We describe an inference task in which a set of timestamped event observations must be clustered into an unknown number of temporal sequences with independent and varying rates of observations. Various existing approaches to multi-object tracking assume a fixed number of sources and/or a fixed observation rate; we develop an approach to inferring structure in timestamped data produced by a mixture of an unknown and varying number of similar Markov renewal processes, plus independent clutter noise. The inference simultaneously distinguishes signal from noise as well as clustering signal observations into separate source streams. We illustrate the technique via a synthetic experiment as well as an experiment to track a mixture of singing birds.

The improvement of medical care quality is a significant interest for the future years. The fight against nosocomial infections (NI) in the intensive care units (ICU) is a good example. We will focus on a set of observations which reflect the dynamic aspect of the decision, result of the application of a Medical Decision Support System (MDSS). This system has to make dynamic decision on temporal data. We use dynamic Bayesian network (DBN) to model this dynamic process. It is a temporal reasoning within a real-time environment; we are interested in the Dynamic Decision Support Systems in healthcare domain (MDDSS).

A large number of transients visit big cities on any given day and they visit crowded areas and come in contact with many people. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. In the present work, we extend a synthetic population model of Washington DC metro area to include leisure and business travelers. This approach involves combining Census data, activity surveys, and geospatial data to build a detailed minute-by-minute simulation of population interaction. We simulate a flu-like disease outbreak both with and without the transient population to evaluate the effect of the transients on outbreak size and peak day in terms of number of residents infected. Results show that there are significantly more infections when transients are considered. We also evaluate interventions like closing big museums and encouraging use of hand sanitizers at those musuems. Surprisingly closing musuems does not result in a significant difference in the epidemic. However, we find that if the use of hand sanitizer reduces the infectivity and suceptibility to 80% or 60% of the original values, it is as effective as closing museums for a few days or entirely eliminating the effect of transients. If infectivity and susceptibility are reduced to 40% or 20%, it reduces the number of resident infections over the period of 120 days by 10% and 13%.

Learning new motor tasks autonomously from interaction with a human being is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. In this paper, we take the task of learning table tennis as an example and present a new framework which allows a robot to learn cooperative table tennis from interaction with a human. Therefore, the robot first learns a set of elementary table tennis hitting movements from a human teacher by kinesthetic teach-in, which is compiled into a set of dynamical system motor primitives (DMPs). Subsequently, the system generalizes these movements to a wider range of situations using our mixture of motor primitives (MoMP) approach. The resulting policy enables the robot to select appropriate motor primitives as well as to generalize between them. Finally, the robot plays with a human table tennis partner and learns online to improve its behavior.

Statistical models almost always yield predictions that are more accurate than those of human experts. However, humans are better at data acquisition and at recognizing atypical circumstances. We use prediction markets to combine predictions from groups of humans and artificial-intelligence agents and show that they are more robust than those from groups of humans or agents alone.

Semantic role labeling (SRL) is a technique of semantic interpretation of text on the sentence level. In this paper, we present a corpus that is labeled with semantic roles for biological transport events. The corpus was built using domain knowledge provided by ontologies. We also report on a word-chunking approach for identifying semantic roles of biomedical predicates describing transport events. We trained a first-order Conditional Random Fields (CRF) for chunking applications with the traditional role labeling features and also domain-specific features. The results show that the system performance varies between different roles and the performance was not improved for all roles by introducing domain specific features.

In the current generation The main results issued from our first experiments (Legras of UV Systems, several ground operators operate a single et al. 2008; Coppin and Legras 2012) were that the swarm vehicle with limited autonomous capabilities, whereas, approach seemed to be robust and adapted for simple mission in the next generation of UV Systems, a ground operator of surveillance, but that the operators in charge of will have to supervise a system of several cooperating vehicles such a system were not ready to understand and dialog with performing a joint mission, i.e. a Multi-Agent System this new kind of system, so that the global performance of (MAS) (Johnson 2003; Coppin and Legras 2012). In order the system was potentially spoiled by human intervention.

Ironic utterances promise an expected meaning that never arrives, and deliver instead a meaning that exposes the failure of our expectations. Though they can appear contextually inappropriate, ironic statements succeed when they subvert their context of use, so it is the context rather than the utterance that is shown to be incongruous. Every ironic statement thus poses two related questions: the first, “what is unexpected about my meaning?” helps us answer the second, “what is unexpected about my context of use?”. Like metaphor, irony is not overtly marked, and relies instead on a listener’s understanding of stereotypical norms to unpack its true meaning. In this paper we consider how irony relies upon and subverts our stereotypical knowledge of a domain, and show how this knowledge can be exploited to both recognize and generate ironic similes for a topic.

We aim to identify and control unintentional humor occurring in human-computer interaction, and recreate it intentionally. In this research we focus on text prediction systems, a type of interactive programs employed in mobile phones, search engines, and word processors. More specifically, we identified two design principles, inspired by humor and emotion theories, and implemented them in a proof-of-concept tool simulating a specific type of text prediction.

This study extends humor theory to explain puns originating from orthographic conversion in Japanese computer-mediated communication (CMC). Standard word-processing software converts Romanized input to appropriate orthographic output consisting of phono-graphic kana and ideographic kanji . Such software may produce an output often semantically incongruent with the intended output, which can be humorous. The dataset analyzed here consists of 492 online submissions to the “Humorous Misconversion Contest” held by the Japan Kanji Aptitude Testing Foundation. Since not all misconversions are funny, the study accounts for how misconversions satisfy funniness conditions of the Semantic Script Theory of Humor. The study finds that script interpretability, as the basis of script compatibility, and script opposition are of most importance in humor perception. It also finds that script oppositeness resides not only within texts but also in outer contexts. As yet, very few academic studies have discussed humor in Japanese CMC. Since a majority of verbal humor is researched on alphabet-based languages, the observations here are expected to enrich and broaden our knowledge of humor.