Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring.The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-one-person-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features.Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring.These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.

There have been significant advances in developing each phase of the mission. Secondly, based on our everyday algorithms that allow researchers to examine these experience with physical objects in our environment, behaviors in simulation (Luke et al. 2005), generally assuming the three major physical states of matter, solid, liquid and noise-free estimates of the agents' own, neighbors' and gas, represent a natural and intuitive means of describing the targets' positions. However, the actual information flow into types of motions a swarm of mobile robots can perform as biological agents' in terms of the sensing, processing and they cluster, transit or wander (Gage 1992).

Addressing the ambitious research agendas put forward by many scientific disciplines requires meeting a multitude of challenges in intelligent systems, information sciences, and human-computer interaction. Many aspects of the scientific discovery process are often largely manual and could be automated, improved, or made more efficient. Better interfaces for collaboration, visualization, and understanding would significantly improve scientific practice. Scientific data, publications, and tools could be published in open formats with appropriate semantic descriptions and metadata annotations to improve sharing and dissemination. Opportunities for broader participation in well-defined scientific tasks enable human contributors to provide large amounts of data, annotations, or complex processing results that could not otherwise be obtained. Improvements and innovations across the spectrum of scientific processes and activities will have a profound impact on the rate of scientific discoveries.

As biological data about genes and their interactions proliferates, scientists have the opportunity to identify sets of proteins whose interactions make them worthy of further investigation. This paper reports on a knowledge discovery technique to support that work. Foretell is an algorithm originally designed to support search for solutions to constraint satisfaction problems. Recent adaptations enable Foretell to detect sets of genes that interact heavily with one another. We provide empirical results, and describe ongoing work on biological meaning and knowledge infusion from the user.

List of organizers of the AAAI Fall Symposium on Robots Learning Interactively from Human Teachers.

Social networking websites such as Twitter have invigorated a wide range of studies in recent years ranging from consumer opinions on products to tracking the spread of diseases. While sentiment analysis and opinion mining from tweets have been studied extensively, surveillance of beliefs, especially those related to public health, have received considerably less attention. In our previous work, we proposed a model for surveillance of health beliefs on Twitter relying on the use of hand-picked probe statements expressing various health-related propositions. In this work we extend our model to automatically discover various probes related to public health beliefs. We present a data driven approach based on two distinct datasets and study the prevalence of public belief, disbelief or doubt for newly discovered probe statements.

Twitter provides the freshest source of data about what is happening in the lives people across the world. The publicly available streams of status updates available on Twitter have been used to track earthquakes, forest fires and most especially flu outbreaks. Current techniques for tracking flu outbreaks rely on count data for a number of keywords. However, count data alone on the noisy Twitter streams is not reliable enough for health officials to make critical decisions. We propose a semi-automatic outbreak detection system. Rather than providing only alarms backed by count data, we propose a summarization system that will allow health officials to quickly verify outbreak alarms. This will lead to higher levels of trust in the system and allow the system to be used by health organizations around the world. We experimentally verify our summarization system and have found system users to have an accuracy of 0.86 when identifying multi-tweet summaries.

An anticipated increase in the number of people withdementia will lead to an escalation in health and socialcare spending unless it is altered by a major breakthroughin treatment or prevention. Behavioral symptomsassociated with dementia (BSD) are some of themost difficult problems faced by caregivers. Severalmeasurement issues have hampered the progress oftimely intervention for BSD. Sensor technology mayoffer a solution to the early detection of BSD that willguide the development of tailored interventions. Similarly,a clinical conceptualization of BSD and its measurementissues can facilitate the engineering of sensornetworks and algorithms for activity recognition. Multidisciplinarycollaboration and the consideration of ethicalissues will improve the adoption of these technologiesin healthcare research.

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.

In this paper we describe the architecture of the OntoGene Relation mining pipeline and some of its recent applications. With this research overview paper we intend to provide a contribution towards the recently started discussion towards standards for information extraction architectures in the biomedical domain. Our approach delivers domain entities mentioned in each input document, as well as candidate relationships, both ranked according to a confidency score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation.