Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Elliptical Slotted Patch Antenna for 5G Communications

In this paper, a broadband elliptical slotted patch antenna has been proposed for 5G wireless communications. The proposed antenna apparatus has a minimal size of 0. 308λ0 × 0. 308λ0 × 0. 009λ0 at 22 GHz. The antenna bandwidth is enhanced for an elliptical ground plane. Microstrip feed line has been connected by a stub, to achieve better impedance. Array design has been designed for better gain, therefore array design has been made. The array has a high gain of about 18 dBi at 28 GHz and 21dBi at 38 GHz. The proposed antenna covers a wide band area from 22 GHz to 48 GHz. The proposed antenna covers 28/38 GHz. The antenna additionally gives omnidirectional to required 5G bands.
Answering Sky-Not query in MapReduce

Constrained skyline is one of the most popular variants of Skyline query. In multi-dimensional dataset, skyline query returns objects to the user if no other object is preferable across all dimensions. This leaves only the objects most likely to appeal to an arbitrary user. However, some dominated objects may still be interesting to an arbitrary user. Conventional skyline query cannot satisfy such interest. In order to satisfy such interests, constrained skyline query was introduced where the user specifies his/her constraints with bounding corners Ql and Qu, and the query engine returns the non-dominated objects within that boundary. Meanwhile, in Sky-not query, given a query object p, a dataset DS, and constraints with bounding corners Ql and Qu, the query returns the alternative constraint $Q_l^{^\prime }$ closest to Ql for which p will be in the skyline. The result of such query can be used by management or marketing agents to introduce some promotional offer for the users who are surfing for interesting objects within a constrained region. Alternative lower constraint $Q_l^{^\prime }$ closest to Ql can influence the user to modify their constraint boundary. Hence, some particular products could have been promoted to be in users’ interested region. In this paper, we have proposed a method to answer the Sky-not query in MapReduce framework.
Causal Testing: Understanding Defects' Root Causes

Understanding the root cause of a defect is critical to isolating and repairing buggy behavior. We present Causal Testing, a new method of root-cause analysis that relies on the theory of counterfactual causality to identify a set of executions that likely hold key causal information necessary to understand and repair buggy behavior. Using the Defects4J benchmark, we find that Causal Testing could be applied to 71% of real-world defects, and for 77% of those, it can help developers identify the root cause of the defect. A controlled experiment with 37 developers shows that Causal Testing improves participants' ability to identify the cause of the defect from 80% of the time with standard testing tools to 86% of the time with Causal Testing. The participants report that Causal Testing provides useful information they cannot get using tools such as JUnit. Holmes, our prototype, open-source Eclipse plugin implementation of Causal Testing, is available at http://holmes.cs. umass.edu/.
A Multi-label Image Classification Algorithm Based on Attention Model

Convolutional neural network (CNN) has shown great success in single-label image classification, but in real world images generally have multiple labels. In this paper, we utilize long short-term memory network (LSTM) as the "decoder" to generate multi labels of an image. Meanwhile, in order to reduce the ‘semantic gap’ between the visual features and the richness of human semantics we propose a label embedding approach to generate a semantic label for an image. Experimental results demonstrate that the proposed architecture achieves a good performance on the multi-label image classification.
Automatic Categorization of Traditional Clothing Using Convolutional Neural Network

The research proposes an approach that can automatically classify real world pictures of some traditional clothing worn in Bangladesh into the predefined classes using Convolutional Neural Networks (CNN). The research is driven by considering the growing market of online shops in mind. For classification purpose, we have collected clothing images from several online stores and labeled them accordingly. Our CNN model is based on the Google Inception model. For comparison purposes we have tried several architectures of CNN and some variations to see how our model perform against them. We have tested our own model with three different optimizers – SGD, Adam and RmsProp. Among these optimizers, RmsProp performed the best. The final result shows our model could classify the images of the training and testing set with 92.05% and 89.22% accuracy respectively.
Analysis of the Art of War of Sun Tzu by Text Mining Technology

This paper studies the development history and research status of text mining. Text mining technology commonly used to obtain valuable information and knowledge. Based on the use of open source Python modules , " Art of War of Sun Tzu " was analyzed through the visualization technology to show the key contents of the book, so that everyone has a more vivid and intuitive understanding of this famous masterpiece.
A Study on Deep Learning Based Sauvegrain Method for Measurement of Puberty Bone Age

This study applies a technique to expand the number of images to a level that allows deep learning. And the applicability of the Sauvegrain method through deep learning with relatively few elbow X-rays is studied. The study was composed of processes similar to the physicians' bone age assessment procedures. The selected reference images were learned without being included in the evaluation data, and at the same time, the data was extended to accommodate the number of cases. In addition, we adjusted the X-ray images to better images using U-Net and selected the ROI with RPN + so as to be able to perform bone age estimation through CNN. The mean absolute error of the Sauvegrain method based on deep learning is 2.8 months and the Mean Absolute Percentage Error (MAPE) is 0.018. This result shows that X - ray analysis using the Sauvegrain method shows higher accuracy than that of the age group of puberty even in the deep learning base. This means that deep learning of the Suvegrain method can be measured at a level similar to that of an expert, based on the extended X-ray image with the image data extension technique. Finally, we applied the Sauvegrain method to deep learning for accurate measurement of bone age at puberty. As a result, the present study is based on deep learning, and compared with the evaluation results of experts, it is possible to overcome limitations of the method of measuring bone age based on machine learning which was in TW3 or Greulich & Pyle due to lack of X- I confirmed the fact. And we also presented the Sauvegrain method, which is applicable to adolescents as well.
Structure Damage Detection Based on Ensemble Learning

Structure damage detection is of great significance for structure maintenance, repair and rehabilitation soon after damage occurs. Data driven approaches to structure damage detection based on signal processing methods and Machine Learning algorithms have long been investigated. This paper proposed applications of Ensemble Learning to boost damage detection performance. Cross correlation function and wavelet packet decomposition are adopted to extract damage sensitive features from raw sensor data. Ensemble Learning algorithm such as Random Forest and XGBoost are used to train the damage pattern classifier. Results obtained in experiments validate Ensemble Learning algorithms' effectiveness in structure damage detection. This method can be used on practical structure damage detection problem in Aerospace Engineering.
Heuristic Patterns of Ethical Decision Making

This article describes the context of ethical decision making in research and suggests that direct attention to the ways in which decisions are actually made in such environments is needed. A decision-making model based on the literature on heuristic processing is proposed and is followed by a review of the method, data, and results of the authors’ research on this model. The implications of the research are developed, and a research agenda is outlined. Key findings were that competent actors do indeed process ethics problems heuristically and in ways that interweave intuitive, affective, and more rational phases. This processing does not typically follow a simple progressive pattern but evidences a sort of trial-and-error processing that is consistent with the logic of heuristic processing more generally. Finally, while diverse, participant-level processing attempts appear to follow patterns which are associated with the experience and training of the actors.
Degree centrality and voxel-mirrored homotopic connectivity in children with nocturnal enuresis: A functional MRI study

Aim: To determine the characteristics of brain development in children with nocturnal enuresis, we investigated the intensity of functional connectivity both among the nodes in the brain network and between the two hemispheres of the brain. Materials and Methods: Twenty-three children with nocturnal enuresis (NE) and an equal number of normal children were examined using resting-state functional magnetic resonance imaging (fMRI) scans. Data analysis was done via the degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) approaches. Moreover, we compared the children's psychological status by utilizing the self-concept scale. Results: In four areas of the brain, the the DC values of the NE group were obviously lower than that of the normal controls. These four areas were the posterior cerebellar lobe, anterior cingulate cortex (ACC), medial frontal gyrus, and superior left temporal gyrus (P < 0.05, after correction). We also found two brain areas where the VMHC values of the NE group were obviously lower than that of the normal controls. The two groups were the cerebellar lobe and the anterior cingulate cortex (ACC) [P < 0.05, after correction]. A psychological comparison between the children with NE and that in the normal group on the self-concept scale was also performed. The scores of the children with NE were lower than normal controls regarding behavior, appearance and property, anxiety, gregariousness, happiness, and satisfaction (P < 0.05). Conclusions: These findings provide evidence of the deficit of urination control in children with NE. Furthermore, through the methods of DC and VMHC, which are based on functional connectivity, it was also possible to explain why children with NE often have the concomitant symptoms of attention, control, and memory problems. The analysis of the self-concept scale suggests that children with NE lack self-confidence.
The Promising Effects of Transplanted Umbilical Cord Mesenchymal Stem Cells on the Treatment in Traumatic Brain Injury

Abstract Many studies have reported the recovery ability of umbilical cord-derived mesenchymal stem cells (UC-MSCs) for neural diseases. In this study, the authors explored the roles of UC-MSCs to treat the traumatic brain injury. Umbilical cord-derived mesenchymal stem cells were isolated from healthy neonatal rat umbilical cord immediately after delivery. The traumatic brain injury (TBI) model was formed by the classical gravity method. The authors detected the behavior changes and measured the levels of inflammatory factors, such as interleukin-l&bgr; and tumor necrosis factor-&agr; by enzyme linked immunosorbent assay (ELISA) at 1, 2, 3, 4 weeks after transplantation between TBI treated and untreated with UC-MSCs. Simultaneously, the expression of glial cell line-derived neurotrophic factor (GDNF) and brain derived neurotrophic factor (BDNF) were measured by real-time–polymerase chain reaction and ELISA. The authors found that the group of transplantation UC-MSCs has a significant improvement than other group treated by phosphate buffered saline. In the behavioral test, the Neurological Severity Scores of UC-MSCs + TBI group were lower than TBI group (P < 0.05), but not obviously higher than control group at 2, 3, and 4week, respectively. The inflammatory factors are significantly reduced comparison with TBI group (P < 0.05), but both GDNF and BDNF were higher than TBI group (P < 0.05). The results indicated that UC-MSCs might play an important role in TBI recovery through inhibiting the release of inflammatory factors and increasing the expression of GDNF and BDNF.
Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.
A Methodology for Search Space Reduction in QoS Aware Semantic Web Service Composition

The semantic information regulates the expressiveness of a web service. State-of-the-art approaches in web services research have used the semantics of a web service for different purposes, mainly for service discovery, composition, execution etc. In this paper, our main focus is on semantic driven Quality of Service (QoS) aware service composition. Most of the contemporary approaches on service composition have used the semantic information to combine the services appropriately to generate the composition solution. However, in this paper, our intention is to use the semantic information to expedite the service composition algorithm. Here, we present a service composition framework that uses semantic information of a web service to generate different clusters, where the services are semantically related within a cluster. Our final aim is to construct a composition solution using these clusters that can efficiently scale to large service spaces, while ensuring solution quality. Experimental results show the efficiency of our proposed method.
Algebraic Multi-Grid Based Multi-Focus Image Fusion Using Watershed Algorithm

This paper proposes a new multi-focus image fusion method named AMGW, and it is based on algebraic multi-grid (AMG) algorithm and watershed segmentation method. In the implementation, the coarse grids of the source images are first extracted with the affinity matrix, and with a spatial interpolation function the approximation of the source image can be reconstructed from the coarse grids. A considerable amount of edge and textural information is still preserved in such approximation. The two source images are compared with their corresponding approximation block by block respectively by employing the mean square error (MSE) as a sharpness criterion. The MSE values are then used to identify the blocks of higher fidelity from the source images. The watershed segmentation is applied to those uncertain blocks in one source image. The two source images are compared again with the MSEs of the segmented regions. The fused image is obtained by reserving the blocks and regions with higher MSEs and applying a post-processing operation. Experimental results demonstrate that the AMG-based method outperforms the state-of-the-art fusion approaches in terms of selected objective image quality assessments. The details of the source images are well preserved in the fused image.
Combinational Linear Programming Approach for Daily Optimal Operation of Customers with DG/ESS under TOU Pricing for Customer EMS Software Applications

Fast calculation under limited computation resources is a basic requirement for a customer energy management system. After discussion of several existing models for optimal operation of customers with DG and ESS, a novel optimization model is presented. A possible energy storage system with binary states of charge/discharge is developed according to a sectionalized time-of-use pricing structure, followed by the derivation of the binary state of power flow direction at the point of common connection with the power grid concerned. The number of such comprehensive state combinations is not large, and in case that those state decisions are known, the daily optimal operation of a customer with DG and ESS can be formulated as a linear dynamic system. Therefore it can be solved by a global optimal promised linear programming. Different types of customers with different DG daily generation curves are employed as case studies to demonstrate the high computation efficiency with less computation resources for customer EMS application.
Oxidative Stress-Protective and Anti-Melanogenic Effects of Loliolide and Ethanol Extract from Fresh Water Green Algae, Prasiola japonica

Loliolide is a monoterpenoid hydroxylactone found in many algae, including fresh water green algae, Prasiola japonica. To date, loliolide and compounds in P. japonica have not been studied systematically with respect to skin pharmacology. In this study, we investigated oxidative stress-protective and anti-melanogenic effects of loliolide and P. japonica ethanol extract (Pj-EE), known to contain loliolide, in human keratinocyte (HaCaT) cells and mouse melanoma (B16F10) cells. Loliolide suppressed the transcription of genes encoding matrix metalloproteinases (MMPS), which were induced in HaCaT cells by hydrogen peroxide (H2O2) treatment. Loliolide and Pj-EE not only reduced the melanin secretion and content in B16F10 cells but also increased the expression of the antioxidant proteins nuclear factor (erythroid-derived 2)-like 2 (NRF2) and heme oxygenase-1 (HO-1) in HaCaT cells subjected to H2O2 treatment. Furthermore, loliolide and Pj-EE decreased expression of the anti-melanogenic protein microphthalmia-associated transcription factor (MITF) and tyrosinase in B16F10 cells subjected to α-melanocyte-stimulating hormone (α-MSH) treatment. Our findings demonstrate that loliolide and Pj-EE have antioxidant and anti-melanogenic effects on skin.
Phylogenetic relationships of the family Gryporhynchidae (Cestoda: Cyclophyllidea) inferred through SSU and LSU rDNA sequences

Abstract Tapeworms of the family Gryporhynchidae are endoparasites of fish-eating birds distributed worldwide. Currently the family contains 16 genera classified on the basis of the morphology of the rostellar apparatus, rostellar hooks and strobilar anatomy. However, the phylogenetic relationships among the genera are still unknown. In this study, sequences of the near complete 18S (SSU) and 28S (LSU) from rDNA of 13 species of gryporhynchids (adult specimens) representing eight genera (Cyclustera, Dendrouterina, Glossocercus, Gryporhynchidae gen. sp., Neovalipora, Paradilepis, Parvitaenia, Valipora) and one species of metacestode from fish (Neovalipora) were generated. Additionally, sequences of metacestodes of the genera Amirthalingamia, Neogryporhynchus, Paradilepis, Parvitaenia and Valipora from Africa recently added to the GenBank database were analysed. Phylogenetic relationships were inferred using maximum-likelihood (ML) and Bayesian inference of each (SSU and LSU) dataset. The phylogenetic analyses indicated that the family Gryporhynchidae is a well-supported monophyletic group within the Cyclophyllidea. The trees inferred with SSU and LSU datasets had similar topologies and suggested that the genera Glossocercus (two species sequenced) and Paradilepis (four spp.) are monophyletic. In contrast, Dendrouterina, Parvitaenia and Valipora are paraphyletic, suggesting that the species composition of these genera should be critically reviewed. Interestingly, species of the genera that use the same groups of definitive hosts such as herons (Ardeidae), cormorants (Phalacrocoracidae) and ibis (Threskiornithidae) are together in the phylogenetic tree, even though they differ markedly from each other in some morphological characters, especially shape and size of rostellar hooks.
IoT and Computer Vision Based Driver Safety Monitoring System with Risk Prediction

A system to monitor driver safety was developed to alarm the driver when he becomes drowsy and also to centrally update driver condition in a database. To measure the concentration level of the driver we averaged few factors such as drowsiness, his face in frame. As most of the methods lack accuracy we used robust facial landmark detectors to detect face in frame and to also detect eyes. By few algorithms, driver drowsiness and drivers face position in frame was detected to alarm the driver locally. At the same time data was uploaded to a database for further inspection. We averaged the data to measure their concentration level and finally made a prediction by using their previous data. This system can be used to notify the driver and take proper actions to prevent the accident before happening
Physicochemical characterization and thermal behavior of hexosomes containing ketoconazole as potential topical antifungal delivery system

Abstract Objective: The main purpose of this article is to show the valuable characteristics that liotropic liquid crystal systems possess to be employed as new drug delivery systems. Significance: Colloidal aqueous dispersions of lyotropic liquid crystal mesophases such as the identified as cubosomes and hexosomes, and so on, have received considerable attention due to their unique nanostructures and their thermodynamic properties, which provide the potential as a sustained drug release matrix. Additionally, their large surface area and similarity with the liquid crystal structures of intercellular lipids of stratum corneum enhances the interaction with the skin and mucous, increasing the potential for topical drug delivery efficiency of biopharmaceutical class II drugs as the antifungal ketoconazole. Methods: This article presents the results in morphological characteristics, particle size, ζ potential, flow, thermal behavior and drug release studies of hexosomes containing ketoconazole (LHLC-K) obtained with glycerol monooleate, propylene glycol monolaurate, poloxamer, and water mixtures. Results: This colloidal system exhibits a Newtonian-type flow and a hexagonal nanostructure with a median particle size of 107 ± 20 nm and ζ potential of +4.45 ± 0.50 mV. Through differential scanning calorimetry studies, the LHLC-K demonstrated physical and chemical stability for more than six months and mesophasic thermal reversibility between 10 and 50 °C. Finally, LHLC-K releases ketoconazole following a kinetics described by the first order model. Conclusions: Physicochemical properties of the hexosomes containing ketoconazole are important for topical mycosis treatment administration, conditions of storage, and for its incorporation into the formulation of semi-solid dosage forms.