Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-shape and Multi-scale Edge Detection Algorithm Based on Mathematical Morphology

In the process of edge detection based on mathematical morphology,the structural elements of different shapes/scales play different roles in noise filtering and keeping edge details intact.An adaptive edge detection algorithm based on multi-shape/scale structural elements was therefore proposed,where the image edge was extracted using different directions/sizes structural elements.Then,the weight factors were determined adaptively by computing the information entropy so as to integrate the edges detected by multi-shape and multi-scale structural elements.Experimental results showed that the proposed algorithm can suppress the interference of noise more effectively in comparison with several classical edge detection algorithm,thus improving the detection accuracy and robustness of different images.
A Semi-Supervised Speech Deception Detection Algorithm Combining Acoustic Statistical Features and Time-Frequency Two-Dimensional Features

Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the generalization ability of the widely used semi-supervised speech deception detection model worse. Because of this, this paper proposes a semi-supervised speech deception detection algorithm combining acoustic statistical features and time-frequency two-dimensional features. Firstly, a hybrid semi-supervised neural network based on a semi-supervised autoencoder network (AE) and a mean-teacher network is established. Secondly, the static artificial statistical features are input into the semi-supervised AE to extract more robust advanced features, and the three-dimensional (3D) mel-spectrum features are input into the mean-teacher network to obtain features rich in time-frequency two-dimensional information. Finally, a consistency regularization method is introduced after feature fusion, effectively reducing the occurrence of over-fitting and improving the generalization ability of the model. This paper carries out experiments on the self-built corpus for deception detection. The experimental results show that the highest recognition accuracy of the algorithm proposed in this paper is 68.62% which is 1.2% higher than the baseline system and effectively improves the detection accuracy.
Prediction of Contaminated Areas Using Ultraviolet Fluorescence Markers for Medical Simulation: A Mobile Phone Application Approach

The use of ultraviolet fluorescence markers in medical simulations has become popular in recent years, especially during the COVID-19 pandemic. Healthcare workers use ultraviolet fluorescence markers to replace pathogens or secretions, and then calculate the regions of contamination. Health providers can use bioimage processing software to calculate the area and quantity of fluorescent dyes. However, traditional image processing software has its limitations and lacks real-time capabilities, making it more suitable for laboratory use than for clinical settings. In this study, mobile phones were used to measure areas contaminated during medical treatment. During the research process, a mobile phone camera was used to photograph the contaminated regions at an orthogonal angle. The fluorescence marker-contaminated area and photographed image area were proportionally related. The areas of contaminated regions can be calculated using this relationship. We used Android Studio software to write a mobile application to convert photos and recreate the true contaminated area. In this application, color photographs are converted into grayscale, and then into black and white binary photographs using binarization. After this process, the fluorescence-contaminated area is calculated easily. The results of our study showed that within a limited distance (50–100 cm) and with controlled ambient light, the error in the calculated contamination area was 6%. This study provides a low-cost, easy, and ready-to-use tool for healthcare workers to estimate the area of fluorescent dye regions during medical simulations. This tool can promote medical education and training on infectious disease preparation.
Attracting Effect of Pinpoint Auditory Glimpse on Digital Signage

Advertising signage installed in public spaces is intended to be seen by passersby to increase recognition. However, display blindness often occurs because people either assume that the displayed content is not relevant to them or the display blurs into their surroundings and they remain unaware of it. To improve advertisement recognition, it is necessary to solve display blindness. Although emitting a sound might be a solution to make passersby be aware of its presence, it would become noisy and make the space discomfort. We propose the use of a pinpoint auditory glimpse that stimulates a passerby to pay attention to signage by playing a pinpoint sound. Through acoustic measurements and participant experiments conducted in a pseudo-store, it was found that (1) the use of a pinpoint auditory glimpse can effectively trigger passersby to pay attention to signage and (2) the pinpoint auditory glimpse does not negatively impact purchasing behavior, despite emitting sound. This paper presents findings on a novel sound-based solution to the problem of display blindness.
FAST RANK ALGORITHMS WITH MULTISCALE HISTOGRAMS LAZY UPDATING 1

Rank algorithms propose good solutions for image smoothing and impulse noise removing. In our previous work on the subject we have proposed methods for fast computations of eV and KNV neighborhood average based on multiscale histograms. In this paper a new method for multiscale histograms fast updating is described and analyzed. Performance comparisons of the combined methods with fastest known median filtering algorithm are given. We have achieved the processing speed for eV and KNV neighborhood average algorithms only a few times slowly than the fastest known median filtering algorithm.
Learning from Adaptations to the COVID-19 Pandemic: How Teleconsultation Supported Cancer Care Pathways at a Comprehensive Cancer Center in Northern Italy

Simple Summary Multidisciplinary team (MDT) meetings are widely recognized as the gold standard for care management of cancer patients. During the pandemic, cancer care delivery was a priority to be maintained through cancer care pathways (CCPs) and MDT meetings, which were forcibly converted from in-person to telematic format. The aim of our retrospective study was to report the evolution of MDT meeting performance following the shift to teleconsultation by analyzing four MDT meeting indicators between 2019 and 2022. We observed that the MDT meeting teleformat strengthened the overall CCP performance by boosting the participation of MDT members and the number of discussed cases, all without compromising either the annual frequency or duration of MDT meetings. Considering the rapidity, extent, and intensity with which telematic tools have been adopted due to the COVID-19 pandemic, the results of this study help to understand the effects of these tools on health care and the parties involved. Abstract Multidisciplinary team (MDT) meetings are recognized as the gold standard for care management of cancer patients, and during the COVID-19 pandemic they were considered a priority to be maintained. Due to pandemic-related restrictions, MDT meetings were forcibly converted from in-person to telematic format. This retrospective study evaluated the annual performance of four MDT meeting indicators (MDT members’ attendance, number of discussed cases, frequency of MDT meetings, and duration) between 2019 and 2022 to report on the implementation of teleconsultation in MDT meetings related to 10 cancer care pathways (CCPs). Over the study period, MDT member participation and the number of discussed cases improved or did not change in 90% (9/10) and 80% (8/10) of the CCPs, respectively. We did not observe significant differences in any of the CCPs included in the study regarding the annual frequency and duration of MDT meeting. Considering the rapidity, extent, and intensity with which telematic tools were adopted due to the COVID-19 pandemic, the results of this study showed that MDT teleconsultation supported the CCPs, and consequently, the delivery of cancer care in COVID-19 times, helping to understand the effects of telematic tools on health care performance and the parties involved.
Analysis of a robust implicit scheme for space–time fractional stochastic nonlinear diffusion wave model

The current paper develops and analyzes a numerical scheme for the space–time fractional stochastic nonlinear diffusion wave equations. The implicit scheme is based on the matrix transform technique for discretizing the Riesz-space fractional derivative, -order approximation to the Caputo-fractional derivative in time and Taylor's series method to linearize the nonlinear source term, and has been successfully applied to solve a class of nonlinear fractional diffusion wave equation. We prove that the implicit scheme is convergent with β-order in space and order in time, respectively. The optimum error estimates in the temporal-spatial direction and unconditional stability of the implicit scheme have been theoretically investigated. Moreover, several specific numerical experiments confirm the consistency and high efficacy of the provided algorithms, which minimizes the computational costs.
Fast Two-Phase Image Deblurring Under Impulse Noise

In this paper,we propose a two-phase approach to restore images corrupted by blur and impulse noise.In the first phase,we identify the outlier candidates-the pixels that are likely to be corrupted by impulse noise.We consider that the remaining data pixels are essentially free of outliers.Then in the second phase,the image is deblurred and denoised simultaneously by a variational method by using the essentially outlier-free data.The experiments show several dB's improvement in PSNR with respect to the typical variational methods.
SELF-TUNING CONTROLLER FOR NEURAL NETWORK PREDICTIVE DEVIATION COMPENSATION BASED ON DAMPED LEAST SQUARE

In this paper, we propose a kind of neural network predictive deviation compensation self-tuning controller, using linear model to control nonlinear system. The predictive deviation's compensation is completed by neural network. The damped least square method was applied in both identification of the linear model and study of the neural network. The simulation results show that this kind of controller can control nonlinear system effectively and has small overshoot and good stability.
Spectral, modal and coherence analysis of sum and difference scattering of narrowband noise in turbomachinery

The phenomenon of sum and difference frequency scattering of narrowband noise as it interacts with ducted rotating turbomachinery is studied experimentally in this article. This phenomenon will affect the spectral and modal distribution of the noise downstream of rotating turbomachinery, which must be considered in order to optimise any acoustic mitigation methods installed inside the engine. In the experimental set-up, narrowband noise is generated by a loudspeaker located at the upstream end of the test duct which then propagates through a rotor stage. A microphone array located downstream of the rotor is used to examine the spectral and modal distribution of the acoustic pressure field at a single axial location. The results from this analysis are compared with pre-existing theory which had not previously been experimentally validated in the literature. This theory may be used to predict the modal content of the scattered noise. A coherence analysis is also presented using microphones upstream and downstream of the rotor stage in order to assess whether sum and difference scattering is best predicted by a linear or non-linear model. The results from this study serve to enhance our current understanding of sum and difference scattering of narrowband noise through turbomachinery stages, and highlight interesting results from this interaction such as potential overlapping of incident and scattered noise which may otherwise be overlooked.
Rationalization of neural network training parameters on WWII poster classification example

Investigation of the possibility of studying the Bauhaus painting heritage with deep learning techniques. The efficiency of this approach is basically limited by the small size of the dataset, limited possibilities of using augmentation and significantly different nature of the images studied in comparison to typical datasets such as ImageNet. The example of classifying World War II posters using transfer learning and fine-tuning of the MobileNetV2 network illustrates the basic points regarding the use of deep learning to solve typical research problems in the Bauhaus heritage. The effects of augmentation, batch size, learning rate, and number of pre-training layers on the quality of learning are investigated. Practical recommendations useful to humanities researchers of art collections are presented.
Phylogeography and cohesion species delimitation of California endemic trapdoor spiders within the Aptostichus icenoglei sibling species complex (Araneae: Mygalomorphae: Euctenizidae)

Abstract Species delimitation is an imperative first step toward understanding Earth's biodiversity, yet what constitutes a species and the relative importance of the various processes by which new species arise continue to be debatable. Species delimitation in spiders has traditionally used morphological characters; however, certain mygalomorph spiders exhibit morphological homogeneity despite long periods of population‐level isolation, absence of gene flow, and consequent high degrees of molecular divergence. Studies have shown strong geographic structuring and significant genetic divergence among several species complexes within the trapdoor spider genus Aptostichus, most of which are restricted to the California Floristic Province (CAFP) biodiversity hotspot. Specifically, the Aptostichus icenoglei complex, which comprises the three sibling species, A. barackobamai, A. isabella, and A. icenoglei, exhibits evidence of cryptic mitochondrial DNA diversity throughout their ranges in Northern, Central, and Southern California. Our study aimed to explicitly test species hypotheses within this assemblage by implementing a cohesion species‐based approach. We used genomic‐scale data (ultraconserved elements, UCEs) to first evaluate genetic exchangeability and then assessed ecological interchangeability of genetic lineages. Biogeographical analysis was used to assess the likelihood of dispersal versus vicariance events that may have influenced speciation pattern and process across the CAFP's complex geologic and topographic landscape. Considering the lack of congruence across data types and analyses, we take a more conservative approach by retaining species boundaries within A. icenoglei.
Coupling of a High-Resolution Atmospheric Model and an Ocean Model for the Baltic Sea

Abstract The coupling between a high-resolution weather forecasting model and an ocean model is investigated. It is demonstrated by several case studies that improvements of short-range weather forecasting in the area of the Baltic Sea require an accurate description of the lower boundary condition over sea. The examples are taken from summer situations without sea ice as well as from winter situations with extreme sea ice conditions. It is shown that the sea state variables used in the model influence the weather forecast both directly on the local scale due to the local impact of surface fluxes of latent and sensible heat and on regional and larger scales. The convective snowbands during winters with cold airmass outbreaks over the open water surfaces of the Baltic Sea are extreme examples of the influence of sea state variables on a regional scale. It is furthermore demonstrated that the sea state conditions may change considerably within forecasting periods up to 48 h. This implies the necessary appli.
Deep Learning for Visual SLAM: The State-of-the-Art and Future Trends

Visual Simultaneous Localization and Mapping (VSLAM) has been a hot topic of research since the 1990s, first based on traditional computer vision and recognition techniques and later on deep learning models. Although the implementation of VSLAM methods is far from perfect and complete, recent research in deep learning has yielded promising results for applications such as autonomous driving and navigation, service robots, virtual and augmented reality, and pose estimation. The pipeline of traditional VSLAM methods based on classical image processing algorithms consists of six main steps, including initialization (data acquisition), feature extraction, feature matching, pose estimation, map construction, and loop closure. Since 2017, deep learning has changed this approach from individual steps to implementation as a whole. Currently, three ways are developing with varying degrees of integration of deep learning into traditional VSLAM systems: (1) adding auxiliary modules based on deep learning, (2) replacing the original modules of traditional VSLAM with deep learning modules, and (3) replacing the traditional VSLAM system with end-to-end deep neural networks. The first way is the most elaborate and includes multiple algorithms. The other two are in the early stages of development due to complex requirements and criteria. The available datasets with multi-modal data are also of interest. The discussed challenges, advantages, and disadvantages underlie future VSLAM trends, guiding subsequent directions of research.
Goodness Evaluation and Precision Analysis of Tree Biomass Equations

Taking the establishment of single-tree biomass equations for larch (Larix) of the northeast and Masson Pine (Pinus massoniana) of the south in China as the example, the goodness evaluation and precision analysis were studied in this paper. Firstly, based on the comprehensive analysis of various statistical indices for model evaluation, six essential evaluation indices were presented which proved to be feasible and effective through evaluating the tree biomass models of two species above. Secondly, according to basic assumptions of linear regression estimation and related formulas of confidence intervals for prediction, the approach to estimate confidence intervals of conditional mean and single predicted value of tree biomass models was provided, and the confidence intervals of tree biomass models of two species were determined. Finally, the Monte Carlo simulation was used for random resampling test, and the test results showed that examination of applicability based on validation samples was inadvisable, and the adequate procedure was to utilize all sample trees for constructing the model, not splitting into two groups for model construction and model validation, which could make full use of the information of entire sample and decreased the prediction error to the highest degree.
The EB II ‘metallic ware’ from Tell el-Far‘ah North (West Bank): typology, technology and petrography of a ceramic industry of the central hill country

The present work reports the results of the typological, technological and archaeometric study undertaken on Early Bronze Age ceramic fragments from the site of Tell el-Far‘ah North (West Bank), which macroscopic analysis has recognized as representative of ‘metallic ware’. The fragments belong to a distinctive class of medium-sized carinated bowls dating to the south Levantine EB II/ESL 4. Petrographic (OM), mineralogical (XRPD) and chemical (SEM-EDS) analyses have yielded the identification of a ‘metallic ware’ industry, which used a low calcareous clay where quartz is dominant, along with feldspars, fragments of sedimentary and siliceous rocks, nodules of iron oxides, and was fired at a temperature in a range between 800–900 °C. Petrographic and mineralogical data have made it possible to discuss the nature of raw materials and to investigate aspects of the production technology. Finally, through a comparison with other ceramics from the site, the fragments have been examined against the background of the local pottery tradition. The metallic ware bowls from Tell el-Far‘ah North have proved to be representative of a distinctive specialized ceramic industry of the central hill country, linked to the so-called ‘Aphek family’ bowls.
Hydromagnetic Flow of Micropolar Fluid between two Horizontal Plates, both the Plates being Stretching Sheets

Hydromaganetic flow of micropolar fluid between two horizontal parallel plates, the lower is a stretching sheet and the upper one is a porous stretching sheet. The flow is in the presence of a transverse magnetic field is studied numerically. The effects of transverse magnetic field parameter M, Reynolds number R and suction parameter λ on the velocity and microrotation fields have been observed. The governing partial differential equations of motion are transformed to ordinary differential form by using appropriate similarity transformations. The resulting equations of motion are solved numerically using SOR method and Simpson's (1/3) Rule. The numerical results have been improved by using Richardson extrapolation to the limit.
Hybrid Forecasting Methods—A Systematic Review

Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.
How Do Corporate Governance Factors Influence Banks’ Value? Evidence from Russia

In this research study we built 3 models that evaluate the panel data of 30 Russian banks with the largest assets and highest reliability. Comparison of all three models by means of specification tests led us to the conclusion that the OLS model with the explanatory power of 67% is optimal. The presence of women on the board of directors negatively affects the banks’ valuation, while the number of the board of directors’ meetings, number of directors and presence of an audit committee have a positive impact on the net asset value of banks. If the share of women increases by 1%, the bank’s net asset value will decrease by 86%. If the board of directorshas a functioning risk management committee, the bank’s net asset value will grow by 225%. In case of an increase in the number of the board of directors’ members by 1%, the bank’s net asset value will grow by 4.4%. If the number of meetings of the board of directors per year grows twofold, the bank’s net asset value will increase by 118%.