicine

Background: Medicine is characterized by its inherent uncertainty, i.e., the difficulty of identifying and obtaining exact outcomes from available data. Electronic Health Records aim to improve the exactitude of health management, for instance using automatic data recording techniques or the integration of structured as well as unstructured data. However, this data is far from perfect and is usually noisy, implying that epistemic uncertainty is almost always present in all biomedical research fields. This impairs the correct use and interpretation of the data not only by health professionals but also in modeling techniques and AI models incorporated in professional recommender systems. Method: In this work, we report a novel modeling methodology combining structural explainable models, defined on Logic Neural Networks which replace conventional deep-learning methods with logical gates embedded in neural networks, and Bayesian Networks to model data uncertainties. This means, we do not account for the variability of the input data, but we train single models according to the data and deliver different Logic-Operator neural network models that could adapt to the input data, for instance, medical procedures (Therapy Keys depending on the inherent uncertainty of the observed data. Result: Thus, our model does not only aim to assist physicians in their decisions by providing accurate recommendations; it is above all a user-centered solution that informs the physician when a given recommendation, in this case, a therapy, is uncertain and must be carefully evaluated. As a result, the physician must be a professional who does not solely rely on automatic recommendations. This novel methodology was tested on a database for patients with heart insufficiency and can be the basis for future applications of recommender systems in medicine.
Modeling and Moderation of COVID-19 Social Network Chat

Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method trains a nine-state Hidden Markov Model (HMM) initialized from a biclustering of 23 features extracted from online messages. We apply our method to 16,000 conversations (1.5 million messages) that took place on the Facebook pages of 15 Canadian newspapers following COVID-19 news items, and show that it can effectively extract the conversation structure and discover the main themes of the messages. Furthermore, we demonstrate how the PageRank algorithm and the conversation graph discovered can be used to simulate the impact of five different moderation strategies, which makes it possible to easily develop and test new strategies to limit the spread of harmful messages. Although our work in this paper focuses on the COVID-19 pandemic, the methodology is general enough to be applied to handle communications during future pandemics and other crises, or to develop better practices for online community moderation in general.
Analysis of the Accuracy Potential of a Stereo High-Speed Camera System in 3D Measurements in Highly Dynamic Experiments

In the context of setting up a stereo high-speed camera system for accurate 3D measurements in highly dynamic experiments, the potential of a "Fastcam SA-X2" stereo system is evaluated by testing different camera configurations and motion scenarios. A thorough accuracy analysis is performed using spatial rigid-body transformations and relative measurement analyses of photogrammetrically reconstructed surfaces of nondeformable objects. The effects of camera calibration, exposure time, object velocity, and object surface pattern quality on the quality of adjusted 3D coordinates are taken into consideration. While the exposure time does not significantly influence the quality of the static measurements, the results of dynamic experiments demonstrate that not only an insufficient frame rate but also an increased noise level resulting from short exposure times affects 3D coordinate accuracy. Using appropriate configurations to capture dynamic events, the errors in dynamic experiments do not differ significantly from the errors obtained in static measurements. A spatial mapping error of less than 1 μm is obtained through the experiments, with proper testing configurations for an object surface area of 5×20 mm. These findings are relevant for users of high-speed stereo imaging techniques to perform geometric 3D measurements, deformation, and crack analyses.
Factorization of broad expansion for broad learning system

The broad learning system (BLS) based on the random vector functional link neural network is a new versatile non-iterative neural network for rapidly selecting models. One of its fascinating features is the broad expansion, a dynamic adjustment strategy in the changing environment. However, the added enhancement nodes often suffer from information redundancy and loss, a failure in the broad expansion. Therefore, the factorization of enhancement nodes, which makes each code independent of others and achieves minimal redundancy and loss representation, is critical to successful broad expansion. Inspired by this idea, in this study, a factorial broad expansion (FBE) strategy is proposed to aid BLS (FBE-BLS) during broad expansion. The strategy is proposed as a component for reducing the information redundancy and loss in the classical BLS. This strategy is an adversarial framework to learn the factorial codes at the enhancement layer. The factorial and predictor networks adversarially learn factorial codes by chasing independence, minimizing predictability among enhancement nodes. In experiments, considering that few works focused on information redundancy and loss in the broad expansion of BLS, the FBE-BLS is compared with the classical BLS, and state-of-the-art methods are selected. The results of FBE-BLS on UCI and real-world image data are very promising, exhibiting higher accuracy during broad expansion.
High-resolution restoration of solar images degraded by atmospheric turbulence effect using improved CycleGAN

Due to the atmospheric turbulence effect, the solar images received by solar telescopes are generally severely degraded. In order to restore and improve the solar images, we proposed an improved Cycle Generative Adversarial Network (CycleGAN). By incorporating the identity loss and the perceptual loss into the loss function of CycleGAN, the ability of the network to capture image pixels and high-frequency information is significantly improved. On the basis, the influence of the turbulence intensity and the image noise on the restoration effect of solar images is analyzed. Results show that the improved CycleGAN has the advantages of smaller error and higher clarity of the restored images. In addition, the proposed method does not require paired images as training datasets, providing a new processing method for optical system telescopes and other fields of image processing.
A Novel Segmentation-Free Approach for Handwritten Sentence Recognition

This is the era of digitization where everything must be available in digital format for easy processing of data. Through advancements in areas of text processing using improvised and futuristic ML and DL algorithms, digitization of handwritten text occupies an unparalleled position. This paper proposes a segmentation-free approach to convert a handwritten sentence to digital text. It uses convolutional neural networks (CNNs), recurrent neural networks (RNNs) and connectionist temporal classifier (CTC) function. The novel approach adopted is a segmentation-free approach which overcomes the increase in training time and character redundancy. The model achieves an accuracy of ~86% and is able to perform handwritten text recognition on sentences which include alphabets (both upper and lower case), digits and commonly used special symbols. Further, a UI has been added to the model for ease of use.
Generating ISL Using Audio Speech

The communication gap between other people and deaf people is bigger than ever. The main aim of our project is to help people with hearing disabilities understand the spoken content using an interface that accepts audio/text as input and converts them to corresponding sign language for deaf people. The audio input will be given by a person with limited knowledge and is therefore not able to communicate with deaf people. The system automatically uses various artificial intelligence and natural language processing to produce the corresponding sign language gesture. The system uses speech to text API, lemmatization for reducing the given form of word or words making sure of the root word to be recognized, tokenization for a thorough understanding of the word or phrase entered, parts of speech tagging for the understanding of the context according to the grammar rules, and parsing to produce the best possible output.
Fine-Tuning BART for Abstractive Reviews Summarization

Abstractive text summarization is a widely studied problem in sequence-to-sequence (seq2seq) architecture. BART is the state-of-the-art (SOTA) model for sequence-to-sequence architecture. In this paper, we have implemented abstractive text summarization by fine-tuning the BART architecture which improves the model significantly resulting in the optimized overall summarization quality. Here, use of Sortish sampling allows the model to become smoother and faster. Also usage of weight decay increments the performance by adding regularization to the model. For the improvement of the input data, BartTokenizerFast is used for tokenization. The paper concludes with the comparison of the proposed optimization strategy with previously published models. The effectiveness is evaluated using a ROUGE score which is giving similarity between two documents. Experiments indicate that the fine-tuned BART model proposed achieves significant improvement in ROUGE scores on Amazon reviews data.