Realistic humanoid robots have emerged in the last two decades but the emotional intelligence of these machines has been limited. To teach humanoids how to emotionally communicate with humans, researchers have been increasingly relying on machine learning algorithms. While the software used to implement machine learning algorithms is largely open source, facially expressive humanoid robots are expensive and inaccessible to most people, thus limiting the number of researchers in this field. This paper aims to aid potential artificial intelligence researchers by providing a relatively inexpensive, open-source robot that can serve as a platform for research into emotional communication between humans and machines. Eva, the robot described in this paper, is an adult-sized humanoid head that can emulate human facial expressions, head movements, and speech through the use of 25 muscles, including 12 facial muscles that can produce a maximum skin displacement of 15 mm.
competing risks, reaction times, speed/accuracy tradeoff, time-to-event model, working memory tasks,
While the majority of CAD systems today have the ability to engage in online collaboration, most systems emphasize engineer-to-engineer collaboration and have little features for creating highly polished interactive presentations for general collaboration. Some of the systems do have web centric collaboration tools, which allow general collaboration, but like all browser-based web-systems, they lack the level of interaction that can be achieved with a dedicated interactive multimedia tool. The author presents a methodology for creating fully interactive 3D multiuser collaborative presentations through the extraction of 3D geometrical data from a CAD source. The data is then assembled and scripted using shockwave 3D studio. The end result is an on-line experience with accurate models and engaging multimedia content.
The ability to collaboratively work on engineering graphics is of a great advantage. This paper details the development of a multiuser multimedia tool specifically designed to enhance collaboration in the engineering design graphics arena. The system provides users with the capabilities to collaboratively explore 3D environments, videoteleconference, and share applications files. The system provides a centralized application that combines traditional CD-ROM multimedia tools with web tools to provide a media-rich collaborative environment. Specifically, the system is built around Macromedia Director and Microsoft's Active Server Pages. Macromedia's Shockwave Multiuser server is used as a conduit for synchronous communications, and ASP pages are used for the storage and administration of shared spaces. Microsoft's Media Services and Active-X components are used to enhance the collaborative capabilities of the system. The system also address on-site collaboration through the integration of an intelligent mechanism capable of identifying users accessing the system from wireless PDA's and delivering alternate content through IEEE 802.11b Wireless LAN.
yale university Carlos J. Morales worcester polytechnic instituteWe welcome Rouder's thoughtful comments, which provide valuable context for our work. We especially appreciate the amount of time that he has placed into examining features and properties of our model. Rouder points out the close connection of our modeling framework with the deadline model framework of Ollman and Billington (1972). In doing so, he explains some of the difficulties that have been discovered with deadline models, summarizing work by Ruthruff (1996) as well as his own with Ratcliff (Ratcliff and Rouder, 1998). Our response to Rouder's discussion allows us to clarify a bit further the assumptions underlying our model.While Rouder is correct in noticing that our error-free statistical model is nearly identical to the deadline model of Ollman and Billington (1972), our model is, in fact, different in a crucial respect. The deadline model, like our error-free modeling framework, assumes a race between two processes. The process of interest, the discrimination process, infallibly produces correct responses. The model assumes that a participant's underlying discrimination time distribution depends only on the type of stimulus presented, so that it is not affected by the relative stress on speed versus accuracy. Meanwhile, the deadline time process is under control of the participant, and is manipulated by the individual according to the demands of a trial. In other words, the deadline time is set by a participant before a trial begins, and is meant to capture the relative demands on speed versus accuracy.In our modeling framework, we also posit two processes that are in a race; one, the error-free process, which always produces correct responses, and the other, the guessing process, which randomly produces correct responses. But unlike the deadline model, our error-free modeling framework assumes that the two processes occur in parallel, and are meant to reflect all cognitive processes that are participant-specific for a fixed experimental condition. Thus, by contrast, our framework allows for the possibility that both processes can be affected by speed versus accuracy demands, not just one of the two processes (i.e., the guessing process). If speed or accuracy demands are experimentally manipulated (e.g., by asking the participant to be as accurate as possible rather than as quick as possible), then they can affect the error-free process insofar as changing speed or accuracy demands constitutes a change in the experimental condition. In our framework, all differences in an experimental setting, whether they are explicit changes in stress on speed versus accuracy, change in the difficulty of the task, change in induced emotion, etc., are
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