Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Performance Analysis of Apache Spark and Map reduce using K-Means

: All around the globe, computer science grabbed its interest inBig Data that has developed extremely high with its continuous raise of data generation in social media and the aim for industrial and borstal Hercules institutions to facilitate additionalinvestigation of their knowledge. This paper provides a deliberate correlation regarding two Apache frameworks such as Apache HadoopMapReduce and Apache Spark (advanced). The two frameworks present a design structure to partition the tremendous data to appropriate information. Although these preferences rely on the BigData objective, individual achievement conflict from collective perspective utilization and its implementation purpose. Such an idea alternates the two commendable analyses for the variation and selection of BigDatain dynamic possibilities. In this paper, we contradict the mentioned two designed framework structures together and offereffectivenessin evaluating throughhandling anapproved machine learning approaches for data assembling using K-Means. From the beginning, the observationsof this workingpaper determinethe relative performance measuresand approximating specifications for MapReducesuch asvelocity, throughput, and dynamism consumption of energy.
A study of temperature and strain-rate dependent glass fracture behaviour

Micro Abstract In this contribution, the numerical simulation of brittle fracture of glass materials under room temperature is carried out on a continuum-mechanical scale using the theory of linear elasticity and J-integral theory, extended by a phase-field modelling (PFM) approach. Following this, behaviours such as crack nucleation, propagation, and bulk material fracture can be realised. Moreover, fracture stresses and J-integral values are compared to analyse material toughness.
Vigorous plants suffer most from herbivores: latitude, nitrogen and biological control of the weed Salvinia molesta

The weevil, Cyrtobagous salviniae, and moth, Samea multiplicalis, were introduced into Australia for biological control of the floating weed Salvinia molesta and formed an unusually simple plant/herbivore system. Rates of weevil and moth population increase and damage to the plant were measured in field cages at latitudes 23? S, 27? S and 33? S in the presence and absence of increased nitrogen (N) availability. All of four weevil populations died out during winter at 33? S and the weevil is unlikely to persist where daily maximum air temperatures remain below 20? C for several months. Two out of four moth populations died out in mid-summer at 23? S. The moth is likely to persist throughout the range of S. molesta but with populations crashing when daily maximum air temperature exceeeds 34? C on several consecutive days. Greater N availability reduced moth but not weevil mortality caused by extreme temperatures. Surviving populations of both insects increased faster on N-rich plants. Models based on earlier studies of effects of temperature and N were not very successful in predicting rates of insect development and population increase. N-rich plants grew faster and suffered less damage per insect individual but, because the insects were more numerous, N-rich plants sustained more total damage i.e. herbivore numerical response to N dominated functional response. When optimal environmental conditions for a plant and its herbivores are similar, vigorous plants are likely to sustain most damage and biological control is most likely to succeed in places where growth by the target weed is most vigorous.
A Real Time and Interactive Web-Based Platform for Visualizing and Analyzing COVID-19 in Canada

In the midst of the global outbreak with over 300,000 worldwide death cases of COVID-19, Canada has reported 79,101 confirmed cases of the novel coronavirus (COVID-19) as of May 19, 2020, in which the severity differs from region to region. To provide a timely view and understanding of the evolving pandemic in Canada, we develop a real time interactive web-based platform which primarily includes data visualization and statistical analysis. The website highlights real time tracking of the development of COVID-19 with visualized graphs and forecasts future trends with applications of different statistical predictive models. By providing research-based statistical analysis, we are able to shed the light on the epidemiological characteristics of COVID-19. We also provide timely social news and preventive measures from the government on the website.
EARLY GENERATION SELECTION FOR YIELD AND ITS COMPONENTS IN 1Mansukhlal

Five F were studied in consecutive years. Ten F as for low expression of pods per plant, seeds per plant, harvest index and seed yield per plant. A total of 226 selected F succeeding year. Selection for high leve seeds per plant and seed yield. Effectiveness may be considered as moderately successful because of 40% of high yielding F Response of selection through pods per plant found effective for improvement of seed yield in all the crosses. No definite selection pattern was found for identification of superior yielding lines. However, some superior yielding F traits. Selecting for high pod number in early generation, a foremost consideration needs to be the influence of environment on the effectiveness of selection.
Design of Adaptive Filters for Satellite Communication

: In the last few years, many ruthless digital platforms have been developed for secure satellite communication. These platforms finds wide application in military oriented programs and are largely used for command, tracking and telemetry. Adaptive digital filters are used to enhance the performance of the standard direct sequence spread spectrum by suppressing the interference. To increase the robustness of on-board transponder for jammer attack, the SST equipment for next programs will be featured with adaptive digital rejection filter. Even in DSSS systems, it make use of much larger bandwidth for transmission than that of which is required for the transmission of the information. By using this excess bandwidth the system becomes advantageous as it will be less sensitive to multiple interferences, which may include jammers too. Adaptive Interference Rejection (AIR) algorithms are based on Lest Mean Square (LMS), Normalized Least Mean Square (NLMS) technique. Adaptive Filter is a type of filter which has the quality of self-designing in a non-stationary environment by operating on a recursive algorithm. Structural basis for implementation of adaptive filter is by using transversal filter. Tap weights are used to define the finite impulse response. Optimum filter is obtained by minimizing the mean square error of the error signal which is the difference between desired response and the transversal filter output. The dependence of the mean-squared error on the unknown tap weights may be viewed to be in the form of a multidimensional paraboloid with the unique minimum point, where the paraboloid is referred as error performance surface.
IoT and Cloud based Feature Extraction and Classification Model for Automatic Glaucoma Detection

: Internet of Things (IoT) and cloud computing are interdependent domains that find useful for monitoring the patients located in remote areas by provisioning incessant support from physicians. For effective disease management, it is needed to design a model that makes use of IoT gadgets to predict the likelihood of the presence of a disease. In this paper, a new automated glaucoma recognition and classifier approach for the identification of the glaucoma diseases from the color fundus images. At first, the real color fundus images hold a collection of three RGB channels and the red channel gets extracted for further processes. Afterward, the contrast enhancement process gets executed by the use of an adaptive histogram equalization (AHE) model for improvising the contrast level of the image. Subsequently, a bilateral filtering technique is given to discard the noise that exists in the image. Afterward, a Directional local ternary quantized extrema pattern (DLTerQEP) based feature extraction process takes place. Then, the extracted features undergo classification by the use of a multi-class support vector machine (MSVM) model to classify the presence of glaucoma. The proposed model is tested using a DRISHTI-GS holds a sum of 101 retinal fundus images. The simulation outcome presented the advantage of the projected approach over the compared approaches.
ENSEMBLE OF AUTO-ENCODER BASED AND WAVENET LIKE SYSTEMS FOR UNSUPERVISED ANOMALY DETECTION

In this paper we report an ensemble of models used to perform anomaly detections for DCASE Challenge 2020 Task 2. Our solution comprises three families of models: Heteroskedas-tic Variational Auto-encoders, ID Conditioned Auto-encoders and a WaveNet like network. Noisy recordings are preprocessed using a U-Net trained for noise removal on training samples augmented with noised obtained from the AudioSet. Models operate either on OpenL3 embeddings or log-mel power spectra. Heteroskedastic VAEs have a non-standard loss function which uses model’s own error estimation to weigh typical MSE loss. Model architecture i.e. sizes of layers, dimension of latent space and size of an error estimating network are independently selected for each machine type. ID Conditioned AEs are an adaptation of the class conditioned auto-encoder approach designed for open set recognition. Assuming that non-anomalous samples constitute distinct IDs, we apply the class conditioned auto-encoder with machine IDs as labels. Our approach omits the classiﬁcation subtask and reduces the learning process to a single run. We simplify the learning process further by ﬁxing a target for non-matching labels. Anomalies are predicted either by poor reconstruction or attribution of samples to the wrong machine IDs. The third solution is based on a convolutional neural network. The architecture of the model is inspired by the WaveNet and uses causal convolutional layers with growing dilation rates. It works by predicting the next frame in the spectrogram of a given recording. Anomaly score is derived from the reconstruction error. We present results obtained by each kind of models separately, as well as, a result of an ensemble obtained by averaging anomaly scores computed by individual models
The effect of decision-maker characteristics on the selection and utilization of strategic decision-making tools

This research examines the factors that affect the selection and implementation of strategic decision-making tools. Following a literature review, thirty five tools classified into three categories, and a set of factors related to decision-maker characteristics, are analyzed and investigated. According to the research model, three main hypotheses are formulated and tested. The results are discussed in terms of the importance of tools in strategic decisionmaking. Two main hypotheses out of three are supported, hypothesis one which focused on the variation of decision-maker characteristics’ effect on the selection of specific strategy tools, and hypothesis three which focused on the effect of awareness of different strategic decision-making tools on the selection of these tools, while hypothesis two regarding strategic decision-makers’ preference to select simple and easily applied tools, was not supported.
Prospective Validation of the Emergency Heart Failure Mortality Risk Grad for Acute Heart Failure: The Acute Congestive Heart Failure Urgent Care Evaluation (ACUTE) Study

ICES, Toronto, Ontario, Canada; Peter Munk Cardiac Centre and University Health Network, Toronto, Ontario, Canada; University of Toronto, Toronto, Ontario, Canada; Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Schwartz/Reisman Emergency Medicine Institute, Sinai Health System, Toronto, Ontario, Canada; University of Western Ontario, London, Ontario, Canada; Trillium Health Partners, Mississauga, Ontario, Canada; Wayne State University Department of Emergency Medicine and Integrative Biosciences
A TOPOLOGICAL DERIVATIVE-BASED HYDRAULIC FRACTURE MODEL IN BRITTLE MATERIALS

This paper deals with a novel hydraulic fracture model based on the concept of topological derivative. The basic idea consists in adapting the Francfort-Marigo damage model to the context of hydraulic fracture. The Francfort-Marigo damage model is a variational approach to describe the behavior of brittle materials under the quasi-static loading assumption, focusing on the evolution of damage regions under an irreversibility constraint. In our problem, the loading comes out from a prescribed pressure acting within the damage region, which is used to trigger the hydraulic fracturing process. A shape functional given by the sum of the total potential energy of the system with a Griffith-type dissipation energy term is minimized with respect to a set of ball-shaped pressurized inclusions by using the topological derivative concept. In particular, the topological asymptotic expansion of the shape functional with respect to the nucleation of a circular inclusion endowed with non-homogeneous transmission condition on its boundary is rigorously developed. The associated topological derivative, which corroborates with the famous Eshelby theorem, is used to devise a simple topology optimization algorithm specifically designed to simulate the whole nucleation and propagation process of hydraulic fracturing. To assess our model, some numerical examples are presented, showing typical features of hydraulic fracture phenomenon, including the characterization of the fault-activation pressure and specific crack path growth, allowing for kinking and bifurcations.
Predicting Execution Times With Partial Simulations in Virtual Memory Research: Why and How

Computer architects frequently use cycle-accurate simulations, which incur heavy overheads. Recently, virtual memory studies increasingly employ a lighter-weight methodology that utilizes partial simulations—of only the memory subsystem— whose output is fed into a mathematical linear model that predicts execution runtimes. The latter methodology is much faster, but its accuracy is only assumed, never rigorously validated.We question the assumption and put it to the test by developing Mosalloc, the Mosaic Memory Allocator. Mosalloc backs the virtual memory of applications with arbitrary combinations of 4KB, 2MB, and 1GB pages (each combination forms a "mosaic" of pages). Previous studies used a single page size per execution (either 4KB or 2MB) to generate exactly two execution samples, which defined the aforementioned linear model. In contrast, Mosalloc can generate numerous samples, allowing us to test instead of assume the model’s accuracy. We find that prediction errors of existing models can be as high as 25%–192%. We propose a new model that bounds the maximal error below 3%, making it more reliable and useful for exploring new ideas. "The phenomena surrounding computers are deep and obscure, requiring much experimentation to assess their nature. " (A. Newell and H. A. Simon)
Transient seismic wave propagation in a multilayered cracked geological region

A hybrid boundary integral equation method (BIEM) for transient problems is developed here as a means for investigating ground motion phenomena in geological regions with complex geometry, variable material properties and in the presence of both interface and internal cracks. Two different aspects of the problem are considered, namely computation of (a) ground motions in the form of synthetic seismograms that are manifested at the free surface of the geological region as it is swept by a seismically induced pressure wave and (b) evaluation of the near crack-tip stress concentration field that develops around cracks buried within the deposit for the same type of loading. The present method combines both displacement and regularized traction BIEM in the Laplace transformed domain for the crack-free and cracked states, respectively, while the transient nature of the wave scattering phenomenon is reconstructed through use of the numerical inverse Laplace transformation. Furthermore, plane strain conditions are assumed to hold and the response of the geological region remains within the linear elastic range. The basic strategy, whereby the aforementioned two states are superimposed, has been successfully used in the past for problems in fracture mechanics. Following numerical implementation of the hybrid BIEM, two validation-type examples serve to calibrate the methodology. Finally, the method is used for solving the seismic response of a complex geological region so as to reach a series of conclusions regarding the relative influence of various key parameters of the problem (layering, surface canyon, crack interaction) on the scattered displacement field and on the stress concentration factor. r 2003 Elsevier Ltd. All rights reserved. ARTICLE IN PRESS *Corresponding author. E-mail addresses: petia@imbm.bas. bg (P.S. Dineva), gdm@civil.auth. gr (G.D. Manolis), rangelov@math.bas. bg (T.V. Rangelov). 0022-460X/$ see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.jsv. 2003.04. 002
A Fuzzy Logic based Approach for Replacing Mouse by Facial Expressions for People with Disability in Movement

: Many authors had proposed solutions in the past three decades for replacing the mouse for people with disability in the movement who have not yet received a fair chance like others to use the standard input devices of a personal computer. In the camera-based systems, the web camera is used as the mouse that reduces the overhead of using head-mounted devices. Tracking the user’s facial expression of different users with different head pose through the camera and converting accurately into the mouse cursor movement and click events are the research challenges and opportunities. The current systems lose the tracked feature during the user’s unintentional head movements and they are only comfortable in moving the cursor on a slanting direction. The proposed system applies fuzzy logic in its decision-making to simplify and improve the efficiency of controlling the cursor and its interactions on the Graphical user interfaces to make the people with disability in the movement to use the computer conveniently and easily. The system addresses the problem of feature loss by mapping the mouse cursor movement only with the intentional head movement ignoring the usual head movements. The system also achieves the horizontal and vertical movement of the cursor. The mouse operations are replaced by the head movement and the eyes-blinks captured by the camera. The head movement controls the mouse cursor; left and right eye-blinks replace the actions of the left-click and right-click of the mouse.
Spatial patterns of random walkers under evolution of the attractiveness: persistent nodes, degree distribution, and spectral properties

In this paper we explore the features of a graph generated by random walkers with nodes that have evolutionary attractiveness and Boltzmann-like transition probabilities that depend both on the euclidean distance between the nodes and on the ratio (β ) of the attractiveness between them. We show that persistent nodes, i.e., nodes that never been reached by random walker in asymptotic times are possible in the stationary case differently from the case where the attractiveness is fixed and equal to one for all nodes (β = 1). Simultaneously, we also investigate the spectral properties and statistics related to the attractiveness and degree distribution of the evolutionary network. Finally, we study a crossover between persistent phase and no persistent phase and we also show the existence of a special type of transition probability that leads to a power law behaviour for the time evolution of the persistence.
Soil Metagegomics: Approaches, Bioinformatics Tools and Applications

Review Article Most of soil microbes have not yet been individuated and their metabolisms are unknown. Recent studies indicate that classic plate counting techniques were able to determine only a part of the overall soil microbial community and that the metagenomics approach, based on a culture independent method, allows to study it in depth and to overcome the previous limits. The relevance of this approach is growing as it allows exploring factors affecting the soil fertility, to deepen the interactions between microorganisms and plants, and to identify new molecules with pharmacological activities such as antibiotics. This work investigates the metagenomics approach and the most commonly used sequencing methods, focusing on bioinformatics tools for each of them. Some of the most recent metagenomics soil applications, concerning both amplicon and whole genome shotgun sequencing are described. Therefore, metagenomics, used in combination with other omics approaches, based on metabolomics, transcriptomics and/or proteomics, is discussed and focused as it can provide a comprehensive soil microbiome draw.
INVESTIGATION OF COSMIC RAY INDUCED DEFECTS IN CIGS SOLAR CELLS

In October 2018 CIGS solar cells were exposed to cosmic radiation during a 3.5 hour long flight with the BEXUS 27 high altitude balloon, the balloon reached an altitude of 28 km where it floated for 2 hours. The experiment studied the cosmic radiation induced defects in the solar cells and was performed by the LODESTAR project part of the REXUS/BEXUS programme [10]. We report no variation of Voc and Isc after an estimated dose of 40μJ/cm from cosmic ray exposure, the measured variation of Voc and Isc was due to temperature and illumination variation. For the IVT and CVT characterization we see a difference post-flight which could be caused by cosmic ray induced microscopic defects, one instance of this is the reduction of the CIGS activation energy from 166.4 meV pre-flight to 151.0 meV post-flight.
AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin

Driving a vehicle is an indispensable part of their everyday life for many people. However, sometimes this everyday life does not go as expected, as a lot of accidents happen on the public roads, and most of these accidents are due to inattentive driver behavior. Modern driver monitoring systems evaluate driver behavior by means of distinctive sensor technology and, if necessary, indicate undesirable driving behavior. However, many roadworthy vehicles do not have the possibility to implement such systems. Therefore, it seems to be interesting to investigate the implementation of such systems based on commodity hardware, e.g., smartphones, because nowadays almost every driver has a powerful smartphone equipped with many sensors at hand in the vehicle. Furthermore, recent advances in Machine Learning (ML) made it possible to analyze large amounts of data and to generate new outcomes. In this work we discuss how ML can be used for driver behavior recognition by improving an already existing threshold-based driver monitoring system with different ML-based techniques, Neural Networks and Random Forests, and evaluate their performance. We propose to use Microsoft Azure platform to analyze data generated by a Driver Monitoring System (DMS). Our results indicate ML as a useful technique for learning and adapting threshold-based reasoning about individual drivers’ states.