On the mopfloor: researching employment relations in the hidden world of commercial cleaning

This chapter explores some of the challenges and tribulations of studying the hidden and hitherto unresearched world of work in the New South Wales (Australia) cleaning industry. The findings and analysis presented here are informed by my research into employment relations, labour management and the organisation of work in the NSW commercial cleaning industry (Ryan, 2007). This research used primarily a case study of an industry exemplar, and made extensive use of participant observation to obtain evidence on the perceived realities of work and organisation for those on the front line of cleaning work - the cleaners and their supervisors - and to understand how they make sense of their working world. The discussions and findings presented in this chapter support the use of an ethnographic approach to work, and provide some guidance as to how a participant observation study might be carried out and what might be found through the use of this method.

Theorizing normalization as hidden privilege

This chapter provides a theoretical overview of some of the ways in which the process of normalization functions as forms of hidden privilege. It outlines how privileged groups come to represent the dominant norm whereby white, male, able-bodied, heterosexual, middle-class people in Western societies come to embody what it means to be normal. It also explores strategies for challenging the normalization of privilege by encouraging the development of responsibility not only for individual actions but also for the social practices which create them.

Green planning heritage/industrial nature : the park lands or 'regeneration reserves' of Broken Hill

Freestone (1989+) has extensively surveyed town planning visions and model communities for Australia, but one settlement has been forgotten. The significant mining settlement of Broken Hill in far western New South Wales does not figure in his thematic and historical analyses yet its park lands are so integral to its physical cultural legacy and human health that it warrants enhanced standing. In the last 2 years the Commonwealth has been considering the potential nomination of the municipality of Broken Hill for inclusion onto the National Heritage List principally due to its mining, social and economic contributions to Australia’s heritage and identity. A component in their deliberations is the Park Lands, or ‘Regeneration Reserves’, that encompass this urban settlement and its mine leaseholds. Within these Regeneration Reserves, international arid zone ecological restoration theory and practice was pioneered by Albert and Margaret Morris in the 1930s that serves as the method for all mining revegetation practice in Australia today. This paper reviews the theory and evolution of the Broken Hill Regeneration Reserves, having regard to the Adelaide Park Lands and Garden City discourses of the 1920s-30s, arguing that the Broken Hill Regeneration Reserves have a valid and instrumental position in the planning and landscape architectural histories of Australia.

Gen Y and their 'backpacks of hidden treasures'

This paper reports on part of a teacher/researcher’s PhD action research study conducted in an Australian public high school, investigating the use of online social networking as a classroom learning environment where students were encouraged to find and use their hidden treasures. It examines student reactions and online activity whilst they are interacting with a range of Web 2.0 and social media tools. The study may inform the rising generation of higher education learners and their course developers and explores the move from passive to active learning through Gen Y’s engagement with online social media. It looks at a range of innovative practices that were made possible through using a Ning online social network as a classroom environment whilst encouraging Gen Y to take more responsibility for learning. It also looks at the pedagogical implications that come with the use of social media as they challenge traditional models of ‘instructional order’. This study primarily adopts a social constructivist approach to teaching with the focus being on learning, rather than teaching. The study found that having such a flexible curriculum environment encouraged Gen Y to share their ‘hidden treasures’ and, hence, informal classroom learning became more visible and more readily documented. Gen Y students appreciated the opportunities to publish and to gain feedback from more than their teacher alone. Students, in some cases, could be seen to be engaged in their own cycle of learning where they valued the opinion of their peers which, in turn, helped them to improve their work.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

The hidden permutation model and location-based activity recognition

Permutation modeling is challenging because of the combinatorial nature of the problem. However, such modeling is often required in many real-world applications, including activity recognition where subactivities are often permuted and partially ordered. This paper introduces a novel Hidden Permutation Model (HPM) that can learn the partial ordering constraints in permuted state sequences. The HPM is parameterized as an exponential family distribution and is flexible so that it can encode constraints via different feature functions. A chain-flipping Metropolis-Hastings Markov chain Monte Carlo (MCMC) is employed for inference to overcome the O(n!) complexity. Gradient-based maximum likelihood parameter learning is presented for two cases when the permutation is known and when it is hidden. The HPM is evaluated using both simulated and real data from a location-based activity recognition domain. Experimental results indicate that the HPM performs far better than other baseline models, including the naive Bayes classifier, the HMM classifier, and Kirshner's multinomial permutation model. Our presented HPM is generic and can potentially be utilized in any problem where the modeling of permuted states from noisy data is needed.

Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model

The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and Inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.

Hierarchical hidden Markov models with general state hierarchy

The hierarchical hidden Markov model (HHMM) is an extension of the hidden Markov model to include a hierarchy of the hidden states. This form of hierarchical modeling has been found useful in applications such as handwritten character recognition, behavior recognition, video indexing, and text retrieval. Nevertheless, the state hierarchy in the original HHMM is restricted to a tree structure. This prohibits two different states from having the same child, and thus does not allow for sharing of common substructures in the model. In this paper, we present a general HHMM in which the state hierarchy can be a lattice allowing arbitrary sharing of substructures. Furthermore, we provide a method for numerical scaling to avoid underflow, an important issue in dealing with long observation sequences. We demonstrate the working of our method in a simulated environment where a hierarchical behavioral model is automatically learned and later used for recognition.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.