Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Scratch Software in Scientific Production for 20 Years: Programming in Education to Develop Computational Thinking and STEAM Disciplines

Scratch is an educational software based on visual programming blocks. It was created in 2003 by the Massachusetts Institute of Technology Media Lab (MIT) and it develops computational thinking (CT) skills from an early age in schools and allows STEM (science, technology, engineering and mathematics) projects to be carried out. The aim of this research is to know the development of the scientific production of the Scratch programme in the educational field in scientific articles in WoS and its link with the STEM field. The methodology used in this study is of a bibliometric nature with an analysis of the development in the scientific literature and co-words. The Scratch in Education (Scratch-EDU) programme has been studied using the Web of Science (WoS) database. WoS, Vosviewer and SciMAT were used to extract the results and a total of 579 manuscripts were analysed. The results of the study show that the first scientific article on Scratch published in WoS dates back to 2004, although it is from 2011 when a considerable volume of studies began to appear in the scientific literature, and moreover, in recent years the scientific literature relates Scratch-EDU with topics and keywords related to the STEM field. The conclusions of the study are that the Scratch programme has had a progressive evolution in the scientific field related to education from 2012 to 2020, mainly in proceedings papers, with a decrease in manuscripts in the last two years. The emerging themes and keywords that have most influenced Scratch-EDU manuscripts in recent years are related to the terms “Implementation” and “Curriculum”, connected in turn, with terms such as “pedagogy”, “public school” or “students”. Another term that stands out in the development of scientific evolution is “Computational Thinking”, associated with topics such as “Primary Education”, “Learning” or “Problem Solving”. Finally, a discussion and conclusion of the results has been carried out, which can serve as a turning point for future lines of research on programming and CT in the STEM field from an early age in education.
Observation of Communicative Behavior: an updated protocol from 0 to 72 months

ABSTRACT Purpose: to describe the updating of the Observation of Communicative Behavior protocol in the age group from 0 to 72 months. Methods: the Observation of Communicative Behavior protocol was revised and updated, with the insertion of the child development milestones in its various areas, becoming a child development screening tool. Results: the protocol includes 188 items, distributed in 10 age groups from zero to 72 months. The items were organized as follows: from zero to 24 months, divided by quarter (four age groups); from 24 to 36 months per semester (two age groups); and from 36 to 72 months, divided by year (four age groups). The items covered the areas of child development and its main milestones. The score is registered in the protocol and a score of 0 - does not perform the action or behavior, 1 - performs the action in an atypical, restricted manner or it is being acquired, 2 - properly performs the action/behavior, is attributed to the response, after analysis. Few materials and a structured environment are needed to apply the protocol. Conclusion: the updating of the Observation of Communicative Behavior protocol has a direct impact on the initial assessment of children with or without suspected delay in child development and should be used by health professionals in monitoring typical child development (routine) or in cases where the child receives specific stimulation.
B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation

Within the Natural Language Processing (NLP) framework, Named Entity Recognition (NER) is regarded as the basis for extracting key information to understand texts in any language. As Bangla is a highly inflectional, morphologically rich, and resource-scarce language, building a balanced NER corpus with large and diverse entities is a demanding task. However, previously developed Bangla NER systems are limited to recognizing only three familiar entities: person, location, and organization. To address this significant limitation, we introduce a novel Bangla NER dataset B-NER, which was created using 22,144 manually annotated Bangla sentences collected from Bangla newspapers and Bangla Wikipedia. This dataset includes a total of 9,895 unique words which were manually categorized into eight different entity types, such as a person, organization, event, artifact, time indicator, natural phenomenon, geopolitical entity, and geographical location. Inter-annotator agreement experiments were conducted to validate the quality of annotations performed by three annotators, resulting in a Kappa score of 0.82. In this paper, we provide an outline of the annotation guideline illustrated with examples, discuss the B-NER dataset properties, and present benchmark evaluations of the dataset. To establish that B-NER is more comprehensive and balanced in comparison to other publicly accessible datasets, we conducted cross-dataset modeling and validation, i.e. trained NER model on one dataset while tested on another, and found that the model trained on B-NER performed the best in that settings. Furthermore, we performed exhaustive benchmark evaluations based on Bidirectional LSTM with fastText embeddings and sentence transformer models. Among these models, fine-tuned NR/IndicbnBERT achieved noticeable results with a Macro-F1 of 86%. This dataset and baseline results will be publicly available under a CC-BY 4.0 license in the CoNLL-2002 format to facilitate further research on Bangla NER.
Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel–Ziv Complexity under Varying Conditions

The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel–Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0–1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel–Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel–Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel–Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.
Intelligent Urban Sensing for Gas Leakage Risk Assessment

In order to mitigate gas leakage damage, it is important to assess systematic risk of gas leakage in utility networks. In practice, leakage detection systems heavily rely on tedious and expensive human efforts, such as manually checking and assessing the risk, and thus only a very limited portion of the gas pipeline network can usually be assessed during a period of time. Therefore, it is critical to develop tools for automatically and systematically evaluating the whole gas pipeline network and revealing high-risk sites for checking in a timely fashion. To this end, in this paper, we develop an intelligent gas leakage risk assessment system based on the analysis of large-scale multi-source data, such as gas pipeline data, Point of Interests (POIs) data, and human mobility data. However, it is a non-trivial endeavor to design such a risk assessment system due to the unclear leakage mechanism, complex environmental conditions, and large size of the pipeline network. To address these challenges, both internal features of gas pipelines and external environmental features should be exploited. Specifically, we design a novel urban sensing technique to extract environmental features by analyzing human mobility data. Then, a joint learning neural network is developed to assess the leakage risk by integrating both internal and external features. Moreover, an intelligent risk assessment system is implemented and deployed for experiments in real-world scenarios. The results show the effectiveness of our system for citywide gas leakage risk assessment. Indeed, the proposed system could help to locate the potential leakage sites in a timely way.
Depth Recovery With Large-Area Data Loss Guided by Polarization Cues for Time-of-Flight Imaging

Time-of-Flight imaging is one of the quintessential techniques in three-dimensional reconstruction. However, depth missing is a common problem in Time-of-Flight imaging, which can be classified into structure-based depth loss and large-area one according to various reasons. Large-area depth loss generally occurs due to close-range overexposure resulting in limited dynamic range in depth sensing. Compared to structure-based depth loss, the recovery of large-area depth missing is more challenging and has been rarely studied. In this paper, a large-area depth recovery framework guided by polarization cues is proposed stemmed from a solid physical basic concerning depth and polarization, to realize high dynamic range in applications. Inspired by RGB-D system and shape-from-polarization technique, a dual camera system is utilized including infrared Time-of-Flight camera and visible polarized camera. A physical model between depth map and polarization cues, specifically depth-gradient and degree-of-polarization, is investigated and established. Based on the physical basics, a corresponding polarization-guided depth recovery algorithm with statistical analysis and image processing approach is introduced. Experimental results towards different targets demonstrate the effectiveness of the proposed method qualitatively and quantitatively, accompanied with outcome analysis and detailed discussions about strengths and future works, which indicates a great potential for the applications of high-quality three-dimensional reconstruction and depth sensing.
Changes in Calprotectin (S100A8-A9) and Aldolase in the Saliva of Horses with Equine Gastric Ulcer Syndrome

Simple Summary Equine gastric ulcer syndrome (EGUS) is a frequent disease that considerably reduces the quality of life of horses, while also affecting their physical performance, even when the disease is subclinical. In this study, we hypothesized that two analytes, calprotectin (CALP) and aldolase, could be measured in saliva with commercially available assays and be potential biomarkers of EGUS in horses. We investigated the changes in the salivary CALP and aldolase in 131 horses divided into 5 different groups: controls (healthy horses), with equine squamous gastric disease (ESGD), with equine glandular gastric disease (EGGD), with combined ESGD and EGGD, and horses with other intestinal pathologies but clinical signs similar to EGUS. The two assays were precise and accurate, and, in both cases, they showed differences between horses with EGUS and healthy horses, but they did not show significant differences between horses with EGUS and horses with other diseases. Our data showed that CALP has a high ability to differentiate between healthy horses and horses with EGUS. Therefore, it could have potential use as a biomarker since a value of CALP in the range of healthy individuals could indicate that the animal is not likely to have EGUS at gastroscopy. Abstract Equine gastric ulcer syndrome (EGUS) is a highly prevalent disease that affects horses worldwide. Within EGUS, two different forms have been described: equine squamous gastric disease (ESGD) and equine glandular gastric disease (EGGD). The associated clinical signs cause detrimental activity performance, reducing the quality of life of animals. Saliva can contain biomarkers for EGUS that could be potentially used as a complementary tool for diagnosis. The objective of this work was to evaluate the measurements of calprotectin (CALP) and aldolase in the saliva of horses as potential biomarkers of EGUS. For this purpose, automated assays for the quantification of these two proteins were analytically validated and applied for detecting EGUS in a total of 131 horses divided into 5 groups: healthy horses, ESGD, EGGD, combined ESGD and EGGD, and horses with other intestinal pathologies. The assays showed good precision and accuracy in analytical validation, and they were able to discriminate between horses with EGUS and healthy horses, especially in the case of CALP, although they did not show significant differences between horses with EGUS and horses with other diseases. In conclusion, salivary CALP and aldolase can be determined in the saliva of horses and further studies are warranted to elucidate the potential of these analytes as biomarkers in EGUS.
Chaotic Multivariate Autoregressive Model of Daily Runoff Prediction Based on Wavelet De-noising

The saturated embedding dimension as the member of the controlled variable of the multivariate autoregressive model was computed by the daily runoff time series which was de-noised by wavelet technology. The daily runoff was predicted with the built above model. Compared with the model gained by the original daily runoff time series, the prediction precision is increased obviously. The eligibility rate increases 8% and the mean absolute percentage error is 9.53%. So it is essential that the hydrological time series are de-noised by wavelet method before chaos identification and prediction of the hydrologic system.
Real Time Dynamic Threshold Clustering Method for Ancient Chinese Character Identification

Character image clustering can find similar characters which have much reference value to ancient character identification. But the classical clustering method depends much on the threshold. To alter the threshold dynamically and get result in real time, a improved clustering method is proposed. The classical BIRCH CF tree was amended to chain hierarchical structure, and the new tree was built from bottom to top with KNN clustering method. Based on this structure, relative similarity degree was transformed to character number in clustering result. By converting the dynamic threshold clustering problem to finding the different cluster range, this method could get the clustering result in real time.
BER and Outage Performance for Multi-hop User-Relays NOMA with Imperfect SIC and CSI

This paper examines a downlink multi-hop user relays assisted non-orthogonal multiple access (NOMA). The system is used to combat channel disorders, extend the coverage area, and improve the quality of service (QoS). Both imperfect successive interference cancellation (SIC) and channel state information (CSI) are taken into consideration. Under imperfect SIC and CSI, we obtain the bit error rate (BER) and outage probability (OP) expressions for the helper user. Then, when imperfect CSI is available at all hops, we compute the end-to-end BER and OP for far user message at each hop. Theoretical analyses are validated by computer simulations. The findings indicate that the BER and OP performance deteriorate as the hops increase. Additionally, at the high signal-to-noise ratio (SNR), the erroneous SIC and CSI create an error floor and impact the BER and OP performance at all hops.
Another scale-guided parallel transformer for image aesthetic assessment

Abstract. Image aesthetic assessment (IAA) is a challenging task in computer vision fields, which aims to automatically evaluate image beauty by simulating human perception on image aesthetic. With the development of deep learning, although convolutional neural network (CNN)-based IAA approaches have achieved extraordinary progress, CNN experiences difficulty to capture long-distance relationships among visual elements. There is a strong correlation between image layout and image semantic information for image aesthetic. In order to solve this problem, an another scale-guided parallel transformer is proposed, including a multiscale local feature extractor (ME), a feature projection (FP), and an another scale-guided parallel feature fusion transformer (AST). The ME captures primary local features with classic ResNet at multiple scales. The FP performs dimension transformation on feature maps for each scale, which can obtain feature token and aesthetic token. The AST with two parallel transformer encoders is exploited to highlight the significant regions in the holistic image, in which the feature tokens and the aesthetic token from another scale are grouped together to obtain interscale guidance. The final score distribution is achieved by weighting multiple aesthetic tokens with learnable parameters for unified aesthetics assessment. Extensive experiments on two public datasets, including aesthetic visual analysis and aesthetics and attributes database, demonstrate that the proposed method outperforms the state-of-the-art methods across three different tasks.
The bizarre spectrum of SS 433.

We report extensive spectrophotometric observations of Stephenson-Sanduleak 433, a peculiar 14th magnitude emission-line object recently identified with a variable nonthermal radio source, and probably also associated with the X-ray source A1909+04. Cassegrain image-tube scanner spectra obtained with the KPNO 4 m and Lick 3 m and 0.6 m reflectors show broad (400 km s/sup -1/), intense Balmer, He I, and He II emission lines which vary dramatically in profile and intensity on a time scale of one day. Coude scanner data from the Hale 5 m and Mount Wilson 2.5 m telescopes reveal complex, composite H.cap alpha. profiles. These data also show the interstellar NaD lines to each have equivalent widths of 1.9 +- 0.2 A, and velocity range remarkably similar to the 21 cm emission profile in this direction, implying a distance for SS 433 in excess of 3.5 kpc. There is no evidence for orbital motion in the emission lines for periods in the range 1--30 days, nor any sign of an underlying stellar absorption spectrum. The most bizarre spectral features, however, are three very strong (equivalent widths 50--150 A), broad emission features in the green red, and near-infrared, which change in intensity, profile, and wavelength daily, butmore » apparently aperiodically. These features have been seen to vary in wavelength by up to 600 A in 30 days. In addition, we present image-tube photography, photoelectric photometry, and proper-motion observations, in an effort to discern the nature of this unusual object. The total proper motion does not exceed 0''. 01 yr/sup -1/; the object is variable by at least 1 mag on a time scale of years. « less
Minimally invasive interventions for biopsy of malignancy-suspected pulmonary nodules: a systematic review and meta-analysis

ABSTRACT BACKGROUND: Imaging tests are important for diagnosis during the management of pulmonary nodules; however, biopsy is required to confirm the malignancy. OBJECTIVES: To compare the effects of different techniques used for the biopsy of a pulmonary nodule. DESIGN AND SETTING: Systematic review and meta-analysis were conducted using Cochrane methodology in São Paulo, São Paulo, Brazil. METHODS: We conducted a systematic review of randomized controlled trials (RCTs) on minimally invasive techniques, including tomography-guided percutaneous biopsy (PERCUT), transbronchial biopsies with fluoroscopy (FLUOR), endobronchial ultrasound (EBUSR), and electromagnetic navigation (NAVIG). The primary outcomes were diagnostic yield, major adverse events, and need for another approach. RESULTS: Seven RCTs were included (913 participants; 39.2% female, mean age: 59.28 years). Little to no increase was observed in PERCUT over FLUOR (P = 0.84), PERCUT over EBUSR (P = 0.32), and EBUSR over NAVIG (P = 0.17), whereas a slight increase was observed in NAVIG over FLUOR (P = 0.17); however, the evidence was uncertain. EBUSR may increase the diagnostic yield over FLUOR (P = 0.34). PERCUT showed little to no increase in all bronchoscopic techniques, with uncertain evidence (P = 0.02). CONCLUSION: No biopsy method is definitively superior to others. The preferred approach must consider availability, accessibility, and cost, as safety and diagnostic yield do not differ. Further RCTs planned, conducted, and reported with methodological rigor and transparency are needed, and additional studies should assess cost and the correlation between nodule size and location, as well as their association with biopsy results. SYSTEMATIC REVIEW REGISTRATION: PROSPERO database, CRD42018092367 -https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=92367.
Particle Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. In this thesis we propose a new Monte Carlo framework in which we build efficient high-dimensional proposal distributions using SMC methods. This allows us to design effective MCMC algorithms in complex scenarios where standard strategies fail. We demonstrate these algorithms on a number of example problems, including simulated tempering, nonlinear non-Gaussian state-space model, and protein folding.
The Hamy symmetric function and its generalization

In the paper we investigate and generalize the Hamy symmetric function: Fn(x, r) = ∑ 1 i1<i2<. <ir n (∏r j=1 xij ) 1 r . The Schur-convexity is discussed and some analytic inequalities are established by use of the theory of majorization. Mathematics subject classification (2000): 0E05, 26D20.
Machine Learning in Network Slicing—A Survey

5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.