Miracle-dz/UniFAR-FTUs huggingface dataset:
Speckle noise reduction for optical coherence tomography images via non-local weighted group low-rank representation

In this work, a non-local weighted group low-rank representation (WGLRR) model is proposed for speckle noise reduction in optical coherence tomography (OCT) images. It is based on the observation that the similarity between patches within the noise-free OCT image leads to a high correlation between them, which means that the data matrix grouped by these similar patches is low-rank. Thus, the low-rank representation (LRR) is used to recover the noise-free group data matrix. In order to maintain the fidelity of the recovered image, the corrupted probability of each pixel is integrated into the LRR model as a weight to regularize the error term. Considering that each single patch might belong to several groups, and multiple estimates of this patch can be obtained, different estimates of each patch is aggregated to obtain its denoised result. The aggregating weights are exploited depending on the rank of each group data matrix, which can assign higher weights to those better estimates. Both qualitative and quantitative experimental results on real OCT images show the superior performance of the WGLRR model compared with other state-of-the-art speckle removal techniques.
Using the DREAM Methodology for Course Assessment in the Field of ICT-Enabled Education for Sustainability

This study explores the application of the DREAM methodology for course assessment in three South East Asian universities aiming to embed sustainability and sustainable development goals (SDGs) in multiple academic disciplines enabled by information and communication technologies (ICTs). A mixing of content and thematic analysis was used, which aligns with the underpinning philosophy of the Diagnosing, Reviewing/Reflecting, Explaining, Assessing, Managing (DREAM) methodology. The DREAM methodology integrates five processes, starting from diagnosing, to reviewing/reflecting, explaining, assessing, and, finally, managing. Results show that merging semantic and latent themes has contributed to uncovering what messages students’ narratives convey and provided a space for focusing both on the surface and explicit meanings of the data as well as on theory building and policy making. They also show the effectiveness of the DREAM methodology in constructing new knowledge and generating meaningful interpretations and suggestions to teacher educators and other academic teaching staff, as well as higher education institutions’ policymakers and planners.
Role of labeling mediation in speech perception: Evidence from a voiced stop continuum perceived in different surrounding sound contexts

The theory of categorical perception of speech sounds traditionally suggests that speech sound discrimination is conducted based on phonemic labeling, which is an abstract speech representation that listeners are hypothesized to have. However, recent research has found that the impact of labeling on perception of an English / /–/l/ contrast may depend on surrounding sound contexts: the effects of phonemic labeling may disappear when the speech sounds to be discriminated are presented in a sentence. The purpose of the present research is to investigate (1) the effects of the sound contexts on categorical perception of speech sounds, and (2) cross linguistic extensibility of such an effect. The experiments employed a Japanese voiced stop consonant continuum, i.e., /ba/– /da/, and tested discrimination of sounds on the continuum by native speakers of Japanese. Experiment 2 in particular investigated whether sounds on such a continuum are discriminated in accordance with the labeling when the sound in question is inserted into a sentence. Through experiments, the cross linguistic effects of surrounding sound contexts are found although there may be some exceptional cases. The research proposes reconsideration of the role of labeling mediation in speech perception.
Assessment of Diet Quality in Children and Adolescents with Overweight or Obesity in Greece

The adoption of healthy nutritional habits constitutes one of the most important determinants of healthy growth and development in childhood. Few studies in Greece have examined children’s diet quality using diet indices. The present study aimed to assess the diet quality of a large cohort of children and adolescents with overweight or obesity. Study participants (n = 1335), aged 2–18, were recruited through the Out-patient Clinic for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence, Aghia Sophia Children’s Hospital, Athens, Greece. Anthropometric, socio-demographic, and behavioral data were collected using standard methods and equipment. The Diet Quality Index (DQI), which includes four subcomponents (i.e., dietary diversity, dietary quality, dietary equilibrium, and meal index), was calculated to assess each subject’s diet quality. According to the results of this study, children’s total DQI score was 63.1%. It was observed that 66.7% of the children had at least moderate diet quality (total DQI ≥ 59.34%). Boys had higher values of the total DQI and certain components of the DQI (i.e., dietary equilibrium score and meal index) compared to girls. Three out of ten children with overweight/obesity had poor diet quality (i.e., DQI ≤ 59.33). Younger children (2–5 years old) were found to have the lowest values of dietary equilibrium compared to older children (6–9 and 12–18 years old). Moreover, boys had higher values of the total DQI score and of specific components of this index (i.e., dietary equilibrium and meal index) compared to girls. Children living in urban areas had higher values in the dietary quality score compared to those living in rural areas. Children with overweight had higher values of the dietary quality score and the total DQI score compared to children with obesity. The present study highlighted that children and adolescents with overweight or obesity have poor diet quality. Multilevel and higher intensity interventions should be designed specifically for this group to achieve tangible outcomes.
A Review Based On Classifying Regular And Improper Gastrointestinal Waves Using CNN

Stomach motility problems are associated with abnormal slow movements in the stomach. Human studies have documented normal and pathological slow wave propagation. In this study, multiple gastrointestinal electrocardiograms from electrogastrograms were used to distinguish between normal and abnormal patterns (EGG). Methods: Human stomach and abdomen models were obtained from computed tomography. The simulated stomach surface slow wave does not include normal and pathological slow waves. The advanced model diffuses the existing dipoles on the stomach surface to virtual electrodes in the stomach. They developed a deep convolutional network (CNN) armature to distinguish between normal and abnormally slow signals from multiple generators. We investigated how parameters such as moving electrode array position, particle size, body mass index (BMI) and signal-to-noise ratio (SNR) affect work. Using the progression to extract spatial information, we estimate the performance of our deep CNNs compared to direct classifiers. The results are better than 90 for all SNRs above 0dB, with vertical offsets at 3 cm, vertical offsets at 6 cm, and navel towel depth at 6 cm, demonstrating the strength of the depth of the central distribution capabilities of the CNN. SNR, electrode location and BMI are more likely for isolated lines. Changes in electrode array position, small groups, body mass index (BMI), weak signal-to-noise ratio (SNR), and. We compared the performance of deep CNNs with random operators using spatial wavelet emitting data. In conclusion, this study using high-resolution EGG data is the first to successfully use a deep CNN to distinguish between healthy and abnormal gastrointestinal slow motion. These results suggest the use of gastrointestinal skin data as a multidisciplinary health screening tool for gastrointestinal disorders.
Domain decomposition methods for coupled Stokes-Darcy flows

This thesis studies the numerical methods for coupled Stokes-Darcy problem. It consists of three major parts: First, a non-overlapping domain decomposition method is presented for Stokes-Darcy problem by partitioning the computational domain into multiple subdomains, upon which families of coupled local problems of lower complexity are formulated. The coupling is based on appropriate interface matching conditions. The global problem is reduced to an interface problem by eliminating the interior subdomain variables, which can be solved by an iterative procedure. FETI approach is used for floating Stokes subdomains. The condition number of the resulting algebraic system is analyzed and numerical tests on matching grids verifying the theoretical estimates are provided. Second, a multiscale flux basis algorithm is developed based on the domain decomposition with multiscale mortar mixed finite element method. The algorithm involves precomputing a multiscale flux basis, which consists of the flux (or velocity trace) response from each mortar degree of freedom. It is computed by each subdomain independently before the interface iteration begins. The subdomain solves required at each iteration are substituted by a linear combination of the multiscale basis. This may lead to a significant reduction in computational cost since the number of subdomain solves is fixed, depending only on the number of mortar degrees of freedom associated with a subdomain. Several numerical examples are carried out to demonstrate the efficiency of the multiscale flux basis implementation. Third, a multiscale flux basis implementation is presented for coupled Stokes-Darcy flows with stochastic permeability, with its log represented as a sum of local Karhunen-Lo\`{e}ve expansions. The problem is approximated by stochastic collocation on either a tensor product or a sparse grid, coupled with multiscale mortar mixed finite element method using non-overlapping domain decomposition for the spatial discretization. Two algorithms based on deterministic or stochastic multiscale flux basis are introduced. Some numerical tests are presented to illustrate the performances of these algorithms, with the stochastic multiscale flux basis showing a great advantage in computational cost among all.
Make Rental Reliable: Blockchain-Based Network Slice Management Framework with SLA Guarantee

To provide customized and high-quality network services under limited network resources, the 5G introduces the network slicing technology that divides physical networks into several logically independent virtual networks, improving the performance of network utilization. The slice management should satisfy the chief concerns of network operators and slice tenants who are the two most important participants, i.e., slice allocation for operators and Service Level Agreement (SLA) guarantee for tenants. However, for slice allocation, traditional centralized schemes cannot well support multi-operator slicing due to the lack of trust. And for SLA guarantee, existing solutions only provide global SLA based on game theory but cannot handle each dispute between operators and tenants. To solve the problems, we propose a blockchain-based network slice management framework consisting of a slice committee and three protocols: slice, audit, and dispute. With the help of the decentralization and reliability of blockchain, the proposed scheme achieves collaborative slice management among multiple operators with SLA guarantee. Through security and performance analysis, we prove that the proposed scheme can defend against possible dishonest behaviors of entities in the system, and is practical in terms of performance.
EVALUATING NONLINEAR CROSSED RANDOM EFFECTS MODELS FOR COMPARING TEMPERATURE OF FEEDING PIGS UNDER DIFFERENT THERMAL ENVIRONMENTS

The thermal environment plays a large role in an animal’s ability to convert feed into weight gain. A better understanding of a pig’s metabolism will help swine producers select environmental specifications for optimizing feed conversion. The objectives of this study are to 1) characterize the thermoregulatory responses of pigs during a feeding event 2) compare those responses for three thermal environmental treatments applied in a Latin Square design 3) investigate different procedures for fitting nonlinear mixed-effect models with crossed random effects (NLME function in R, %NLINMIX macro in SAS, random effects modeling in AD Model Builder: ADMB-RE). We found that the threeparameter first-order compartment model provides a reasonable representation of the tympanic temperatures of feeding pigs during feeding events. The thermal environmental treatments (28oC + High air speed) and (18oC + Low air speed) are significantly different from the reference treatment (28oC + Low air speed), at the 5% level. Both NLME and ADMB-RE successfully fit the nonlinear mixed-effects model and produce similar results. The %NLINMIX macro did not converge unless restrictions were placed on the model. The estimates of fixed and random effects from the restricted model using %NLINMIX macro were generally different from those from NLME and ADMB-RE.
Multisensor Temporal Unsupervised Domain Adaptation for Land Cover Mapping With Spatial Pseudo-Labeling and Adversarial Learning

With the huge variety of Earth observation satellite missions available nowadays, the collection of multisensor remote sensing information depicting the same geographical area has become systematic in practice, paving the way to the further breakthroughs in automatic land cover mapping with the aim to support decision makers in a variety of land management applications. In this context, along with the increase in the volume of data available, the availability of ground-truth (GT) data to train supervised models, which is usually time-consuming and costly, may even be more critical. In this scenario, the possibility to transfer a model learned on a particular time span (source domain) to a different period of time (target domain), over the same geographical area, can be advantageous in terms of both cost and time efforts. However, such model transfer is challenging due to different climate, weather, or environmental conditions affecting remote sensing data collected at different time periods, resulting in possible distribution shifts between the source and target domains. With the aim to cope with the multisensor temporal transfer scenario in the context of land cover mapping, where multitemporal and multiscale information are used jointly, we propose a Multisensor, Multitemporal, and Multiscale SPatially Aware Domain Adaptation (M3SPADA) framework, a deep learning methodology that jointly exploits self-training and adversarial learning to transfer a multisensor land cover classifier (MSLCC) from a time period (year) to a different one on the same geographical area. Here, we consider the case in which each domain (source and target) is described by a pair of remote sensing datasets: a satellite image time series (SITS) of optical images and a single very high spatial resolution (VHR) scene. Experimental evaluation on a real-world study case located in Burkina Faso and characterized by operational constraints shows the quality of our proposal to deal with the temporal multisensor transfer in the context of land cover mapping.
Robust rotation search in computer vision

School of Computer Science Doctor of Philosophy Robust Rotation Search in Computer Vision by Álvaro Joaqúın Parra Bustos Rotation search is a fundamental problem which has significant importance in geometric computer vision. In many practical settings, data or measurements for rotation search are usually contaminated with large errors, leading to the existence of outliers in the data. As a consequence, traditional least-squares rotation estimation methods are not suitable in many practical applications. A more appropriate approach is to search for the rotation based on a robust criterion. However, optimisation problems involving robust criteria are hard to solve since the objective functions are usually non-differentiable and non-convex. This thesis makes several fundamental contributions in robust rotation search. In contrast to approximations or local methods that are typically used by current practitioners, the presented methods in this thesis guarantee global optimality. The main challenge for robust rotation search algorithms is to find an optimal result in reasonable time (to be practical in out-of-lab applications). The work in this thesis is a contribution in this direction. To efficiently solve robust rotation search, several strategies are presented based on new insights into the geometry of rotations, from the perspective of global optimisation. Firstly, for point set registration on horizontally levelled data, the presented algorithms make it possible to globally find the best rotation in real-time. Secondly, for the fully unconstrained 3D rotation search problem, the presented algorithms outperform previous methods by an order of magnitude. The final contribution of this thesis is an algorithm to safely remove true outliers when rotation is computed on outlier contaminated point correspondences. Substantial speed-up can be obtained when the proposed outlier removal is used as a preprocessor to globally optimal algorithms. Since no inliers are discarded, global optimality is guaranteed. The contributions in this thesis can impact on computer vision problems where rotation search is invoked as a subroutine. This thesis presents examples from 3D point cloud registration and image stitching.
ENTREPRENEURIAL ELEMENTS IN RURAL TOURISM - FINDINGS FROM PLS-SEM

The aim of this paper is to determine the interrelationship between innovation, proactivity and risk-taking, as a dimension of entrepreneurial orientation and their connection with relational capital, in order to indicate the entrepreneurial behavior that gives the best results for the development of sustainable rural tourism. The research was conducted on the basis of questionnaires collected on the territory of the Republic of Serbia during the year 2022. The data were analyzed using the PLS-SEM method. The results show a positive relationship between proactivity and innovation, between relational capital and innovation, and proactivity and relational capital, while a positive relationship between risktaking and innovation and risk-taking and relational capital was not confirmed. Results provide important elements for making decisions about innovation and competitiveness of rural tourism destinations.
Classification of ISAR Ship Imagery Using Transfer Learning

Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterize and classify non-cooperative targets. Traditional classification approaches use geometric features extracted from the images of known targets to form a training dataset that is later used to classify an unknown target. While these approaches work reasonably well, deep learning-based techniques have demonstrated significant improvements over conventional processing schemes in many areas of radar. However, the application of ISAR image classification is difficult when there are only small training datasets available. In this article, we address the small dataset problem by utilizing transfer learning. Different approaches are considered that can take advantage of the ship aspect angle to improve the overall stability and improve the final classification result. The new classification results are then compared with a traditional classification approach and a simple three-layer convolutional neural network. In addition, to better understand how the neural networks are working, saliency maps are used to visualize the trained network.
Nonparametric kernel methods for curve estimation and measurement errors

Abstract We consider the problem of estimating an unknown density or regression curve from data. In the parametric setting, the curve to estimate is modelled by a function which is known up to the value of a finite number of parameters. We consider the nonparametric setting, where the curve is not modelled a priori. We focus on kernel methods, which are popular nonparametric techniques that can be used for both density and regression estimation. While these methods are appropriate when the data are observed accurately, they cannot be directly applied to astronomical data, which are often measured with a certain degree of error. It is well known in the statistics literature that when the observations are measured with errors, nonparametric procedures become biased, and need to be adjusted for the errors. Correction techniques have been developed, and are often referred to as deconvolution methods. We introduce those methods, in both the homoscedastic and heteroscedastic error cases, and discuss their practical implementation.
Hegemony and authority in health: The World Health Organization in Indonesian online news coverage on COVID-19 pandemic

The idea of hegemony and authority in language practice do exist in the development of linguistics and critical discourse studies. It attracts great attention at any time, moreover during time of crisis, such as the COVID-19 Pandemic. This study uncovers the realization of the hegemony and authority of the World Health Organization (WHO) in the coverage of the COVID-19 Pandemic by online Indonesian media. More specifically, the focus is on the representation of the hegemony and authority of the WHO and their linguistic realization in Indonesian news discourse. The study used the van Dijk’s framework of Critical Discourse Studies to examine five news reports from five Indonesian media related to the WHO stance and standards in the handling of the COVID-19 Pandemic. The study employed van Dijk’s macrostructure analysis to uncover the surface of the news; and Halliday’s transitivity as the microstructure analysis. Overall, the study found that the online Indonesian media coverage on COVID-19 Pandemic automatedly reinforced and maintained the power and the authority of the WHO. On the one hand, the fact is beneficial for the readers since they will receive coverage with reputable sources. On the other hand, there is a potential that the WHO’s power and authority can be misused to enhanced some parties’ interests, for instance in promoting one’s vaccine brand.
Work Stress and Personal and Relational Well-Being Among Chinese College Teachers: The Indirect Roles of Sense of Control and Work-Related Rumination

Purpose The association between work stress and well-being has been well documented. However, the underlying mechanism for such association is not clear, especially in terms of how work stress relates to both personal and relational well-being. Based on the Conservation of Resources Theory and the Stress Process Model, the present study examined the potential indirect roles of the sense of control and the work-related rumination in the associations between work stress and both personal and relational well-being. Methods Data were collected from 536 married Chinese university teachers (Mage = 39.40 + 7.64, 38.6% males) through an online survey. Analyses were conducted using structural equation modeling via Mplus. Results Work stress was indirectly associated with life satisfaction through (a) sense of control, (b) work-related rumination, and (c) a sequential pathway from sense of control to work-related rumination. Work stress was indirectly associated with relationship satisfaction through sense of control. Conclusion Findings suggest that sense of control would be an important linking mechanism underlying the association between work stress and college teachers’ well-being. Personal well-being may be more vulnerable to work-related rumination than relational well-being. Insights for prevention and intervention efforts in enriching college teachers’ well-being are discussed.