Miracle-dz/UniFAR-FTUs huggingface dataset:
Femur morphology in healthy infants and young children

The objective of this study was to characterize femur morphology in healthy infants and young children. Anterior–posterior (AP) radiographs of the femur from children age 0–3 years with no history of bone disease were obtained from two children's hospitals and one medical examiner's office. Femur morphological measures (bone length, minimum diaphysis diameter, growth plate width, and femur radius of curvature) and sectional structural measures were determined. Measures were described and compared based on subject age and mass. Relationships between measures and age and mass were evaluated. The 169 AP femur radiographs were obtained from 99 children (59.6% males, median age = 12.0 months, IQR = 0–27.5 months, median body weight = 10.0 kg, IQR = 4.4–15.6 kg). Femur length (rs = 0.97, p < 0.001; rs = 0.89, p < 0.001), trochanter width (rs = 0.86, p < 0.001; rs = 0.85, p < 0.001), minimum diaphysis diameter (rs = 0.91, p < 0.001; rs = 0.87, p < 0.001), and growth plate width (rs = 0.91, p < 0.001; rs = 0.84, p < 0.001) increased with age and weight, respectively. Cross‐sectional area (rs = 0.87; rs = 0.86; p < 0.01), polar moment of inertia (rs = 0.91; rs = 0.87; p < 0.001), moment of inertia (rs = 0.91; rs = 0.87; p < 0.001), polar modulus (rs = 0.91; rs = 0.87; p < 0.001) and medullary canal diameter (rs = 0.83, p < 0.001; rs = 0.73, p < 0.001) at the minimum diaphysis also increased with age and weight, respectively. Changes during rapid bone growth are important to understanding fracture risk in infants and young children as they transition to independent walking. Femur length, trochanter width, minimum diaphysis diameter and growth plate width increased with age and weight. Structural properties associated with fracture resistance also increased with age and weight.
Analysis of 3D scanning data for optimal custom footwear manufacture

Very few standards exist for tting products to people. Footwear fit is a noteworthy example for consumer consideration when purchasing shoes. As a result, footwear manufacturing industry for achieving commercial success encountered the problem of developing right footwear which is fulfills consumer's requirement better than it's competeries. Mass customization starts with understanding individual customer's requirement and it finishes with fulllment process of satisfying the target customer with near mass production efficiency. Unlike any other consumer product, personalized footwear or the matching of footwear to feet is not easy if delivery of discomfort is predominantly caused by pressure induced by a shoe that has a design unsuitable for that particular shape of foot. Footwear fitter have been using manual measurement for a long time, but the combination of 3D scanning systems with mathematical technique makes possible the development of systems, which can help in the selection of good footwear for a given customer. This thesis, provides new approach for addressing the computerize footwear fit customization in industry problem. The design of new shoes starts with the design of the new shoe last. A shoe last is a wooden or metal model of human foot on which shoes are shaped. Despite the steady increase in accuracy, most available scanning techniques cause some deficiencies in the point cloud and a set of holes in the triangle meshes. Moreover, data resulting from 3D scanning are given in an arbitrary position and orientation in a 3D space. To apply sophisticated modeling operations on these data sets, substantial post-processing is usually required. We described a robust algorithm for filling holes in triangle mesh. First, the advance front mesh technique is used to generate a new triangular mesh to cover the hole. Next, the triangles in initial patch mesh is modified by estimating desirable normals instead of relocating them directly. Finally, the Poisson equation is applied to optimize the new mesh. After obtaining complete 3D model, the result data must be generated and aligned before taking this models for shape analysis such as measuring similarity between foot and shoe last data base for evaluating footwear it. Principle Component Analysis (PCA), aligns a model by considering its center of mass as the coordinate system origin, and its principle axes as the coordinate axes. The purpose of the PCA applied to a 3D model is to make the resulting shape independent to translation and rotation asmuch as possible. In analysis, we applied "weighted" PCA instead of applying the PCA in a classical way (sets of 3D point-clouds) for alignment of 3D models. This approach is based on establishing weights associated to center of gravity of triangles. When all of the models are aligned, an efficient algorithm to cut the model to several sections toward the heel and toe for extracting counters is used. Then the area of each contour is calculated and compared with equal sections in shoe last data base for finding best footwear fit within the shoe last data base.
Using RSSI Simple Localization Method to Implement the Context-Aware and Social Recommendation System

This study integrates the concept of context-awareness with association algorithms and social media to establish the Context-aware and Social Recommendation System (CASRS). The Simple RSSI Indoor Localization Module (SRILM) locates the user position; integrating SRILM with Apriori Recommendation Module (ARM) provides effective recommended product information. The Social Media Recommendation Module (SMRM) connects to users social relations, so that the effectiveness for users to gain product information is greatly enhanced. This study develops the system based on actual context.
Class-Disentanglement and Applications in Adversarial Detection and Defense

What is the minimum necessary information required by a neural net D ( · ) from an image x to accurately predict its class? Extracting such information in the input space from x can allocate the areas D ( · ) mainly attending to and shed novel insights to the detection and defense of adversarial attacks. In this paper, we propose “class-disentanglement” that trains a variational autoencoder G ( · ) to extract this class-dependent information as x − G ( x ) via a trade-off between reconstructing x by G ( x ) and classifying x by D ( x − G ( x )) , where the former competes with the latter in decomposing x so the latter retains only necessary information for classiﬁcation in x − G ( x ) . We apply it to both clean images and their adversarial images and discover that the perturbations generated by adversarial attacks mainly lie in the class-dependent part x − G ( x ) . The decomposition results also provide novel interpretations to classiﬁcation and attack models. Inspired by these observations, we propose to conduct adversarial detection and adversarial defense respectively on x − G ( x ) and G ( x ) , which consistently outperform the results on the original x . In experiments, this simple approach substantially improves the detection and defense against different types of adversarial attacks.
Soil Fungal Diversity of the Aguarongo Andean Forest (Ecuador)

Simple Summary The Kingdom Fungi is one of the richest in species, most of which are still unknown. Many fungal species are hidden in the tropics, the area richest in biodiversity on earth. In this paper, a mycological analysis is presented on a vast number of soil samples collected in the Aguarongo forest, an important Andean Natural Reserve of Ecuador. The study was carried out by analyzing the total DNA extracted from the soil and unveiled a total of more than 400 species of fungi. The most abundant species belong to Ascomycota and Mortierellomycota; some are important beneficial fungi for the environments such as antagonistics of fungal pathogens or nematode predators, while others are well-known producers of nutraceutical and pharmaceutical compounds. Based on the results of this study, a picture of the mycodiversity of Aguarongo forest soil was obtained. This area hides a huge number of unknown fungal species that could be discovered; thus, the protection of the Aguarongo forest is mandatory. Abstract Fungi represent an essential component of ecosystems, functioning as decomposers and biotrophs, and they are one of the most diverse groups of Eukarya. In the tropics, many species are unknown. In this work, high-throughput DNA sequencing was used to discover the biodiversity of soil fungi in the Aguarongo forest reserve, one of the richest biodiversity hotspots in Ecuador. The rDNA metabarcoding analysis revealed the presence of seven phyla: Ascomycota, Basidiomycota, Mortierellomycota, Mucoromycota, Glomeromycota, Chytridiomycota, and Monoblepharomycota. A total of 440 identified species were recorded. They mainly belonged to Ascomycota (263) and Basidiomycota (127). In Mortierellomycota, 12 species were recorded, among which Podila verticillata is extremely frequent and represents the dominant species in the entire mycobiota of Aguarongo. The present research provides the first account of the entire soil mycobiota in the Aguarongo forest, where many fungal species exist that have strong application potential in agriculture, bioremediation, chemical, and the food industry. The Aguarongo forest hides a huge number of unknown fungal species that could be assessed, and its protection is of the utmost importance.
Compact shape of vivaldi antenna for water detection using ground pentrating radar

A novel design of a Vivaldi antenna for water detection is presented for developing the ground penetrating radar system. In the newly developed Vivaldi antenna periodic dumbbell slots have been etched on both edges to reduce the antenna size and produce much better properties than the conventional one. The −10 dB impedance bandwidth extends from 69 to 250 MHz with 25% size reduction with average gain around 3.5 dBi using thin substrate. For thick substrate, the bandwidth extended from 50 to 500 MHz with 40% size reduction and average gain of about 4 dBi. Both simulation and experimental results show that the Vivaldi antennas have stable directional end fire radiation patterns, which are suitable for various ultra‐wideband applications. The effect of sandy soil, water layer, and the distance between sand surface and proposed antennas are also studied. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:1801–1809, 2014
Bioelectrical Impedance Vector Analysis of Young Elite Team Handball Players

Team handball is a highly dynamic sport where physical demands differ between categories and roles. Thus, physical characteristics are fundamental for the final performance. This study aims to (a) characterize a sample of young male and female elite team handball players with a non-athletic reference population; (b) to generate their 50%, 75%, and 95% percentiles of the bioelectrical variables. The study included 55 young elite team handball players (Males, n = 37, age = 17.0 ± 1.2 yrs, height = 185.8 ± 7.3 cm, weight = 82.0 ± 11.0 kg, body mass index (BMI) = 23.7 ± 2.5; Females, n = 18, age = 17.8 ± 0.9 yrs, height = 171.2 ± 6.4 cm, weight = 67.4 ± 7.2 kg, BMI = 23.0 ± 2.0). Height and bioelectrical variables were assessed in a state of euhydration and standard conditions. Bioelectrical impedance vector analysis (BIVA) was used to characterize the bioelectrical vector (BIA vector) distribution pattern for each group. Compared to the reference values, BIA vector showed statistically significant differences in males U17 (n = 19, T2 = 51.0, p < 0.0001), males U19 (n = 18, T2 = 82.0, p < 0.0001) and females U19 (n = 18, T2 = 85.8, p < 0.0001). Male groups were also bioelectrically different (T2 = 13.7, p = 0.0036). BIVA showed specific bioelectrical characteristics in young male and female elite handball players. This study provides an original data set of bioelectrical impedance reference values of young male and female elite team handball players. Our result might help to interpret individual bioimpedance vectors and define target regions for young handball players.
Wideband and compact erbium‐doped fiber amplifier using parallel double‐pass configuration

In this article, a wide‐band erbium doped fiber amplifier (EDFA) that operates in both C‐ and L‐band wavelength regions is demonstrated. The amplifier employs two pieces of 1.5 m and 9 m‐long erbium doped fibers (EDFs) optimized for C‐band and L‐band operations, respectively, in double‐pass parallel configuration to achieve a wideband operation from 1530 to 1605 nm. The chirp fiber Bragg grating is used in both stages to allow a double propagation of signal and thus increases the attainable gain in both C‐ and L‐band regions. At an input signal power of −30 dBm, a measured gain varies from 23 to 36 dB within a wide wavelength region from 1530 to 1605 nm. The corresponding noise figure varies from 5.6 to 8.2 dB within this wavelength region. The gain and noise figure of the proposed parallel configuration is much better when compared to that of a serial amplifier. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:629–631, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26599
SOFTWARE TESTING DEFECT PREDICTION MODEL-A PRACTICAL APPROACH

Software defects prediction aims to reduce software testing efforts by guiding the testers through the defect classification of software systems. Defect predictors are widely used in many organizations to predict software defects in order to save time, improve quality, testing and for better planning of the resources to meet the timelines. The application of statistical software testing defect prediction model in a real life setting is extremely difficult because it requires more number of data variables and metrics and also historical defect data to predict the next releases or new similar type of projects. This paper explains our statistical model, how it will accurately predict the defects for upcoming software releases or projects. We have used 20 past release data points of software project, 5 parameters and build a model by applying descriptive statistics, correlation and multiple linear regression models with 95% confidence intervals (CI). In this appropriate multiple linear regression model the R-square value was 0.91 and its Standard Error is 5.90%. The Software testing defect prediction model is now being used to predict defects at various testing projects and operational releases. We have found 90.76% precision between actual and predicted defects.
EXPLORING TRAVEL SELFIES: THE CASE OF BARCELONA

This paper aims to explore the relationship between characteristics of selfie-takers and the destination image depicted in the background of the photographs. Through a content analysis based on a sample of 1,537 selfies taken in Barcelona’s most visited places and shared on Instagram, the authors were able to identify the most relevant elements and tourist scenarios involved in these photographs, as well as the relationship that exists among certain features of the profile of selfie-takers. The study reveals that travel selfies are a viable tool, which contributes to destination image and is aligned with the image destination management organizations promote.
120-GHz wireless link using photonic techniques for generation, modulation, and emission of millimeter-wave signals

We present a wireless link system that uses millimeter-wave (MMW) photonic techniques. The photonic transmitter in the wireless link consists of an optical 120-GHz MMW generator, an optical modulator, and a high-power photonic MMW emitter. A uni-traveling carrier photodiode (UTC-PD) was used as the photonic emitter in order to eliminate electronic MMW amplifiers. We evaluated the dependence of UTC-PD output power on its transit-time limited bandwidth and its CR-time constant limited bandwidth, and employed a UTC-PD with the highest output power for the photonic emitter. As for the MMW generation, we developed a 120-GHz optical MMW generator that generates a pulse train and one that generates a sinusoidal signal. The UTC-PD output power generated by a narrow pulse train was higher than that generated by sinusoidal signals under the same average optical power condition, which contributes to reducing the photocurrent of the photonic emitter. We have experimentally demonstrated that the photonic transmitter can transmit data at up to 3.0 Gb/s. The wireless link using the photonic transmitter can be applied to optical gigabit Ethernet signals.
Infrastructure-Enabled GPS Spoofing Detection and Correction

Accurate and robust localization is crucial for supporting high-level driving automation and safety. Modern localization solutions rely on various sensors, among which GPS has been and will continue to be essential. However, GPS can be vulnerable to malicious attacks and GPS spoofing has been identified as a high threat. With transportation infrastructure becoming increasingly important in supporting emerging vehicle technologies and systems, this study explores the potential of applying infrastructure data for defending against GPS spoofing. We propose an infrastructure-enabled framework using roadside units as an independent, secured data source. A real-time detector, based on the Isolation Forest, is constructed to detect GPS spoofing. Once spoofing is detected, GPS measurements are isolated, and the potentially compromised location estimator is corrected using secure infrastructure data. We test the proposed method using both simulation and real-world data and show its effectiveness in defending against various GPS spoofing attacks, including stealthy attacks that are proposed to fail the production-grade autonomous driving systems.
Why Do Off‐Shored Indian Call Centre Workers Want to Leave Their Jobs?

Employee turnover is a major problem in off‐shored Indian call centres. Agents who service Western customers often face hostility and racial abuse because of who they are and where they are located. A substantial part of job‐related training focuses on teaching employees to manage their identity and modify their accent. Based on a sample (n = 211) of Indian call centre workers servicing international customers, we explore these issues and investigate how they affect employee turnover intentions. The study utilises Taylor and Bain's (2005) distinction between factors particular to the Indian context and those more generic to the call centre labour process to better understand the drivers of turnover. We found that a number of distinctive factors including accent modification difficulty, stigma consciousness, racial abuse and perceived favouritism were associated with turnover intentions. The study also revealed that certain job‐related factors related to intentions to leave, including routinisation and poor promotional opportunities.
Smart Garbage Bin Based on AIoT

Waste management and monitoring is a major concern in the context of the environment, and has a significant impact on human health. The concept of the Artificial Intelligence of Things (AIoT) can help people in everyday tasks in life. This study proposes a smart trash bin to help solve the problem of waste management and monitoring. Traditional methods of garbage disposal require human labor, and pose a hazard to the worker. The proposed smart garbage bin can move itself by using ultrasonic sensors and a web camera, which serves as its “eyes. ” Because the smart garbage bin is designed for indoor use and mobility, we use a Bluetooth beacon for location tracking, whereby the bin transmits a constant signal that the other devices in the environment can detect. A smart garbage bin was developed in this study to be used in the E315 research room in the Electrical Engineering Department of the National Chin-Yi University of Technology. An Android application was also developed to help users monitor waste in the smart bin.
A Hybrid Method Based on Feature Fusion for Breast Cancer Classification using Histopathological Images

A Hybrid Method Based on Feature Fusion for Breast Cancer Classification using Histopathological Images. European Journal of Science and Technology , (29), 129-137. Abstract Breast cancer is the most common type of cancer in women today, and it ranks second after lung cancer with a very high mortality rate. If it is detected late, the treatment of breast cancer becomes very difficult. Although there are various methods for the detection of breast cancer, there is still a need for auxiliary diagnosis and treatment methods. In this study, a hybrid method is proposed to investigate the development of basal-like breast tumors and classify basal-like breast cancer types using histopathological images. In the study, firstly, appropriate features that support the accurate classification between tumor and non-tumor regions are extracted from histopathological images. Then the dataset is created by combining the obtained features. In the last stage of the study, the classification of images is carried out by using bag of words (BoW) and deep neural networks (DNN) techniques in a hybrid manner. Generally, immunohistochemical markers are used for this classification, but the performance of these markers remains at 60%. The performance of the classification accuracy of the proposed system is increased with the proposed hybrid classifier based on feature fusion. As a result of the study, 94.5% classification accuracy is achieved on the training set, while 80.8% classification accuracy is succeed on the test set. As a result, it is verified that successful results are achieved in the classification of basal-like breast cancer on histopathological images using the proposed hybrid method based on feature fusion.
Quasi-phase matched optical frequency comb generation through cascaded second order nonlinearity in a micro-ring resonator

We propose optical frequency comb generation in a monolithic micro-ring resonator. Being different from the previously reported nonlinear optical frequency combs, our scheme is based on more efficient quadratic frequency conversion rather than the third-order nonlinearity. To overcome the phase mismatch, a partly poled nonlinear ring is employed. Cascading second harmonic generation and parametric down conversion processes thus are realized through quasi-phase matching (QPM). Coupling equations are used to describe the related nonlinear interactions among different whispering-gallery modes, showing some interesting characteristics that are different from conventional QPM technology.
Reduced Simulation: Real-to-Sim Approach toward Collision Detection in Narrowly Confined Environments

Recently, several deep-learning based navigation methods have been achieved because of a high quality dataset collected from high-quality simulated environments. However, the cost of creating high-quality simulated environments is high. In this paper, we present a concept of the reduced simulation, which can serve as a simplified version of a simulated environment yet be efficient enough for training deep-learning based UAV collision avoidance approaches. Our approach deals with the reality gap between a reduced simulation dataset and real world dataset and can provide a clear guideline for reduced simulation design. Our experimental result confirmed that the reduction in visual features provided by textures and lighting does not affect operating performance with the user study. Moreover, by conducting collision detection experiments, we verified that our reduced simulation outperforms the conventional cost-effective simulations in adaptation capability with respect to realistic simulation and real-world scenario.