Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Learning Process in the Higher Education Through the Use of a Predictive Tool (Dashboard)

Improving the learning process in the high education through the use of a predictive tool (dashboard). De Jesús Liriano, Rubėn and Sevillano, María C. , 2018: A Florida Distance Learning Association Journal (FDLA) Paper to be published in a special section of the US Distance Learning Association Professional Journal Distance Learning. Interaction/Predictive Tools/Dashboard/Evaluation Learning Process/Automated Process/eLearning Environment/Learning Environment/Learning Management System (LMS)/Cloud Computing/ Learning Performance, and Learning Analytics. The purpose of this study was the initial step in providing a clear understanding of the use of a predictive tool (dashboard) to improving the learning process in the higher education field. Through further applications and research, the predictive tool must be refined to increase accuracy, and provide expeditious, reliable feedback about the students’ progress in an online environment. This information will benefit e-learning facilitators in the following ways: 1) knowing their students and, therefore, identifying how best to deliver content to them; 2) applying the right predictive tool to capitalizing on the new experience and new opportunity; 3) taking value-added approaches to student engagement; 4) bridging connections in a precise manner between enriched content and e-learning; and 5) transforming e-learning into digital experience. 2 FDLA Journal, Vol. 4 [2019], Art. 7 https://nsuworks.nova. edu/fdla-journal/vol4/iss1/7
Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points’ spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available.
CRACK: qualitative reasoning about fatigue and fracture in steel bridges

The consultant reasoning about cracking knowledge (CRACK) system, which uses qualitative reasoning to analyze fatigue and fracture in steel bridges, is described. CRACK's architecture includes three reasoning levels-heuristic, qualitative, and quantitative-linked by a common representation of the bridge's physical structure. CRACK explains a failure by matching a crack progression sequence to the observed facts. The system establishes the type of problem using design critiques, predictive modeling, or failure analysis describes the problem by gathering information on the girder's geometry, service history, material properties, and observed symptoms. It then hypothesizes a cause, qualitatively simulates possible crack progression sequences to guide quantitative analysis, calculates the fracture mechanics to determine critical crack sizes and fatigue lives, and evaluates the hypothesis. Finally it states a conclusion. Several difficulties that arose in developing a simple, theoretically defensible, qualitative model which translates numerical relationships into a correct set of behaviors are discussed. <<ETX>>
Detect Globally, Label Locally: Learning Accurate 6-DOF Object Pose Estimation by Joint Segmentation and Coordinate Regression

Coordinate regression has established itself as one of the most successful current trends in model-based 6 degree of freedom (6-DOF) object pose estimation from a single image. The underlying idea is to train a system that can regress the three-dimensional coordinates of an object, given an input RGB or RGB-D image and known object geometry, followed by a robust procedure such as RANSAC to optimize the object pose. These coordinate regression based approaches exhibit state-of-the-art performance by using pixel-level cues to model the probability distribution of object parts within the image. However, they fail to capture global information at the object level to learn accurate foreground/background segmentation. In this letter, we show that combining global features for object segmentation and local features for coordinate regression results in pixel-accurate object boundary detections and consequently a substantial reduction in outliers and an increase in overall performance. We propose a deep architecture with an instance-level object segmentation network that exploits global image information for object/background segmentation and a pixel-level classification network for coordinate regression based on local features. We evaluate our approach on the standard ground-truth 6-DOF pose estimation benchmarks and show that our joint approach to accurate object segmentation and coordinate regression results in the state-of-the-art performance on both RGB and RGB-D 6-DOF pose estimation.
Needed on Simulation to Enhance Plastic Surgery Education and Training ?

Changes in health care triggered major shifts in health sciences education, including a move to simulation in education and training. Simulation enhances student learning, provides controlled and safe practice opportunities, and shapes the acquisition of doctors’ clinical skills/professional competence. Myriad research opportunities exist in the field of simulation-based medical education (SBME). The overall goal of this research was to reflect on the purpose and value of research on simulation and was aimed at improving plastic surgery education and training (teaching, learning and professional competence). The methodology is based on the conceptualisation and contextualisation of SBME. This article offers recommendations on the role and value of simulation in education; the enhancement of learning; integrating simulationbased education into training curricula; standardising plastic surgery training globally; the revalidation of competency in continuing medical education, and the engagement of teachers and students in educational research. Simulation has potential to play an integral role in developing better and safer health care services for patients worldwide.
SINGULAR NONCENTRAL AND DOUBLY NONCENTRAL MATRIX VARIATE BETA DISTRIBUTIONS

In this paper, we determine the symmetrised density of doubly noncentral singular matrix variate beta type I and II distributions under different definitions. As particular cases we obtain the noncentral singular matrix variate beta type I and II distributions and the corresponding joint density of the nonnull eigenvalues. In addition, we propose an alternative approach to find the corresponding nonsymmetrised densities. From the latter, we solve the integral proposed by Constantine (1963) and Khatri (1970) and reconsidered in Farrell (1985, p. 191), see also Dı́az-Garćıa and Gutiérrez-Jáimez (2006a), for the singular and nonsingular cases.
Anaerobes in Biofi lm-Based Healthcare-Associated Infections

G. Donelli (ed.), Biofi lm-based Healthcare-associated Infections: Volume I, Advances in Experimental Medicine and Biology 830, DOI 10.1007/978-3-319-11038-7_6, © Springer International Publishing Switzerland 2015 Abstract Anaerobic bacteria can cause an infection when they encounter a permissive environment within the host. These opportunistic pathogens are seldom recovered as single isolates but more frequently are involved in polymicrobial infections, together with other anaerobes or aerobes. Nowadays it’s known that some anaerobic bacteria are also able to grow as biofi lm even if this feature and its role in the healthcare-associated infections (HAIs) are still poorly characterized. As consequence, the involvement of biofi lmforming anaerobic bacteria in infections related to healthcare procedures, including surgery and medical devices implantation, is underestimated. The current knowledge on the role of biofi lmgrowing anaerobes in HAIs has been here reviewed, with particular reference to respiratory, intestinal, intra-abdominal, wound, and urogenital tract infections. Even if the data are still scarce, the ability to form biofi lm of opportunistic anaerobic species and their possible role as causative agents of HAIs should alert even more clinicians and microbiologists on the need to search for anaerobes in clinical samples when their presence can be reasonably assumed.
Fabrication of Full-3D Printed Electronics RF Passive Components and Circuits

This paper presents a process for full-3D circuit and RF passive component fabrication based on two main steps: additive manufacturing of the plastic or ceramic substrate, through a stereolitographic 3D printer, and a copper electroless plating metallization process. The metallization results are discussed in terms of resistivity, comparing them to the State-of-the-Art on 3D printed electronics. The capabilities and accuracy of the process have been demonstrated and discussed through the fabrication of conical inductors.
Envir onmental Monitoring Ar ound Indian Antar ctic Stations

Environmental monitoring is integral part of Environmental Impact Assessment (EIA). Continuous ef forts of Scientific Committee on Antarctic Research (SCAR), Committee for Environmental Protection (CEP), Council of Managers of National Antarctic Programme (COMNAP) and establishment of Convention for Conservation of Marine Living Resources (CCAMLR) has given impetus to establish scientific approach towards environmental monitoring thus formed a core part of Antarctica Treaty System (A TS).
Analysis of Blind Signal Separation of Mixed Signals Based on Fast Discrete Curvelet Transform

This paper investigates the technique of Fast Discrete Curvelet Transform (FDCT) de-noising with the Independent Component Analysis (ICA) for the separation of noisy signals. Two approaches are presented for this purpose. In the first approach, noisy mixed signals are separated using fast ICA algorithm and then Curvelet thresholding is used to de-noise the results. The second approach uses Curvelet thresholding to denoise the mixed signals and then the fast ICA algorithm to separate the de-noised signals. The simulation results show a better performance for image de-noising followed by separation. The Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE) are used as metrics to evaluate the quality of the separated signals. Index Terms Blind Source Separation (BSS), ICA, FDCT.
Android authorship attribution through string analysis

With the rising popularity of Android mobile devices, the amount of malicious applications targeting the Android platform has been increasing tremendously. To mitigate the risk of malicious apps, there is a need for an automated system to detect these applications. Current detection techniques rely on the signatures of well-documented malware, and hence may not be able to detect new malware samples. Instead of generating signatures for malware samples themselves, in this work, we propose to develop a lightweight system that can generate signatures of malware writers by leveraging the string components present in their Android binaries. Using these author signatures, we can effectively detect a wide range of existing, as well as any new, malware samples generated by particular authors. The proposed system achieved 98%, 96%, and 71% accuracy over datasets of 1559 benign, 262 malicious, and 96 obfuscated Android applications, respectively. The string-based approach achieved 71% of accuracy compared to only 50% obtained with the existing Ding and Samadzadeh's system.
Estimation of Directed Information to Processes Assuming Continuous Values with CTW Algorithm

This paper introduces the use of a directed information estimator for discrete-valued processes to continuousvalued processes by previously discretizing the continuous-valued processes according to three different and generally applicable methods. The directed information estimator uses context tree weighting algorithm (CTW). The three discretization methods are called equidistant, equipopulated and symbolic methods. Simulated results indicate that faster and more conservative results are found by using few discretization levels with equipopulated method. Keywords— Directed information, Causality, Estimation, Discretization.
Detection of some heavy metals in fish (Tilapia nilotica and Clarias lazera) at Menofia governorate

A grand total of eighty random samples of Tilapia nilotica and Clarias lazera (40 of each) were collected after catching in Shebin El-kom, Menofia Governorate. The samples were directly transferred to the laboratory for determination of their contents of mercury, lead and cadmium. The evaluation revealed that the mean values of mercury concentration (mg/kg) in the examined fresh water fish were 0.46 ± 0.03 mg/ kg for Tilapia nilotica and 0.52 ± 0.04 mg/ kg for Clarias lazera. Furthermore, the mean values of lead concentration (mg/kg) in the examined samples of Tilapia nilotica and Clarias lazera were 0.08 ± 0.02 and 0.14 ± 0.02 mg/ kg, respectively. In addition, the mean values of cadmium concentration in the examined samples of fresh water fish were 0.10 ± 0.01 mg / kg for Tilapia nilotica and 0.14 ± 0.02 mg/ kg for Clarias lazera. The public health importance of such heavy metal residues were discussed.
A Sensitivity Function Approach to the Design of Rudder Roll Stabilization Controller

This paper is concerned with the design of a rudder roll stabilization (RRS) system by the internal model control (IMC) method. The proposed method directly shapes the output sensitivity function which relates the wave disturbance to the ship roll motion, to achieve good disturbance rejection. Under the IMC structure, the sensitivity functions depend linearly on a design transfer function and this makes it easy to satisfy the internal stability condition. A reference conditioning technique is employed in the design that takes the limited actuator authority into the controller design consideration to avoid unknowingly pushing the actuator beyond its saturation and slew rate limitation boundaries. Numerical results indicate that very good roll reduction is achieved for sinusoidal disturbance and reasonable roll reduction is obtained for a narrow band type of disturbance generated by passing a white noise sequence through a second order shaping filter.
One Permutation Hashing for Efficient Search and Learning

Recently, the method of b-bit minwise hashing has been applied to large-scale linear learning and sublinear time near-neighbor search. The major drawback of minwise hashing is the expensive preprocessing cost, as the method requires applying (e.g.,) k=200 to 500 permutations on the data. The testing time can also be expensive if a new data point (e.g., a new document or image) has not been processed, which might be a significant issue in user-facing applications. We develop a very simple solution based on one permutation hashing. Conceptually, given a massive binary data matrix, we permute the columns only once and divide the permuted columns evenly into k bins; and we simply store, for each data vector, the smallest nonzero location in each bin. The interesting probability analysis (which is validated by experiments) reveals that our one permutation scheme should perform very similarly to the original (k-permutation) minwise hashing. In fact, the one permutation scheme can be even slightly more accurate, due to the "sample-without-replacement" effect. Our experiments with training linear SVM and logistic regression on the webspam dataset demonstrate that this one permutation hashing scheme can achieve the same (or even slightly better) accuracies compared to the original k-permutation scheme. To test the robustness of our method, we also experiment with the small news20 dataset which is very sparse and has merely on average 500 nonzeros in each data vector. Interestingly, our one permutation scheme noticeably outperforms the k-permutation scheme when k is not too small on the news20 dataset. In summary, our method can achieve at least the same accuracy as the original k-permutation scheme, at merely 1/k of the original preprocessing cost.
Backside Optical I/O Module for Si Photonics Integrated with Electrical ICs Using Fan-Out Wafer Level Packaging Technology

We propose a novel Si photonics module that overcomes the issues of conventional Si photonics modules such as package structure and electrical connection. The module incorporates an optical fiber socket fabricated by blind via socket (BVS) technology, which implements backside optical I/O in a photonic IC (PIC) by forming blind via holes on the backside. High-speed high-density electrical connection to both the PIC and an electrical IC (EIC) is also obtained in the module by fan-out wafer level packaging (FOWLP) technology. These technologies achieve a surface-mountable substrate-less fan-out optical module. It realizes a practicable integrated module of optoelectronic devices excellent in terms of electrical characteristics such as signal integrity (SI) and power integrity (PI), heat characteristics, and miniaturization. This paper presents a BVS module that enables optical coupling between a III-V/Si photodiode (PD) fabricated on a Si substrate and a multi-mode optical fiber by passive alignment of only insertion of the fiber into a blind via hole on the backside. High-speed optical signal transmission is also demonstrated with a fan-out optical module in which a BVS and an EIC are integrated by FOWLP and a vertical-cavity surface-emitting laser (VCSEL) or PD is mounted on the BVS.
THE SIGNAL PROCESSING BY EMPIRICAL MODE DECOMPOSITION FOR INCOHERENT DOPPER WIND LIDAR BASED ON IODINE FILTER

The atmospheric wind field can be retrieved from the combined Mie and Rayleigh backscattering by use of an incoherent Doppler lidar. In this paper, we present a detailed analysis of the signal processing and wind field retrieving from backscattered lidar signal. The backscattering lidar signal can be denoised based on the method of Empirical Mode Decomposition (EMD). And a new approach is proposed to eliminate the edge problem that comes with EMD. The Signal-to-Noise Ratio (SNR) by using the EMD method is improved no less three times than the original’s. The result of wind field retrieving shows that this denoising method is effective and superior to traditional method.
A hybrid assessment approach for medical device software development companies

Medical device software development organizations are bound by regulatory requirements and constraints to ensure that developed medical devices will not harm patients. Medical devices have to be treated as complete systems and be evaluated in this manner. Instead of manufacturers having to ensure compliance to various regulatory standards individually, the authors previously developed a medical device software process assessment framework called MDevSPICE® that integrates the regulatory requirements from all the relevant medical device software standards. The MDevSPICE® was developed in a manner that suits plan‐driven software development. To improve the usability of MDevSPICE® in agile settings, we extended the assessment approach. The hybrid assessment approach described here combines the MDevSPICE®‐based process assessment method with steps for prioritization of improvement needs through value stream mapping and enabling process improvement through the use of KATA technique. This approach integrates agile methods into the medical device software development process while adhering to the requirements of the regulatory standards. This paper describes the implementation of the approach within 4 organizations that develop software in line with medical device regulations.
Building Reference Datasets to Support Socialbots Detection

Reference datasets are databases that guide the development and the evaluation of tools and methods in several areas of Computer Science. In the field of Information Security, in particular, there is a notable need for tools devoted to detection and classification. Hence, the availability of such datasets is fundamental: the reference dataset is seen as a standard against which a tool must be tested characterize its accuracy. In this sense, the reference dataset is analogous to the classic metrology primary standard, in a sense that it provides the most trustworthy reference against which an object under evaluation can be compared to. It is therefore of great importance to devote efforts to the development of methods that assure the quality of reference datasets. In the present work, we discuss the challenges faced by the currently available datasets and propose directions towards the development of reliable datasets. Finally, we propose a methodology for the construction of reference datasets for Online Social Networks and present a case study for the construction of a Twitter dataset for the detection of social bots.
Alterations in Pharmacokinetics of Orally Administered Carbamazepine in Rats Treated with Sodium alginate: Possible Interaction between Therapeutic Drugs and Semi-solid Enteral Nutrients

Abstract Objective The use of enteral nutrients plays an extremely important role in accurate nutrition management. Sodium alginate (SA) is frequently used for the semi-solidification of enteral nutrients. In the present study, we investigated whether the pharmacokinetic profile of orally administered carbamazepine (CBZ) is altered by a treatment with SA immediately before and after dosing of the drug. Furthermore, the adsorption effects of SA on CBZ were examined using an in vitro analysis. Method SA was orally administered to rats just before and immediately after CBZ dosing. The CBZ concentration profile following its oral administration was analyzed by a non-compartmental method. The adsorption of CBZ onto SA was evaluated after centrifugation using an ultrafiltration device. Findings The serum concentration of orally administered CBZ at each sampling point was reduced by the treatment with SA, and the extent of the decrease observed in the concentration of CBZ was larger when SA was ingested immediately after administration of the drug, which was consistent with the alteration observed in the value of the area under the curve (AUC). No significant differences were noted in the elimination rate at the terminal phase (ke) among the groups. In the in vitro assay, CBZ was adsorbed by SA in the solution used to reflect fluid in the intestinal tract. Conclusions The pharmacological efficacies of CBZ might be reduced by SA through the pharmacokinetic interactions, and that the careful attention should be paid to the timing of administration of CBZ and semi-solid enteral nutrients.