Unusual event detection in crowded scenes

Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.

Knowledge management challenges in small-and medium-sized enterprises

Background & Research Focus Managing knowledge for innovation and organisational benefit has been extensively investigated in studies of large firms (Smith, Collins & Clark, 2005; Zucker, et al., 2007) and to a large extent there is limited research into studies of small- and medium- sized enterprises (SMEs). There are some investigations in knowledge management research on SMEs, but what remains to be seen in particular is the question of where are the potential challenges for managing knowledge more effectively within these firms? Effective knowledge management (KM) processes and systems lead to improved performance in pursuing distinct capabilities that contribute to firm-level innovation (Nassim 2009; Zucker et al. 2007; Verona and Ravasi 2003). Managing internal and external knowledge in a way that links it closely to the innovation process can assist the creation and implementation of new products and services. KM is particularly important in knowledge intensive firms where the knowledge requirements are highly specialized, diverse and often emergent. However, to a large extent the KM processes of small firms that are often the source of new knowledge and an important element of the value networks of larger companies have not been closely studied. To address this gap which is of increasing importance with the growing number of small firms, we need to further investigate knowledge management processes and the ways that firms find, capture, apply and integrate knowledge from multiple sources for their innovation process. This study builds on the previous literature and applies existing frameworks and takes the process and activity view of knowledge management as a starting point of departure (see among others Kraaijenbrink, Wijnhoven & Groen, 2007; Enberg, Lindkvist, & Tell, 2006; Lu, Wang & Mao, 2007). In this paper, it is attempted to develop a better understanding of the challenges of knowledge management within the innovation process in small knowledge-oriented firms. The paper aims to explore knowledge management processes and practices in firms that are engaged in the new product/service development programs. Consistent with the exploratory character of the study, the research question is: How is knowledge integrated, sourced and recombined from internal and external sources for innovation and new product development? Research Method The research took an exploratory case study approach and developed a theoretical framework to investigate the knowledge situation of knowledge-intensive firms. Equipped with the conceptual foundation, the research adopted a multiple case study method investigating four diverse Australian knowledge-intensive firms from IT, biotechnology, nanotechnology and biochemistry industries. The multiple case study method allowed us to document in some depth the knowledge management experience of the theses firms. Case study data were collected through a review of company published data and semi-structured interviews with managers using an interview guide to ensure uniform coverage of the research themes. This interview guide was developed following development of the framework and a review of the methodologies and issues covered by similar studies in other countries and used some questions common to these studies. It was framed to gather data around knowledge management activity within the business, focusing on the identification, acquisition and utilisation of knowledge, but collecting a range of information about subject as well. The focus of the case studies was on the use of external and internal knowledge to support their knowledge intensive products and services. Key Findings Firstly a conceptual and strategic knowledge management framework has been developed. The knowledge determinants are related to the nature of knowledge, organisational context, and mechanism of the linkages between internal and external knowledge. Overall, a number of key observations derived from this study, which demonstrated the challenges of managing knowledge and how important KM is as a management tool for innovation process in knowledge-oriented firms. To summarise, findings suggest that knowledge management process in these firms is very much project focused and not embedded within the overall organisational routines and mainly based on ad hoc and informal processes. Our findings highlighted lack of formal knowledge management process within our sampled firms. This point to the need for more specialised capabilities in knowledge management for these firms. We observed a need for an effective knowledge transfer support system which is required to facilitate knowledge sharing and particularly capturing and transferring tacit knowledge from one team members to another. In sum, our findings indicate that building effective and adaptive IT systems to manage and share knowledge in the firm is one of the biggest challenges for these small firms. Also, there is little explicit strategy in small knowledge-intensive firms that is targeted at systematic KM either at the strategic or operational level. Therefore, a strategic approach to managing knowledge for innovation as well as leadership and management are essential to achieving effective KM. In particular, research findings demonstrate that gathering tacit knowledge, internal and external to the organization, and applying processes to ensure the availability of knowledge for innovation teams, drives down the risks and cost of innovation. KM activities and tools, such as KM systems, environmental scanning, benchmarking, intranets, firm-wide databases and communities of practice to acquire knowledge and to make it accessible, were elements of KM. Practical Implications The case study method that used in this study provides practical insight into the knowledge management process within Australian knowledge-intensive firms. It also provides useful lessons which can be used by other firms in managing the knowledge more effectively in the innovation process. The findings would be helpful for small firms that may be searching for a practical method for managing and integrating their specialised knowledge. Using the results of this exploratory study and to address the challenges of knowledge management, this study proposes five practices that are discussed in the paper for managing knowledge more efficiently to improve innovation: (1) Knowledge-based firms must be strategic in knowledge management processes for innovation, (2) Leadership and management should encourage various practices for knowledge management, (3) Capturing and sharing tacit knowledge is critical and should be managed, (4)Team knowledge integration practices should be developed, (5) Knowledge management and integration through communication networks, and technology systems should be encouraged and strengthen. In sum, the main managerial contribution of the paper is the recognition of knowledge determinants and processes, and their effects on the effective knowledge management within firm. This may serve as a useful benchmark in the strategic planning of the firm as it utilises new and specialised knowledge.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

The use of multimodal technologies to enhance reflective writing in teacher education

For preservice teachers new to the teaching profession, reflective practice can be a difficult process. Yet reflective writing, once mastered, has the capacity to support preservice teachers to make connections between teaching theory and professional practice, and to start to take control of their own professional learning journey. The reflective practice described in this chapter was scaffolded through a framework for writing, the use of annotated work samples and explicit teaching. This approach was enhanced through multimodal resources including written peer assessment, audio teacher feedback and a video recording of the class presentation. The video footage assisted the preservice teachers to reconcile the feedback that they received from multiple sources. This chapter describes and analyses the implementation of the PRT Pattern (Prompting Reflection using Technology). Results of this practice revealed that the multiple forms of feedback assisted the preservice teachers to analyse their performance in terms of their developing professional identity and practice.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Bi-modal biometric authentication on mobile phones in challenging conditions

This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.

Quality of information for quality of life: Healthcare big data analytics

Big data analysis in healthcare sector is still in its early stages when comparing with that of other business sectors due to numerous reasons. Accommodating the volume, velocity and variety of healthcare data Identifying platforms that examine data from multiple sources, such as clinical records, genomic data, financial systems, and administrative systems Electronic Health Record (EHR) is a key information resource for big data analysis and is also composed of varied co-created values. Successful integration and crossing of different subfields of healthcare data such as biomedical informatics and health informatics could lead to huge improvement for the end users of the health care system, i.e. the patients.

Detection of anuran calling activity in long field recordings for bio-acoustic monitoring

This paper presents a system to analyze long field recordings with low signal-to-noise ratio (SNR) for bio-acoustic monitoring. A method based on spectral peak track, Shannon entropy, harmonic structure and oscillation structure is proposed to automatically detect anuran (frog) calling activity. Gaussian mixture model (GMM) is introduced for modelling those features. Four anuran species widespread in Queensland, Australia, are selected to evaluate the proposed system. A visualization method based on extracted indices is employed for detection of anuran calling activity which achieves high accuracy.

The development and preliminary testing of an instrument for assessing fatigue self-management outcomes in patients with advanced cancer

Background: Fatigue is one of the most distressing and commonly experienced symptoms in patients with advanced cancer. Although the self-management (SM) of cancer-related symptoms has received increasing attention, no research instrument assessing fatigue SM outcomes for patients with advanced cancer is available. Objectives: to describe the development and preliminary testing of an interviewer administered instrument for assessing the frequency, and perceived levels of effectiveness and self-efficacy associated with fatigue SM behaviors in patients with advanced cancer. Methods: The development and testing of the Self-efficacy in Managing Symptoms Scale- Fatigue Subscale for Patients with Advanced Cancer (SMSFS-A) involved a number of procedures: item-generation using a comprehensive literature review and semi-structured interviews, content validity evaluation using expert panel reviews, and face validity and test-retest reliability evaluation using pilot testing. Results: Initially, 23 items (22 specific behaviors with one global item) were generated from the literature review and semi-structured interviews. After two rounds of expert panel review, the final scale was reduced to 17 items (16 behaviors with one global item). Participants in the pilot test (n=10) confirmed that the questions in this scale were clear and easy to understand. Bland-Altman analysis showed agreement of results over a one-week interval. Conclusions: The SMSFS-A items were generated using multiple sources. This tool demonstrated preliminary validity and reliability. Implications for practice: The SMSFS-A has the potential to be used for clinical and research purposes. Nurses can use this instrument for collecting data to inform the initiation of appropriate fatigue SM support for this population.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.

Multiple sulfur isotope analyses support a magmatic model for the volcanogenic massive sulfide deposits of the Teutonic Bore volcanic complex, Yilgarn Craton, Western Australia

We report sensitive high mass resolution ion microprobe, stable isotopes (SHRIMP SI) multiple sulfur isotope analyses (32S, 33S, 34S) to constrain the sources of sulfur in three Archean VMS deposits—Teutonic Bore, Bentley, and Jaguar—from the Teutonic Bore volcanic complex of the Yilgarn Craton, Western Australia, together with sedimentary pyrites from associated black shales and interpillow pyrites. The pyrites from VMS mineralization are dominated by mantle sulfur but include a small amount of slightly negative mass-independent fractionation (MIF) anomalies, whereas sulfur from the pyrites in the sedimentary rocks has pronounced positive MIF, with ∆33S values that lie between 0.19 and 6.20‰ (with one outlier at −1.62‰). The wall rocks to the mineralization include sedimentary rocks that have contributed no detectable positive MIF sulfur to the VMS deposits, which is difficult to reconcile with the leaching model for the formation of these deposits. The sulfur isotope data are best explained by mixing between sulfur derived from a magmatic-hydrothermal fluid and seawater sulfur as represented by the interpillow pyrites. The massive sulfide lens pyrites have a weighted mean ∆33S value of −0.27 ± 0.05‰ (MSWD = 1.6) nearly identical with −0.31 ± 0.08‰ (MSWD = 2.4) for pyrites from the stringer zone, which requires mixing to have occurred below the sea floor. We employed a two-component mixing model to estimate the contribution of seawater sulfur to the total sulfur budget of the two Teutonic Bore volcanic complex VMS deposits. The results are 15 to 18% for both Teutonic Bore and Bentley, much higher than the 3% obtained by Jamieson et al. (2013) for the giant Kidd Creek deposit. Similar calculations, carried out for other Neoarchean VMS deposits give value between 2% and 30%, which are similar to modern hydrothermal VMS deposits. We suggest that multiple sulfur isotope analyses may be used to predict the size of Archean VMS deposits and to provide a vector to ore deposit but further studies are needed to test these suggestions.

Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Extending the advanced forensic format to accommodate multiple data sources, logical evidence, arbitrary information and forensic workflow

Forensic analysis requires the acquisition and management of many different types of evidence, including individual disk drives, RAID sets, network packets, memory images, and extracted files. Often the same evidence is reviewed by several different tools or examiners in different locations. We propose a backwards-compatible redesign of the Advanced Forensic Formatdan open, extensible file format for storing and sharing of evidence, arbitrary case related information and analysis results among different tools. The new specification, termed AFF4, is designed to be simple to implement, built upon the well supported ZIP file format specification. Furthermore, the AFF4 implementation has downward comparability with existing AFF files.

Statistical analysis and reduction of multiple access interference in MC-CDMA systems

Multicarrier code division multiple access (MC-CDMA) is a very promising candidate for the multiple access scheme in fourth generation wireless communi- cation systems. During asynchronous transmission, multiple access interference (MAI) is a major challenge for MC-CDMA systems and significantly affects their performance. The main objectives of this thesis are to analyze the MAI in asyn- chronous MC-CDMA, and to develop robust techniques to reduce the MAI effect. Focus is first on the statistical analysis of MAI in asynchronous MC-CDMA. A new statistical model of MAI is developed. In the new model, the derivation of MAI can be applied to different distributions of timing offset, and the MAI power is modelled as a Gamma distributed random variable. By applying the new statistical model of MAI, a new computer simulation model is proposed. This model is based on the modelling of a multiuser system as a single user system followed by an additive noise component representing the MAI, which enables the new simulation model to significantly reduce the computation load during computer simulations. MAI reduction using slow frequency hopping (SFH) technique is the topic of the second part of the thesis. Two subsystems are considered. The first sub- system involves subcarrier frequency hopping as a group, which is referred to as GSFH/MC-CDMA. In the second subsystem, the condition of group hopping is dropped, resulting in a more general system, namely individual subcarrier frequency hopping MC-CDMA (ISFH/MC-CDMA). This research found that with the introduction of SFH, both of GSFH/MC-CDMA and ISFH/MC-CDMA sys- tems generate less MAI power than the basic MC-CDMA system during asyn- chronous transmission. Because of this, both SFH systems are shown to outper- form MC-CDMA in terms of BER. This improvement, however, is at the expense of spectral widening. In the third part of this thesis, base station polarization diversity, as another MAI reduction technique, is introduced to asynchronous MC-CDMA. The com- bined system is referred to as Pol/MC-CDMA. In this part a new optimum com- bining technique namely maximal signal-to-MAI ratio combining (MSMAIRC) is proposed to combine the signals in two base station antennas. With the applica- tion of MSMAIRC and in the absents of additive white Gaussian noise (AWGN), the resulting signal-to-MAI ratio (SMAIR) is not only maximized but also in- dependent of cross polarization discrimination (XPD) and antenna angle. In the case when AWGN is present, the performance of MSMAIRC is still affected by the XPD and antenna angle, but to a much lesser degree than the traditional maximal ratio combining (MRC). Furthermore, this research found that the BER performance for Pol/MC-CDMA can be further improved by changing the angle between the two receiving antennas. Hence the optimum antenna angles for both MSMAIRC and MRC are derived and their effects on the BER performance are compared. With the derived optimum antenna angle, the Pol/MC-CDMA system is able to obtain the lowest BER for a given XPD.