Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.

Increasing stochastic perturbations enhances acquisition and learning of complex sport movements

Traditionally, the aquisition of skills and sport movement has been characterised by numerous repetitions of presumed model movement pattern to be acquired by learners. This approach has been questioned by research identifying the presence of individualised movement patterns and the low probability of occurrence of two identical movements within and between individuals. In contrast, the differential learning approach claims advantage for incurring variability in the learning process by adding stochastic perturbations during practice. These ideas are exemplified by data from a high jump experiment which compared the effectiveness of classical and a differential training approach with pre-post test design. Results showed clear advantages for the group with additional stochastic perturbation during the aquisition phase in comparison to classically trained athletes. Analogies to similar phenomenological effects in the neurobiological literature are discussed.

Action learning of online marketing via a global contest

Introduction: The Google Online Marketing Challenge is a global competition in which student teams run advertising campaigns for small and medium-sized businesses (SMEs) using AdWords, Google’s text-based advertisements. In 2008, its inaugural year, over 8,000 students and 300 instructors from 47 countries representing over 200 schools participated. The Challenge ran in undergraduate and graduate classes in disciplines such as marketing, tourism, advertising, communication and information systems. Combining advertising and education, the Challenge gives student hands-on experience in the increasingly important field of online marketing, engages them with local businesses and motivates them through the thrill of a global competition. Student teams receive US$200 in AdWords credits, Google’s premier advertising product that offers cost-per-click advertisements. The teams then recruit and work with a local business to devise an effective online marketing campaign. Students first outline a strategy, run a series of campaigns, and provide their business with recommendations to improve their online marketing. Teams submit two written reports for judging by 14 academics in eight countries. In addition, Google AdWords experts judge teams on their campaign statistics such as success metrics and account management. Rather than a marketing simulation against a computer or hypothetical marketing plans for hypothetical businesses, the Challenges has student teams develop and manage real online advertising campaigns for their clients and compete against peers globally.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Use of advanced computer graphics and data visualisation technologies in teaching and learning of steel structural design

A teaching and learning development project is currently under way at Queens-land University of Technology to develop advanced technology videotapes for use with the delivery of structural engineering courses. These tapes consist of integrated computer and laboratory simulations of important concepts, and behaviour of structures and their components for a number of structural engineering subjects. They will be used as part of the regular lectures and thus will not only improve the quality of lectures and learning environment, but also will be able to replace the ever-dwindling laboratory teaching in these subjects. The use of these videotapes, developed using advanced computer graphics, data visualization and video technologies, will enrich the learning process of the current diverse engineering student body. This paper presents the details of this new method, the methodology used, the results and evaluation in relation to one of the structural engineering subjects, steel structures.

Contextualising the teaching and learning of measurement within Torres Strait Islander schools

A one year mathematics project that focused on measurement was conducted with six Torres Strait Islander schools and communities. Its key focus was to contextualise the teaching and learning of measurement within the students’ culture, communities and home languages. There were six teachers and two teacher aides who participated in the project. This paper reports on the findings from the teachers’ and teacher aides’ survey questionnaire used in the first Professional Development session to identify: a) teachers’ experience of teaching in Torres Strait Islands, b) teachers’ beliefs about effective ways to teach Torres Strait Islander students, and c) contexualising measurement within Torres Strait Islander culture, Communities and home languages. A wide range of differing levels of knowledge and understanding about how to contextualise measurement to support student learning were identified and analysed. For example, an Indigenous teacher claimed that mathematics and the environment are relational, that is, they are not discrete and in isolation from one another, rather they interconnect with mathematical ideas emerging from the environment of the Torres Strait Communities.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Entry into Valhalla : contextualising the learning of legal ethics through the use of Second Life machinima

An ability to recognise and resolve ethical dilemmas was identified by the Australian Law Reform Commission as one of the ten fundamental lawyering skills. While the ‘Priestley 11’ list of areas of law required to qualify for legal practice includes ethics and professional responsibility, the commitment to ethics learning in Australian law schools has been far from uniform. The obligation imposed by the Priestley 11 is frequently discharged by a traditional teaching and learning approach involving lectures and/or tutorials and focusing on the content of the formal rules of professional responsibility. However, the effectiveness of such an approach is open to question. Instead, a practical rather than a theoretical approach to the teaching of legal ethics is required. Effective final-year student learning of ethics may be achieved by an approach which engages students, enabling them to appreciate the relevance of what they are learning to the real world and facilitating their transition from study to their working lives. Entry into Valhalla comprises a suite of modules featuring ‘machinima’ (computer-generated imagery) created using the Second Life virtual environment to contextualise otherwise abstract concepts. It provides an engaging learning environment which enables students to obtain an appreciation of ethical responsibility in a real-world context and facilitates understanding and problem-solving ability.

Contextualising the teaching and learning of measurement in Torres Strait Islander schools

This paper reports on a mathematics project conducted with six Torres Strait Islander schools and communities by the research team at the YuMi Deadly Centre at QUT. Data collected is from a small focus group of six teachers and two teacher aides. We investigated how measurement is taught and learned by students, their teachers and teacher aides in the community schools. A key focus of the project was that the teaching and learning of measurement be contextualised to the students’ culture, community and home languages. A significant finding from the project was that the teachers had differing levels of knowledge and understanding about how to contextualise measurement to support student learning. For example, an Indigenous teacher identified that mathematics and the environment are relational, that is, they are not discrete and in isolation from one another, rather they mesh together, thus affording the articulation and interchange among and between mathematics and Torres Strait Islander culture.