Using virtual meeting spaces for work integrated learning

Work integrated learning activities provide students with the opportunity to apply the knowledge and skills they have developed through their tertiary education to authentic work place problems. This paper reports on the outcome of a virtual work integrated learning activity undertaken by third year IT students. Students used a synchronous communication tool to participate in meetings with their virtual teammates. They were required to produce minutes and a report of their meeting. The majority of students completed the exercise successfully with some student groups using the meeting facility for subsequent collaboration during the remainder of the unit.

Late palaeozoic global changes affecting high-latitude environments and biotas : an introduction

The Late Palaeozoic Ice Age (LPIA), spanning approximately from ~320 Ma (Serpukhovian, late Mississippian) to 290 Ma (mid-Sakmarian, Early Permian), represents the vegetated Earth’s largest and most long-lasting regime of severe and multiple glaciations, involving processes and patterns probably comparable to those of the Last Ice Age. Accompanying the LPIA occurred a number of broadly synchronous global environmental and biotic changes. These global changes, as briefly reviewed and summarized in this introductory paper, comprised (but are not limited to) the following: massive continental reorganization in the lead up to the final assembly of Pangea resulting in profound changes in global palaeogeography, palaeoceanography and palaeobiogeogarphy; substantially lowered global atmospheric carbon dioxide concentrations (pCO₂), coupled with an unprecedented increase in atmospheric oxygen concentrations reaching Earth's all-time high in its last 600 million year history; sharp global temperature and sea-level drops (albeit with considerable spatial and temporal variability throughout the ice age); and apparently a prolonged period of global sluggish macro-evolution with both low extinction and origination rates compared to other times. In the aftermath of the LPIA, the world's climate entered into a transitional climate phase through the late Early to Middle Permian before its transformation into a greenhouse state towards the end-Permian. In recent years, considerable amount of data and interpretations have been published concerning the physical evidence in support of the LPIA, its broad timeframe and eustatic and ecosystem responses from the lower latitudes, but relatively less attention has been drawn to the impact of the ice age on late Palaeozoic high-latitude environments and biotas. It is with this mission in mind that we have organized this special issue, with the central focus on late Palaeozoic high latitude regions of both hemispheres, that is, Gondwana and northern Eurasia. Our aim is to gather a set of papers that not only document the physical environmental changes that had occurred in the polar regions of Gondwana and northern Eurasia during the LPIA, but also review on the biotic responses at different taxonomic, ecological and spatial scales to these physical changes in a refined chronological timeframe.

This introductory paper is designed to provide a global context for the special issue, with a brief review of key late Palaeozoic global environmental changes (including: changes in global land-sea configurations, atmospheric chemistry, global climate regimes, global ocean circulation patterns and sea levels) and large -scale biotic (biogeographic and evolutionary) responses, followed by a summary of what we see as unresolved scientific issues and various working hypotheses concerning late Palaeozoic global changes and, in particular, the LPIA, as a possible reference to future research.

Investigating Ways of Enhancing Online Research

We live in an era characterized as ‘the Digital Age’ and the ways in which we engage in teaching, learning and research are evolving with the increased use of digital technologies. This paper describes a study that investigated the ways in which a cohort of Education students in Victoria, Australia engaged in online research projects using Information Communication Technologies (ICT) as the main form of communication during the research process. When an array of technologies, related resources and training are made available to staff and University students, what are the key influences that effect their adoption and application of the selected mediums? Understanding the answer to this question is important in informing instruction and technological pedagogies for distance education and research. Data was gathered from students and their research supervisors via the use of online surveys. The research identified a number of key factors that influenced people’s preferences for using certain digital technologies. The study revealed that there was a tendency for people to prefer the use of asynchronous forms of digital communication. It is argued that more research is needed in this area in order to improve the application of online modes of communication and ensure that those researching via distance/technological modes are not disadvantaged in their research and learning experiences.

Human action recognition based on pyramid histogram of oriented gradients

Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e.; Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities.

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

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.

A smith-waterman local alignment approach for spatial activity recognition

In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accommodate gaps, resulting from tracking system errors. Unlike other approaches SW is able to locate and quantify activities embedded within extraneous spatial data. Through experimentation with a three class data set, we show that the proposed SW algorithm is capable of recognising accurately and inaccurately segmented spatial sequences. To benchmark the techniques classification performance we compare it to the discrete hidden markov model (HMM). Results show that SW exhibits higher accuracy than the HMM, and also maintains higher classification accuracy with smaller training set sizes. We also confirm the robust property of the SW approach via evaluation with sequences containing artificially introduced noise.

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.

Spatial activity recognition in a smart home environment using a chemotactic model

Spatial activity recognition is challenging due to the amount of noise incorporated during video tracking in everyday environments. We address the spatial recognition problem with a biologically-inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to change motile behaviour in relation to environmental gradients. Through adoption of chemotactic principles, we propose the chemotactic model and evaluate its performance in a smart house environment. The model exhibits greater than 99% recognition performance with a diverse six class dataset and outperforms the Hidden Markov Model (HMM). The approach also maintains high accuracy (90-99%) with small training sets of one to five sequences. Importantly, unlike other low-level spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities.

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.

Human action segmentation via controlled use of missing data in HMMs

Segmentation of individual actions from a stream of human motion is an open problem in computer vision. This paper approaches the problem of segmenting higher-level activities into their component sub-actions using Hidden Markov Models modified to handle missing data in the observation vector. By controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. The approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. The advantage of this method over sliding windows and Viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.

Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.