In this paper we describe the production and consumption of linked data in the scenario of the Italian news agency ANSA portal. The goal of the use-case is to provide viewers of a news item with background information and links to related news articles contained on the portal. This information enrichment process is entity-based: ANSA news archive is analyzed using Name Entity Recognition, and each detected entity is annotated with a unique identifier. These identifiers are obtained using the Entity Name Server developed within the scope of the OKKAM European project. Subsequently the news are published on the portal using RDFa and linked to a semantic search engine that provides background information harvested from sources such as DBpedia and links to additional news sources. The presented project has the potential to contribute to Linked Data by creating and publishing a large quantity of entities and assertions about them coming from the ANSA news archive.

While recommender systems can greatly enhance the user experience, the entry barriers in terms of data acquisition are very high, making it hard for new service providers to compete with existing recommendation services. This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborative recommender systems. We describe how to aggregate data about object centred sociality from different sources and how to process it for collaborative recommendation. To demonstrate the validity of our approach, we augment the data from a closed collaborative music recommender system with Linked Data, and significantly improve its precision and recall.

Anticipating the actions of others is key to coordinating joint activities. We propose the notion of anticipatory action and perception for for robots acting with humans. We describe four systems in which anticipation has been modeled for human-robot interaction; two in a teamwork setting, and two in a human-robot joint performance setting. In evaluating the effects of anticipatory agent activity, we find in one study that anticipation aids in team efficiency, as well as in the perceived commitment of the robot to the team and its contribution to the team's fluency and success. In another study we see anticipatory action and perception affect the human partner's sense of team fluency, the team's improvement over time, the robot’s contribution to the efficiency and fluency, the robot's intelligence, and the robot’s adaptation to the task. We also find that subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence.

In this paper, we describe our initial experiences using a mobile robot as a teaching aid in a stage movement class, taught in the Performing Arts Department of Washington University in St. Louis. The robot participated in a number of exercises, intended to teach the fundamentals of movement, and interacted closely with human acting students. We describe these exercises, what they are designed to teach the students, and discuss how using a robot as a teaching aid can enhance the students' experience. We describe two classes in which a robot participated, one under tele-operation and one fully autonomously, and discuss both the students' reaction to the robots, and our subjective evaluations of the systems success.

Similarity networks are important abstractions in many information management applications such as recommender systems, corpora analysis, and medical informatics. For instance, by inducing similarity networks between movies rated similarly by users, or between documents containing common terms, and or between clinical trials involving the same themes, we can aim to find the global structure of connectivities underlying the data, and use the network as a basis to make connections between seemingly disparate entities. In the above applications, composing similarities between objects of interest finds uses in serendipitous recommendation, in storytelling, and in clinical diagnosis, respectively. We present an algorithmic framework for traversing similarity paths using the notion of `hammock' paths which are generalization of traditional paths. Our framework is exploratory in nature so that, given starting and ending objects of interest, it explores candidate objects for path following, and heuristics to admissibly estimate the potential for paths to lead to a desired destination. We present three diverse applications: exploring movie similarities in the Netflix dataset, exploring abstract similarities across the PubMed corpus, and exploring description similarities in a database of clinical trials. Experimental results demonstrate the potential of our approach for unstructured knowledge discovery in similarity networks.

A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client is associated with an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real-time from the clients and codify the information in a common database. The information coded in this database can be used by all the clients to solve their individual learning task, so that each client can exploit the informative content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization theory and kernel methods, uses a suitable class of mixed effect kernels. The new method is illustrated through a simulated music recommendation system.

We describe a hierarchical, probabilistic model that learns to extract complex motion from movies of the natural environment. The model consists of two hidden layers: the first layer produces a sparse representation of the image that is expressed in terms of local amplitude and phase variables. The second layer learns the higher-order structure among the time-varying phase variables. After training on natural movies, the top layer units discover the structure of phase-shifts within the first layer.

We describe a hierarchical, probabilistic model that learns to extract complex motion from movies of the natural environment. The model consists of two hidden layers: the first layer produces a sparse representation of the image that is expressed in terms of local amplitude and phase variables. The second layer learns the higher-order structure among the time-varying phase variables. After training on natural movies, the top layer units discover the structure of phase-shifts within the first layer.

We describe a hierarchical, probabilistic model that learns to extract complex motion frommovies of the natural environment. The model consists of two hidden layers: the first layer produces a sparse representation of the image that is expressed interms of local amplitude and phase variables. The second layer learns the higher-order structure among the time-varying phase variables. After training onnatural movies, the top layer units discover the structure of phase-shifts within the first layer.

We consider the problem of learning probabilistic models for complex relational structures between various types of objects. A model can help us "understand" a dataset of relational facts in at least two ways, by finding interpretable structure in the data, and by supporting predictions, or inferences about whether particular unobserved relations are likely to be true. Often there is a tradeoff between these two aims: cluster-based models yield more easily interpretable representations, while factorization-based approaches have given better predictive performance on large data sets. We introduce the Bayesian Clustered Tensor Factorization (BCTF) model, which embeds a factorized representation of relations in a nonparametric Bayesian clustering framework. Inference is fully Bayesian but scales well to large data sets. The model simultaneously discovers interpretable clusters and yields predictive performance that matches or beats previous probabilistic models for relational data.