The fundamental challenge for human-computer interaction (HCI) is to invent and design a culture in which humans can express themselves and engage in personally meaningful activities. Cultures are substantially defined by their media and tools for thinking, working, learning, and collaborating. New media change (1) the structure and contents of our interests, (2) the nature of our cognitive and physical tools, and (3) the social environment in which thoughts originate and evolve, and mindsets develop. Unfortunately, a large number of new media are designed from a perspective of seeing and treating humans primarily as consumers. The possibility for humans to be and to act as designers (in cases in which they desire to do so) should be accessible not only to a small group of high-tech scribes, but rather to all interested individuals and groups. 1. Introduction Cultures are substantially defined by their media and their tools for thinking, working, learning, and collaborating. A large...

The emphasis on processors that are both low power and high performance has resulted in the incorporation of dynamic voltage scaling into processor designs. This feature allows one to make fine granularity trade-offs between power use and performance, provided there is a mechanism in the OS to control that trade-off. In this paper, we describe a novel software approach to automatically controlling dynamic voltage scaling in order to optimize energy use. Our mechanism is implemented in the Linux kernel and requires no modification of user programs. Unlike previous automated approaches, our method works equally well with irregular and multiprogrammed workloads. Moreover, it has the ability to ensure that the quality of interactive performance is within user specified parameters. Our experiments show that as a result of our algorithm, processor energy savings of as much as 75% can be achieved with only a minimal impact on the user experience.

this paper but makes the predictive model more complete. 2 Experiment

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Although engineering models of user behavior have enjoyed a rich history in HCI, they have yet to have a widespread impact due to the complexities of the modeling process. In this paper we describe a development system in which designers generate predictive cognitive models of user behavior simply by demonstrating tasks on HTML mock-ups of new interfaces. Keystroke-Level Models are produced automatically using new rules for placing mental operators, then implemented in the ACT-R cognitive architecture. They interact with the mock-up through integrated perceptual and motor modules, generating behavior that is automatically quantified and easily examined. Using a query-entry user interface as an example [19], we demonstrate that this new system enables more rapid development of predictive models, with more accurate results, than previously published models of these tasks. Author Keywords

We describe a memory management toolkit for language implementors. It offers efficient and flexible generation scavenging garbage collection. In addition to providing a core of languageindependent algorithms and data structures, the toolkit includes auxiliary components that ease implementation of garbage collection for programming languages. We have detailed designs for Smalltalk and Modula-3 and are confident the toolkit can be used with a wide variety of languages. The toolkit approach is itself novel, and our design includes a number of additional innovations in flexibility, efficiency, accuracy, and cooperation between the compiler and the collector. This project is supported by National Science Foundation Grant CCR-8658074, and by Digital Equipment Corporation, GTE Laboratories, and Apple Computer. 1 Introduction As part of an ongoing effort to implement Persistent Smalltalk and Persistent Modula-3, we have designed a high performance garbage collector toolkit that can be us...

A variety of information visualization tools have been developed recently, but relatively little effort has been made to evaluate the effectiveness and utility of the tools. This article describes results from two empirical studies of two visualization tools for depicting hierarchies, in particular, computer file and directory structures. The two systems examined implement space-filling methodologies, one rectangular, the Treemap method, and one circular, the Sunburst method. Participants performed typical file/directory search and analysis tasks using the two tools. In general, performance trends favored the Sunburst tool with respect to correct task performance, particularly on initial use. Performance with Treemap tended to improve over time and use, suggesting a greater learning cost that was partially recouped over time. Each tool afforded somewhat different search strategies, which also appeared to influence performance. Finally, participants strongly preferred the Sunburst tool,...

nvergence and ubiquity. At the same time, inexpensive computing power is enabling a quiet revolution in the machine perception of human action. In the near future, we expect machine perception to converge with ubiquitous computing and communication. Exploring machine vision for human-computer interaction. THINGS THAT SEE COMMUNICA 0 A OF THE AE March 2000/V4 43, No. 3 55 PUI Figure 1. Interacting with the Magic Board (iihm.imag.fr/demos/magicboard/). Physical whiteboard Workstation Video projector Video camera (a) The apparatus of the Magic Board; (b) Selecting a physical drawing with the finger; (c) Copying the selected drawing; (d) Completing the drawing with physical markers; (e) The menu at the top of the physical board to facilitate reinitialization. a c b d e What Can Machine Vision Do For You? Machine vision is the observation of an environment using cameras. It differs from image

The emphasis on processors that are both low-power and high-performance has resulted in the incorporation of dynamic voltage scaling into processor designs. This feature allows one to make fine granularity trade-offs between power use and performance, provided there is a mechanism in the OS to control that trade-off. In this paper, we describe a novel software approach to automatically controlling dynamic voltage scaling to optimize energy use. Our mechanism is implemented in the Linux kernel and requires no modification of user programs. Unlike previous automated approaches, our method works equally well with irregular and multiprogrammed workloads. Moreover, it has the ability to ensure that the quality of interactive performance is within user specified parameters. Our experiments show that as a result of our algorithm, processor energy savings of as much as 75 % can be achieved with only a minimal impact on the user experience.

To contribute to systems that reason about human attention, our work empirically demonstrates how a user’s mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.