Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for Multilevel Parallelism on GPU Clusters: Application to a Multigrid Accelerated Navier-Stokes Solver

Computational Fluid Dynamics (CFD) is an important field in high performance computing with numerous applications. Solving problems in thermal and fluid sciences demands enormous computing resources and has been one of the primary applications used on supercomputers and large clusters. Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can accelerate simulation science applications substantially. While significant speedups have been obtained with single and multiple GPUs on a single workstation, large problems require more resources. Conventional clusters of central processing units (CPUs) are now being augmented with GPUs in each compute-node to tackle large problems. The present research investigates methods of taking advantage of the multilevel parallelism in multi-node, multi-GPU systems to develop scalable simulation science software. The primary application the research develops is a cluster-ready GPU-accelerated Navier-Stokes incompressible flow solver that includes advanced numerical methods, including a geometric multigrid pressure Poisson solver. The research investigates multiple implementations to explore computation / communication overlapping methods. The research explores methods for coarse-grain parallelism, including POSIX threads, MPI, and a hybrid OpenMP-MPI model. The application includes a number of usability features, including periodic VTK (Visualization Toolkit) output, a run-time configuration file, and flexible setup of obstacles to
Modify Flipped Model of Co-regulation and Shared-regulation Impact in Higher Education, and Role of Facilitator on Student’s Achievement

Flipped learning approach is a well-organized learning model leading to efficient active learning, effective peer-to-peer collaboration, and student-teacher interaction. To date, strategic implementation of co-regulation and shared regulation is rare in a flipped model in higher education, but they are become necessary to apply them.This research is to propose and modify the current flipped learning model by adding some elements such as providing some co-regulation and shared regulation strategies to enhance the level of student’s self-regulation skills to better student’s academic achievements next to instructor support, and peer-to-peer interactive way by creating a regulation questions to collaboratively assessment of the student’s self-regulation resulted.
On `initial regime' cross ow-induced vibration predictions of rigid cylindrica lstructures with high wake turbulence

Copyright: © 2017 Elbanhawy AY, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author: AY Elbanhawy, Mechanical Power Department, Faculty of Engineering, Ain Shams University, 1 ElSarayat Street, Abbasia, Cairo, Egypt, E-mail: amr.elbanhawy@eng.asu. edu Received: March 27, 2017: Accepted: August 02, 2017: Published: August 05, 2017
Multi-Task Deep Neural Networks for Multimodal Personality Trait Prediction

Artificial intelligence (AI) is being increasingly integrated into the hiring process. A prominent example is video interviews used by large organizations to quickly screen job candidates. The personality traits of job candidates, such as the Big Five characteristics, are predicted using computer vision and affective computing approaches. Past methods have used feature extraction, text analysis, and other multimodal methods to achieve a high prediction accuracy. We build upon past approaches by using a multi-task deep neural network (MTDNN) to predict personality traits and job interview scores of individuals. An MTDNN shares lower layers to learn features which apply across outputs, and contains task-specific layers to predict each individual trait, thereby providing an advantage over single-task approaches since personality traits are determined by features (e.g., emotion, gestures, and speech) shared across traits. Our model is trained using the CVPR 2017 First Impressions V2 competition dataset, containing 10,000 videos of individuals and their Big Five personality and interview scores. We also use scene, audio, and facial features from the state-of-the-art model from the competition. A 5-fold cross-validation approach is used to evaluate our results. We achieve a prediction accuracy for all traits on par with state-of-the-art models, while reducing training time and parameter tuning to a single network.
Towards Bidirectional Live Programming for Incomplete Programs

Bidirectional live programming not only allows software developers to see continuous feedback on the output as they write the program, but also allows them to modify the program by directly manipulating the output, so that the modified program can get the output that was directly manipulated. Despite the appealing of existing bidirectional live programming systems, there is a big limitation: they cannot deal with incomplete programs where code blanks exist in the source programs. In this paper, we propose a framework to support bidirectional live programming for incomplete programs, by extending the output value structure, introducing hole binding, and formally defining bidirectional evaluators that are well-behaved. To illustrate the usefulness of the framework, we realize the core bidirectional evaluations of incomplete programs in a tool called Bidirectional Preview. Our experimental results show that our extended back-ward evaluation for incomplete programs is as efficient as that for complete programs in that it is only $21 ms$ slower on a program with 10 holes than that on its full program, and our extended forward evaluation makes no difference. Furthermore, we use quick sort and student grades, two nontrivial examples of incomplete programs, to demonstrate its usefulness in algorithm teaching and program debugging.
Low-cost COTS GNSS interference detection and classification platform: Initial results

Astract- Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference needs to be detected, classified, its purpose determined, and localized, such that it can be eliminated. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This paper introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-possessing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human in the loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers to be deployed, resulting in a significantly higher probability of intercept (POI). The initial results of controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches.
Organic Anion Transport in Short-term Cultured Rat Hepatocytes and Isolated Perfused Rat Liver

Transport of 35S-labeled sulfobromophthalein V'SIBSP was studied in short-term cultured rat hepatocytes incubated in bovine serum albumin. At 37°C, initial uptake of V35SIBSP was 5-10fold that at 4°C, linear for at least 15 min. saturable, inhibited by bilirubin, and reduced by > 70% after ATP depletion or isosmotic substitution of sucrose for NaCI in medium. Replacement of Na+ by K+ or Li' did not alter uptake, whereas replacement of Clby HCO3 or gluconatereduced uptake by 40%. Substitution ofClby the more permeant NOenhanced initial BSP uptake by 30%. Efflux of V5SIBSP from cells to media was inhibited by 40% after ATP depletion or sucrose substitution. To confirm these results in a more physiologic system, transport of 3Hlbilirubin was studied in isolated livers perfused with control medium or medium in which Clwas replaced by gluconate-. Perfusion data analyzed by the model of Goresky, revealed 4050% reductions in influx and efflux with gluconatesubstitution. These results are consistent with existence of a Cl-/organic anion-exchange mechanism similar to that described by others in renal tubules.
APPROACHES TO THE GAS CONTROL IN UCG

Underground Coal Gasification represents an alternative for conventional coal mining. This technology is also less expensive than traditional mining. It is expected that coal will be an important energy source in the coming decades. In requirement to improve the gasification process we must ensure that the combustion reactions generated enough energy to heat the reactants. This can be achieved by controlling the flow of oxidizing agents and the underpressure control at the exit of the reactor UCG. This paper aims to propose the stabilization of air flow as a main gasification agent injected to the gasifier, underground temperature and concentration of O2 in syngas. Also there is proposed the mechanism that could cope with uncertainties in the process of UCG and its control on stabilization level. Paper presents utilization of discrete controller with adaptation in order to stabilization of UCG process variables. The controllers were verified on experimental ex-situ reactor (generator).
Lateral Flow Test Interpretation with Residual Networks

Lateral flow tests (LFTs) are a cost-effective, quick, and frequently used testing method in many domains such as food safety and environmental and clinical applications. However, a major challenge is accurate interpretation of LFT results. Often, if a low level of target substance is present in the input liquid, the test line indicator may appear faint, causing a test interpreter to read the test result as a false negative. Therefore, to address this problem, we propose a deep-learning-based method to interpret images of LFT results. Our model is based on ResNet-101, a state-of-the-art image classification model that uses residual networks, or skip-connections between layers to improve learning on the training dataset. We further improve our model by using data augmentation to generate additional and more difficult images of LFTs for the model to learn from, thereby improving its performance and reducing overfitting to the training dataset. Our approach is also trained and tested on a dataset of SARS-CoV-2 LFT images, containing both positive and negative results. We compare our ResNet approach to a baseline convolutional neural network model. Our results show the ResNet model achieves a higher specificity and sensitivity than the baseline model to interpret LFT results.
A Deep Learning BERT-Based Approach to Person-Job Fit in Talent Recruitment

Although the widespread use of the Internet provides job recruiters with a larger pool to select the most qualified candidates, the tedious process of going over hundreds of resumes makes a fair and objective decision making more difficult. This paper proposes an end-to-end BERT-based framework to decrease the workload and expedite the shortlisting process of job applicants. Utilizing historical-records data of thousands failed and successful job applications, our model simulates the recruiters’ decision-making process by the state-of-the-art BERT algorithm. The results show that BERT outperforms a variety of models by a high margin.
Combining Technologies for Aiding Search Missions with Drones

This paper presents the combination of technologies in an application for aiding search missions. Unmanned Aerial Vehicle (UAV), wireless communications, and image processing and classification technologies are combined for generating warnings to an operator in a search mission. Thus, the UAV would be used to cover a specific ground area communicating images the system should be able to classify. In case a positive image is classified (i.e., one with the object/s being searched for), an operator is warned so that the final decision is made by an expert. In this way, the mostly bored task of looking at images without object being looked for is avoided, and the expert attention could be focused on images with a high probability of having the object/s being looked for. A cloud service is used for image classification (IBM Watson) along with a previously proposed UAV system architecture and a low-cost drone SDK. A proof of concept is described, giving implementation details, and preliminary results. Obtained results show that the general system is a viable solution and set a starting point for enhancing several subsystems for better performance.
An Exploratory Study of Productivity Perceptions in Software Teams

Software development is a collaborative process requiring a careful balance of focused individual effort and team coordination. Though questions of individual productivity have been widely examined in past literature, less is known about the interplay between developers' perceptions of their own productivity as opposed to their team's. In this paper, we present an analysis of 624 daily surveys and 2899 self-reports from 25 individuals across five software teams in North America and Europe, collected over the course of three months. We found that developers tend to operate in fluid team constructs, which impacts team awareness and complicates gauging team productivity. We also found that perceived individual productivity most strongly predicted perceived team productivity, even more than the amount of team interactions, unplanned work, and time spent in meetings. Future research should explore how fluid team structures impact individual and organizational productivity.
Online Summarizing Alerts through Semantic and Behavior Information

Alerts, which record details about system failures, are crucial data for monitoring a online service system. Due to the complex correlation between system components, a system failure usually triggers a large number of alerts, making the traditional manual handling of alerts insufficient. Thus, automatically summarizing alerts is a problem demanding prompt solution. This paper tackles this challenge through a novel approach based on supervised learning. The proposed approach, OAS (Online Alert Summarizing), first learns two types of information from alerts, semantic information and behavior information, respectively. Then, OAS adopts a specific deep learning model to aggregate semantic and behavior repre-sentations of alerts and thus determines the correlation between alerts. OAS is able to summarize the newly reported alert online. Extensive experiments, which are conducted on real alert datasets from two large commercial banks, demonstrate the efficiency and the effectiveness of OAS.
Impact and Mitigation of High-Frequency Side-Channel Noise Intrusion on the Low-Frequency Performance of an Inverter

This letter investigates and demonstrates, experimentally, the impact of a high-frequency side-channel noise intrusion (SNI) on an output-voltage feedback signal on the low-frequency performance of a three-phase inverter. The high-frequency SNI originates at frequencies that are in the vicinity of the sampling frequency (and its multiples) of the inverter. The resultant noise-injected output-voltage feedback signal is fed to a controlling digital signal processor that experiences feedback aliasing effect due sub-Nyquist-frequency sampling. The impacts of these aliasing effects are observed at sub- or super-60-Hz frequencies of the output voltages of the inverter. Subsequently, a Kalman-filter-estimation-based control approach is pursued to mitigate this detrimental effect and its efficacy demonstrated experimentally.
Self-guided Proposal Generation for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) in remote sensing images remains a challenging task when learning object detectors with only image-level labels. As we know, object proposal generation plays a crucial role in WSOD. At present, the proposal generation of most existing WSOD methods mainly relies on heuristic strategies such as selective search and Edge Boxes. However, the proposals obtained by the above methods cannot well cover the entire objects, severely hindering the performance of WSOD. To address this issue, this paper proposes a Self-guided Proposal Generation approach, termed SPG. It can be easily implemented with most WSOD methods in a unified framework. To this end, we first introduce a confidence propagation approach to obtain the objectness confidence map for each image, which on the one hand highlights informative object locations, and on the other hand aggregates discriminative feature representation by combining the objectness confidence map with the deep features. Then, the proposal generation is implemented by mining informative regions as proposals on the objectness confidence map. Extensive evaluations on two challenging datasets demonstrate that our SPG significantly improves the baseline methods Online Instance Classifier Refinement (OICR) and Min-Entropy Latent Model (MELM) by large margins (for OICR: 15.86% mAP and 12.89% CorLoc gains on the NWPU VHR-10.v2 dataset, 3.65% mAP and 4.87% CorLoc gains on the DIOR dataset; for MELM: 20.51% mAP and 23.54% CorLoc gains on the NWPU VHR-10.v2 dataset, 7.11% mAP and 4.96% CorLoc gains on the DIOR dataset) and achieves the state-of-the-art results compared with existing methods.
Using Bandit Algorithms for Selecting Feature Reduction Techniques in Software Defect Prediction

Background: Selecting a suitable feature reduction technique, when building a defect prediction model, can be challenging. Different techniques can result in the selection of different independent variables which have an impact on the overall performance of the prediction model. To help in the selection, previous studies have assessed the impact of each feature reduction technique using different datasets. However, there are many reduction techniques, and therefore some of the well-known techniques have not been assessed by those studies. Aim: The goal of the study is to select a high-accuracy reduction technique from several candidates without preliminary assessments. Method: We utilized bandit algorithm (BA) to help with the selection of best features reduction technique for a list of candidates. To select the best feature reduction technique, BA evaluates the prediction accuracy of the candidates, comparing testing results of different modules with their prediction results. By substituting the reduction technique for the prediction method, BA can then be used to select the best reduction technique. In the experiment, we evaluated the performance of BA to select suitable reduction technique. We performed cross version defect prediction using 14 datasets. As feature reduction techniques, we used two assessed and two non-assessed techniques. Results: Using BA, the prediction accuracy was higher or equivalent than existing approaches on average, compared with techniques selected based on an assessment. Conclusions: BA can have larger impact on improving prediction models by helping not only on selecting suitable models, but also in selecting suitable feature reduction techniques.
Circuit Analysis of a Coplanar Waveguide (CPW) Terminated With a Step-Impedance Resonator (SIR) for Highly Sensitive One-Port Permittivity Sensing

This paper presents a single-frequency reflective-mode phase-variation microwave sensor devoted to the dielectric characterization of materials. The device is implemented in coplanar waveguide (CPW) technology and consists of two parts: (i) the sensing region, a step-impedance resonator (SIR) as termination of a CPW transmission line, and (ii) the design region, a cascade of high/low impedance quarter-wavelength inverters, used to boost up the sensitivity. By placing the so-called material under test (MUT) on top of the sensing region, the capacitance of the SIR is altered due to the effects of the dielectric constant of the MUT. This modifies the phase of the reflection coefficient seen from the input port, the output variable. From a circuit analysis, it is demonstrated that the sensitivity for small perturbations in the vicinity of the dielectric constant of a reference (REF) material can be optimized by setting the operation frequency of the sensor to the resonance frequency of the SIR loaded with such REF material. The maximum sensitivity in one of the reported sensors is as high as 66.5°, and the main figure of merit, defined as the ratio between the maximum sensitivity and the area of the sensing region expressed in terms of the squared guided wavelength, is FoM = 3643°/λ2. Such figure of merit represents a significant improvement as compared to the one of the equivalent sensor implemented by means of an open-ended quarter-wavelength sensing line. Such equivalence between the semi-lumped element (i.e., SIR-based) sensor and the fully distributed counterpart is also analyzed in the paper.
Electron Scattering at Surfaces and Grain Boundaries in Rh Layers

The resistivity size effect in Rh is investigated by quantifying electron scattering at surfaces and grain boundaries in polycrystalline Rh layers with thickness <inline-formula> <tex-math notation="LaTeX">${d} =9$ </tex-math></inline-formula>–261 nm. Sputter deposition on SiO<sub>2</sub>/Si(001) at <inline-formula> <tex-math notation="LaTeX">${T}_{s} =20$ </tex-math></inline-formula>, 350, and 350 °C followed by <italic>in situ</italic> stepwise annealing to 750 °C yields three series of 111-textured Rh layers with increasing average lateral grain sizes <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>. Electron backscatter diffraction (EBSD) maps show that <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> for annealed layers increases with layer thickness from <inline-formula> <tex-math notation="LaTeX">${D} =89$ </tex-math></inline-formula> to 134 nm, matching the surface morphological lateral correlation length <inline-formula> <tex-math notation="LaTeX">${{\xi }}=86$ </tex-math></inline-formula>–154 nm measured by atomic force microscopy (AFM). <italic>In situ</italic> transport measurements yield resistivity <inline-formula> <tex-math notation="LaTeX">${{\rho }}$ </tex-math></inline-formula> versus <inline-formula> <tex-math notation="LaTeX">${d}$ </tex-math></inline-formula> data, which are described with a combined Fuchs–Sondheimer (FS) and Mayadas–Shatzkes (MS) model and indicate a Rh electron mean free path <inline-formula> <tex-math notation="LaTeX">${{\lambda }} =9.5$ </tex-math></inline-formula> unboldmath± 0.8 nm and a reflection coefficient <inline-formula> <tex-math notation="LaTeX">${R} =0.41$ </tex-math></inline-formula> unboldmath± 0.05 for grain boundaries characterized by a rotation about the <inline-formula> <tex-math notation="LaTeX">$\langle 111\rangle $ </tex-math></inline-formula> axis. As-deposited layers have considerably smaller grains, leading to a threefold and sixfold increase in <inline-formula> <tex-math notation="LaTeX">${{\rho }}$ </tex-math></inline-formula> above the bulk value for <inline-formula> <tex-math notation="LaTeX">${d} =10$ </tex-math></inline-formula> nm and <inline-formula> <tex-math notation="LaTeX">${T}_{s} =350$ </tex-math></inline-formula> and 20 °C, respectively. The overall results indicate a conductance benefit of Rh versus Cu for narrow interconnect lines and reveal the importance of Rh processing to achieve a large (>10 nm) grain size, which is essential to realize the conductivity advantage.