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Most people who purchased the first Roombas were early adopters of technology and liked the idea of a personal robot to do their bidding. To watch a Roomba roaming around their living room, cleaning up after a mess, was to experience in a small way life in the future. Unfortunately, the Roomba wasn t very hackable by the normal gadgeteer. If you wanted to easily reprogram your Roomba to alter its behavior or make it do tricks, you were out of luck. At the least you had to take the Roomba apart, definitely voiding its warranty. Once inside perhaps you could reverse engineer the small computer (also known as the microcontroller) used as its brain, maybe replace it completely, and hook into the motors and sensors, effectively destroying it for its original purpose. Communities devoted to hacking the Roomba in this low-level way grew and flourished. The hacking section of Roomba Review (http:// roombareview.com/hack/) is one of the most famous, and the accompanying forum is still the best place to go to discuss Roomba hacking. Other sites like Hackaday (http:// hackaday.com/) and Makezine (http://makezine.com/) routinely featured projects that used stripped-down or heavily modified Roombas. But hacking the Roomba was a difficult and expensive task, only suitable for the most experienced engineers. Recently this has changed.

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failure to meet higher reliability standards resulted in underestimation of these correlations We claim, however, that the main reason for the failure to precisely measure psychological variables is the reliance on correlational methods, rather than meeting the basic assumptions of measurement Two examples for this claim are given next Meeting Measurement Assumptions in Sport and Exercise Psychology Measures We assume that most of the scales used in the sport and exercise psychology domains are not meeting the main assumptions of measurement (ie, lack of accepted origin, unit of measurement, linearity, and being sample-free) We demonstrate our claim with two instruments widely used in our two domains: the TEOSQ (Duda & Nicholls, 1992) and Borg s (1970) 15-category Rating of Perceived Exertion scale (RPE) The TEOSQ consists of two scales, Task and Ego, with 7 and 6 items, respectively, using a 5-point Likert-type response format.

84 Classless Inter-Domain Routing (continued)

Though it has been extensively tested and used, it was first subjected to rating scale Rasch analysis (Wright & Masters, 1982) by Tenenbaum and Fogarty (1998), only once, using 1,591 adolescents active in sport and exercise The items locations on the Task and Ego Orientations continua for males and females are presented in Figure 347 As one can see (a) the items do not cover a sufficient space (ie, narrow scales) on the linear continua, indicating insufficient use of the 5 categories; ( b) three of the Ego items produced misfit values; (c) the items of the Task and Ego scales do not share similar locations for males and females; and (d) many Task responses resulted in being misfit because most persons rated the items by selecting only the 4 and 5 categories.

To be considered a sound measurement tool for eliciting goal orientation in sport, substantial additional work is needed Borg s (1970) RPE scale (6 to 20) consists of a set of numerical response categories in a stimulus-response context analogous to heart rate (HR) range between 60 and 200 b/min (1 10 ratio) Tenenbaum, Falk, and Bar-Or (2002) applied a Rasch FACET analysis (Linacre, 1989) on children s and adolescents RPE responses to cycle-ergometry testing, which corresponds to their HR The results are presented in Figure 348 The analysis indicates that when exertive sensations are evoked during a progressive maximal aerobic power test, and HR is the physiological criterion against which exertion feelings are estimated, the qualitative and quantitative semantics do not share equal units.

hop away from the leader. Thus, every cluster is a two-hop subnetwork [18]. We note that a number of issues are still open. For example, it is highly desirable to elect as a leader of a cluster a station that is optimal in some sense. One optimality criterion would be a central position within the cluster. Yet another nontrivial and very important such criterion is to elect as local leader a station that has the largest remaining power level.

Persons move faster in the upper section of the scale than in the lower section, meaning it takes more to move from 8 to 10 than from 18.