Shape and tone depiction for implicit surfaces

We present techniques for rendering implicit surfaces in different pen-and-ink styles. The implicit models are rendered using point-based primitives to depict shape and tone using silhouettes with hidden-line attenuation, drawing directions, and stippling. We present sample renderings obtained for a variety of models. Furthermore, we describe simple and novel methods to control point placement and rendering style. Our approach is implemented using HRBF Implicits, a simple and compact representation, that has three fundamental qualities: a small number of point-normal samples as input for surface reconstruction, good projection of points near the surface, and smoothness of the gradient field. These qualities of HRBF Implicits are used to generate a robust distribution of points to position the drawing primitives.     

Impervious surface mapping with Quickbird imagery

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of "salt-and-pepper" pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance.     

Hybrid Classification Ensemble Using Topology-preserving Clustering

This study presents a novel hybrid intelligent system using both unsupervised and supervised learning that can be easily adapted to be used in an individual or collaborative system. The system divides the classification problem into two stages: firstly it divides the input data space into different parts, according to the input space distribution of the data set. Then, it generates several simple classifiers that are used to correctly classify samples that are contained in one of the previously determined parts. This way, the efficiency of each classifier increases, as they can specialize in classifying only related samples from certain regions of the input data space. This specialization of the single classifiers enables them to learn more specific patterns or characteristics of the data space, avoiding the risk of obtaining a general algorithm that over-fits to the data. The hybrid system presented has been tested with artificial and real data sets. A comparative study of the results obtained by the novel model with those obtained from other common classification methods is also included in the present work.     

Analysis of Virtual Irrigation Water. Application to Water Resources Management in a Mediterranean River Basin

Virtual water, understood as the volume of water needed to produce a good or service, in the context of trade among countries, allows assessing the relative dependency of different countries on imported virtual water. The assessment of virtual water flows in virtual water trade studies is generally carried out at the national level, thus concealing the spatial variability of many countries that comprise a wide range of agro-climatic areas. A finer spatial resolution is needed to apply the virtual water concept with a view to improving local water resources management at the regional or basin scale. This paper presents a more detailed analysis of blue agricultural virtual water at sub-basin scale, which is aggregated to the basin scale for the Guadalquivir River basin in southern Spain. Given that around 80% of the available blue water resources of this region are used in agriculture, improving irrigation water management is of great interest to the basin water resources management. The main objective of this paper is to estimate the virtual irrigation water balance; an important component of the agricultural water footprint that provides additional information to the basin blue water balance and contributes to achieving a more rational and efficient management of basin water resources. Management recommendations focusing on the spatial redistribution of irrigated crops according to both their water efficiency and productivity are proposed with the aim of saving blue water for other purposes such as drought mitigation or environmental flows, among others. In particular, the proposed measures affect irrigated olive trees; the most widespread irrigated crop in this basin.     

Assessing the influence of stereotypes on the comprehension of UML sequence diagrams: A family of experiments

Context

The conventional wisdom states that stereotypes are used to clarify or extend the meaning of model elements and consequently should be helpful in comprehending the diagram semantics.

Objective

The main goal of this work is to present a family of experiments that we have carried out to investigate whether the use of stereotypes improves the comprehension of UML sequence diagrams.

Method

The family of experiments consists of an experiment and two replications carried out with 78, 29 and 36 undergraduate Computer Science students, respectively. The comprehension of UML sequence diagrams with and without stereotypes was analyzed from three different perspectives borrowed from the Cognitive Theory of Multimedia Learning (CTML): semantic comprehension, retention and transfer. In addition, we carried out a meta-analysis study to integrate the different data samples.

Results

The statistical analysis and meta-analysis of the data obtained from each experiment separately indicates that the use of the proposed stereotypes helps improving the comprehension of the diagrams, especially when the subjects are not familiar with the domain.

Conclusions

The set of stereotypes presented in this work seem to be helpful for a better comprehension of UML sequence diagrams, especially with not well-known domains. Although further research is necessary for strengthening these results, introducing these stereotypes both in academia and industry could be an interesting practice for checking the validity of the results.     

Modelling with knowledge: A review of emerging semantic approaches to environmental modelling

Models, and to a lesser extent datasets, embody sophisticated statements of environmental knowledge. Yet, the knowledge they incorporate is rarely self-contained enough for them to be understood and used – by humans or machines – without the modeller's mediation. This severely limits the options in reusing environmental models and connecting them to datasets or other models. The notion of “declarative modelling” has been suggested as a remedy to help design, communicate, share and integrate models. Yet, not all these objectives have been achieved by declarative modelling in its current implementations.Semantically aware environmental modelling is a way of designing, implementing and deploying environmental datasets and models based on the independent, standardized formalization of the underlying environmental science. It can be seen as the result of merging the rationale of declarative modelling with modern knowledge representation theory, through the mediation of the integrative vision of a Semantic Web. In this paper, we review the present and preview the future of semantic modelling in environmental science: from the mediation approach, where formal knowledge is the key to automatic integration of datasets, models and analytical pipelines, to the knowledge-driven approach, where the knowledge is the key not only to integration, but also to overcoming scale and paradigm differences and to novel potentials for model design and automated knowledge discovery.     

Graph visualization techniques for web clustering engines

One of the most challenging issues in mining information from the World Wide Web is the design of systems that present the data to the end user by clustering them into meaningful semantic categories. We show that the analysis of the results of a clustering engine can significantly take advantage of enhanced graph drawing and visualization techniques. We propose a graph-based user interface for Web clustering engines that makes it possible for the user to explore and visualize the different semantic categories and their relationships at the desired level of detail     

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.     

A unified short-cut model approach to size safe venting stacks and other blow-off devices for hazardous vapours

Starting from a general model of the atmospheric dispersion of upward-directed high velocity releases, a unified short-cut method is developed for properly designing flares, vents and other safety devices for hazardous material releases and/or to verify the adequacy of the existing ones. By comparison with the design constraints arising from the prevention and protection criteria against the risk of ignition, explosion and exposure of people to radiating heat and/or toxic compounds, the proposed procedure directly gives size, height and location of the venting system as a function of significant release characteristics. A way of economical optimisation is also considered in flare design, where raising the emission also increases the safe areas available to workers.