In multilabel learning, the precise mining and appropriate application of label correlation can improve the effectiveness and generalization of prediction models. In order to characterize label correlation more carefully, the concept of operator-valued kernel is introduced. The value of operator-valued kernel is an operator on Hilbert space, and when applied to a practical problem, the function-valued operator degenerates into a positive-definite matrix, which aims to describe the label correlation. However, existing works focus on the basic theory of operator-valued kernel, and lack way to learn specific kernel from specific datasets, thus the application of operator-valued kernel in practical problem is greatly hindered. In this article, we focus on learning operator-valued kernels with fuzzy rough sets from multilabel datasets and designing learning algorithm for multilabel classification. First, the importance distribution of feature set to different labels at each sample is measured by using kernelized fuzzy rough sets. For a single sample, label correlation matrix is constructed based on the consistency of the importance distribution of features to labels, so as to characterize the correlation information between different labels. By considering the interaction information between two label correlation matrices, the label incidence matrix between two samples is obtained. Therefore, a new operator-valued kernel is defined by using label incidence matrices as elements. This operator-valued kernel is further proved to be an entangled and transformable kernel. On the basis, the proposed operator-valued kernel is applied to develop an efficient learning algorithm for multilabel classification. The generalization error bound of the prediction function is measured by Rademacher complexity. In order to illustrate the effectiveness of our algorithm, the classification experiments and statistical analysis results on twelve multilabel datasets are provided, which are compared with seven high performance algorithms.

We present a computational model of visual search on graphical layouts. It assumes that the visual system is maximising expected utility when choosing where to fixate next. Three utility estimates are available for each visual search target: one by unguided perception only, and two, where perception is guided by long-term memory (location or visual feature). The system is adaptive, starting to rely more upon long-term memory when its estimates improve with experience. However, it needs to relapse back to perception-guided search if the layout changes. The model provides a tool for practitioners to evaluate how easy it is to find an item for a novice or an expert, and what happens if a layout is changed. The model suggests, for example, that (1) layouts that are visually homogeneous are harder to learn and more vulnerable to changes, (2) elements that are visually salient are easier to search and more robust to changes, and (3) moving a non-salient element far away from original location is particularly damaging. The model provided a good match with human data in a study with realistic graphical layouts.

Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.

This paper deals with a novel local arc length estimator for curves in gray-scale images. The method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels, and then computes the sum of the spline segments’ lengths. In this model, the second derivatives and y coordinates at the knots are required in the computation; the spline polynomial coefficients need not be computed explicitly. We provide the algorithm pseudo code for estimation and preprocessing, both taking linear time. Implementation shows that the proposed model gains a smaller relative error than other state-of-the-art methods.

The following topics were dealt with: system architecture comparison; requirements and design objectives; notification service in context; notification service interfaces; notification service components; federation of notification servers; other issues and future directions.