Miracle-dz/UniFAR-FTUs huggingface dataset:
Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation

Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.
Twitter as an Enabler for Gathering and Interpreting Sentiments: an Example Choosing the Portuguese Word of the Year

Desde 2009 que a Porto Editora elege em “infopedia.pt” a palavra que melhor representa os anos que terminam. Este trabalho apresenta uma forma alternativa a essa eleicao, substituindo a votacao dos cidadaos pela recolha de dados da rede social Twitter ao longo do ano, e procedendo a analise dos mesmos em substituicao da votacao. Assim sendo, foram recolhidos dados associados as dez palavras finalistas incluidas no conjunto da palavra do ano 2014, os quais foram armazenados em ambiente Hadoop para seguidamente e recorrendo a dois lexicons ser possivel a classificacao dos tweets. Os lexicons utilizados incluem, por um lado, a lista de palavras positivas e negativas e, por outro, as polaridades associadas as palavras em conjugacao com o top vinte e cinco de emoticons utilizados no Twitter. Os resultados obtidos permitem identificar a palavra mais referida e o sentimento, positivo ou negativo associado a mesma.
Targetless Extrinsic Calibration of Multiple Small FoV LiDARs and Cameras Using Adaptive Voxelization

Determining the extrinsic parameter between multiple light detection and rangings (LiDARs) and cameras is essential for autonomous robots, especially for solid-state LiDARs, where each LiDAR unit has a very small field-of-view (FoV), and multiple units are often used collectively. The majority of extrinsic calibration methods are proposed for 360° mechanical spinning LiDARs where the FoV overlap with other LiDAR or camera sensors is assumed. A few research works have been focused on the calibration of small FoV LiDARs and cameras nor on the improvement of the calibration speed. In this work, we consider the problem of extrinsic calibration among small FoV LiDARs, and cameras, with the aim to shorten the total calibration time and further improve the calibration precision. We first implement an adaptive voxelization technique in the extraction and matching of LiDAR feature points. Such a process could avoid the redundant creation of $k$ -d trees in LiDAR extrinsic calibration and extract LiDAR feature points in a more reliable and fast manner than existing methods. We then formulate the multiple LiDAR extrinsic calibration into a LiDAR bundle adjustment (BA) problem. By deriving the cost function up to second order, the solving time and precision of the nonlinear least square problem are further boosted. Our proposed method has been verified on data collected in four targetless scenes and under two types of solid-state LiDARs with a completely different scanning pattern, density, and FoV. The robustness of our work has also been validated under eight initial setups, with each setup containing 100 independent trials. Compared with the state-of-the-art methods, our work has increased the calibration speed 15 times for LiDAR-LiDAR extrinsic calibration (averaged result from 100 independent trials) and 1.5 times for LiDAR–camera extrinsic calibration (averaged result from 50 independent trials) while remaining accurate. To benefit the robotics community, we have also open-sourced our implementation code on GitHub.
Ellipticalness index – a simple measure of the complexity of oval leaf shape

Plants have diverse leaf shapes that have evolved to adapt to the environments they have experienced over their evolutionary history. Leaf shape and leaf size can greatly influence the growth rate, competitive ability, and productivity of plants. However, researchers have long struggled to decide how to properly quantify the complexity of leaf shape. Prior studies recommended the leaf roundness index (RI = 4π A / P 2 ) or dissection index (DI = √ ⁄ ), where P is leaf perimeter and A is leaf area. However, these two indices merely measure the extent of the deviation of leaf shape from a circle, which is usually invalid as leaves are seldom circular. In this study, we proposed a simple measure, named the ellipticalness index (EI), for quantifying the complexity of leaf shape based on the hypothesis that the shape of any oval leaf can be regarded as a variation from a standard ellipse. 2220 leaves from nine species of Magnoliaceae were sampled to check the validity of the EI. We also tested the validity of the Montgomery equation (ME), which assumes a proportional relationship between leaf area and the product of leaf length and width, because the EI actually comes from the proportionality coefficient of the ME. We also compared the ME with five other models of leaf area. The ME was found to be the best model for calculating leaf area based on consideration of the trade-off between model fit vs. complexity, which strongly supported the robustness of the EI for describing oval leaf shape. The new index can account for both leaf shape and size, and we conclude that it is a promising method for quantifying and comparing oval leaf shapes across species in future studies.
Predicting credit card fraud using multipurpose classification based on evolutionary rules

Due to advances in Internet technology, credit card transactions are increasing faster than ever before. This has led to a fraud problem that affects businesses, cooperating institutions, and government agencies. Credit card fraud occurs when a third party uses your credit card or credit account for an unauthorized transaction. Given the increased security of credit card transactions, fraudsters are developing new tricks or exploiting new vulnerabilities. Thanks to evolving technology, it is possible to analyze the use of maliciously obtained data by studying the time and cost associated with account switching transactions. In this study, we propose the ENORA and NSGA‐II methods to create rule‐based classifiers that can be easily interpreted using the credit card fraud diagnosis dataset created by analyzing the time intervals of individuals' credit card usage. In this experiment, credit card payments are classified as fraudulent or nonfraudulent based on several variables. The experiments were conducted in full training mode and 10‐fold cross‐validation mode. The performance of the algorithm was measured by accuracy, area under the receiver operating characteristic (ROC) curve, mean square error, proportion of true positives, proportion of false positives, precision, recall, F‐measure, area under the curve (AUC), and Matthews correlation coefficient. The classification performance of the model using the correct classification ratio (ACC) is as follows: NSGA‐II = 94.244%, ENORA = 93.236%. The performance of the other metrics is also discussed in detail in the results section. These results illustrate the significant predictive power of the proposed credit card theft models. After a thorough statistical analysis of our results, we found that the proposed strategy is capable of producing accurate and easy‐to‐understand categorization models.
Superconducting Resonators on Thin Film Flexible Substrates

In cryogenic electronics applications and experiments, i.e., for cold detectors and low temperature quantum computing, the base temperature is typically below 100 milli-Kelvin. In order to reach this temperature range, a significant effort is undertaken to reduce the thermal load of the system by using microwave and dc cables with a low thermal conductivity. As the number of connections increase in superconducting cryogenic computers, the more difficult it becomes to cool the system down. One practical solution to address this issue is to reduce the cross-sectional area of the cables used in the cryogenic system. For cryogenic cables and interconnects that deliver microwave signals, we want low loss dielectric materials to interface different kinds of microwave devices, especially superconductor devices. Therefore, we want to minimize losses in the substrate by selectively choosing dielectrics that are low-loss and mechanically compliant when used as free-standing flex cables. Half-wavelength, capacitively coupled superconducting microstrip resonators have been constructed on various flexible substrates including 50.8 μm (2 mil) thick free-standing Kapton polyimide and 20 μm thick spin-on polyimide. Cu normal conducting and Nb superconducting resonators were designed and fabricated on these substrates. We extracted the complex dielectric permittivity of the films over a wide frequency range and at cryogenic temperatures below 9 K. In order to better characterize these thin and low loss dielectric films, we require high-quality factor resonators to allow extraction of the dielectric properties. We designed these resonators to be weakly coupled to reduce the coupling loss, this involved several iterative designs and measurements at cryogenic temperatures. The metal stack-up of these superconducting resonators mainly consisted of a 250 nm thick superconducting Nb film to limit conductor loss. The fabricated resonators exhibited high-quality factors (Q) at a temperature of ∼1.2 K of 7000 for E-series Kapton film, 13 000 for a spin-on PI-2611 and
Hand Hygiene Auditing: Is It a Roadway to Improve Adherence to Hand Hygiene Among Hospital Personnel?

Background Over the years, there has been an increase in hospital-acquired infections (HAIs) among patients in India. One of the main reasons is a lack of compliance with infection control guidelines, such as hand hygiene. So the present study was conducted to determine the compliance of hand hygiene among healthcare workers in a private tertiary care teaching hospital in South India. Materials and methods The prospective observational study was carried out between April 2017 and March 2020. Nineteen areas were directly observed for hand hygiene (HH) compliance. At each location, HH audit was conducted for one hour per day for five days per month. HH complete adherence rate (HHCAR) and HH partial adherence rate (HHPAR) were analyzed. Results Nine hundred and twenty observation periods were completed during the entire study period. Overall, hand hygiene complete adherence rate was 29.9% (11,981/39,998); partial adherence rate was 45.3% (18,131/39,998) and the non-adherence rate was 24.7% (9886/39,998). A better adherence rate was seen among nurses (44.7%), followed by other staff (33.7%) and doctors (33.04%). Moment-specific adherence rates show almost equal adherence rates of 50.7%, 50.75%, and 50.1%, respectively, for moments 2, 3, and 4, and comparatively low for moments 1 and 5 (48.4% and 47.6%, respectively). Conclusion Despite adequate hand hygiene facilities, compliance remains low. Hand hygiene is a bundle care approach that needs to consider factors including healthcare staff, clinical, institutional, environmental, and behavioral changes. Multimodal interventions and multidisciplinary commitment are mandatory for sustained compliance.
Tracking Global Patterns of Drought‐Induced Productivity Loss Along Severity Gradient

Droughts significantly impact land carbon uptake by reducing the gross primary productivity (GPP). Projections suggest an increase in severe and extreme droughts in the twenty‐first century. Despite the importance of droughts on carbon uptakes, it remains unclear about the GPP responses under different drought severity and its changes under current climate changes. Using four state‐of‐the‐science GPP products, we showed that moderate (−1 ≤ SPEI < −1.5), severe (−1.5 ≤ SPEI < −2), and extreme (SPEI < −2) droughts caused on average 5%, 7%, and 9% GPP reductions, respectively, across the globe during the recent decades. Although the mean reductions varied among GPP products, we observed similar spatial patterns. The reductions increased significantly along the severity gradient, while the sharpest increases were observed in semi‐arid and grassland ecosystems. On a biome level, we found that the reductions increased nonlinearly with increasing drought severities for almost all vegetation types, but the trends and slopes varied significantly. In addition, we found that the mean drought‐induced GPP reductions had increased in dry ecosystems but decreased in humid ecosystems from 2001–2008 to 2009–2016. The result highlights the potential increase of drought‐induced land carbon sink loss responding to more extreme climates and offers new perspectives to improve the prediction of global carbon fluxes in a changing climate.
Do tourists’ perceptions of tourism destination change across seasons? A mixed big data analysis

ABSTRACT Many scholars use big data analysis methods to investigate tourist destination image. However, research on the internal relations between the key elements of tourist destination image and its revolution trends is rare, especially with respect to seasonal trends. To bridge this gap in the literature, our research is the first to comprehensively use multiple big data analysis methods to explore tourist destination image based on tourist gaze theory. By analyzing 22,035 tourists’ reviews of Huangshan, we had three main findings: First, ‘convenient’, ‘worthy’, ‘beautiful scenery’, ‘satisfactory’, ‘adequate facilities’, and ‘great experience’ are the six main tourism destination image dimensions of Huangshan; second, the keywords of each dimension display various co-occurrence relations; finally, the tourism destination image dimensions exhibit different seasonal trends over time. Our study not only greatly enhances the existing literature, but also provides important practical implications for destination marketing organizations.
Bioinspired nacre-like PEEK material with superior tensile strength and impact toughness

A bioinspired PEEK material with hard “bricks” of nanoscale lamellae and micron-scale deformed spherulites bonded by soft “mortar” of a rigid amorphous fraction was produced with a pressure-induced flow (PIF) processing applied in the solid-state. Novel mechanisms were proposed for the marked and simultaneous improvement in the strength and toughness, where the tensile strength and impact strength could be increased to ∼200% and ∼450%, respectively. On one hand, the rotation, recombination and restacking of the crystalline blocks formed an oriented and stratified morphology similar to the “brick-and-mortar” structure in nacre, and resulted in the confined crack propagations and the tortuous energy dissipating paths. On the other hand, the PIF-relaxation due to the newly generated rigid amorphous fraction further contributed to the improvement of the impact strength. The efficiency of enhancement could be controlled by the molding temperature, the compression ratio, and the volume fraction of chopped carbon fiber. As a result, PIF-processing might endow the PEEK material with improved mechanical matching with the surrounding tissues and extended service life in biomedical applications while retaining excellent biocompatibility with no external substances introduced.
Impact of the pandemic on social media influencer marketing in fashion: a qualitative study

Purpose In spite of the rise of social media influencers observed in the 2010s, the direction and future of influencers are ambiguous. This popular yet overused marketing tool has shown certain problems and limitations such as a decrease in perceived authenticity and market saturation. Additionally, the outbreak of COVD-19 has amplified the significance of these factors and made many companies and influencers reconsider their involvement in influencer marketing. Within this context, this paper aims to explore whether influencers were impacted by diminishing perceived authenticity, market saturation or the prolonged pandemic. Also, the authors aim to investigate influencers’ perception of the future of influencer marketing post-pandemic. Design/methodology/approach To gain insight into trends in influencer marketing from the influencer’s perspective, this paper uses qualitative research in the form of interviews with influencers and industry professionals. Findings The findings highlight the importance of perceived authenticity for success in influencer marketing. Most interviewees indicated that they had noticed a boom in social media influencer marketing before the pandemic, yet provided mixed views regarding the market during the pandemic. Several believe that influencers will continue to be relevant in the increasingly digital world (e.g. increasing digital marketing spend and e-commerce), whereas an expectation of new digital platforms and innovations was also observed. In the long term, saturation and decreased effectiveness were predicted by several interviewees. Originality/value This under-researched topic is of relevance especially to consumer goods companies, as social media marketing and influencer marketing are currently highly effective and popular tools. To refine marketing strategies designed around influencers, understanding the limitations, in the context of COVID-19, is crucial.
High-dynamic-range displays : contributions to signal processing and backlight control

Compared to a conventional RGB LCD, an RGBW LCD is known to result in 50% higher light transmission. However, the reduced aperture for the RGB subpixels lead to reduced luminance for saturated pixels. Consequently, colorful images may appear dull due to a lack of sufficient luminance. In this work it is proposed to address the luminance shortage for bright saturated pixels by boosting a spatially modulated backlight. Whereas best-in-class methods for deriving backlight drive levels either results in high contrast or temporal stability, we propose a reformulation which provide superior contrast and temporal stability. By employing local backlight boosting, the 99.5%-percentile of ∆E94 is reduced from 12.3 JND to 4.3 JND on average. Power consumption is reduced by 18% compared to an RGB LCD with local backlight dimming. Additionally, we propose to increase luminance of desaturated pixels in regions of backlight boosting, which results in an average luminance increase of 16% for pixels which are boosted. Reproduced from M Hammer and KJG Hinnen (2014). „Local Luminance Boosting of an RGBW LCD”. in: IEEE/OSA J. Display Technol. 10.1, pp. 33–42.
AI-TAM: a model to investigate user acceptance and collaborative intention inhuman-in-the-loop AI applications

More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities to provide advanced features; on the other hand, human-in-the-loop approaches are on the rise to involve people in AI-powered pipelines for data collection, results validation and decision-making.Does the introduction of AI features affect user acceptance? Does the AI result quality affect people willingness to use such applications? Does the additional user effort required in human-in-the-loop mechanisms change the application adoption and use? This study aims to provide a reference approach to answer those questions. We propose a model that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to AI (user trust in AI and perceived quality of AI output, from XAI literature) and collaborative intention (willingness to contribute to AI pipelines). We tested the proposed model with an application for car damage claim reporting with AI-powered damage estimation for insurance customers. The results showed that the XAI related factors have a strong and positive effect on the behavioural intention, the perceived usefulness and the ease of use of the application. Moreover, there is a strong link between the behavioural intention and the collaborative intention, indicating that indeed human-in-the-loop approaches can be successfullyadopted in final user applications.
Automatic removal of both EOG and EMG artifacts in EEG by a fast ICA and an IVA

Electroencephalogram (EEG) signals are easy to be interfered by the artifacts of both electrooculogram (EOG) and electromyogram (EMG) during data acquisition. The two artifacts are unwanted in EEG signals processing but difficult to remove. In this paper, a novel blind source separation method for removal of both EOG and EMG based on the fast independent component analysis (FICA) and the independent vector analysis (IVA) is presented. The system uses both high-order statistics and second-order statistics to analyze the non-Gaussian and weak correlation of the artifact signals. Firstly, the FICA algorithm is used to decompose EEG into independent components. Next, the EOG artifacts will be identified and removed according to the window width of the separated EEG signals. Finally, an IVA Gauss-Laplace distribution (IVA-GLD) algorithm with an adaptive step size is exploited to remove the EMG artifacts. Experiments were carried out by the data set collected by a self-developed equipment. The root mean square error (RMSE) of the proposed method is 19.431, which is lower than the similar method without the peak window (23.118), and the correlation coefficient (CC) is 0.895, which is higher than the similar method without the peak window (0.799).