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With the exception of urgent situations, school psychologists define the parameters of confidentiality at the onset of offering services (Davis & Sandoval, 1982; also EP Principle E, 402; NASP-PPE, III, A, #11) The parameters of the promise of confidentiality will vary depending on the nature of the services offered In the paragraphs that follow, we discuss confidentiality and its limits in providing direct services to the student, services that involve collaboration with the teacher or parent, and consultative services to the teacher Confidentiality and Direct Services to the Student For our purposes, the provision of direct services to the student means that the practitioner works with the student directly (eg, individual coun-.

The H-T-P technique: A qualitative and quantitative method Journal of Clinical Psychology, 4, 317 396 Burns, R C, & Kaufman, S H (1970) Kinetic Family Drawings (K-F-D): An introduction to understanding children through kinetic drawings New York: Brunner/Mazel Butcher, J N, Dahlstrom, W G, Graham, J R, Tellegen, A, & Kaemmer, B (1989) Minnesota Multiphasic Personality Inventory (MMPI-2): Manual for administration and scoring Minneapolis: University of Minnesota Press Butcher, J N, & Rouse, S V (1996) Personality: Individual differences and clinical assessment Annual Review of Psychology, 47, 87 111 Butcher, J N, Williams, C L, Graham, J R, Archer, R, Tellegen, A, Ben-Porath, Y S, et al (1992) MMPI-A manual for administration, scoring, and interpretation Minneapolis: University of Minnesota Press Butler, M, Retzlaff, P, & Vanderploeg, R (1991) Neuropsychological test usage Professional Psychology, 22, 510 512..

(j) follows from Jensen s inequality and the convexity of the conditional rate distortion function (Lemma 1591) 1 (k) follows from the de nition of D = E[ n n d(Xi , Xi )] i=1 It is easy to see the parallels between this converse and the converse for rate distortion without side information (Section 104) The proof of achievability is also parallel to the proof of the rate distortion theorem using strong typicality However, instead of sending the index of the codeword that is jointly typical with the source, we divide these codewords into bins and send the bin index instead If the number of codewords in each bin is small enough, the side information can be used to isolate the particular codeword in the bin at the receiver.

and Parsia B: Pellet: an OWL DL reasoner Proceedings of the 2004 International Workshop on Description Logics (DL 04, Whistler, British Columbia, Canada, June 2004), pp 212 213 [104] Sohn T, Grisworld WG, Scott J, LaMarca A, Chawathe Y, Smith I and Chen MY: Experiences with Place Lab: an open source toolkit for location-aware computing Proceedings of the 28th International Conference on Software Engineering (ICSE 06, Shanghai, China, May 2006), ACM Press, 2006, pp 462 471 [105] Staab S and Studer R (eds): Handbook on Ontologies Springer-Verlag, Berlin, 2004 [106] SWOOP semantic web ontology editor Online: codegooglecom/p/swoop/ [107] Tapia EM, Intille SS and Larson K: Activity Recognition in the Home Setting using Simple and Ubiquitous Sensors Proceedings of the 2nd International Conference on Pervasive Computing (PERVASIVE 2004, Vienna, Austria, April 2004), Springer-Verlag, Berlin, 2004, pp.

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Hence again we are combining random binning with rate distortion encoding to nd a jointly typical reproduction codeword We outline the details of the proof below Proof: (Achievability of Theorem 1591) Fix p(w|x) and the function f (w, y) Calculate p(w) = x p(x)p(w|x) Generation of codebook: Let R1 = I (X; W ) + Generate 2nR iid codewords W n (s) n p(wi ), and index them by s {1, 2, , 2nR1 } i=1 Let R2 = I (X; W ) I (Y ; W ) + 5 Randomly assign the indices s {1, 2, , 2nR1 } to one of 2nR2 bins using a uniform distribution over the bins Let B(i) denote the indices assigned to bin i There are approximately 2n(R1 R2 ) indices in each bin.

158 175 [108] Teller A and Stivoric J: The BodyMedia platform: continuous body intelligence Proceedings of the 1st ACM Workshop on Continuous Archival and Retrieval of Personal Experiences (CARPE 2004, New York, New York, October 2004), ACM Press, 2004, pp 114 115 [109] Toyama N, Ota T, Kato F, Toyota Y, Hattori T and Hagino T: Exploiting Multiple Radii to Learn Signi cant Locations Proceedings of the International Workshop on Location- and Context-Awareness (LoCA 2005, Oberpfaffenhofen, Germany, May 2005), Lecture Notes in Computer Science 3479, Springer-Verlag, 2005, pp 157 168 [110] vCard: The Electronic Business Card, Version 21 Speci cation, September 18, 1996 Online: wwwimcorg/ pdi/vcard-21txt [111] Wang X, Zhang DQ, Gu T and Pung HK: Ontology-based Context Modeling and Reasoning using OWL .