Automatic generation of explanations: AGE

Explaining how engineering devices work is important to students, engineers, and operators. In general, machine generated explanations have been produced from a particular perspective. This paper introduces a system called automatic generation of explanations (AGE) capable of generating causal, behavioral, and functional explanations of physical devices in natural language. AGE explanations can involve different user selected state variables at different abstraction levels. AGE uses a library of engineering components as building blocks. Each component is associated with a qualitative model, information about the meaning of state variables and their possible values, information about substances, and information about the different functions each component can perform. AGE uses: (i) a compositional modeling approach to construct large qualitative models, (ii) causal analysis to build a causal dependency graph, (iii) a novel qualitative simulation approach to efficiently obtain the system's behavior on large systems, and (iv) decomposition analysis to automatically divide large devices into smaller subsystems. AGE effectiveness is demonstrated with different devices that range from a simple water tank to an industrial chemical plant.     

Octree generation from object silhouettes in perspective views

Octrees are useful for object representation when fast access to coarse spatial occupancy information is necessary. This paper presents an efficient algorithm for generating octrees from multiple perspective views of an object. The algorithm first obtains a polygonal approximation of the object silhouette. This polygon is then decomposed into convex components. For each convex component, a pyramid is formed treating the view point as its apex and the convex components as a cross section. The octree representation of each of these pyramids is obtained by performing intersection detection of the object with the cubes corresponding to octree nodes. The intersection detection step is made efficient by decomposing it into a coarse-to-fine sequence of intersection tests. The octree for one silhouette is obtained by taking the union of octrees obtained for each component. An intersection of octrees corresponding to different viewing directions gives the final octree of the object. An implementation of the algorithm is given. The accuracy of the octree representation of the objects is evaluated. The ratio of the actual volume of the object to the volume of the object reconstructed from the octree representation is used as a performance index of the algorithm.     

Parallel adaptive mesh generation and decomposition

An important class of methodologies for the parallel processing of computational models defined on some discrete geometric data structures (i.e. meshes, grids) is the so calledgeometry decomposition or splitting approach. Compared to the sequential processing of such models, the geometry splitting parallel methodology requires an additional computational phase. It consists of the decomposition of the associated geometric data structure into a number of balancedsubdomains that satisfy a number of conditions that ensure the load balancing and minimum communication requirement of the underlying computations on a parallel hardware platform. It is well known that the implementation of the mesh decomposition phase requires the solution of a computationally intensive problem. For this reason several fast heuristics have been proposed. In this paper we explore a decomposition approach which is part of a parallel adaptive finite element mesh procedure. The proposed integrated approach consists of five steps. It starts with a coarse background mesh that isoptimally decomposed by applying well known heuristics. Then, the initial mesh is refined in each subdomain after linking the new boundaries introduced by its decomposition. Finally, the decomposition of the new refined mesh is improved so that it satisfies the objectives and conditions of the mesh decomposition problem. Extensive experimentation indicates the effectiveness and efficiency of the proposed parallel mesh and decomposition approach.     

Polyhedral objects identification through the orthographic projection views generation

This paper presents a new method to automatic recognition of polyhedra. Given images taken from different viewpoints of designated polyhedra, algorithms are developed to interpret them as the same object. Based on the heuristic inference of the polyhedral scene regularities and the gradient space analysis, this method will restore the 3-D information of the polyhedra. The 3-D data is used for the generation of the orthographic projection views of the observed object which consist of the top view, the front view, and the side view.     

Dynamic generation of adaptive Internet-based courses

In this paper we describe a new approach for developing adaptive Web based courses. These courses are defined by means of teaching tasks which correspond to basic knowledge units, and rules which describe how teaching tasks are divided into subtasks. Both tasks and rules are used at execution time to guide the students during their learning process by determining the set of achievable tasks to be presented to the student at every step. Adaptivity is implemented by presenting students with different HTML pages depending on their profile, their previous actions, and the active learning strategy. The HTML pages presented to the students are generated dynamically from general information about the type of media elements associated to each task and their layout. The whole approach is exemplified by means of a course on traffic signs.     

Intelligent adaptive assistance and its automatic generation

Manuals and interactive help are tedious to provide, difficult to maintain, and difficult to ensure that they remain correct, even for simple systems. The result is a loss in product quality, felt particularly by users and designers committed to long-term product development.The paper shows that it is possible to systematically put a system specification and its documentation into exact correspondence. It follows that much previously manual work can be done automatically — and with considerable advantages, including guaranteed correctness and completeness, as well as supporting powerful new features such as intelligent adaptive assistance. This paper shows how interactive assistance can be provided to answer ‘how to?’, ‘why not?’ and other questions.     

Links between the personalities,views and attitudes of software engineers

Context:Successful software development and management depends not only on the technologies, methods and processes employed but also on the judgments and decisions of the humans involved. These, in turn, are affected by the basic views and attitudes of the individual engineers.Objective:The objective of this paper is to establish if these views and attitudes can be linked to the personalities of software engineers.Methods:We summarize the literature on personality and software engineering and then describe an empirical study on 47 professional engineers in ten different Swedish software development companies. The study evaluated the personalities of these engineers via the IPIP 50-item five-factor personality test and prompted them on their attitudes towards and basic views on their professional activities.Results:We present extensive statistical analyses of their responses to show that there are multiple, significant associations between personality factors and software engineering attitudes. The tested individuals are more homogeneous in personality than a larger sample of individuals from the general population.Conclusion:Taken together, the methodology and personality test we propose and the associated statistical analyses can help find and quantify relations between complex factors in software engineering projects in both research and practice.     

Feature-preserving adaptive mesh generation for molecular shape modeling and simulation

We describe a chain of algorithms for molecular surface and volumetric mesh generation. We take as inputs the centers and radii of all atoms of a molecule and the toolchain outputs both triangular and tetrahedral meshes that can be used for molecular shape modeling and simulation. Experiments on a number of molecules are demonstrated, showing that our methods possess several desirable properties: feature-preservation, local adaptivity, high quality, and smoothness (for surface meshes). We also demonstrate an example of molecular simulation using the finite element method and the meshes generated by our method. The approaches presented and their implementations are also applicable to other types of inputs such as 3D scalar volumes and triangular surface meshes with low quality, and hence can be used for generation/improvement of meshes in a broad range of applications.     

Parallel generation of adaptive multiresolution structures for image processing

In the paper we present an algorithm for creating region-adjacency-graph (RAG) pyramids on TurboNet, an experimental parallel computer system. Each level of these hierarchies of irregular tessellations is generated by independent stochastic processes that adapt the structure of the pyramid to the content of the image. RAGs can be used in multiresolution image analysis to extract connected components from labeled images. The implementation of the algorithm is discussed and performance results are presented for three different communication techniques which are supported by the TurboNet's hybrid architecture. The results indicate that efficient communications are vital to good performance of the algorithm. © 1997 by John Wiley & Sons, Ltd.     

Efficient generation of discontinuity-preserving adaptive triangulations from range images

This paper presents an efficient technique for generating adaptive triangular meshes from range images. The algorithm consists of two stages. First, a user-defined number of points is adaptively sampled from the given range image. Those points are chosen by taking into account the surface shapes represented in the range image in such a way that points tend to group in areas of high curvature and to disperse in low-variation regions. This selection process is done through a noniterative, inherently parallel algorithm in order to gain efficiency. Once the image has been subsampled, the second stage applies a two and one half-dimensional Delaunay triangulation to obtain an initial triangular mesh. To favor the preservation of surface and orientation discontinuities (jump and crease edges) present in the original range image, the aforementioned triangular mesh is iteratively modified by applying an efficient edge flipping technique. Results with real range images show accurate triangular approximations of the given range images with low processing times.     

Optofluidics: a novel generation of reconfigurable and adaptive compact architectures

The integration of microfluidics and microphotonics brings the ability to tune and reconfigure ultra-compact optical devices. This flexibility is essentially provided by three characteristics of fluids that are scalable at the micron-scale: fluid mobility, large ranges of index modulation, and abrupt interfaces that can be easily reshaped. Several examples of optofluidic devices are presented here to illustrate the achievement of flexible devices on (semi) planar and compact platforms. First, we report an integrated geometry for a compact and tunable interferometer that exploits a sharp and mobile air/water interface. We then describe a class of optically controlled devices that rely on the actuation of optically trapped micron-sized objects within a fluid environment. The last architecture results from the infiltration of photonic crystal devices with fluids. This produces tunable and reconfigurable photonic devices, like optical switches. Higher degrees of functionality could be achieved with sophisticated optofluidic platforms that associate complex microfluidic delivery and mixing schemes with microphotonic devices. Moreover, optofluidics offers new opportunities for realizing highly responsive and compact sensors.     

自适应采样与融合的增强现实阴影生成算法

针对现有增强现实(AR)应用中阴影生成算法绘制软阴影的真实性不足,提出一种自适应采样与背景融合的阴影生成算法。首先结合考虑遮挡的平面阴影算法计算虚拟对象投影阴影的空间位置分布;其次,改进膨胀腐蚀算法中软阴影的生成过程,提出一种依据近似点光源的形状类型进行自适应采样,从而获取假想点光源集合的软阴影绘制方法;最后,针对基于膨胀腐蚀算法使用阴影灰度图方法造成阴影的颜色被限制在单通道上,提出基于多通道与背景融合的方法。实验结果表明所提算法计算软阴影的颜色更加合理,软阴影的绘制方法更为有效,提高了阴影生成算法绘制软阴影的真实性。     

Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment

This paper proposes a fuzzy rule-based system for an adaptive scheduling, which dynamically selects and applies the most suitable strategy according to the current state of the scheduling environment. The adaptive scheduling problem is generally considered as a classification task since the performance of the adaptive scheduling system depends on the effectiveness of the mapping knowledge between system states and the best rules for the states. A rule base for this mapping is built and evolved by the proposed fuzzy dynamic learning classifier based on the training data cumulated by a simulation method. Distributed fuzzy sets approach, which uses multiple fuzzy numbers simultaneously, is adopted to recognize the system states. The developed fuzzy rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop more effective and robust rules than the traditional job-dispatching rules and a neural network approach.     

Model driven test case generation and optimization using adaptive cuckoo search algorithm

Innovations in Systems and Software Engineering - Software testing is leading toward automation that reduces the effort to find errors or bugs. The identification of test cases and its critical...     

A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems

In this paper, a generalized adaptive ensemble generation and aggregation (GAEGA) method for the design of multiple classifier systems (MCSs) is proposed. GAEGA adopts an “over-generation and selection” strategy to achieve a good bias-variance tradeoff. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data globally with different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking into consideration both the ability of each ensemble to fit the validation data locally and reducing the risk of overfitting. In this paper, the performance of GAEGA is assessed experimentally in comparison with other multiple classifier aggregation methods on 16 data sets. The experimental results demonstrate that GAEGA significantly outperforms the other methods in terms of average accuracy, ranging from 2.6% to 17.6%.     

Scenario generation using adaptive sampling: The case of resource scarcity

Static input-oriented sampling approaches are often used for generating model-based scenarios. However, for models of deeply uncertain and dynamically complex issues, there is no guarantee that such approaches reveal the total behavioral spectrum that could be generated by simulating them. In this paper, we present an adaptive output-oriented sampling approach for exploring the full behavioral spectrum that could be generated by computational models in view of generating interesting, even previously undiscovered, scenarios. In this paper, we use a resource scarcity model to illustrate the approach, show the difference between static sampling and adaptive sampling, and demonstrate the usefulness for scenario discovery of the latter combined with other methods. We show that this approach can be used for revealing the behavioral spectrum of models, identifying regions of the input space that generate particular behaviors, and selecting (sets of) scenarios that are representative in terms of output and input spaces.     

Causes and explanations in the structural-model approach: Tractable cases

This paper continues the research on the computational aspects of Halpern and Pearl's causes and explanations in the structural-model approach. To this end, we first explore how an instance of deciding weak cause can be reduced to an equivalent instance in which irrelevant variables in the (potential) weak cause and the causal model are removed, which extends previous work by Hopkins. We then present a new characterization of weak cause for a certain class of causal models in which the causal graph over the endogenous variables has the form of a directed chain of causal subgraphs, called decomposable causal graph. Furthermore, we also identify two important subclasses in which the causal graph over the endogenous variables forms a directed tree and more generally a directed chain of layers, called causal tree and layered causal graph, respectively. By combining the removal of irrelevant variables with this new characterization of weak cause, we then obtain techniques for deciding and computing causes and explanations in the structural-model approach, which can be done in polynomial time under suitable restrictions. This way, we obtain several tractability results for causes and explanations in the structural-model approach. To our knowledge, these are the first explicit ones. They are especially useful for dealing with structure-based causes and explanations in first-order reasoning about actions, which produces large causal models that are naturally layered through the time line, and thus have the structure of layered causal graphs. Furthermore, an important feature of the tractable cases for causal trees and layered causal graphs is that they can be recognized efficiently, namely in linear time. Finally, by extending the new characterization of weak cause, we obtain similar techniques for computing the degrees of responsibility and blame, and hence also novel tractability results for structure-based responsibility and blame.     

The second generation of self-organizing adaptive penalty strategy for constrained genetic search

Penalty function approaches have been extensively applied to genetic algorithms for tackling constrained optimization problems. The effectiveness of the genetic searches to locate the global optimum on constrained optimization problems often relies on the proper selections of many parameters involved in the penalty function strategies. A successful genetic search is often completed after a number of genetic searches with varied combinations of penalty function related parameters. In order to provide a robust and effective penalty function strategy with which the design engineers use genetic algorithms to seek the optimum without the time-consuming tuning process, the self-organizing adaptive penalty strategy (SOAPS) for constrained genetic searches was proposed. This paper proposes the second generation of the self-organizing adaptive penalty strategy (SOAPS-II) to further improve the effectiveness and efficiency of the genetic searches on constrained optimization problems, especially when equality constraints are involved. The results of a number of illustrative testing problems show that the SOAPS-II consistently outperforms other penalty function approaches.