User interface

About: User interface is a research topic. Over the lifetime, 85402 publications have been published within this topic receiving 1728377 citations. The topic is also known as: UI & input method.

Making sense of sensing systems: five questions for designers and researchers

Abstract: This paper borrows ideas from social science to inform the design of novel "sensing" user-interfaces for computing technology. Specifically, we present five design challenges inspired by analysis of human-human communication that are mundanely addressed by traditional graphical user interface designs (GUIs). Although classic GUI conventions allow us to finesse these questions, recent research into innovative interaction techniques such as 'Ubiquitous Computing' and 'Tangible Interfaces' has begun to expose the interaction challenges and problems they pose. By making them explicit we open a discourse on how an approach similar to that used by social scientists in studying human-human interaction might inform the design of novel interaction mechanisms that can be used to handle human-computer communication accomplishments

MVC (Model-View-Controller) based multi-modal authoring tool and development environment

Abstract: Application development tools and method for building multi-channel, multi-device and multi-modal applications, and in particular, to systems and methods for developing applications whereby a user can interact in parallel with the same information via a multiplicity of channels and user interfaces, while a unified, synchronized views of the information are presented across the various channels or devices deployed by the user to interact with the information. In a preferred embodiment, application frameworks and development tools are preferably based on a MVC (Model-View-Controller) design paradigm that is adapted to provide synchronized multi-modal interactions. Multi-channel authoring can be developed using a similar methodology.

Systems and methods for implementing modular DOM (Document Object Model)-based multi-modal browsers

Abstract: Systems and methods for building multi-modal browsers applications and, in particular, to systems and methods for building modular multi-modal browsers using a DOM (Document Object Model) and MVC (Model-View-Controller) framework that enables a user to interact in parallel with the same information via a multiplicity of channels, devices, and/or user interfaces, while presenting a unified, synchronized view of such information across the various channels, devices and/or user interfaces supported by the multi-modal browser. The use of a DOM framework (or specifications similar to DOM) allows existing browsers to be extended without modification of the underling browser code. A multi-modal browser framework is modular and flexible to allow various fat client and thin (distributed) client approaches.

System and method enabling awareness of others working on similar tasks in a computer work environment

Abstract: A computer system and method provide networked computer users with information about which other users are task proximate to the user, thereby facilitating spontaneous communications regarding task-related, or other, issues. The information about other users is displayed in a user interface window on each computer that presents a visual representation of each user who is task proximate to the user operating the computer. Task proximity to other users may change as the user context switches between applications, and the user interface window is updated accordingly. Task proximity is determined individually by different applications. One exemplary system architecture for providing the information includes a person object representing each user, and storing the visual representation of the user. An encounter window on each computer displays the visual representations. A number of encounter-aware applications may execute on each computer. An encounter server on each computer provides communication between the encounter-aware applications of the positions of each user, position being determined, for example, by the function the user is using, the data, and the time. At least one encounter-aware application includes a match object that accepts information from the encounter servers about user positions and determines the task proximity of the users. The match object informs the encounter server of the task proximity of the user. The encounter server then updates the appropriate encounter window. The encounter windows further provide a number of communication mechanisms so that users can efficiently contact those other users who are task proximate.

Mining User Mobility Features for Next Place Prediction in Location-Based Services

Abstract: Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.