Proactive detection of high collision concentration locations on highways

In previous research (Chung et al., 2009), the potential of the continuous risk profile (CRP) to proactively detect the systematic deterioration of freeway safety levels was presented. In this paper, this potential is investigated further, and an algorithm is proposed for proactively detecting sites where the collision rate is not sufficiently high to be classified as a high collision concentration location but where a systematic deterioration of safety level is observed. The approach proposed compares the weighted CRP across different years and uses the cumulative sum (CUSUM) algorithm to detect the sites where changes in collision rate are observed. The CRPs of the detected sites are then compared for reproducibility. When high reproducibility is observed, a growth factor is used for sequential hypothesis testing to determine if the collision profiles are increasing over time. Findings from applying the proposed method using empirical data are documented in the paper together with a detailed description of the method.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Producing the intercultural citizen in the International Baccalaureate

The International Baccalaureate Diploma (IBD) is currently offered in 2,718 schools across 138 countries, and explicitly aims to produce ‘internationally-minded’ citizens with a sense of belonging to both the local and the global community. It thus offers an opportunity to enquire how a school curriculum might produce more intercultural or global dispositions, knowledge and skills, and the challenges inherent in such design. To frame this empirical enquiry, the chapter distinguishes between the fact of living together in difference as a life circumstance, and a range of ethical dispositions for such living together, including cosmopolitanism, internationalism, interculturality and global citizenship. These alternatives are understood as competing social imaginaries with different premises and logics. This chapter offers an empirical exploration of how the IBD’s curricular goal of ‘international-mindedness’ is interpreted firstly in current official documents, then reinterpreted by teachers and students in three case study schools in Australia. Traces of these overlapping but distinct discourses are found in the teachers’ recontextualisation of the IBD’s ‘internationalmindedness’ producing diffuse and contradictory versions of what ‘internationalmindedness’ means, and looks like in educational settings.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Designing university, government and industry community engagement to build informed and effective interventions in the area of road safety

Individuals, community organisations and industry have always been involved to varying degrees in efforts to address the Queensland road toll. Traditionally, road crash prevention efforts have been led by state and local government organisations. While community and industry groups have sometimes become involved (e.g. Driver Reviver campaign), their efforts have largely been uncoordinated and under-resourced. A common strength of these initiatives lies in the energy, enthusiasm and persistence of community-based efforts. Conversely, a weakness has sometimes been the lack of knowledge, awareness or prioritisation of evidence-based interventions or their capacity to build on collaborative efforts. In 2000, the Queensland University of Technology’s Centre for Accident Research and Road Safety – Queensland (CARRS-Q) identified this issue as an opportunity to bridge practice and research and began acknowledging a selection of these initiatives, in partnership with the RACQ, through the Queensland Road Safety Awards program. After nine years it became apparent there was need to strengthen this connection, with the Centre establishing a Community Engagement Workshop in 2009 as part of the overall Awards program. With an aim of providing community participants opportunities to see, hear and discuss the experiences of others, this event was further developed in 2010, and with the collaboration of the Queensland Department of Transport and Main Roads, the RACQ, Queensland Police Service and Leighton Contractors Pty Ltd, a stand-alone Queensland Road Safety Awards Community Engagement Workshop was held in 2010. Each collaborating organisation recognised a need to mobilise the community through effective information and knowledge sharing, and recognised that learning and discussion can influence lasting behaviour change and action in this often emotive, yet not always evidence-based, area. This free event featured a number of speakers representing successful projects from around Australia and overseas. Attendees were encouraged to interact with the speakers, to ask questions, and most importantly, build connections with other attendees to build a ‘community road safety army’ all working throughout Australia on projects underpinned by evaluated research. The workshop facilitated the integration of research, policy and grass-roots action enhancing the success of community road safety initiatives. For collaboration partners, the event enabled them to transfer their knowledge in an engaged approach, working within a more personal communication process. An analysis of the success factors for this event identified openness to community groups and individuals, relevance of content to local initiatives, generous support with the provision of online materials and ongoing communication with key staff members as critical and supports the view that the university can directly provide both the leadership and the research needed for effective and credible community-based initiatives to address injury and death on the roads.

CAT Detect (Computer Activity Timeline Detection) : a tool for detecting inconsistency in computer activity timelines

The construction of timelines of computer activity is a part of many digital investigations. These timelines of events are composed of traces of historical activity drawn from system logs and potentially from evidence of events found in the computer file system. A potential problem with the use of such information is that some of it may be inconsistent and contradictory thus compromising its value. This work introduces a software tool (CAT Detect) for the detection of inconsistency within timelines of computer activity. We examine the impact of deliberate tampering through experiments conducted with our prototype software tool. Based on the results of these experiments, we discuss techniques which can be employed to deal with such temporal inconsistencies.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

An epistemic community as influencer and implementer in local government accounting in Australia

This paper adopts an epistemic community framework to explicate the dual role of epistemic communities as influencers of accounting policy within regulatory space and as implementers who effect change within the domain of accounting. The context is the adoption and implementation of fair value accounting within local government in New South Wales (NSW). The roles and functions of Australian local government are extensive, and include the development and maintenance of infrastructure, provision of recreational facilities, certain health and community services, buildings, cultural facilities, and in some cases, water and sewerage (Australian Local Government Association, 2009). The NSW state Department of Local Government (DLG) is responsible for legislation and policy development to ensure that local councils are able to deliver ‘quality services to their communities in a sustainable manner’ (DLG, 2008c). These local councils receive revenue from various sources including property rates, government grants and user-pays service provision. In July 2006 the DLG issued Circular 06-453 to councils (DLG, 2006c), mandating the staged adoption of fair value measurement of infrastructure assets. This directive followed the policy of NSW State Treasury (NSW Treasury, 2007),4 and an independent inquiry into the financial sustainability of local councils (LGSA, 2006). It was an attempt to resolve the inconsistency in public sector asset valuation in NSW Local Governments, and to provide greater usefulness and comparability of financial statements.5 The focus of this study is the mobilization of accounting change by the DLG within this wider political context. When a regulatory problem arises, those with political power seek advice from professionals with relevant skill and expertise (Potter, 2005). This paper explores the way in which professionals diffuse accounting ‘problems’ and the associated accounting solutions ‘across time and space’ (Potter, 2005, p. 277). The DLG’s fair value accounting policy emanated from a ‘regulatory space’ (Hancher and Moran, 1989)6 as a result of negotiations between many parties, including accounting and finance professionals. Operating within the local government sector, these professionals were identified by the DLG as being capable of providing helpful input. They were also responsible for the implementation of the new olicy within local councils. Accordingly they have been dentified as an pistemic community with the ability to ranslate regulatory power by changing he domain of ccounting (Potter, 2005, p. 278).7 The paper is organised as follows. The background to the LG’s decision to require the introduction of fair value accounting for infrastructure assets is explored. Following this, the method of the study is described, and the epistemic community framework outlined. In the next sections, evidence of the influencing and implementing roles of epistemic groups is provided. Finally, conclusions are drawn about the significance of these groups both within regulatory space in developing accounting regulation, and in embedding change within the domain of accounting.

Research in action for community informatics : A matter for conversation (editorial introduction)

This special issue of the Journal of Community Informatics brings together people from a diverse range of disciplines to discuss how academic researchers and community practitioners and activists can work together to explore the use of information and communication technologies, social media, augmented reality, and other forms of network technologies for research and action in pursuit of social responsibility. The aim is to connect people with ideas, ideas with research projects, and harness new media to further inquiry into socially just outcomes in our community. Some of the papers are based on presentations given at the "Research for Action: Networking University and Community for Social Responsibility" workshop chaired by Matthew Allen and Marcus Foth, at the Making Links 2010 conference in Perth, WA on 15 Nov 2010.

Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Psychometric properties and health correlates of the Geriatric Anxiety Inventory in Australian community-residing older women

Abstract OBJECTIVE: To assess the psychometric properties and health correlates of the Geriatric Anxiety Inventory (GAI) in a cohort of Australian community-residing older women. METHOD: Cross-sectional study of a population-based cohort of women aged 60 years and over (N = 286). RESULTS: The GAI exhibited sound internal consistency and demonstrated good concurrent validity against the state half of the Spielberger State Trait Anxiety Inventory and the neuroticism domain of the NEO five-factor inventory. GAI score was significantly associated with self-reported sleep difficulties and perceived memory impairment, but not with age or cognitive function. Women with current DSM-IV Generalized Anxiety Disorder (GAD) had significantly higher GAI scores than women without such a history. In this cohort, the optimal cut-point to detect current GAD was 8/9. Although the GAI was designed to have few somatic items, women with a greater number of general medical problems or who rated their general health as worse had higher GAI scores. CONCLUSION: The GAI is a new scale designed specifically to measure anxiety in older people. In this Australian cohort of older women, the instrument had sound psychometric properties.

Classifying Socially-Valued Role Functioning Among Community Residents with Psychiatric Disorders

An increased emphasis on community-based care has not ensured that people recovering from psychiatric disorders return to active and valued roles in their local communities. Although clinical recovery remains a priority for mental health services there is increasing recognition of the need for functional recovery to be attained and demonstrated in roles valued by the wider community. With this need in mind, a method for classifying socially-valued role functioning among people with schizophrenia or schizoaffective disorder was developed and trialed. Participants (n = 104) were recruited via mental health, psychosocial rehabilitation, and other community support services. Socially-valued roles were investigated via participation in five categories: (1) self-care and home duties; (2) caring for others; (3) self-development, voluntary work or rehabilitation; (4) formal education or training; and (5) employment. Activities were classified by primary role type and role status level at baseline, six, and 12 months. Current role status was assessed along with highest and lowest status in the previous year. Preliminary psychometric results were favorable. Research applications are now recommended for monitoring socially-valued role functioning in community settings.

Using participatory action research in a community-based initiative addressing complex mental health needs

Objective: This paper describes the first phase of a larger project that utilizes participatory action research to examine complex mental health needs across an extensive group of stakeholders in the community. Method: Within an objective qualitative analysis of focus group discussions the social ecological model is utilized to explore how integrative activities can be informed, planned and implemented across multiple elements and levels of a system. Seventy-one primary care workers, managers, policy-makers, consumers and carers from across the southern metropolitan and Gippsland regions of Victoria, Australia took part in seven focus groups. All groups responded to an identical set of focusing questions. Results: Participants produced an explanatory model describing the service system, as it relates to people with complex needs, across the levels of social ecological analysis. Qualitative themes analysis identified four priority areas to be addressed in order to improve the system's capacity for working with complexity. These included: (i) system fragmentation; (ii) integrative case management practices; (iii) community attitudes; and (iv) money and resources. Conclusions: The emergent themes provide clues as to how complexity is constructed and interpreted across the system of involved agencies and interest groups. The implications these findings have for the development and evaluation of this community capacity-building project were examined from the perspective of constructing interventions that address both top-down and bottom-up processes.