Context‐Dependent Crowd Evaluation

Many times, even if a crowd simulation looks good in general, there could be some specific individual behaviors which do not seem correct. Spotting such problems manually can become tedious, but ignoring them may harm the simulation's credibility. In this paper we present a data‐driven approach for evaluating the behaviors of individuals within a simulated crowd. Based on video‐footage of a real crowd, a database of behavior examples is generated. Given a simulation of a crowd, an analog analysis is performed on it, defining a set of queries, which are matched by a similarity function to the database examples. The results offer a possible objective answer to the question of how similar are the simulated individual behaviors to real observed behaviors. Moreover, by changing the video input one can change the context of evaluation. We show several examples of evaluating simulated crowds produced using different techniques and comprising of dense crowds, sparse crowds and flocks.     

A type-centric framework for specifying heterogeneous, large-scale, component-oriented, architectures

Maintaining integrity and consistency, and effecting conformance in architectures of large-scale systems require specification and enforcement of many different forms of structural constraints. While type systems have proved effective for enforcing structural constraints in programs and data structures, most architectural modeling frameworks include only weak notions of typing or rely on first order logic constraint languages that have steep learning curves associated with them and that become unwieldy when scaling to large systems.We present the Cadena Architecture Language with Meta-modeling (CALM) — that uses multi-level type systems to specify and enforce a variety of architectural constraints relevant to the development of large-scale component-based systems. Cadena is a robust and extensible tool that has been used to specify a number of industrial strength component models and applied in multiple industrial research projects on model-driven development and software product lines.     

Mesh Snapping: Robust Interactive Mesh Cutting Using Fast Geodesic Curvature Flow

This paper considers the problem of interactively finding the cutting contour to extract components from a given mesh. Some existing methods support cuts of arbitrary shape but require careful and tedious input from the user. Others need little user input however they are sensitive to user input and need a postprocessing step to smooth the generated jaggy cutting contours. The popular geometric snake can be used to optimize the cutting contour, but it cannot deal with the topology change. In this paper, we propose a geodesic curvature flow based framework to overcome all these problems. Since in many cases the meaningful cutting contour on a 3D mesh is locally shortest in the sense of some weighted curve length, the geodesic curvature flow is an ideal tool for our problem. It evolves the cutting contour to the nearby local minimum. We should mention that the previous numerical scheme, discretized geodesic curvature flow (dGCF) is too slow and has not been applied to mesh segmentation. With a careful observation to dGCF, we devise here a fast computation scheme called fast geodesic curvature flow (FGCF), which only needs to solve a smaller and easier problem. The initial cutting contour is generated by a variant of random walks algorithm, which is very fast and gives reasonable cutting result with little user input. Experiment results on the benchmark mesh segmentation data set show that our proposed framework is robust to user input and capable of producing good results reflecting geometric features and human shape perception.     

Mesh Decomposition with Cross‐Boundary Brushes

We present a new intuitive UI, which we call cross‐boundary brushes, for interactive mesh decomposition. The user roughly draws one or more strokes across a desired cut and our system automatically returns a best cut running through all the strokes. By the different natures of part components (i.e., semantic parts) and patch components (i.e., flatter surface patches) in general models, we design two corresponding brushes: part‐brush and patch‐brush. These two types of brushes share a common user interface, enabling easy switch between them. The part‐brush executes a cut along an isoline of a harmonic field driven by the user‐specified strokes. We show that the inherent smoothness of the harmonic field together with a carefully designed isoline selection scheme lead to segmentation results that are insensitive to noise, pose, tessellation and variation in user's strokes. Our patch‐brush uses a novel facet‐based surface metric that alleviates sensitivity to noise and fine details common in region‐growing algorithms. Extensive experimental results demonstrate that our cutting tools can produce user‐desired segmentations for a wide variety of models even with single strokes. We also show that our tools outperform the state‐of‐art interactive segmentation tools in terms of ease of use and segmentation quality.     

Hybrid Simulation of Miscible Mixing with Viscous Fingering

By modeling mass transfer phenomena, we simulate solids and liquids dissolving or changing to other substances. We also deal with the very small‐scale phenomena that occur when a fluid spreads out at the interface of another fluid. We model the pressure at the interfaces between fluids with Darcy's Law and represent the viscous fingering phenomenon in which a fluid interface spreads out with a fractal‐like shape. We use hybrid grid‐based simulation and smoothed particle hydrodynamics (SPH) to simulate intermolecular diffusion and attraction using particles at a computable scale. We have produced animations showing fluids mixing and objects dissolving.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

Guiding students to the right questions: adaptive navigation support in an E‐Learning system for Java programming

Rapid growth of the volume of interactive questions available to the students of modern E‐Learning courses placed the problem of personalized guidance on the agenda of E‐Learning researchers. Without proper guidance, students frequently select too simple or too complicated problems and ended either bored or discouraged. This paper explores a specific personalized guidance technology known as adaptive navigation support. We developed JavaGuide, a system, which guides students to appropriate questions in a Java programming course, and investigated the effect of personalized guidance a three‐semester long classroom study. The results of this study confirm the educational and motivational effects of adaptive navigation support.     

Cognitive,social and teaching presence in a virtual world and a text chat

Using a framework of cognitive, social, and teaching presence, the nature of learning experiences in a three-dimensional virtual world environment (Second Life) and a text-chat learning environment without visuals (TeachNet) were investigated. A mixed method of code frequencies, coherence graphs, interviews, and a survey was used. The results revealed that the TeachNet debates included more cognitive presence codes that indicate higher levels of cognitive processing than in SL debates. The teams were significantly different from each other in the collaboration style for developing arguments and in the ways to use utterances associated with cognitive, social, and teaching presences, and the groups’ collaboration style became more established with more experience with the tasks. The three critical factors–tool, tasks and group cohesion–that affect cognitive, teaching, and social presence are discussed.     

Overview of the applications of thermodynamic databases to steelmaking processes

Computerized thermodynamic databases for solid and liquid steel, slags and solid oxide solutions, for large numbers of components, have been developed over the last three decades by critical evaluation/optimization of all available phase equilibrium and thermodynamic data. The databases contain model parameters specifically developed for molten slags, liquid and solid steel and solid oxide solutions. With user-friendly software, which accesses these databases, complex chemical reactions and phase equilibria occurring throughout the steelmaking process can be calculated over wide ranges of temperature, oxygen potential and pressure. In the present article, the thermodynamic models and databases for molten slag and liquid steel included in well-known thermochemical packages and their applications to complex steelmaking processes involving molten slag, steel, inclusions, refractories and gases are reviewed.