Learning the Semantics of Manipulation Action

In this paper we present a formal computational framework for modeling manipulation actions. The introduced formalism leads to semantics of manipulation action and has applications to both observing and understanding human manipulation actions as well as executing them with a robotic mechanism (e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The goal of the introduced framework is to: (1) represent manipulation actions with both syntax and semantic parts, where the semantic part employs $\lambda$-calculus; (2) enable a probabilistic semantic parsing schema to learn the $\lambda$-calculus representation of manipulation action from an annotated action corpus of videos; (3) use (1) and (2) to develop a system that visually observes manipulation actions and understands their meaning while it can reason beyond observations using propositional logic and axiom schemata. The experiments conducted on a public available large manipulation action dataset validate the theoretical framework and our implementation

Rethinking affordance

n/a – Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory

Acquiring and processing verb argument structure : distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings

Big data and the SP theory of intelligence

This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK), helping to reduce the diversity of formalisms and formats for knowledge and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualisation of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.Comment: Accepted for publication in IEEE Acces

SBNet: Sparse Blocks Network for Fast Inference

Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network. We show that such computation masks can be used to reduce computation in the high-resolution main network. Variants of sparse activation CNNs have previously been explored on small-scale tasks and showed no degradation in terms of object classification accuracy, but often measured gains in terms of theoretical FLOPs without realizing a practical speed-up when compared to highly optimized dense convolution implementations. In this work, we leverage the sparsity structure of computation masks and propose a novel tiling-based sparse convolution algorithm. We verified the effectiveness of our sparse CNN on LiDAR-based 3D object detection, and we report significant wall-clock speed-ups compared to dense convolution without noticeable loss of accuracy.Comment: 10 pages, CVPR 201

Techno-historical limits of the interface: the performance of interactive narrative experiences

This thesis takes the position that current analyses of digitally mediated interactive experiences that include narrative elements often lack adequate consideration of the technical and historical contexts of their production.From this position, this thesis asks the question: how is the reader/player/user's participation in interactive narrative experiences (such as hypertext fiction, interactive fiction, computer games, and electronic art) influenced by the technical and historical limitations of the interface?In order to investigate this question, this thesis develops a single methodology from relevant media and narrative theory, in order to facilitate a comparative analysis of well known exemplars from distinct categories of digitally mediated experiences. These exemplars are the interactive fiction Adventure, the interactive art work Osmose, the hypertext fiction Afternoon, a story, and the computer/video games Myst, Doom, Half Life and Everquest.The main argument of this thesis is that the technical limits of new media experiences cause significant ‘gaps’ in the reader’s experience of them, and that the cause of these gaps is the lack of a dedicated technology for new media, which instead ‘borrows’ technology from other fields. These gaps are overcome by a greater dependence upon the reader’s cognitive abilities than other media forms. This greater dependence can be described as a ‘performance’ by the reader/player/user, utilising Eco’s definition of an ‘open’ work (Eco 21).This thesis further argues that the ‘mimetic’ and ‘immersive’ ambitions of current new media practice can increases these gaps, rather than overcoming them. The thesis also presents the case that these ‘gaps’ are often not caused by technical limits in the present, but are oversights by the author/designers that have arisen as the product of a craft culture that has been subject to significant technical limitations in the past. Compromises that originally existed to overcome technical limits have become conventions of the reader/player/user’s interactive literacy, even though these conventions impinge on the experience, and are no longer necessary because of subsequent technical advances. As a result, current new media users and designers now think of these limitations as natural.This thesis concludes the argument by redefining ‘immersion’ as the investment the reader makes to overcome the gaps in an experience, and suggests that this investment is an important aspect of their performance of the work