Constraint-aware interior layout exploration for pre-cast concrete-based buildings

Creating desirable layouts of building interiors is a complex task as designers have to manually adhere to various local and global considerations arising from competing practical and design considerations. In this work, we present an interactive design tool to create desirable floorplans by computationally conforming to such design constraints. Specifically, we support three types of constraints: (i) functional constraints such as number of rooms, connectivity among the rooms, target room areas, etc., (ii) design considerations such as user modifications and preferences, and (iii) fabrication constraints such as cost and convenience of manufacturing. Based on user specifications, our system automatically generates multiple floor layouts with associated 3D geometry that all satisfy the design specifications and constraints, thus exposing only the desirable family of interior layouts to the user. In this work, we focus on pre-cast concrete-based constructions, which lead to interesting discrete and continuous optimization possibilities. We test our framework on a range of complex real-world specifications and demonstrate the control and expressiveness of the exposed design space relieving the users of the task of manually adhering to non-local functional and fabrication constraints.     

Game level layout from design specification

The design of video game environments, or levels, aims to control gameplay by steering the player through a sequence of designer‐controlled steps, while simultaneously providing a visually engaging experience. Traditionally these levels are painstakingly designed by hand, often from pre‐existing building blocks, or space templates. In this paper, we propose an algorithmic approach for automatically laying out game levels from user‐specified blocks. Our method allows designers to retain control of the gameplay flow via user‐specified level connectivity graphs, while relieving them from the tedious task of manually assembling the building blocks into a valid, plausible layout. Our method produces sequences of diverse layouts for the same input connectivity, allowing for repeated replay of a given level within a visually different, new environment. We support complex graph connectivities and various building block shapes, and are able to compute complex layouts in seconds. The two key components of our algorithm are the use of configuration spaces defining feasible relative positions of building blocks within a layout and a graph‐decomposition based layout strategy that leverages graph connectivity to speed up convergence and avoid local minima. Together these two tools quickly steer the solution toward feasible layouts. We demonstrate our method on a variety of real‐life inputs, and generate appealing layouts conforming to user specifications.     

Utilizing hierarchical feature domain values for prediction

We propose a Bayesian learning framework which can exploit hierarchical structures of discrete feature domain values to improve the prediction performance on sparse training data. One characteristic of our framework is that it provides a principled way based on mean–variance analysis to transform an original feature domain value to a coarser granularity by exploiting the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation for prediction. We have conducted comparative experiments using three real-world data sets. The results demonstrate that utilizing domain value hierarchies gains benefits for prediction.     

Configuring Hierarchical Layouts to Address Research Questions

We explore the effects of selecting alternative layouts in hierarchical displays that show multiple aspects of large multivariate datasets, including spatial and temporal characteristics. Hierarchical displays of this type condition a dataset by multiple discrete variable values, creating nested graphical summaries of the resulting subsets in which size, shape and colour can be used to show subset properties. These 'small multiples' are ordered by the conditioning variable values and are laid out hierarchically using dimensional stacking. Crucially, we consider the use of different layouts at different hierarchical levels, so that the coordinates of the plane can be used more effectively to draw attention to trends and anomalies in the data. We argue that these layouts should be informed by the type of conditioning variable and by the research question being explored. We focus on space-filling rectangular layouts that provide data-dense and rich overviews of data to address research questions posed in our exploratory analysis of spatial and temporal aspects of property sales in London. We develop a notation ('HiVE') that describes visualisation and layout states and provides reconfiguration operators, demonstrate its use for reconfiguring layouts to pursue research questions and provide guidelines for this process. We demonstrate how layouts can be related through animated transitions to reduce the cognitive load associated with their reconfiguration whilst supporting the exploratory process.     

Reasoning about memory layouts

Verification methods for memory-manipulating C programs need to address not only well-typed programs that respect invariants such as the split-heap memory model, but also programs that access through pointers arbitrary memory objects such as local variables, single struct fields, or array slices. We present a logic for memory layouts that covers these applications and show how proof obligations arising during the verification can be discharged automatically using the layouts. The framework developed in this way is also suitable for reasoning about data structures manipulated by algorithms, which we demonstrate by verifying the Schorr-Waite graph marking algorithm.     

Techniques for Edge Stratification of Complex Graph Drawings

We propose an approach that allows a user (e.g., an analyst) to explore a layout produced by any graph drawing algorithm, in order to reduce the visual complexity and clarify its presentation. Our approach is based on stratifying the drawing into layers with desired properties; to this aim, heuristics are presented. The produced layers can be explored and combined by the user to gradually acquire details. We present a user study to test the effectiveness of our approach. Furthermore, we performed an experimental analysis on popular force-directed graph drawing algorithms, in order to evaluate what is the algorithm that produces the smallest number of layers and if there is any correlation between the number of crossings and the number of layers of a graph layout. The proposed approach is useful to explore graph layouts, as confirmed by the presented user study. Furthermore, interesting considerations arise from the experimental evaluation, in particular, our results suggest that the number of layers of a graph layout may represent a reliable measure of its visual complexity. The algorithms presented in this paper can be effectively applied to graph layouts with a few hundreds of edges and vertices. For larger drawings that contain lots of crossings, the time complexity of our algorithms grows quadratically in the number of edges and more efficient techniques need to be devised. The proposed approach takes as input a layout produced by any graph drawing algorithm, therefore it can be applied in a variety of application domains. Several research directions can be explored to extend our framework and to devise new visualization paradigms to effectively present stratified drawings.     

A linear algebra framework for automatic determination of optimaldata layouts

This paper presents a data layout optimization technique for sequential and parallel programs based on the theory of hyperplanes from linear algebra. Given a program, our framework automatically determines suitable memory layouts that can be expressed by hyperplanes for each array that is referenced. We discuss the cases where data transformations are preferable to loop transformations and show that under certain conditions a loop nest can be optimized for perfect spatial locality by using data transformations. We argue that data transformations can also optimize spatial locality for some arrays without distorting temporal/spatial locality exhibited by others. We divide the problem of optimizing data layout into two independent subproblems: 1) determining optimal static data layouts, and 2) determining data transformation matrices to implement the optimal layouts. By postponing the determination of the transformation matrix to the last stage, our method can be adapted to compilers with different default layouts. We then present an algorithm that considers optimizing parallelism and spatial locality simultaneously. Our results on eight programs on two distributed shared-memory multiprocessors, the Convex Exemplar SPP-2000 and the SGI Origin 2000, show that the layout optimizations are effective in optimizing spatial locality and parallelism     

A hierarchical visual saliency detection method by combining distinction and background probability maps

We propose a bottom-up method for image saliency detection by combining the distinction map and the background probability map. The contributions of our work are as follows. First, a novel distinction measure is proposed by weighted combination of the color contrast and the spatial distance distribution criterions in previous works. Second, a background pixel distribution approximation method using patches sampled near the image borders is introduced. Finally, the distinction map and the background probability map are incorporated into a hierarchical framework to generate the final saliency map. We have compared our method with several recent works experimentally and observe that competitive results can be achieved.     

Efficient provably-secure hierarchical key assignment schemes

A hierarchical key assignment scheme is a method to assign some private information and encryption keys to a set of classes in a partially ordered hierarchy, in such a way that the private information of a higher class can be used to derive the keys of all classes lower down in the hierarchy.In this paper we design and analyze hierarchical key assignment schemes which are provably secure and support dynamic updates to the hierarchy with local changes to the public information and without requiring any private information to be re-distributed.

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We first consider the problem of constructing a hierarchical key assignment scheme by using as a building block a symmetric encryption scheme. We propose a new construction which is provably secure with respect to key indistinguishability, requires a single computational assumption, and improves on previous proposals.

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Then, we show how to construct a hierarchical key assignment scheme by using as a building block a public-key broadcast encryption scheme. In particular, one of our constructions provides constant private information and public information linear in the number of classes in the hierarchy.

     

Template Assembly for Detailed Urban Reconstruction

We propose a new framework to reconstruct building details by automatically assembling 3D templates on coarse textured building models. In a preprocessing step, we generate an initial coarse model to approximate a point cloud computed using Structure from Motion and Multi View Stereo, and we model a set of 3D templates of facade details. Next, we optimize the initial coarse model to enforce consistency between geometry and appearance (texture images). Then, building details are reconstructed by assembling templates on the textured faces of the coarse model. The 3D templates are automatically chosen and located by our optimization‐based template assembly algorithm that balances image matching and structural regularity. In the results, we demonstrate how our framework can enrich the details of coarse models using various data sets.     

Modelling multilevel data in multimedia: A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.     

SAFE: Structure‐aware facade editing

Many man‐made objects, in particular building facades, exhibit dominant structural relations such as symmetry and regularity. When editing these shapes, a common objective is to preserve these relations. However, often there are numerous plausible editing results that all preserve the desired structural relations of the input, creating ambiguity. We propose an interactive facade editing framework that explores this structural ambiguity. We first analyze the input in a semi‐automatic manner to detect different groupings of the facade elements and the relations among them. We then provide an incremental editing process where a set of variations that preserve the detected relations in a particular grouping are generated at each step. Starting from one input example, our system can quickly generate various facade configurations.     

Synthesizing MPI Implementations from Functional Data-Parallel Programs

Distributed memory architectures such as Linux clusters have become increasingly common but remain difficult to program. We target this problem and present a novel technique to automatically generate data distribution plans, and subsequently MPI implementations in C++, from programs written in a functional core language. The main novelty of our approach is that we support distributed arrays, maps, and lists in the same framework, rather than just arrays. We formalize distributed data layouts as types, which are then used both to search (via type inference) for optimal data distribution plans and to generate the MPI implementations. We introduce the core language and explain our formalization of distributed data layouts. We describe how we search for data distribution plans using an adaptation of the Damas–Milner type inference algorithm, and how we generate MPI implementations in C++ from such plans.     

Intensity- and gradient-based stereo matching using hierarchicalGaussian basis functions

We propose a stereo correspondence method by minimizing intensity and gradient errors simultaneously. In contrast to conventional use of image gradients, the gradients are applied in the deformed image space. Although a uniform smoothness constraint is imposed, it is applied only to nonfeature regions. To avoid local minima in the function minimization, we propose to parameterize the disparity function by hierarchical Gaussians. Both the uniqueness and the ordering constraints can be easily imposed in our minimization framework. Besides, we propose a method to estimate the disparity map and the camera response difference parameters simultaneously. Experiments with various real stereo images show robust performances of our algorithm     

Dynamic hierarchical reactive controller synthesis

In the formal approach to reactive controller synthesis, a symbolic controller for a possibly hybrid system is obtained by algorithmically computing a winning strategy in a two-player game. Such game-solving algorithms scale poorly as the size of the game graph increases. However, in many applications, the game graph has a natural hierarchical structure. In this paper, we propose a modeling formalism and a synthesis algorithm that exploits this hierarchical structure for more scalable synthesis. We define local games on hierarchical graphs as a modeling formalism that decomposes a large-scale reactive synthesis problem in two dimensions. First, the construction of a hierarchical game graph introduces abstraction layers, where each layer is again a two-player game graph. Second, every such layer is decomposed into multiple local game graphs, each corresponding to a node in the higher level game graph. While local games have the potential to reduce the state space for controller synthesis, they lead to more complex synthesis problems where strategies computed for one local game can impose additional requirements on lower-level local games. Our second contribution is a procedure to construct a dynamic controller for local game graphs over hierarchies. The controller computes assume-admissible winning strategies that satisfy local specifications in the presence of environment assumptions, and dynamically updates specifications and strategies due to interactions between games at different abstraction layers at each step of the play. We show that our synthesis procedure is sound: the controller constructs a play that satisfies all local specifications. We illustrate our results through an example controlling an autonomous robot in a building with known floor plan and provide simulation results using an implementation of our algorithm on top of LTLMoP.     

Object-oriented product metrics: A generic framework

In spite of considerable prior research, a generic framework has not emerged for structuring work on object-oriented (OO) metrics. We propose such a framework (Generic Framework) for object-oriented product metrics. The framework captures the generic structure of the underlying metrics space (Metrics Space) based on a mereological and set theoretic perspective of the building blocks of OO systems and relational measurement theory. We validate the framework by applying it to a repository of about 350 product metrics. The validation shows that the framework does, indeed, capture the underlying metrics space, and can be useful in identifying gaps and additional metrics that can extend the manner in which Metrics Space is currently populated.     

Metro Transit‐Centric Visualization for City Tour Planning

In general, city trip planning consists of two main steps: knowing Points‐Of‐Interest (POIs), and then planning a tour route from the current point to next preferred POIs. We mainly consider the metro for traveling around touristic cities as the main means of transportation. In this context, existing tools lack a capability to effectively visualize POIs on the metro map for trip planning. To bridge this gap, we propose an interactive framework that holistically combines presentations of POIs and a metro network. Our idea is to identify popular POIs based on visual worth computation, and to introduce POI discovery for effectively identifying POIs within reach of a metro network for users. We use octilinear layouts to highlight the metro network, and show representative POI images in the layout space visualized within a user‐specified viewing window. We have implemented our working prototype showing touristic cities with a metro network. We have factored out various design guidelines that are basis for designing our method, and validated our approach with a user study surveying 70 individuals.