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278 ENTERPRISE INFORMATION SYSTEMS VI A current open issue concerning AAC communication software aids regards human-computer interaction in disables with motor disorders (Lee et al., 2001), that represent about the 60% of verbal impaired people. Motor disordered people are not able to use canonical input devices, such as keybord and mouse, but they can use only ad-hoc devices, such as buttons or switches according to their residual motor capabilities. Such devices operate providing the AAC software aid with an on/off input, so, in order to select AAC symbols from a table, it is required an automatic scansion of such table underlining the symbols they can select. The intelligent scansion mechanisms currently adopted in AAC software do not assure a relevant reduction of the time spent by verbal disables to compose messages: in fact person evidencing motor disorders can spend few minutes to compose a simple sentece. In such perspective we have developed CABA 2 L (Composition Assistant for Bliss Augmentative Alternative Language) an innovative composition assistant that performs a predictive scansion of Bliss symbols and reduces up to 60% the time required to compose a message. CABA 2 L is based on a discrete implementation of auto-regressive hidden Markov model (Rabiner, 1989) called DAR-HMM and it predicts a list of symbols as the most probable according to both the last selected symbol and the semantic categories associated to symbols. Moreover, the predictive model embedded in CABA 2 L can be adapted to the specific disable user to better match his/her peculiar linguistic behavior. The paper is structured as follows. In Section 2, we introduce current scansion mechanisms for verbal disables and underline prediction issues. Section 3 introduces Bliss symbolic prediction issues and probabilistic prediction techniques. Section 4 describes DAR- HMM and its implementation. Section 5 reports the experimental results we have obtained. Finally, Section 6 concludes the paper. 2 SENTENCE COMPOSITION IN PEOPLE WITH MOTOR DISORDERS Currently some AAC software aids provide people suffering motor disorders with an automatic scansion of the symbol table (see Figure 1). A generic scansion mechanism can be described as follows: an highlight moves autonomously on an AAC symbol table according to a specific strategy, when the requested symbol is highlighted the user can select such symbol activating the device, then the highlight starts to move again. Each scansion mechanism is characterized by a specific ad-hoc input device and the scansion strategy. Ad-hoc input devices allow the verbal disable to Figure 1: An AAC symbols table start the scansion, select symbols, close the sentence, and activate features such as vocal synthesis. Each user adopts the device that best matches with his/her residual motor capabilities. The alternative/residual motor capabilities used are usually: blowing with the mouth, closing the eyes, pushing big buttons. The scansion strategy determines what is the next symbol to highlight. In literature several automatic scansion strategies for AAC communication aids can be retrieved (Higginbotham et al., 1998) and each one of them exhibits advantages and drawbacks. Such strategies can be classified in (Swiffin et al., 1987): • linear scansion: the symbols are presented sequentially from the first symbol of the table to the last one; • row-column scansion: at first the rows are scanned, once the disable has selected a row, its columns are scanned (or vice versa) (Simpson and Koester, 1999); • scansion at subgroups: the symbols are presented in groups fewer and fewer up to only one symbol; • predictive scansion: it predicts the most probable symbols the user will use to continue the sentence according to a model of the user, and it presents the most probable ones. The choice of the most adequate scansion strategy for the peculiar user depends on several factors, such as the mental and motor residual capabilities, the adopted AAC language and the size of the user symbols table. With respect to the user mental capabilities, both scansion at subgroups and row-column scansion require the user to remember exactly the place of the desired symbol, so they can not be used
y disables evidencing severe mental impairments. With respect to the size of the symbol table linear scansion, row-column scansion and scansion at subgroups do not offer good performance if the number of symbols is elevate (50 symbols and more). Hence, current non predictive scansion strategies do not allow to verbal disables suffering motor disorders a relevant reduction in the time spent to compose sentences (Lee et al., 2001). Although predictive scansion strategies could assure better performance (Koester and Levine, 1994), they are currently adopted in a small number of AAC assistitive technology aids. In particular symbolic prediction is currently adopted only in VOCAs, but it evidences severe limitations: it predicts symbols according to a strict set of sentences previously registered and do not exploit a linguistic behavior model of the user. In such a way such prediction system allows the user to compose a fixed number of messages and it is not able to generalize allowing the composition of new messages (Higginbotham et al., 1998). In literature numerous predictive techniques have been developed, but they have been applied mainly in alphabetical prediction. In such context the main prediction techniques (Aliprandi et al., 2003) employ a statistical approach (based on hidden Markov models and Bayesian networks), a syntactic and strong syntactic approach (based on linguistic models), a semantic approach (based on semantic networks), and hybrid approaches. To the best of our knowledge, currently symbolic predictive models do not exist. The main issue with alphabetical predictive techniques, that prevents their use for symbolic prediction, is related to the size of the dictionary of items to be predicted and their composition rules. In fact, alphabetical prediction operates on a limited number (about 20) of items, the alphabetic signs, that can be organized in words known a priori. Conversely, symbolic prediction operates on a set of symbols variable in number that can be organized in different sequences according to the peculiar user linguistic capabilities. In addition alphabetical prediction techniques do not match with the symbolic prediction issue. On the other side, a pure statistical approach does not keep into account the peculiar AAC language structure, in fact each verbal impaired user adopts/develops an own syntactic model according to his/her residual mental capacities. This is also the reason for which the utilization of a pure syntactic approach for any user can not be achieved, and a pure semantic approach does not address the variability related to the residual user capacities. We consider an ad-hoc hybrid approach as the right choice in this context; in the following sections of the paper we focus on the description of this prediction model since it represents the most original part of our work. 2 CAB A L A BLISS PREDICTIVE COMPOSITION ASSISTANT 279 3 BLISS SYMBOLS PREDICTION AND GRAPHICAL MODELS In our work we focus on the Bliss language (Bliss, 1966), since it is the most adopted and expressive among AAC languages. In the design of a composition assistant to predicts Bliss symbols, a set of peculiar requirements regarding both the human-computer interface and the prediction model can be established. The composition assistant should suggest a limited number of symbols (around 4-5) not to confuse the disable user (Koester and Levine, 1994), the prediction must be accomplished in real time, and the scansion rate must be adaptable with the user needs (Cronk and Schubert, 1987). This last aspect addresses issues due to the high variability of residual mental and motor capabilities, in fact the composition assistant should be able to adapt the scansion rate according to the time required by the specific disable to read and select the highlighted symbol. With respect to the prediction model, a verbal impaired user can adopt all the Bliss symbols (about 2000), even if he/she usually adopts only a part of them (usually from 6-7 to 200), and it should be taken into account that the symbol to be predicted depends in some extents on the symbols selected previously. We have adopted a semantic/probabilistic approach to model the user language behavior and we use this model in order to predict the most probable symbols to be suggested by an automatic scansion system. We have used the semantic approach to take advantage of a Bliss symbols categorization and the probabilistic approach both to take into account for uncertainties in the user language model and to give a reasonable estimate of the reliability of the proposed prediction. In CABA 2 L we have used a graphical model based on a variation of a classical Hidden Markov Models (HMM). Classical HMMs involve states and symbols, in particular they relate the probability that a particular symbol is emitted to the probability that the system is in particular state. Moreover they use a stochastic process to define the transition from a state to the other (see Figure 2). In HMM a particular sequence of observation (i.e. observed symbols) is generated by choosing at time t = 0 the initial state si ∈ S according to an initial probability distribution π(0), a symbol vk is generated from a multinomial probability distribution b i k associated to state si, and the system move from the present state si to the next state si ′ of the sequence according to a transition probability aii ′ to generate the next symbol. States in this model are not directly observable; symbols represent the only information that can be observed, and this is the reason for the term hidden in the model name. Notice that classical HMMs consider symbols as independent from each
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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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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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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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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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