Tackling the Interleaving Problem in Activity Discovery

Activity discovery (AD) is the unsupervised process of discovering activities in data produced from streaming sensor networks that are recording the actions of human subjects. One major challenge for AD systems is interleaving, the tendency for people to carry out multiple activities at a time a parallel. Following on from our previous work, we continue to investigate AD in interleaved datasets, with a view towards progressing the state-of-the-art for AD

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

The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges

Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

In lifelong learning systems, especially those based on artificial neural networks, one of the biggest obstacles is the severe inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this article, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the immensely popular back-propagation of errors. Grounded in the neurocognitive theory of predictive processing, our model adapts its synapses in a biologically-plausible fashion, while another, complementary neural system rapidly learns to direct and control this cortex-like structure by mimicking the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting as compared to standard neural models and outperforms a wide swath of previously proposed methods even though it is trained across task datasets in a stream-like fashion. The promising performance of our complementary system on benchmarks, e.g., SplitMNIST, Split Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, a new possibility for tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split mnist/fmnist/not-mnist. Task selection/basal ganglia model has been integrate

Using visualization for visualization : an ecological interface design approach to inputting data

Visualization is experiencing growing use by a diverse community, with continuing improvements in the availability and usability of systems. In spite of these developments the problem of how first to get the data in has received scant attention: the established approach of pre-defined readers and programming aids has changed little in the last two decades. This paper proposes a novel way of inputting data for scientific visualization that employs rapid interaction and visual feedback in order to understand how the data is stored. The approach draws on ideas from the discipline of ecological interface design to extract and control important parameters describing the data, at the same time harnessing our innate human ability to recognize patterns. Crucially, the emphasis is on file format discovery rather than file format description, so the method can therefore still work when nothing is known initially of how the file was originally written, as is often the case with legacy binary data. © 2013 Elsevier Ltd

Memory and metacognition in classroom learning : the role of item order in learning with particular reference to the interleaving effect

Education needs to be effective, but previous research suggests that teachers and learners alike are not always aware of which practices lead to lasting, transferable learning and which do not. In particular, research into evidence-based teaching strategies such as the spacing effect, interleaving and retrieval practice have shown a striking mismatch between what classroom choices are supported by the evidence and metacognitive beliefs on the part of learners. In part, this is because these strategies make the process of learning more challenging and error-prone; what Bjork and Bjork (1991) refer to as desirable difficulties tend to lead to poorer performance in the short-term but better learning in the long-term. As such, they are often mistakenly rejected by learners who cannot easily perceived their benefits. This thesis focuses on desirable difficulties that relate to the timing and order in which classroom examples are presented, and in particular on interleaving – the process of mixing or alternating the order of examples during learning. Previous research has established the strength and boundary conditions of other desirable difficulties such as the spacing effect (Cepeda et al., 2006) but a clear picture of these issues was lacking when it came to interleaving. A systematic review was therefore conducted to gauge the strength of the evidence on interleaving, and its potential for application to the secondary classroom. It found that interleaving (as compared to blocking) is associated with high effect sizes which persist across experimental designs and do not appear to be biased by the work of specific labs. However, there was also a gap in the literature when it came to classroom-based field research on the technique, and very little work had been done which related directly to higher-order skills – a key element of many exam-based courses. The next stage of this thesis was therefore to extend investigation of interleaving to classroom situations, focusing particularly on psychology teaching at school level. In a pilot study, high school students engaged in an introductory week for a psychology course experienced spaced and interleaved learning tasks, allowing a computer-based methodology to be tested but revealing no effect of interleaving in the context of brief presentations of factual information. A follow-up which used similar methodology applied to learning the skills of application and evaluation found an advantage of interleaving over blocking. The latter study also found a trend in favour of self-explanation – another desirable difficulty – that did not reach significance. As desirable difficulties are often counterintuitive, this thesis also aimed to investigate whether teachers would endorse these techniques, and what might discourage them from doing so. A wide-ranging survey on learning and memory suggested that teachers’ beliefs about memory are generally more accurate than prior findings among the general public, but are out of line with the scientific consensus when it comes to desirable difficulties such as spacing and retrieval practice. A follow-up study focused on three techniques in particular – interleaving, spacing, and retrieval practice (all desirable difficulties). New student teachers and in-service teachers were shown a set of vignettes, each of which presented a classroom situation relating to one of these techniques and required a response on a 7-point scale to indicate their belief in which of two alternatives (for example, interleaving vs. blocking) would lead to better outcomes. This study found that a minority of teachers favoured the techniques overall, though spacing was more widely endorsed (49% overall) than retrieval practice (30%), and interleaving was endorsed least of all (4%). No relationship was found between years of experience and accuracy across the sample of in-service teachers, and this group were less accurate than the student teachers, supporting the idea that experience does not help when it comes to adopting teaching techniques which are based on counterintuitive features of human memory. Finally, the thesis addresses the implications of these findings for both teaching and professional learning. It considers the role of both interleaving and other related techniques, as well as looking at ways of inculcating research evidence into the profession. It is noted that flawed beliefs about learning and memory often link to teacher identity, and that this is a barrier when it comes to teachers’ choosing to engage with evidence (or not). Some synthesis from the ideas can be achieved by considering the role of desirable difficulties as professional learning tools, and a series of recommendations are set out. The methodology used in the research – systematic reviewing and computer-based field experiments – is also evaluated, and directions for future work identified.Education needs to be effective, but previous research suggests that teachers and learners alike are not always aware of which practices lead to lasting, transferable learning and which do not. In particular, research into evidence-based teaching strategies such as the spacing effect, interleaving and retrieval practice have shown a striking mismatch between what classroom choices are supported by the evidence and metacognitive beliefs on the part of learners. In part, this is because these strategies make the process of learning more challenging and error-prone; what Bjork and Bjork (1991) refer to as desirable difficulties tend to lead to poorer performance in the short-term but better learning in the long-term. As such, they are often mistakenly rejected by learners who cannot easily perceived their benefits. This thesis focuses on desirable difficulties that relate to the timing and order in which classroom examples are presented, and in particular on interleaving – the process of mixing or alternating the order of examples during learning. Previous research has established the strength and boundary conditions of other desirable difficulties such as the spacing effect (Cepeda et al., 2006) but a clear picture of these issues was lacking when it came to interleaving. A systematic review was therefore conducted to gauge the strength of the evidence on interleaving, and its potential for application to the secondary classroom. It found that interleaving (as compared to blocking) is associated with high effect sizes which persist across experimental designs and do not appear to be biased by the work of specific labs. However, there was also a gap in the literature when it came to classroom-based field research on the technique, and very little work had been done which related directly to higher-order skills – a key element of many exam-based courses. The next stage of this thesis was therefore to extend investigation of interleaving to classroom situations, focusing particularly on psychology teaching at school level. In a pilot study, high school students engaged in an introductory week for a psychology course experienced spaced and interleaved learning tasks, allowing a computer-based methodology to be tested but revealing no effect of interleaving in the context of brief presentations of factual information. A follow-up which used similar methodology applied to learning the skills of application and evaluation found an advantage of interleaving over blocking. The latter study also found a trend in favour of self-explanation – another desirable difficulty – that did not reach significance. As desirable difficulties are often counterintuitive, this thesis also aimed to investigate whether teachers would endorse these techniques, and what might discourage them from doing so. A wide-ranging survey on learning and memory suggested that teachers’ beliefs about memory are generally more accurate than prior findings among the general public, but are out of line with the scientific consensus when it comes to desirable difficulties such as spacing and retrieval practice. A follow-up study focused on three techniques in particular – interleaving, spacing, and retrieval practice (all desirable difficulties). New student teachers and in-service teachers were shown a set of vignettes, each of which presented a classroom situation relating to one of these techniques and required a response on a 7-point scale to indicate their belief in which of two alternatives (for example, interleaving vs. blocking) would lead to better outcomes. This study found that a minority of teachers favoured the techniques overall, though spacing was more widely endorsed (49% overall) than retrieval practice (30%), and interleaving was endorsed least of all (4%). No relationship was found between years of experience and accuracy across the sample of in-service teachers, and this group were less accurate than the student teachers, supporting the idea that experience does not help when it comes to adopting teaching techniques which are based on counterintuitive features of human memory. Finally, the thesis addresses the implications of these findings for both teaching and professional learning. It considers the role of both interleaving and other related techniques, as well as looking at ways of inculcating research evidence into the profession. It is noted that flawed beliefs about learning and memory often link to teacher identity, and that this is a barrier when it comes to teachers’ choosing to engage with evidence (or not). Some synthesis from the ideas can be achieved by considering the role of desirable difficulties as professional learning tools, and a series of recommendations are set out. The methodology used in the research – systematic reviewing and computer-based field experiments – is also evaluated, and directions for future work identified

Models of Interaction as a Grounding for Peer to Peer Knowledge Sharing

Most current attempts to achieve reliable knowledge sharing on a large scale have relied on pre-engineering of content and supply services. This, like traditional knowledge engineering, does not by itself scale to large, open, peer to peer systems because the cost of being precise about the absolute semantics of services and their knowledge rises rapidly as more services participate. We describe how to break out of this deadlock by focusing on semantics related to interaction and using this to avoid dependency on a priori semantic agreement; instead making semantic commitments incrementally at run time. Our method is based on interaction models that are mobile in the sense that they may be transferred to other components, this being a mechanism for service composition and for coalition formation. By shifting the emphasis to interaction (the details of which may be hidden from users) we can obtain knowledge sharing of sufficient quality for sustainable communities of practice without the barrier of complex meta-data provision prior to community formation