Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification

In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs. Different from most existing classical graph kernels, the proposed HAQJSK kernels can incorporate hierarchical aligned structure information between graphs and transform graphs of random sizes into fixed-sized aligned graph structures, i.e., the Hierarchical Transitive Aligned Adjacency Matrix of vertices and the Hierarchical Transitive Aligned Density Matrix of the Continuous-Time Quantum Walk (CTQW). For a pair of graphs to hand, the resulting HAQJSK kernels are defined by measuring the Quantum Jensen-Shannon Divergence (QJSD) between their transitive aligned graph structures. We show that the proposed HAQJSK kernels not only reflect richer intrinsic global graph characteristics in terms of the CTQW, but also address the drawback of neglecting structural correspondence information arising in most existing R-convolution kernels. Furthermore, unlike the previous Quantum Jensen-Shannon Kernels associated with the QJSD and the CTQW, the proposed HAQJSK kernels can simultaneously guarantee the properties of permutation invariant and positive definiteness, explaining the theoretical advantages of the HAQJSK kernels. Experiments indicate the effectiveness of the proposed kernels

A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs

In this paper, we develop a new graph kernel, namely the Hierarchical Transitive-Aligned kernel, by transitively aligning the vertices between graphs through a family of hierarchical prototype graphs. Comparing to most existing state-of-the-art graph kernels, the proposed kernel has three theoretical advantages. First, it incorporates the locational correspondence information between graphs into the kernel computation, and thus overcomes the shortcoming of ignoring structural correspondences arising in most R-convolution kernels. Second, it guarantees the transitivity between the correspondence information that is not available for most existing matching kernels. Third, it incorporates the information of all graphs under comparisons into the kernel computation process, and thus encapsulates richer characteristics. By transductively training the C-SVM classifier, experimental evaluations demonstrate the effectiveness of the new transitive-aligned kernel. The proposed kernel can outperform state-of-the-art graph kernels on standard graph-based datasets in terms of the classification accuracy

Action Observation for Neurorehabilitation in Apraxia

Neurorehabilitation and brain stimulation studies of post-stroke patients suggest that action-observation effects can lead to rapid improvements in the recovery of motor functions and long-term motor cortical reorganization. Apraxia is a clinically important disorder characterized by marked impairment in representing and performing skillful movements [gestures], which limits many daily activities and impedes independent functioning. Recent clinical research has revealed errors of visuo-motor integration in patients with apraxia. This paper presents a rehabilitative perspective focusing on the possibility of action observation as a therapeutic treatment for patients with apraxia. This perspective also outlines impacts on neurorehabilitation and brain repair following the reinforcement of the perceptual-motor coupling. To date, interventions based primarily on action observation in apraxia have not been undertaken

Identifying and Disentangling Interleaved Activities of Daily Living from Sensor Data

Activity discovery (AD) refers to the unsupervised extraction of structured activity data from a stream of sensor readings in a real-world or virtual environment. Activity discovery is part of the broader topic of activity recognition, which has potential uses in fields as varied as social work and elder care, psychology and intrusion detection. Since activity recognition datasets are both hard to come by, and very time consuming to label, the development of reliable activity discovery systems could be of significant utility to the researchers and developers working in the field, as well as to the wider machine learning community. This thesis focuses on the investigation of activity discovery systems that can deal with interleaving, which refers to the phenomenon of continuous switching between multiple high-level activities over a short period of time. This is a common characteristic of the real-world datastreams that activity discovery systems have to deal with, but it is one that is unfortunately often left unaddressed in the existing literature. As part of the research presented in this thesis, the fact that activities exist at multiple levels of abstraction is highlighted. A single activity is often a constituent element of a larger, more complex activity, and in turn has constituents of its own that are activities. Thus this investigation necessarily considers activity discovery systems that can find these hierarchies. The primary contribution of this thesis is the development and evaluation of an activity discovery system that is capable of identifying interleaved activities in sequential data. Starting from a baseline system implemented using a topic model, novel approaches are proposed making use of modern language models taken from the field of natural language processing, before moving on to more advanced language modelling that can handle complex, interleaved data. As well as the identification of activities, the thesis also proposes the abstraction of activities into larger, more complex activities. This allows for the construction of hierarchies of activities that more closely reflect the complex inherent structure of activities present in real-world datasets compared to other approaches. The thesis also discusses a number of important issues relating to the evaluation of activity discovery systems, and examines how existing evaluation metrics may at times be misleading. This includes highlighting the existence of differing abstraction issues in activity discovery evaluation, and suggestions for how this problem can be mitigated. Finally, alternative evaluation metrics are investigated. Naturally, this dissertation does not fully solve the problem of activity discovery, and work remains to be done. However, a number of the most pressing issues that affect real-world activity discovery systems are tackled head-on, and show that useful progress can indeed be made on them. This work aims to benefit systems that are as “clean slate as possible, and hence incorporate no domain-specific knowledge. This is perhaps somewhat of an artificial handicap to impose in this problem domain, but it does have the advantage of making this work applicable to as broad a range of domains as possible