Miracle-dz/UniFAR-FTUs huggingface dataset:
A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers.
QoS best routing problem based on ant colony algorithm

The paper presents a QoS-constrains best routing algorithm for the problem of service in network—Ant Colony Algorithm(ACA),the algorithm is a new optimization.In this paper,the performance of ACA is approved by dynamically adjusting evaporation rate of pheromone and applying the global updating rule of the best and the worst path.The improved algorithm can find the global optimum at higher convergence speed.The simulation results demonstrate that the above approach is reasonable and efficient.
Snow research in the Dronning Maud Land within the Finnish Antarctic Research Program in 1989-2014

Snow research has been carried out at the Finnish Antarctic station Aboa (73°02.5'S 13°24.4'W) and in its surroundings in the Dronning Maud Land since it was established in 1989. In 1999 the work expanded to cover the spatial variability of snow and its properties in the snow and ice surface layer (0– 10 m) along a 500 km long transect passing Aboa from the shelf edge in the Weddell Sea to the edge of East Antarctic Plateau. The topics have been the mass and heat balance of snow, remote sensing, stratigraphy of snow cover, physical and chemical properties of snow, solar radiation budget, and snow on nunataks. Net accumulation of snow has been typically 200–300 mm of water equivalent per year, increasing towards the oceanic boundary. The distance from the ocean is the most predominant factor controlling the variations in the snow properties. Aerosol chemical composition, physical properties and processes from their origin to deposition have been studied mainly in Antarctic summers at Aboa.
DETECÇÃO DE MUDANÇA DO USO E COBERTURA DA TERRA USANDO O MÉTODO DE PÓS-CLASSIFICAÇÃO NA FRONTEIRA AGRÍCOLA DO OESTE DA BAHIA SOBRE O GRUPO URUCUIA DURANTE O PERÍODO 1988-2011

Este artigo tem como objetivo detectar as mudanças da cobertura e uso da Terra ao longo do período 1988-2011, utilizando imagens de sensoriamento remoto para o Oeste da Bahia (Brasil Central). A área de estudo é restrita aos solos sobre o Grupo Urucuia (Cretáceo Superior), que é composta por arenitos continentais relacionados a ambiente desértico. Esta formação geológica gera áreas planas com predominância de Latossolos, caracterizado por textura média, excessivamente drenados e adequado para o desenvolvimento da agricultura intensiva e mecanizada. Portanto, esta área apresenta um dos maiores índices de crescimento da agricultura no bioma Cerrado. A detecção de mudança foi realizada pelo método de pós-classifi cação a partir de dados multitemporais do sensor TM-Landsat. A classifi cação de imagens através da interpretação visual identifi caram seis classes de uso e cobertura da Terra: agropecuária, áreas urbanas, corpos d'água, refl orestamento, vegetação alterada e vegetação natural. A detecção de mudança foi feita a partir da tabulação cruzada entre as imagens bi-temporais. Durante o período, as principais alterações ocorreram nas classes de vegetação natural (decréscimo de 26,57%) e agropecuária (27,13%). O avanço da agropecuária ocorreu principalmente em áreas de vegetação nativa.
Geometric and appearance modeling of vascular structures in CT and MR

Medical imaging devices such as CT and MRI scanners allows doctors to view their patient’s internal organs noninvasively. However, it can be difficult to see the structure of organs and tissues in the raw intensity data. Segmentation of the intensity data can be helpful for describing the shape of such objects. Vital Images creates software for visualizing medical images. They have algorithms for determining the centerline of a blood vessel as well as determining the contours of the vessel lumen and vessel wall in two-dimensional slices through the three-dimensional image; see for example Figure 1. The centerline and the contours give a good idea of the vessel’s shape, but when meshed directly, they can lead to a rough surface with self-intersections. The primary goal of our project was to give a more complete description of the geometry using the existing segmentation data. Four approaches are described here. The first three are level set methods, in which the target surface (either the vessel lumen or the outer wall) is viewed as the zeroset of a function defined on a space around the vessel, and the goal is to find such a function. The fourth method is a geodesic method for surface reconstruction; an underlying smooth surface is assumed, and an approximate parametrization is obtained by computing the tangent space at each data point and shooting trajectories along the surface.
Age, Gender and Personality Recognition using Tweets in a Multilingual setting

User generated text on social media sites is a rich source of information that can be used to identify different aspects of their authors. Proper mining of this content provides an automatic way of identifying users which is very valuable for applications that rely on personalisation. In this work, we describe the properties of our multilingual software submitted for PAN2015 which recognizes the age, gender and personality traits of Twitter users in four languages, namely, English, Spanish, Dutch and Italian.
A Robot Ping Pong Player Experiment In Real Time Intelligent Control

Thank you for downloading a robot ping pong player experiment in real time intelligent control. Maybe you have knowledge that, people have look numerous times for their favorite readings like this a robot ping pong player experiment in real time intelligent control, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their laptop.
Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

Image registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments.
WCET Analysis for Concurrent Execution of Multiple Applications on Safety Critical Embedded Multicores

The computing hardware layer for modern embedded systems (such as internetof-things) must be able to execute many concurrent applications under tight deterministic execution guarantees to meet the real-time requirements. Those time-critical cyber-physical systems are designed with respect to their Worst Case Execution Time (WCET). Single-chip multicores are attractive for such embedded systems due to their lightweight form factor. However, multicores aggressively share hardware resources, leading to interference that in turn makes hard to determine the WCET for multiple concurrent applications.We propose an approach to use a set of methods to spatio-temporally partition shared multicore resources, analyse the WCET and quantify its overheads.
Optimization to AODV routing protocol in Ad-hoc networks

An optimization scheme to AODV protocol based on location information in Ad-hoc networks is examined and pro-posed.This scheme controls the transmission power and uses regional broadcast.Results obtained by Network Simulation(NS) show that the optimized AODV scheme performs effectively in enhancing packet transfer rate and shortening network time delay,mean-while it avoids affecting other Ad hoc network parameters.
Robust and Efficient Statistical Inference for Clustered Observational Data in Comparative Effectiveness Research

Baiming Zou: Robust and Efficient Statistical Inference for Clustered Observational Data in Comparative Effectiveness Research (Under the direction of Professor Haibo Zhou) Treatment allocations in observational studies are nonrandom and result in the confounding problem and potentially biase treatment effect estimates. Propensity score (PS) methods are commonly used in practice to address the confounding problem. Among different PS methods, PS regression is frequently used in clinical research. Even though the treatment effect estimate from the PS regression model is unbiased under the strongly ignorable treatment assignment assumption, the default variance estimate is biased. In the first topic of this dissertation, an improved variance estimator for the treatment effect estimate is proposed. Many observational data are clustered, for example, by physicians, and are therefore, not independent. A few PS methods consider correlated or clustered samples using mixed effects models with a strong normality assumption on the cluster effects. In the second part of this dissertation, a robust semi-nonparametric propensity score (SNP-PS) regression model is proposed. We relax the normality assumption and model the complex heterogeneity structure in treatment allocation process nonparametrically. The proposed SNP-PS model is robust and provides unbiased treatment effect estimates while parametric mixed effects PS models fail to do so when the cluster effects are non-normally distributed. We establish the asymptotic result for the treatment effect estimate and propose an unbiased variance estimator for it. Computationally, we propose an adaptive quadrature integration EM (expectation-maximization) algorithm to avoid potential large Monte Carlo errors of existing Monte Carlo EM algorithms.
A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

Current PM2.5 retrieval maps have many missing values, which seriously hinders their performance in real applications. This paper presents a framework to map full-coverage daily average PM2.5 concentrations from MODIS C6 aerosol optical depth (AOD) products and fill missing pixels in both the AOD and PM2.5 maps. First, a two-stage inversed variance weights (IVW) algorithm was adopted to fuse the MODIS C6 Terra and Aqua AOD products, which fills missing data in MODIS standard AOD data and obtains a high coverage daily average. After that, using the fused MODIS daily average AOD and ground-level PM2.5 in all grid cells, a two-stage generalized additive model (GAM) was implemented to obtain the full-coverage PM2.5 concentrations. Experiments on the Yangtze River Delta (YRD) in 2013–2016 were carefully designed to validate the performance of our proposed framework. The results show that the two-stage IVW could not only improve the spatial coverage of MODIS AOD against the original standard product by 230%, but could also keep its data accuracy. When compared with the ground-level measurements, the two-stage GAM can obtain accurate PM2.5 concentration estimates (R2 = 0.78, RMSE = 19.177 μg/m3, and RPE = 28.9%). Moreover, our method performs better than the inverse distance weighted method and kriging methods in mapping full-coverage daily PM2.5 concentrations. Therefore, the proposed framework provides a good methodology for retrieving full-coverage daily average PM2.5 concentrations from MODIS standard AOD products.
Using Taxation to Address Noncommunicable Diseases : Lessons from Tonga

Tonga has experienced a high burden of noncommunicable diseases (NCDs) in the past two decades. Data indicate the continuing rise of four major NCDs -cardiovascular disease, diabetes, cancer,and respiratory diseases – as the leading causes of premature death and disability. According to latest data, NCDs accounted for four out of five leading causes of mortality in Tonga, and 99.9 percent of Tongan adults aged 25–64 are at moderate to high risk of developing an NCD. The increase in behavioral-related risk factors such as smoking, poor diet (e.g. high in sugar, salt, trans-fat and saturated fat, and low in fruit, vegetables, legumes, fish etc.), harmful alcohol intake, and physical inactivity are acknowledged as the major contributing factors to the rise in NCDs in Tonga. According to its 2012 STEPS Survey, Tonga has one of the highest rates of overweight and obesity (≥25 kg/m2) in the world – reaching 90.7 percent among some population groups. Both obesity and overweight are common at younger ages. Furthermore, almost one in two men smoke, and smoking appears to be increasing among young women. The study aims to generate policy relevant findings and insights on the implementation of taxation policy on tobacco, alcohol, and unhealthy food consumption behaviors; as well as pricing, government revenues, and the country's response to Tonga's rising NCD burden. The findings are expected to help strengthen the monitoring and evaluation of NCD-related fiscal policies, not only of behavior change but also revenue generation. The findings also intend to help improve the design of future NCD-related fiscal policy – as well as other, non-fiscal policies – to address NCD challenges. These lessons from Tonga will also be relevant and beneficial to other Pacific Island Countries (PICs) that face similar NCD and obesity burdens. This study employs the following assessment tools and sources of data: (i) government administrative data; (ii) baseline and endline household surveys, focusing on the impact of NCD tax on consumption behaviors in Tongatapu and Vava'u which account for over 85 percent of Tonga's population; (iii) retail surveys focusing on the impact of the NCD tax on prices; (iv) tobacco taxation simulation modeling (TaXSiM); (v) a qualitative study that includes focus group discussions and in-depth interviews.
Fish Ecology, Evolution, and Exploitation

Fish are one of the most important global food sources, supplying a significant share of the world's protein consumption. From stocks of wild Alaskan salmon and North Sea cod to entire fish communities with myriad species, fisheries require careful management to ensure that stocks remain productive, and mathematical models are essential tools for doing so. This book is an introduction to the modern size- and trait-based approach to fish populations and communities. It covers the theoretical foundations, mathematical formulations, and real-world applications of this powerful new modeling method, which is grounded in the latest ecological theory and population biology. It begins with fundamental assumptions on the level of individuals and goes on to cover population demography and fisheries impact assessments. The book shows how size- and trait-based models shed new light on familiar fisheries concepts such as maximum sustainable yield and fisheries selectivity—insights that classic age-based theory can't provide—and develops novel evolutionary impacts of fishing. It extends the theory to entire fish communities and uses it to support the ecosystem approach to fisheries management, and forges critical links between trait-based methods and evolutionary ecology. The book unifies the thinking in ecology and fisheries science and is an indispensable reference for anyone seeking to apply size- and trait-based models to fish demography, fisheries impact assessments, and fish evolutionary ecology.
A GIS-Based Approach for Determining Potential Runoff Coefficient and Runoff Depth for the Indian River Lagoon, Florida, USA

The Indian River Lagoon system (IRL), spanning ~40% of Florida’s east coast, is one of the nation’s biggest and most biodiverse estuaries. In 2011, a super algal bloom event occurred in the IRL with total nitrogen and phosphorus levels that exceeded historical levels. Scientists suspect that nonpoint source pollution through surface runoff may have had a significant impact on the recent recurring algal blooms. Digital Elevation Model, land cover/land use, and soil data were used to calculate a runoff coefficient for the IRL drainage basin. Rainfall data were used to calculate runoff depth for the study area between the years of 2006–2016. When the monthly runoff depth data for 2011 were compared to a previous study on the 2011 super algal bloom in the lagoon, areas with high runoff visually matched the areas with higher chlorophyll a concentrations. Land development was a significant variable for determining runoff depth ( p < 0.0001), and although used to derive runoff depths, the influence of precipitation was marginally significant ( p = 0.06). Significant spatial autocorrelation indicated local trends between land development and runoff depth ( p < 0.0001). Outputs will aid with decisions on stormwater management to more sustainable land development planning.
Calibrating the Scientific Ecosystem Through Meta-Research

While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientific ecosystem, topics that have become especially relevant amid widespread concerns about the credibility of the scientific literature. Meta-research may help calibrate the scientific ecosystem toward higher standards by providing empirical evidence that informs the iterative generation and refinement of reform initiatives. We introduce a translational framework that involves ( a) identifying problems, ( b) investigating problems, ( c) developing solutions, and ( d) evaluating solutions. In each of these areas, we review key meta-research endeavors and discuss several examples of prior and ongoing work. The scientific ecosystem is perpetually evolving; the discipline of meta-research presents an opportunity to use empirical evidence to guide its development and maximize its potential.
Mapping the stability of spatial production in integrated crop and pasture systems: Towards zonal management that accounts for both yield and livestock-landscape interactions

Precision farming technologies are now widely applied within Australian cropping systems. However, the use of spatial monitoring technologies to investigate livestock and pasture interactions in mixed farming systems remains largely unexplored. Spatio-temporal patterns of grain yield and pasture biomass production were monitored over a four-year period on two Australian mixed farms, one in the south-west of Western Australia and the other in south-east Australia. A production stability index was calculated for two paddocks on each farm. An example is given here for one paddock from Western Australia. The stability index described here is unique in that it combines spatial and temporal variation across both cropping and pasture phases. Co-efficient of variation in yield was used as the threshold value for determining stability. Production in each stability zone was analysed statistically for consistency and correlation between the cropping and pasture phases. Results indicate that the stability index can be used in mixed farming systems to assist in management decisions and for the paddock described, spatial and temporal variation in production between crop and pasture phases was strongly correlated.
Probabilistic Inference in Piecewise Graphical Models

In many applications of probabilistic inference the models contain piecewise densities that are differentiable except at partition boundaries. For instance, (1) some models may intrinsically have finite support, being constrained to some regions; (2) arbitrary density functions may be approximated by mixtures of piecewise functions such as piecewise polynomials or piecewise exponentials; (3) distributions derived from other distributions (via random variable transformations) may be highly piecewise; (4) in applications of Bayesian inference such as Bayesian discrete classification and preference learning, the likelihood functions may be piecewise; (5) context-specific conditional probability density functions (tree-CPDs) are intrinsically piecewise; (6) influence diagrams (generalizations of Bayesian networks in which along with probabilistic inference, decision making problems are modeled) are in many applications piecewise; (7) in probabilistic programming, conditional statements lead to piecewise models. As we will show, exact inference on piecewise models is not often scalable (if applicable) and the performance of the existing approximate inference techniques on such models is usually quite poor. This thesis fills this gap by presenting scalable and accurate algorithms for inference in piecewise probabilistic graphical models. Our first contribution is to present a variation of Gibbs sampling algorithm that achieves an exponential sampling speedup on a large class of models (including Bayesian models with piecewise likelihood functions). As a second contribution, we show that for a large range of models, the timeconsuming Gibbs sampling computations that are traditionally carried out per sample, can be computed symbolically, once and prior to the sampling process. Among many potential applications, the resulting symbolic Gibbs sampler can be used for fully automated reasoning in the presence of deterministic constraints among random variables. As a third contribution, we are motivated by the behavior of Hamiltonian dynamics in optics —in particular, the reflection and refraction of light on the refractive surfaces— to present a new Hamiltonian Monte Carlo method that demonstrates a significantly improved performance on piecewise models. Hopefully, the present work represents a step towards scalable and accurate inference in an important class of probabilistic models that has largely been overlooked in the literature.