Statistical user interfaces

A statistical user interface is an interface between a human user and a statistical software package. Whenever we use a statistical software package we want to solve a specific statistical problem. But very often at first it is necessary to learn specific things about the software package. Everyone of us knows about the ?religious wars? concerning the question which statistical software package/method is the best for a certain task; see Marron (1996) and Cleveland and Loader (1996) and related internet discussions. Experienced statisticians use a bunch of different statistical software packages rather than a single one; although all of the major companies (at least the marketing departments) tell us that we only need their software package. --

Evaluating advanced search interfaces using established information-seeking model

When users have poorly defined or complex goals search interfaces offering only keyword searching facilities provide inadequate support to help them reach their information-seeking objectives. The emergence of interfaces with more advanced capabilities such as faceted browsing and result clustering can go some way to some way toward addressing such problems. The evaluation of these interfaces, however, is challenging since they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design out-performs another. Nonetheless if we could effectively compare these interfaces we would have a way to determine which was best for a given scenario and begin to learn why. In this article we present a formative framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users, user needs, and user behaviours to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive IR evaluation

Sensory semantic user interfaces (SenSUI)

Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology

Design Principals of Social Navigation

8th Delos Workshop on "User Interfaces for Digital Libraries" (on 21 October it will be held in conjuction with the 4th ERCIM Workshop on "User Interfaces for All"), SICS, Kista, Sweden, 21-23 October 1998PERSON

Disjoint forms in graphical user interfaces

Forms are parts of a graphical user interface (GUI) that show a set of values and allow the user to update them. The declarative form construction library FunctionalForms is extended with disjoint form combinators to capture some common patterns in which the form structure expresses a choice. We demonstrate that these combinators lead to a better separation of logic and layout

Saliency Prediction for Mobile User Interfaces

We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons, text, etc. in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.Comment: Paper accepted at WACV 201

Contextualizing the blogosphere: A comparison of traditional and novel user interfaces for the web

In this paper, we investigate how contextual user interfaces affect blog reading experience. Based on a review of previous research, we argue why and how contextualization may result in (H1) enhanced blog reading experiences. In an eyetracking experiment, we tested 3 different web-based user interfaces for information spaces. The StarTree interface (by Inxight) and the Focus-Metaphor interface are compared with a standard blog interface. Information tasks have been used to evaluate and compare task performance and user satisfaction between these three interfaces. We found that both contextual user interfaces clearly outperformed the traditional blog interface, both in terms of task performance as well as user satisfaction. © 2007 Laqua, S., Ogbechie, N. and Sasse, M. A