Support Vector Machines (SVMs) are a class of classification models in machine learning that are based on computing a maximum-margin separator between two sets of points. The SVM problem has been heavily studied for Euclidean geometry and for a number of kernels. In this paper, we consider the linear SVM problem in the Hilbert metric, a non-Euclidean geometry defined over a convex body. We present efficient algorithms for computing the SVM classifier for a set of n points in the Hilbert metric defined by convex polygons in the plane and convex polytopes in d-dimensional space. We also consider the problems in the related Funk distance.

Geometric data sets that arise in modern applications are often very large and change dynamically over time. A popular framework for dealing with such data sets is the evolving data framework, where a discrete structure continuously varies over time due to the unseen actions of an evolver, which makes small changes to the data. An algorithm probes the current state through an oracle, and the objective is to maintain a hypothesis of the data set’s current state that is close to its actual state at all times. In this paper, we apply this framework to maintaining a set of n point objects in motion in d-dimensional Euclidean space. To model the uncertainty in the object locations, both the ground truth and hypothesis are based on spatial probability distributions, and the distance between them is measured by the Kullback-Leibler divergence (relative entropy). We introduce a simple and intuitive motion model in which, with each time step, the distance that any object can move is a fraction of the distance to its nearest neighbor. We present an algorithm that, in steady state, guarantees a distance of O(n) between the true and hypothesized placements. We also show that for any algorithm in this model, there is an evolver that can generate a distance of Ω(n), implying that our algorithm is asymptotically optimal.

Touchscreen typing requires coordinating the fingers and visual attention for button-pressing, proofreading, and error correction. Computational models need to account for the associated fast pace, coordination issues, and closed-loop nature of this control problem, which is further complicated by the immense variety of keyboards and users. The paper introduces CRTypist, which generates human-like typing behavior. Its key feature is a reformulation of the supervisory control problem, with the visual attention and motor system being controlled with reference to a working memory representation tracking the text typed thus far. Movement policy is assumed to asymptotically approach optimal performance in line with cognitive and design-related bounds. This flexible model works directly from pixels, without requiring hand-crafted feature engineering for keyboards. It aligns with human data in terms of movements and performance, covers individual differences, and can generalize to diverse keyboard designs. Though limited to skilled typists, the model generates useful estimates of the typing performance achievable under various conditions.

Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human–computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., “rotating” a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.

Gaze-based selection has received significant academic attention over a number of years. While advances have been made, it is possible that further progress could be made if there were a deeper understanding of the adaptive nature of the mechanisms that guide eye movement and vision. Control of eye movement typically results in a sequence of movements (saccades) and fixations followed by a ‘dwell’ at a target and a selection. To shed light on how these sequences are planned, this paper presents a computational model of the control of eye movements in gaze-based selection. We formulate the model as an optimal sequential planning problem bounded by the limits of the human visual and motor systems and use reinforcement learning to approximate optimal solutions. The model accurately replicates earlier results on the effects of target size and distance and captures a number of other aspects of performance. The model can be used to predict number of fixations and duration required to make a gaze-based selection. The future development of the model is discussed.

Traditionally, touchscreen typing has been studied in terms of motor performance. However, recent research has exposed a decisive role of visual attention being shared between the keyboard and the text area. Strategies for this are known to adapt to the task, design, and user. In this paper, we propose a unifying account of touchscreen typing, regarding it as optimal supervisory control. Under this theory, rules for controlling visuo-motor resources are learned via exploration in pursuit of maximal typing performance. The paper outlines the control problem and explains how visual and motor limitations affect it. We then present a model, implemented via reinforcement learning, that simulates co-ordination of eye and finger movements. Comparison with human data affirms that the model creates realistic finger- and eye-movement patterns and shows human-like adaptation. We demonstrate the model’s utility for interface development in evaluating touchscreen keyboard designs.

The contour tree is a topological structure associated with a scalar function that tracks the connectivity of the evolving level sets of the function. It supports intuitive and interactive visual exploration and analysis of the scalar function. This paper describes a fast, parallel, and memory efficient algorithm for constructing the contour tree of a scalar function on shared memory systems. Comparisons with existing implementations show significant improvement in both the running time and the memory expended. The proposed algorithm is particularly suited for large datasets that do not fit in memory. For example, the contour tree for a scalar function defined on a 8.6 billion vertex domain (2048×2048×2048 volume data) can be efficiently constructed using less than 10GB of memory.

Up-sampling plays a crucial role while increasing the resolution of an image or a video intra frame through interpolation. Since the up-sampling process is analogous to a low pass filtering operation, it produces undesirable blurring artifacts that deteriorate the signal quality in terms of loss of fine details and critical edge information. In order to resolve this problem, a no reference, fuzzy weighted unsharp masking based DCT interpolation technique is proposed here. The proposed method is an anticipatory, spatial domain, fuzzy logic based preprocessing approach which sharpens the sub-sampled or low resolution video intra frames depending on their region statistics in order to compensate the blurring caused by the subsequent DCT interpolation technique. According to this method, the regions with high variance are sharpened more than the regions with low variance based on the Fuzzy rule base. This consequently results in the restoration of fine details and edge information in the reconstructed up-sampled video intra frame with improved objective and subjective quality.