Miracle-dz/UniFAR-FTUs huggingface dataset:
When Eyes Tell a Story … An Eye-Tracking Approach Towards Creating a Fit-for-Purpose Learning Management System for Higher Education

Learning Management System is a vital organ of any university that exists in today's digital era. Whilst these platforms are meant to promote quality teaching, learning and assessment, developers and designers often ignore learner's visual behavior when implementing or maintaining these applications. Consequently, most of these applications appear as poorly designed and populated by disorganized content- eventually provoking tedious navigations and interactions at the learner's end. Xi'an Jiaotong-Liverpool University uses Learning Mall to organize coursework, assessment and facilitate staff-student interactions. However, there is an absence of evidence whether any usability study on the Learning Mall, vis-à-vis learners' gaze behavior and visual attention, has ever been carried out at the University. This paper reports an ongoing study which is at the very preliminary stage, and linked with Eye-tracking, Participatory Design and Mixed Methods approaches to bring learner-centered improvements in the Learning Mall.
Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach

Free-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records. We analyzed the Pedianet database, which includes data from January 2004 to August 2017. The model categorizes narratives from clinical record diagnoses into six types: no otitis, non-media otitis, non-acute otitis media (OM), acute OM (AOM), AOM with perforation, and recurrent AOM. Utilizing deep learning architectures, including an ensemble model, this study addressed the challenges associated with the manual classification of extensive narrative data. The performance of the model was evaluated according to a gold standard classification made by three expert clinicians. The ensemble model achieved values of 97.03, 93.97, 96.59, and 95.48 for balanced precision, balanced recall, accuracy, and balanced F1 measure, respectively. These results underscore the efficacy of using automated systems for medical diagnoses, especially in pediatric care. Our findings demonstrate the potential of deep learning in interpreting complex medical records, enhancing epidemiological surveillance and research. This approach offers significant improvements in handling large-scale medical data, ensuring accuracy and minimizing human error. The methodology is adaptable to other medical contexts, promising a new horizon in healthcare analytics.
Efficient Frequency and Time-Domain Simulations of Delayed PEEC Models With Proper Orthogonal Decomposition Techniques

The Partial Element Equivalent Circuit (PEEC) method has gained significant recognition as an electromagnetic computational technique known for its ability to represent electromagnetic phenomena using equivalent circuits. This feature makes it particularly valuable for addressing mixed EM-circuit problems. However, PEEC models often exhibit large dimensions, necessitating modeling techniques that can effectively reduce their size while preserving accuracy. Model order reduction (MOR) serves as a highly effective approach to accomplish this objective. This paper presents two MOR techniques based on proper orthogonal decomposition (POD) for PEEC models described by neutral delayed differential equations (NDDEs). The unique characteristics of NDDEs demand specialized MOR approaches, as their formulation is inherently more complex compared to standard quasi-static PEEC models described by non-delayed differential equations. In addition to a traditional one-shot singular value decomposition (SVD), this paper also presents an incrementally computed SVD to evaluate the orthogonal matrix needed to generate the reduced order matrices. The accuracy and efficiency of the proposed PEEC-MOR methods are demonstrated through multiple relevant numerical results in both the frequency-domain and time-domain.
Ultrasonic Characterization of Ethylene Vinyl Acetate (EVA) Crosslinking for Quality Assurance and Lamination Process Control

To increase throughput and reduce cost, module manufacturers seek to minimize lamination processing times. However, too short lamination result in poor Ethylene-Vinyl Acetate (EVA) properties due to unfinished crosslinking and a high content of residual aggressive reaction starters in the completed module. These modules are prone to aging, delamination, mechanical fatigue, corrosion, and yellowing, severely reducing long-term reliability. Identification of unfinished crosslinking is crucial for lamination process surveillance, but since material and process conditions vary locally within the module (and over time), destructive characterization techniques (e.g. differential scanning calorimetry (DSC), Soxhlet extraction or peel testing) on some selected samples only provide limited insight. For that reason, we propose a non-destructive method that uses ultrasound to characterize crosslinking within the solar module. High-frequency longitudinal ultrasound is transmitted through the thickness of the solar module and analyzed. By knowing the thickness of the polymer, the speed of sound and the frequency-dependent ultrasonic attenuation, the degree of crosslinking can be determined. In this paper, we demonstrate an experimental correlation between the DSC degree of crosslinking and the speed of sound, as well as the frequency-dependent attenuation coefficient. After an initial drop in velocity during initial melting (or humidity evaporation), sound velocity and attenuation increased with curing duration. This correlation can be utilized to determine the degree of crosslinking. However, a calibration of the material-specific model with conventional methods is required (e.g. via DSC analysis). The proposed ultrasonic method then allows a non-destructive characterization of the module lamination process, capable of detecting inhomogeneities and deficiencies in crosslinking, to ultimately drive process optimizations, increase production yield and reduce the LCOE.
Spectral–Spatial–Language Fusion Network for Hyperspectral, LiDAR, and Text Data Classification

The fusion classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has gained widespread attention because of its ability to obtain more comprehensive spatial and spectral information. However, the heterogeneous gap between HSI and LiDAR data also adversely affects the classification performance. Despite the excellent performance of traditional multimodal fusion classification models, language information containing much linguistic priori knowledge to enrich visual representations needs to be addressed. Therefore, we design a spectral–spatial–language fusion network (S2LFNet), which can fuse visual and language features to broaden the semantic space using linguistic priori knowledge commonly shared between spectral features and spatial features. First, we propose a dual-channel cascaded image fusion encoder (DCIFencoder) for visual feature extraction and progressive feature fusion of different levels for HSI and LiDAR data. Then, three aspects of text data are designed to extract linguistic priori knowledge using the text encoder. Finally, contrastive learning is utilized to construct a unified semantic space, and spectral–spatial–language fusion features are obtained for classification tasks. We evaluate the classification performance of the proposed S2LFNet on three datasets through extensive experiments, and the results show that it outperforms the state-of-the-art fusion classification methods.
Tracking Control of Shape Memory Alloy Artificial Wrist Joint Based on Generalized Prandtl-Ishlinskii Inverse Model

Shape memory alloys (SMAs) are renowned for their exceptional properties. SMAs can undergo material crystal phase transitions in response to specific temperature variations, allowing them to regain their intended shape and overcome external stresses. However, the nonlinear hysteresis feature within the SMA actuators hinders accurate control of their behavior. In this paper, we propose an approach to construct an inverse hysteresis model based on a detailed examination of the principles underlying the phenomenological GPI model. Using the inverse model as a feedforward control mechanism, nonlinear plants can be approximately linearized through mathematical calculations, leading to improved controllability and superior performance. To further enhance control, we introduce a classical PID feedback controller as a complement to the GPI inverse model, forming a compound control methodology. In addition, we conduct displacement tracking experiments on an artificial wrist joint platform to gain a deeper understanding of the governing principles of SMAs.
Laser Additive Manufacturing System Modification for High-Level Entropy Alloys

The surface properties of aerospace components and coatings must be excellent throughout various temperatures. Steel, aluminum, copper, and high-level entropy alloys were employed to improve the external features of these components. This research used laser additive manufacturing to create functional high-level entropy alloys on an austenitic stainless-steel baseplate and to measure the influence of the laser parameters and density on the hardness and morphology of the produced alloys aimed at aerospace industries. Microstructure, as well as hardness, were studied as a function of experimental factors, laser power as well as scan speed. The data revealed that the coatings predictably stuck to the baseplate. Next to a preheat temperature of 400 °C, the best processing parameters, including individual alloys with defects of free structures, were 1200-1600W, 8–12 mm/s, and layers consisting of face-centered cubic and body-centered cubic structure phases. The quality, as well as the hardness of the belongings of alloys, were affected via the experimental factors. According to the findings, enhanced laser settings by increasing the preheating temperature increased the performance of alloys with prospective coatings and structural materials.
Integrated Earthquake Catalog of the Ossetian Sector of the Greater Caucasus

This article is the continuation of a study by authors to create the most complete and representative earthquake catalogs with a unified magnitude scale. The catalog created of the Ossetian sector of the Greater Caucasus (the territory of the Republic of North Ossetia–Alania and adjacent areas) was formed by the aggregation of all available data from Soviet, modern Russian, and Georgian catalogs, as well as the data from the International Seismological Centre. The integration was carried out using the author’s approach based on the modified nearest neighbor method. The integrated catalog of the Ossetian sector of the Greater Caucasus contains 16,285 events for the period 1962–2022. For all events, magnitude estimates are reduced to a unified “proxy-MW” scale. The integration of data from various sources made it possible to significantly replenish the beginning of the aftershock sequence of the Racha earthquake with MW = 7.0, which occurred on 29 April 1991. There has been a change in the level of registration over time. Thus, there is a significant lack of events for the periods 1967–1970 and 1988–1991; starting from 1995, the catalog is complete for magnitude 3.2, and since 2005 for magnitude 2.2. The integration of Soviet and modern Russian and Georgian catalogs made it possible to significantly increase the completeness and representativeness of seismic events in the studied Ossetian sector of the Greater Caucasus. This once again demonstrates both the fundamental importance of merging seismic data from global, national, and regional catalogs and the effectiveness of the author’s developed method.
ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields

In this paper, we introduce ASUR3D, a novel methodology for the arbitrary-scale upsampling of 3D point clouds employing Local Occupancy Representation. Our proposed implicit occupancy representation enables efficient point classification, effectively discerning points belonging to the surface from non-surface points. Learning an implicit representation of open surfaces, enables one to capture the better local neighbourhood representation, leading to finer refinement and reconstruction with enhanced preservation of intricate geometric details. Leveraging this capability, we can accurately sample an arbitrary number of points on the surface, facilitating precise and flexible upsampling. We demonstrate the effectiveness of ASUR3D on PUGAN and PU1K benchmark datasets. Our proposed method achieves state-of-the-art results on all benchmarks and for all evaluation metrics. Additionally, we demonstrate the efficacy of our methodology on self-proposed heritage data generated through photogrammetry, further confirming its effectiveness in diverse scenarios. The code is publicly available at https://github.com/Akash-Kumbar/ASUR3D.
Development of Passive Liveness Detection System Based on Deep Learning LivenessNet to Overcome Face Spoofing

This paper is focused on developing a real-time and robust passive liveness detection system to overcome a consequential challenge on existing face recognition systems, face spoofing. Our passive liveness detection system is a web-based system that is able to classify between real and 2D spoofing facial images. Our system consists of face detection and liveness detection process. For face detection, we conduct a comparative evaluation of pre-trained models to obtain the best-performing model. For liveness detection, we use LivenessNet, a lightweight Convolutional Neural Network (CNN) architecture. Using LivenessNet, we conduct an experimental evaluation of its hyperparameters value to obtain the most accurate model. The final result of our system can achieve 97.17% validation accuracy, 0% False Positive Rate, 5.68% False Negative Rate, and an inference time of 0.25 ms per frame.
A Novel Framework for 2.5-D Building Contouring From Large-Scale Residential Scenes

This paper introduces a novel methodology for residential building contouring from large-scale airborne point clouds. Unlike other methods that handle linearization and regularization of the linear primitives separately by imposing rigid constraints, we propose an optimization-based linearization and global regularization to form accurate, topologically error-free, and lightweight polygons. To this end, we enhance the classic density-based spatial clustering of applications with noise algorithm to segment individual building entities at the instance level. The initial contours of each individual building are then delineated and further decomposed by a novel topologically aware propagation process and a global optimization technique. The decomposed linear primitives are fed into the global regularization step, from which the regular shapes are learned and enforced hierarchically by imposing constraints, such as parallelism, homogeneity, orthogonality, and collinearity. Based on the concept of hybrid representation, the regularized and unaltered linear primitives are jointly connected in an esthetic way. Various experiments using representative buildings and large-scale residential scenes from the Dutch AHN3 data set have shown that the proposed methodology generates meaningful building contouring representation in terms of accuracy, compactness, topology, and levels of detail abstraction while being robust and scalable.
Development of Poly(sorbitol adipate)-g-poly(ethylene glycol) Mono Methyl Ether-Based Hydrogel Matrices for Model Drug Release

Hydrogels were prepared by Steglich esterification and by crosslinking pre-synthesized poly(sorbitol adipate)-graft-poly(ethylene glycol) mono methyl ether (PSA-g-mPEG) using different-chain-length-based disuccinyl PEG. PSA and PSA-g-mPEG were investigated for polymer degradation as a function of time at different temperatures. PSA-g-mPEG hydrogels were then evaluated for their most crucial properties of swelling that rendered them suitable for many pharmaceutical and biomedical applications. Hydrogels were also examined for their Sol-Gel content in order to investigate the degree of cross-linking. Physical structural parameters of the hydrogels were theoretically estimated using the modified Flory–Rehner theory to obtain approximate values of polymer volume fraction, the molecular weight between two crosslinks, and the mesh size of the hydrogels. X-ray diffraction was conducted to detect the presence or absence of crystalline regions in the hydrogels. PSA-g-mPEG hydrogels were then extensively examined for higher and lower molecular weight solute release through analysis by fluorescence spectroscopy. Finally, the cytotoxicity of the hydrogels was also investigated using a resazurin reduction assay. Experimental results show that PSA-g-mPEG provides an option as a biocompatible polymer to be used for pharmaceutical applications.
Geometric Transformation: Tactile Data Augmentation for Robotic Learning

In recent years, the popularity of robotic tactile learning has grown with data-driven approaches being widely utilized for various tasks. It is generally acknowledged that a large and high quality dataset is critical for a data-driven approach. However, collecting such a dataset is challenging and time consuming with a real robot and tactile sensors. Instead, data augmentation techniques are widely used in visual-based learning because it could increase the size/quality of the dataset and improve the learning performance. In this letter, by considering properties of tactile data, we propose geometry transformation tactile data augmentation methods by adopting conventional image data augmentation methods. Our proposal is tested on two different tactile learning tasks, slip prediction and object recognition on a real robot. Two datasets are collected and our datasets are publicly available. The results show that our proposal could improve the learning accuracy additionally by up to 8.43%. Furthermore, our proposal outperforms conventional image data augmentation methods on tactile learning task overall.
Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network

This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other machine learning (ML) classifier models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC). The 1DCNN architecture consists of 4 major parts, a Convolutional Layer, a Flattened Dense Layer, a Max Pooling Layer and a Fully Connected Multilayer Perceptron (MLP), with 1 Hidden Layer and a SoftMax output. The model repeatedly learns how to better extract prominent features from one-dimensional data and map it to the MLP for increased prediction. Training and validation are achieved using pre-processed time-series ECG signals captured from 35 ECG recordings. Using our unique windowing strategy, the data is shaped into 5 datasets of different window sizes. A total of 15 models (5 for each group, 1DCNNs, RFCs, SVMs) were evaluated using various metrics, with each being run over numerous experiments. Results show the 1DCNN-500 model delivered the greatest degree of accuracy and rapidity in comparison to the best producing RFC and SVM classifiers. 1DCNN-500 (Sensitivity 0.9743, Specificity 0.9708, Accuracy 0.9699); RFC-500 (Sensitivity/Recall (0) 0.90 / (1) 0.94, Precision (0) 0.94 / (1) 0.90, Accuracy 0.91); SVM-500 (Sensitivity (0) 0.94 / (1) 0.50, Precision (0) 0.65 / (1) 0.90, Accuracy 0.72). The model presents a novel approach that could provide support mechanisms in clinical practice to promptly diagnose patients suffering from OSA.
Severe Post-COVID-19 Condition after Mild Infection: Physical and Mental Health Eight Months Post Infection: A Cross-Sectional Study

Severe acute COVID-19 infections requiring intensive care treatment are reported risk factors for the development of post-COVID-19 conditions. However, there are also individuals suffering from post-COVID-19 symptoms after mild infections. Therefore, we aimed to describe and compare the health status of patients who were initially not hospitalized and patients after critical illness due to COVID-19. The outcome measures included health-related quality of life (EQ-5D-5L, visual analogue scale (VAS)); mental health (hospital anxiety and depression scale (HADS)); general disability (WHODAS-12); and fatigue (Fatigue-Severity-Scale-7). Individuals were recruited at Schoen Clinic Bad Aibling, Germany. A total of 52 non-hospitalized individuals (47 ± 15 years, 64% female, median 214 days post-infection) and 75 hospitalized individuals (61 ± 12 years, 29% female, 235 days post-infection) were analyzed. The non-hospitalized individuals had more fatigue (87%) and anxiety (69%) and a decreased health-related quality of life (VAS 47 ± 20) compared to the hospitalized persons (fatigue 45%, anxiety 43%, VAS 57 ± 21; p < 0.010). Severe disability was observed in one third of each group. A decreased quality of life and disability were more pronounced in the females of both groups. After adjusting for confounding, hospitalization did not predict the burden of symptoms. This indicates that persons with post-COVID-19 conditions require follow-up services and treatments, independent of the severity of the acute infection.
Build Energy-Efficient GPU Computing Environment for Machine Learning Algorithms with Register File Packing Technique

Popular machine learning algorithms built with a mass of matrix multiplications can be well paralleled and the GPUs are desirable computing environment for these applications. However, the energy consumption on GPUs becomes a big concern which prevents the further performance increases for machine learning algorithms. In this work, we aim to build an energy-efficient GPU computing environment for famous machine learning algorithms with a GPU register file management theory named narrow width operand packing. First, we observed that RF occupancies of modern machine learning algorithms are relatively low leaving a great waste of GPU's RF leakage energy. Second, we found that the data maintained by the RF contains a large fraction of narrow-width operands for machine learning algorithms. We proposed to pack multiple narrow width operands to a single register. After the register packing, the RF occupancies can be further reduced. Finally, we attempt to save both static and dynamic energy consumption of GPU's RF by smartly shutting down the unused portion of the RF. We evaluated the energy reduction of this GPU RF management with five state-of-the-art machine learning algorithms. The experimental results show that the register packing techniques achieve the total GPU energy consumption reduction, up to 14.14% and 10.71 % on average.