Recognition and Understanding of Meetings Overview of the European AMI and AMIDA Projects

The AMI and AMIDA projects are concerned with the recognition and interpretation of multiparty (face-to-face and remote) meetings. Within these projects we have developed the following: (1) an infrastructure for recording meetings using multiple microphones and cameras; (2) a one hundred hour, manually annotated meeting corpus; (3) a number of techniques for indexing, and summarizing of meeting videos using automatic speech recognition and computer vision, and (4) a extensible framework for browsing, and searching of meeting videos. We give an overview of the various techniques developed in AMI (mainly involving face-to-face meetings), their integration into our meeting browser framework, and future plans for AMIDA (Augmented Multiparty Interaction with Distant Access), the follow-up project to AMI. Technical and business information related to these two projects can be found at www.amiproject.org, respectively on the Scientific and Business portals

How Personalized Networks Can Limit Free Riding: A Multi-Group Version of the Public Goods Game

People belong to many different groups, and few belong to the same network of groups. Moreover, people routinely reduce their involvement in dysfunctional groups while increasing involvement in those they find more attractive. The net effect can be an increase in overall cooperation and the partial isolation of free-riders, even if free-riders are never punished, excluded, or recognized. We formalize and test this conjecture with an agent-based social simulation and a multi-good extension of the standard repeated public goods game. Our initial results from three treatments suggest that the multi-group setting indeed raises overall cooperation and dampens the impact of freeriders. We extend our understanding of this setting by imposing greater heterogeneity between groups through interweaving automated bot players amongst human subjects; whereby initial sessions of this amplify the aforementioned effects

A Neural Computation for Visual Acuity in the Presence of Eye Movements

Humans can distinguish visual stimuli that differ by features the size of only a few photoreceptors. This is possible despite the incessant image motion due to fixational eye movements, which can be many times larger than the features to be distinguished. To perform well, the brain must identify the retinal firing patterns induced by the stimulus while discounting similar patterns caused by spontaneous retinal activity. This is a challenge since the trajectory of the eye movements, and consequently, the stimulus position, are unknown. We derive a decision rule for using retinal spike trains to discriminate between two stimuli, given that their retinal image moves with an unknown random walk trajectory. This algorithm dynamically estimates the probability of the stimulus at different retinal locations, and uses this to modulate the influence of retinal spikes acquired later. Applied to a simple orientation-discrimination task, the algorithm performance is consistent with human acuity, whereas naive strategies that neglect eye movements perform much worse. We then show how a simple, biologically plausible neural network could implement this algorithm using a local, activity-dependent gain and lateral interactions approximately matched to the statistics of eye movements. Finally, we discuss evidence that such a network could be operating in the primary visual cortex

Spatiotemporal dynamics of continuum neural fields

We survey recent analytical approaches to studying the spatiotemporal dynamics of continuum neural fields. Neural fields model the large-scale dynamics of spatially structured biological neural networks in terms of nonlinear integrodifferential equations whose associated integral kernels represent the spatial distribution of neuronal synaptic connections. They provide an important example of spatially extended excitable systems with nonlocal interactions and exhibit a wide range of spatially coherent dynamics including traveling waves oscillations and Turing-like patterns

Quantifying diversity in user experience

Evaluation should be integral to any design activity. Evaluation in innovative product development practices however is highly complicated. It often needs to be applied to immature prototypes, while at the same time users’ responses may greatly vary across different individuals and situations. This thesis has focused on methods and tools for inquiring into users’ experiences with interactive products. More specifically, it had three objectives: a) to conceptualize the notion of diversity in subjective judgments of users’ experiences with interactive products, b) to establish empirical evidence for the prevalence of diversity, and c) to provide a number of methodological tools for the study of diversity in the context of product development. Two critical sources of diversity in the context of users’ experiences with interactive products were identified and respective methodological solutions were proposed: a) understanding interpersonal diversity through personal attribute judgments, and b) understanding the dynamics of experience through experience narratives. Personal Attribute Judgments, and in particular, the Repertory Grid Technique, is proposed as an alternative to standardized psychometric scales, in measuring users’ responses to artifacts in the context of parallel design. It is argued that traditional approaches that rely on the a-priori definition of the measures by the researchers have at least two limitations. First, such approaches are inherently limited as researchers might fail to consider a given dimension as relevant for the given product and context, or they might simply lack validated measurement scales for a relevant dimension. Secondly, such approaches assume that participants are able to interpret and position a given statement that is defined by the researcher to their own context. Recent literature has challenged this assumption, suggesting that in certain cases participants are unable to interpret the personal relevance of the statement in their own context, and might instead employ shallow processing, that is respond to surface features of the language rather than attaching personal relevance to the question. In contrast, personal attributes are elicited from each individual respondent, instead of being a-priori imposed by the experimenter, and thus are supposed to be highly relevant to the individual. However, personal attributes require substantially more complex quantitative analysis procedures. It is illustrated that traditional analysis procedures fail to bring out the richness of the personal attribute judgments and two new Multi-Dimensional Scaling procedures that extract multiple complementary views from such datasets are proposed. An alternative approach for the measurement of the dynamics of experience over time is proposed that relies on a) the retrospective elicitation of idiosyncratic selfreports of one’s experiences with a product, the so-called experience narratives, and b) the extraction of generalized knowledge from these narratives through computational content analysis techniques. iScale, a tool that aims at increasing users’ accuracy and effectiveness in recalling their experiences with a product is proposed. iScale uses sketching in imposing a structured process in the reconstruction of one’s experiences from memory. Two different versions of iScale, each grounded in a distinct theory of how people reconstruct emotional experiences from memory, were developed and empirically tested. A computational approach for the extraction of generalized knowledge from experience narratives, that combines traditional coding procedures with computational approaches for assessing the semantic similarity between documents, is proposed and compared with traditional content analysis. Through these two methodological contributions, this thesis argues against averaging in the subjective evaluation of interactive products. It proposes the development of interactive tools that can assist designers in moving across multiple levels of abstractions of empirical data, as design-relevant knowledge might be found on all these levels

Consciousness as inference in time : a commentary on Victor Lamme

Unraveling the neural correlates of conscious remains one of the great challenges of our time. Victor Lamme proposes that neural integration through feedback loops is what differentiates conscious from unconscious processing. Here, I review his hypothesis, focusing on the spatial scale of integration as well as the possible neural mechanisms involved. I go on to show that any theory of the neural correlates of consciousness is incomplete if it cannot account for how prior knowledge shapes perception and how this form of integration occurs. Finally, I propose that integration across moments in time is a crucial but hitherto neglected aspect of conscious perception, which creates the “flow” of conscious experience

Efficient indexing for skyline queries with partially ordered domains

Master'sMASTER OF SCIENC