By Alessandro Vinciarelli (auth.), Roderick Murray-Smith (eds.)

ISBN-10: 3642543243

ISBN-13: 9783642543241

ISBN-10: 3642543251

ISBN-13: 9783642543258

This ebook comprises papers invited after the 1st foreign Workshop on cellular Social sign Processing, MSSP 2010, held in Lisbon, Portugal, in September 2010. The nine revised papers incorporated during this quantity signify the variety of 2 fields of analysis, cellular HCI and Social sign Processing and parts of overlap. They conceal a variety of issues spanning from methods for potent interplay with cellular and wearable units to modelling, research and synthesis of nonverbal behaviour in human-human and human-machine interactions.

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Extra info for Mobile Social Signal Processing: First International Workshop, MSSP 2010, Lisbon, Portugal, September 7, 2010, Invited Papers

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3). It is thus straightforward to extend the objective function (see Eq. 5) in order to include this type of information i. : S∗ = arg max log p(X|S)p(S) = arg max log p(X|S)p(ϕ(S)) S S (6) In other words, the optimal speaker sequence (and the associated speaker times) can be obtained combining the evidence from the acoustic score p(X|S) together with the prior probability of a given sequence p(S). This is somehow similar to what is done in Automatic Speech Recognition (ASR) where sentences (i.

This is important in order to show how the role typology influences the effectiveness of the recognition, and thus how easily an approach can be ported from one interaction setting to another. Another novelty of the presented approaches is to use the interaction between the persons as features. The Social Affiliation Networks (SAN) [19] allows one to extract these features, which represent the evidence of interactions in terms of proximity in time, from the co-occurence turn-taking patterns structuring the conversations.

The use of neural networks has reduced the length of the string required in order to authenticate a user via keystroke dynamics. [6], which makes authenticating using a short character string viable. Despite the long research history of statistical methods, they Invisible, Passive, Continuous and Multimodal Authentication 39 have been found to produce higher error rates in keystroke dynamics research, when compared to neural networks [5]. However, there are always tradeoffs when selecting an algorithm to use in a computing environment; in this case, neural networks require a large training set in order to correctly classify patterns, they must be re-trained if a new user is added to the network, and they are computationally and memory-intensive pattern classification methods [5,14].

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Mobile Social Signal Processing: First International Workshop, MSSP 2010, Lisbon, Portugal, September 7, 2010, Invited Papers by Alessandro Vinciarelli (auth.), Roderick Murray-Smith (eds.)

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