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Every learner tends to overanalyze the data he or she receives during the coaching experience data collected from others in the organization as part of the coaching, as well as feedback from the developer based on his or her observations. This is particularly true when the information is new to the learner or is something with which the learner disagrees. Although learners are more likely to overanalyze data they perceive as negative, they may also do this with positive information. The three Head Center styles tend to overanalyze data even more than other learners. This is a result of their data-driven, mental-processing orientation, as well as their tendency to analyze information as a means of intellectual stimulation and of avoiding or preparing for fearful situations. While they respect concrete, verifiable data, Fives, Sixes, and Sevens also resist most information unless it is presented logically and in a well-organized manner. When they receive negative information, the Head Center styles will analyze, challenge, and produce counterarguments, then analyze and reanalyze the data again and again. When communicating data to the Head Center styles, it is also important that developers use anonymous anecdotes, quotes, or direct observations to supplement any thematic information collected from other people about the learner. Some Fives, Sixes, and Sevens may use their own logic to invalidate the themes, downplay their importance, or discount the importance of others perceptions in general. As a result, integrating factual information with anecdotal data makes them more receptive.
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The main problem with local command authorization is scalability: if you have one appliance, you only have to create your user accounts and privilege commands once. However, if you have 30 appliances, replicating this information and keeping it in synch would be difficult. Given this scenario, I recommend that you centralize the administrator accounts
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Howard, Toby. Biometrics Come of Age. Personal Computer World. January 2001. Human Identification at a Distance (HID). products/HID.html. Hurley, David J., M. S. Nixon, and J. N. Carter. Force Field Energy Functionals for Image Feature Extraction. Image and Vision Computing. 20 (2002): 311 317. IRID, Inc. (Infrared Identification Incorporated).; Sarcon Technology Could Assist Firefighters, Automobile Makers. 2001. http:// IRID, Inc. Basic of Infrared Imaging. 2002. IR%20Imaging.htm. Izarek, Stephanie. Smart Gun, Smart Solution FoxNews. January 25, 2001.,3566,664,00.html. Jain, A., and S. Pankathi. Automated Fingerprint Identification and Imaging Systems. TJW Research Center, IBM. ecvg/pubs/sharat-forensic.pdf. Jarvis, Angela. Biometric Identification. http:// Krochmal, Mo. Biometrics Makes Scents for Computer Users. TechWeb News. September 1, 1999. Little, James J., and Jeffrey E. Boyd. Recognizing People by Their Gait: The Shape of Motion. Journal of Computer Vision Research. MIT Press. 1, no. 2 (Winter 1998). McBride, Jonah. Human Gait Recognition Curve Matching. May 15, 2002. pres_files/CV-final.html. McCormick, John. Keep Networks Safe with Body Language. Government Computer News. April 27, 1998. rev2.htm. McFarling, Usha Lee. Researchers Smell Success in Developing Robo-Noses. Seattle Times. March 9, 1999. texis.cgi/web/vortex/display slug=nose&date=19990309. Moenssens, Andre A. 1971. Alphonse Bertillon and Ear Prints. Fingertip Techniques. Chilton Book Co. Moenssens, Andre A. Lip Print Identification Anyone Morgan, J. Court Holds Earprint Identification Not Generally Accepted In Scientific Community. http://www.forensic-evidence .com/site/ID/ID_Kunze.html. Nakajima, K., et al. Footprint-Based Personal Recognition. IEEE Trans on Biomedical Engineering. 47, no. 11 (2000): 1,534 1,537.
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