Amongst the best practices that constitute linked data, one of the foremost is to use only HTTP-URIs as identifiers for RDF resources. This is so that the URI will resolve in a Linked Data browser to give information about the named resource. At the same time, Linked Data takes a resource-centric, as opposed to page-centric, approach to resolution. We argue that this approach can, in certain cases, obviate the need for insisting on HTTP-URIs. As a use of our “expanded” notion of Linked Data, we present as an example Life Science Identifiers.

GORT is a system that combines Linked Data from across several Semantic Web data sources to solve guesstimation problems, with user assistance. The system uses customised inference rules over the relationships in the OpenCyc ontology, combined with data from DBPedia, to reason and perform its calculations. The system is extensible with new Linked Data, as it becomes available, and is capable of answering a small range of guesstimation questions.

In this study we show that humans form very similar perceptual spaces when they explore parametrically-defined shell-shaped objects visually or haptically. A physical object space was generated by varying three shape parameters. Sighted participants explored pictures of these objects while blindfolded participants haptically explored 3D printouts of the objects. Similarity ratings were performed and analyzed using multidimensional scaling (MDS) techniques. Visual and haptic similarity ratings highly correlate and resulted in very similar visual and haptic MDS maps providing evidence for one shared perceptual space underlying both modalities. To investigate to which degree these results are transferrable to natural objects, we performed the same visual and haptic similarity ratings and multidimensional scaling analyses using a set of natural sea shells.

Sensor-to-symbol conversion lies at the heart of all embedded intelligent systems. The everyday world occupied by human stakeholders is dominated by objects that have symbolic labels. For an embedded intelligent system to operate in such a world it must also be able to segment its sensory stream into objects and label those objects appropriately. It is our position that development of a consistent and flexible sensor-to-symbol reasoning system (or architecture) is a key component of embedded intelligence.

Time is a concept that highly depends on the socio-cultural context. Its perception by humans is primarily based on the cultures, nations and social environment they belong to. Hence, different socio-cultural contexts imply different understandings of time. This leads to communication problems when their members start interacting with each other. In a dynamic and multi-cultural environment like today’s Web, where both billions of people with different socio-cultural contexts and numerous context dependent software applications interact, similar communication and inter-operability problems are expected. Expressing socio-cultural temporal information in an unambiguous, explicit and machine processable way can, however, help reduce such communication conflicts. In this way, heterogeneous temporal Web application systems can share the same concept of time. In this paper we present an ontology of socio-cultural time expressions that attempts to formalize the notion of socio-cultural time. The resulting model can then be used in a Web based temporal applications such as automated appointment scheduling services or calendars to provide more context sensitive service to its users.

We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.

This paper presents a simulator that is being developed to study the performance of certain types of vehicle navigation. The performance metric looks at a likelihood of accomplishing a task and the cost of the strategy – measuring both robustness and efficiency. We present results involving only autonomous control strategies, yet the simulator will be used to compare human performance in completing the same task.

In visual metaphor processing, one object, the target, is compared to and understood in terms of another object, the source. Several studies suggest that perceptual similarity between two objects enhances a conceptual link between the two. However, little is known about how perceptual features contribute to the establishment of this link. In the present experiment we investigated the processing of the four possible combinations of conceptually and perceptually similar picture pairs using a same-different task. In order to determine whether particular processes are bound to a particular time range, we manipulated the delay between the two successively presented pictures. We expected perceptual processing effects at a short delay and conceptual processing effects at a longer delay. We did not find evidence for this expectation. However, the results did show that (i) it took participants longer to give a ‘different’ response if two objects shared perceptual features than when they did not; (ii) this presence of perceptual similarity also resulted in more response errors; and (iii) if objects shared only perceptual features, participants in the long delay condition produced more erroneous responses than the participants in the short delay condition did. These results are discussed in light of metaphor processing models.

This position paper describes how the nascent area of AI for development can learn from the challenges and successes of its parents: artificial intelligence and information and communication technologies for development (ICT4D). AI suffered from overly ambitious beginnings and years of stumbling before finding its footing, and achieving impactful ICT4D has been an equally challenging endeavor. We describe the history and challenges of both AI and ICT4D research, and present three broad suggestions for AI-for-development researchers: (1) that they spend as much time as possible with the kind of site or the organization they are hoping to impact; (2) that they be ambitious but humble in their goals and expectations; and (3) that they put AI in the service of existing, well-intented, competent development organizations.

For the past several years, we have been using robots in our introductory computer science course. Although this has been challenging for many reasons, it has also been very rewarding on a number of fronts, both for the students and for us. However, in order for this to occur, we had to adapt to what we perceived as “chaotic code.” In this paper we describe lessons learned by watching what the students do, where they have trouble, and what they enjoy. Further, we discuss what the implications of focusing on creativity has had on teaching and assessment.