A Defence of Intentionalism about Demonstratives

Intentionalism about demonstratives is the view that the referent of a demonstrative is determined solely by the speaker's intentions. Intentionalists can disagree about the nature of these intentions, but are united in rejecting the relevance of other factors, such as the speaker's gestures, her gaze, and any facts about the addressee or the audience. In this paper, I formulate a particular version of this view, and I defend it against six objections, old and new

Linked Data based video annotation and browsing for distance learning

We present a pair of prototype tools that enable users to mark up video with annotations and later explore related materials using Semantic Web and Linked Data approaches. The �first tool helps academics preparing Open University course materials to mark up videos with information about the subject matter and audio-visual content. The second tool enables users, such as students or academics, to find video and other materials relevant to their study

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

The Analysis of Online Communities using Interactive Content-based Social Networks

published or submitted for publicationis peer reviewe