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284 ENTERPRISE INFORMATION SYSTEMS VI Higginbotham, D. J., Lesher, G. W., and Moulton, B. J. (1998). Techniques for augmenting scanning communication. Augmentative and Alternative Communication, 14:81–101. ISAAC (1983). Int. Soc. for Augmentative and Alternative Communication, Internet site. http://www.isaaconline.org. Last accessed October 1th, 2003. Juang, B. and Rabiner, L. (1990). The segmental k-means algorithm for estimating parameters of hidden markov models. In IEEE Trans. on Acoustics Speech and Signal processing, ASSP-38, pages 1639–1641. IEEE Computer Society Press. Juang, B., Rabiner, L., and Wilpon, G. (1986). A segmental k-means training procedure for connected word recognition. AT&T Technical Journal, 65:21–31. Koester, H. and Levine, S. (1994). Learning and performance of ablebodied individuals using scanning systems with and without word prediction. Assistive Technology, page 42. Lee, H.-Y., Yeh, C.-K., Wu, C.-M., and Tsuang, M.-F. (2001). Wireless communication for speech impaired subjects via portable augmentative and alternative system. In Proc. of the Int. Conf. of the IEEE on Eng. in Med. and Bio. Soc., volume 4, pages 3777–3779, Washington, DC, USA. Prechelt, L. (1996). Early stopping-but when? In Neural Networks: Tricks of the Trade, pages 55–69. Quillian, M. (1968). Semantic memory. In Minsky ed. Semantic Information Processing. MIT Press, Cambridge. Rabiner, L. (1989). A tutorial on hidden markov models and selected applications in speech recognition. In Proc. of the IEEE, 77, pages 257–286. IEEE Computer Society Press. Shane, B. (1981). Augmentative Communication: an introduction. Blackstone, Toronto, Canada. Simpson, R. C. and Koester, H. H. (1999). Adaptive oneswitch row-column scanning. IEEE Trans. on Rehabilitation Engineering, 7:464–473. Swiffin, A. L., Pickering, J. A., and Newell, A. F. (1987). Adaptive and predictive techniques in a cammunication prosthesis. Augmentative and Alternative Communication, 3:181–191. APPENDIX A In this appendix, we briefly describe the DAR-HMM according to the formalism adopted by Rabiner in (Rabiner, 1989) to specify classical hidden Markov models: • S � {si}, subcategories set with N = |S|; • V � {vj}, predictable symbols set with M = |V |; • V (i) = {v (i) k }, set of symbols predictable in subcategory i with M (i) = |V (i) | and V = � i V (i) ; • O(t) ∈ V , observed symbol at time t; • Q(t) ∈ S, state at time t; • πi(t) =P (Q(t) =si), probability that si is the actual subcategory at time t; • aii ′ = P (Q(t +1)=si|Q(t) =si ′), transition probability from si ′ to si; • b i k = P (O(0) = v (i) k |Q(0) = si), probability of observing v (i) k from subcategory si at t =0; • b ii′ kk ′ = P (O(t) =v(i) k |Q(t) =si,O(t − 1) = v(i′ ) k ′ ), probability of observing v (i) k from the subcategory si having just observed v (i′ ) k ′ . DAR-HMM for symbolic prediction can thus be described using a parameter vector λ =< Π 0 ,A,B >, where Π 0 [N] is the vector of inital subcategory probability πi(0), A[N][N] is the matrix with subcategory transition probabilities aii ′, and B[N][M][M +1]is the emission matrix with symbol probabilities b ii′ kk ′ and b i k.Givenλthe vector of parameters describing a specific language behavior model, we can predict the first observed symbol as the most probable one at time t =0: Ô(0) = arg max v (i) k = argmax v (i) k = argmax v (i) k � P (O(0) = v (i) k |λ) � (P (O (0) |Q (0) ,λ) P (Q(0))) � b i � k · πi(0) . Then mimicking the DAR-HMM generative model, to predict the t th symbol of a sentence we want to maximize the symbol probability in the present (hidden) state given the last observed symbol: � P O(t) =v (i) k ,Q(t) =si|O(t − 1) = v(i′ � ) k ′ ,λ . Recalling that we can compute the probability of the current (hidden) state as: P (Q(t)) = = N� P (Q(t)|Q(t − 1)) P (Q(t − 1)) = N� i ′ =1 πi ′(t − 1)aii ′ = πi(t), we obtain a recursive form for symbol prediction at time t: � b ii′ N� � kk ′ · πi ′(t − 1)aii ′ . Ô(t) = arg max v (i) k i ′ =1
A METHODOLOGY FOR INTERFACE DESIGN FOR OLDER ADULTS Mary Zajicek Department of Computing, School of Technology, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK, Email: mzajicek@brookes.ac.uk Keywords: User models, interface design patterns, design methodology, universal accessibility, older adults, speech systems, memory support, strategies at the interface. Abstract: This paper puts forward a new design method based upon Alexandrian patterns for interface design for particular user groups. The author has created a set of interface design patterns for speech systems for older adults with the aim of supporting the dynamic diversity in this group. The patterns themselves reflect a significant body of research work with this user group uncovering important information about how they interact with speech systems. The design knowledge embedded in these patterns is therefore closely linked to knowledge about the user and enables interface designers to clarify which users are excluded from their software. 1 INTRODUCTION This paper is concerned with a new methodology for interface design for older adults using speech systems, where carefully describing the users for whom the system is defined will promote a better fit between user and system. The paper outlines the characteristics of older adults as computer users and introduces the idea of patterns to encapsulate design recommendations for speech systems for this user group. Older adults to a greater or lesser degree experience memory impairment and a reduction in their ability to build strategies at the interface, characteristics which are modeled within the patterns. The paper discusses the challenge of complying with legislation which promotes software accessibility for all user types and patterns are suggested as providing a useful framework within which software developers can analyse their prospective user group in this regard. Eight interface design patterns are analyzed in terms of the user models they implicitly and explicitly encapsulate, and also in terms of the users they disadvantage. I. Seruca et al. (eds.), Enterprise Information Systems VI, © 2006 Springer. Printed in the Netherlands. 285 285–292. 2 WHY WOULD WE WANT TO MODEL USERS’ ABILITIES? Recently significant legislation has been introduced in the US and UK aimed at encouraging designers to ensure that software does not exclude certain user groups. Age related impairments older adults often exclude older adults from the use of standard software. For example the print may be to small or the multi-tasking interface may rely to heavily on good memory or their ability to build complex strategies in order to use the software. A better understanding of older adults as users through modeling their attributes will enable interface designers to clarify which users are excluded by their software. 3 THE ORIGIN OF INTERFACE DESIGN PATTERNS The idea for patterns and pattern language originated in the domain of architecture; with the publication more than twenty years ago of Christopher Alexander’s book The Timeless Way of Building (Alexander, 1979). He proposed that one could achieve excellence in architecture by learning and using a carefully defined set of design rules, or
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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FEATURE MATCHING IN MODEL-BASED SOF
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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A USER-CENTERED METHODOLOGY TO GENE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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