Predicting intent behind selections in scatterplot visualizations

Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.

Magic Wand Selection Tool for 3D Model Surfaces

Introduction: segmentation of 3d shapes is a fundamental problem in computer graphics and computer-aided design. It has received much plays attention in recent years. The analysis and research methods of 3d mesh models have established reliable mathematical foundations in graphics and geometric modeling. Compared with color and texture, shape features describe the shape information of objects from geometric structure features. And it an important role in a wide range of applications, including mesh parameterization, skeleton extraction, resolution modeling, shape retrieval, character recognitio,, robot navigation, and many others. Methods: The interactive selection surface of models is mainly used for shape segmentation. The common method is boundary-based selection, which requires user input some stokes near the edge of the selected or segmented region. Chen et al.introduced an approach to join the specified points form the boundaries for region segmentation on the surface. Funkhouser et al. improve the Dijkstra algorithm to find segmentation boundary contour. The graph cut algorithm use the distance between the surface and its convex hull as the growing criteria to decompose a shape into meaningful components. The watershed algorithm, widely used for image segmentation, is a region-growing algorithm with multiple seed points. Wu and Levine use simulated electrical charge distributions over the mesh to deal with the 3D part segmentation problem. Other methods using a watershed algorithm for surface decomposition. Results: We implemented our algorithm in C++ and Open MP and conducted the experiments on a PC with a 3.07 GHz Intel(R) Core(TM) i7 CPU and 6 GB memory. Our method can get a similar region under different interaction vertices in specific regions. Figure 6a and Figure 6b are the calculation results of tolerance region selection of this algorithm in a certain region of kitten model at two different interaction points, from which we can see the obtained regions are similar from different vertices in this region. Figure 6c and figure 6d are two different interactive points in the same region, and the region selection results are obtained by Region growing technique. Discussion: In this paper, we proposed a novel magic wand selection tool to interactive select surface of 3D model. The feature vector is constructed by extracting the HKS feature descriptor and means curvature of 3D model surface, which allows users to input the feature tolerance value for region selection and improves the self-interaction of users. Many experiments show that our algorithm has obvious advantages in speed and effectiveness. The interactive generation of region boundary is very useful for many applications including model segmentation. Conclusion: In consideration of a couple of requirements including user-friendliness and effectiveness in model region selection, we propose a novel magic wand selection tool to interactive selection surface of 3D models. First, we pre-compute the heat kernel feature and mean curvature of the surface, and then form the eigenvector of the model. Then we provide two ways for region selection. One is to select the region according to the feature of tolerance value. The other is to select the region that aligns with stroke automatically. Finally, we use the geometry optimization approach to improve the performance of the computing region con-tours. Extensive experimental results show that our algorithm is efficient and effective.

A multi-criteria optimization approach to modeling negotiation process

The paper presents a multi-criteria optimization approach to modeling negotiation process. The negotiation process is modeled as a special multi-criteria problem. The method for finding solutions is the interactive selection process of some proposals. The parties shall submit their proposals on the subjects of the negotiations. These proposals are parameters of the multi-criteria optimization problem. Selection of solutions is accomplished by solving the optimization problem with parameters that define the aspirations of each party involved in the negotiations. Finally, evaluation of the solutions obtained by the parties is made.