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.

Visual voice activity detection using frontal versus profile views

Visual activity detection of lip movements can be used to overcome the poor performance of voice activity detection based solely in the audio domain, particularly in noisy acoustic conditions. However, most of the research conducted in visual voice activity detection (VVAD) has neglected addressing variabilities in the visual domain such as viewpoint variation. In this paper we investigate the effectiveness of the visual information from the speaker’s frontal and profile views (i.e left and right side views) for the task of VVAD. As far as we are aware, our work constitutes the first real attempt to study this problem. We describe our visual front end approach and the Gaussian mixture model (GMM) based VVAD framework, and report the experimental results using the freely available CUAVE database. The experimental results show that VVAD is indeed possible from profile views and we give a quantitative comparison of VVAD based on frontal and profile views The results presented are useful in the development of multi-modal Human Machine Interaction (HMI) using a single camera, where the speaker’s face may not always be frontal.

Detection versus false alarm characterisation of a vision-based airborne dim-target collision detection system

This paper presents a preliminary flight test based detection range versus false alarm performance characterisation of a morphological-hidden Markov model filtering approach to vision-based airborne dim-target collision detection. On the basis of compelling in-flight collision scenario data, we calculate system operating characteristic (SOC) curves that concisely illustrate the detection range versus false alarm rate performance design trade-offs. These preliminary SOC curves provide a more complete dim-target detection performance description than previous studies (due to the experimental difficulties involved, previous studies have been limited to very short flight data sample sets and hence have not been able to quantify false alarm behaviour). The preliminary investigation here is based on data collected from 4 controlled collision encounters and supporting non-target flight data. This study suggests head-on detection ranges of approximately 2.22 km under blue sky background conditions (1.26 km in cluttered background conditions), whilst experiencing false alarms at a rate less than 1.7 false alarms/hour (ie. less than once every 36 minutes). Further data collection is currently in progress.

The use of semantic priming as a paradigm for the detection of faking on self-report psychological tests

It is recognised that individuals do not always respond honestly when completing psychological tests. One of the foremost issues for research in this area is the inability to detect individuals attempting to fake. While a number of strategies have been identified in faking, a commonality of these strategies is the latent role of long term memory. Seven studies were conducted in order to examine whether it is possible to detect the activation of faking related cognitions using a lexical decision task. Study 1 found that engagement with experiential processing styles predicted the ability to fake successfully, confirming the role of associative processing styles in faking. After identifying appropriate stimuli for the lexical decision task (Studies 2A and 2B), Studies 3 to 5 examined whether a cognitive state of faking could be primed and subsequently identified, using a lexical decision task. Throughout the course of these studies, the experimental methodology was increasingly refined in an attempt to successfully identify the relevant priming mechanisms. The results were consistent and robust throughout the three priming studies: faking good on a personality test primed positive faking related words in the lexical decision tasks. Faking bad, however, did not result in reliable priming of negative faking related cognitions. To more completely address potential issues with the stimuli and the possible role of affective priming, two additional studies were conducted. Studies 6A and 6B revealed that negative faking related words were more arousing than positive faking related words, and that positive faking related words were more abstract than negative faking related words and neutral words. Study 7 examined whether the priming effects evident in the lexical decision tasks occurred as a result of an unintentional mood induction while faking the psychological tests. Results were equivocal in this regard. This program of research aligned the fields of psychological assessment and cognition to inform the preliminary development and validation of a new tool to detect faking. Consequently, an implicit technique to identify attempts to fake good on a psychological test has been identified, using long established and robust cognitive theories in a novel and innovative way. This approach represents a new paradigm for the detection of individuals responding strategically to psychological testing. With continuing development and validation, this technique may have immense utility in the field of psychological assessment.

Detection of bacteriophage and respiratory viruses in droplets

Influenza is a widespread disease occurring in seasonal epidemics, and each year is responsible for up to 500,000 deaths worldwide. Influenza can develop into strains which cause severe symptoms and high mortality rates, and could potentially reach pandemic status if the virus’ properties allow easy transmission. Influenza is transmissible via contact with the virus, either directly (infected people) or indirectly (contaminated objects); via reception of large droplets over short distances (one metre or less); or through inhalation of aerosols containing the virus expelled by infected individuals during respiratory activities, that can remain suspended in the air and travel distances of more than one metre (the aerosol route). Aerosol transmission of viruses involves three stages: production of the droplets containing viruses; transport of the droplets and ability of a virus to remain intact and infectious; and reception of the droplets (via inhalation). Our understanding of the transmission of influenza viruses via the aerosol route is poor, and thus our ability to prevent a widespread outbreak is limited. This study explored the fate of viruses in droplets by investigating the effects of some physical factors on the recovery of both a bacteriophage model and influenza virus. Experiments simulating respiratory droplets were carried out using different types of droplets, generated from a commonly used water-like matrix, and also from an ‘artificial mucous’ matrix which was used to more closely resemble respiratory fluids. To detect viruses in droplets, we used the traditional plaque assay techniques, and also a sensitive, quantitative PCR assay specifically developed for this study. Our results showed that the artificial mucous suspension enhanced the recovery of infectious bacteriophage. We were able to report detection limits of infectious bacteriophage (no bacteriophage was detected by the plaque assay when aerosolised from a suspension of 103 PFU/mL, for three of the four droplet types tested), and that bacteriophage could remain infectious in suspended droplets for up to 20 minutes. We also showed that the nested real-time PCR assay was able to detect the presence of bacteriophage RNA where the plaque assay could not detect any intact particles. Finally, when applying knowledge from the bacteriophage experiments, we reported the quantitative recoveries of influenza viruses in droplets, which were more consistent and stable than we had anticipated. Influenza viruses can be detected up to 20 minutes (after aerosolisation) in suspended aerosols and possibly beyond. It also was detectable from nebulising suspensions with relatively low concentrations of viruses.

Classification of packet contents for malware detection

Many existing schemes for malware detection are signature-based. Although they can effectively detect known malwares, they cannot detect variants of known malwares or new ones. Most network servers do not expect executable code in their in-bound network traffic, such as on-line shopping malls, Picasa, Youtube, Blogger, etc. Therefore, such network applications can be protected from malware infection by monitoring their ports to see if incoming packets contain any executable contents. This paper proposes a content-classification scheme that identifies executable content in incoming packets. The proposed scheme analyzes the packet payload in two steps. It first analyzes the packet payload to see if it contains multimedia-type data (such as . If not, then it classifies the payload either as text-type (such as or executable. Although in our experiments the proposed scheme shows a low rate of false negatives and positives (4.69% and 2.53%, respectively), the presence of inaccuracies still requires further inspection to efficiently detect the occurrence of malware. In this paper, we also propose simple statistical and combinatorial analysis to deal with false positives and negatives.

Nobody says no : student self-censorship in a collaborative knowledge building activity

This paper explores student self-censorship within an online learning environment. Self-censorship in group activity can be seen as a two-edged sword. While it can be advantageous that a student censor personal frustration and angst when working with others, if the self-censorship impacts on the cognitive contribution a student makes then this may significantly impact upon the overall quality of the group’s collective knowledge artefact. This paper reports on a study where it was found that students had self censored both their feelings and ideas as they collaboratively worked together.

Fat and thin adaptive HMM filters for vision based detection of moving targets

Computer vision is an attractive solution for uninhabited aerial vehicle (UAV) collision avoidance, due to the low weight, size and power requirements of hardware. A two-stage paradigm has emerged in the literature for detection and tracking of dim targets in images, comprising of spatial preprocessing, followed by temporal filtering. In this paper, we investigate a hidden Markov model (HMM) based temporal filtering approach. Specifically, we propose an adaptive HMM filter, in which the variance of model parameters is refined as the quality of the target estimate improves. Filters with high variance (fat filters) are used for target acquisition, and filters with low variance (thin filters) are used for target tracking. The adaptive filter is tested in simulation and with real data (video of a collision-course aircraft). Our test results demonstrate that our adaptive filtering approach has improved tracking performance, and provides an estimate of target heading not present in previous HMM filtering approaches.

Enhancing workplace learning through collaborative HRD

Human resource development (HRD) has evolved a great deal over the past 20 years. Indeed, developments in HRD theory, research and practice have helped transform HRD from a reactive function focusing on administrative and bureaucratic issues to a proactive function focusing on creating learning and development opportunities for employees that not only allow them to achieve their potential but also make a substantial contribution to the longterm survival and sustainability of the organisation. HRD is now seen as an investment in the future of an organisation. This investment perspective is based on the recognition that HRD is linked to business strategy and the achievement of competitive advantage (Caravan et al. 2002). One reason for the shift in emphasis is that many of the traditional sources of competitive advantage (technology, economies of scale) have diminished in value. Nowadays it is the workforce that has come to be seen as an important source of competitive advantage for the organisation. However, not too long ago employees were viewed by management as a disposable resource rather than an asset to an organisation.

A taint marking approach to confidentiality violation detection

This article presents a novel approach to confidentiality violation detection based on taint marking. Information flows are dynamically tracked between applications and objects of the operating system such as files, processes and sockets. A confidentiality policy is defined by labelling sensitive information and defining which information may leave the local system through network exchanges. Furthermore, per application profiles can be defined to restrict the sets of information each application may access and/or send through the network. In previous works, we focused on the use of mandatory access control mechanisms for information flow tracking. In this current work, we have extended the previous information flow model to track network exchanges, and we are able to define a policy attached to network sockets. We show an example application of this extension in the context of a compromised web browser: our implementation detects a confidentiality violation when the browser attempts to leak private information to a remote host over the network.

The collaborative push : pushing beyond rhetoric and gaining evidence

Collaboration has been enacted as a core strategy by both the government and nongovernment sectors to address many of the intractable issues confronting contemporary society. The cult of collaboration has become so pervasive that it is now an elastic term referring generally to any form of ‘working together’. The lack of specificity about collaboration and its practice means that it risks being reduced to mere rhetoric without sustained practice or action. Drawing on an extensive data set (qualitative, quantitative) of broadly collaborative endeavours gathered over ten years in Queensland, Australia, this paper aims to fill out the black box of collaboration. Specifically it examines the drivers for collaboration, dominant structures and mechanisms adopted, what has worked and unintended consequences. In particular it investigates the skills and competencies required in an embeded collaborative endeavour within and across organisations. Social network analysis is applied to isolate the structural properties of collaborations over other forms of integration as well as highlighting key roles and tasks. Collaboration is found to be a distinctive form of working together, characterised by intense and interdependent relationships and exchanges, higher levels of cohesion (density) and requiring new ways of behaving, working, managing and leading. These elements are configured into a practice framework. Developing an empirical evidence base for collaboration structure, practice and strategy provides a useful foundation for theory extension. The paper concludes that for collaboration, to be successfully employed as a management strategy it must move beyond rhetoric and develop a coherent model for action.

Is the public sector ready to collaborate? Human resource implications of collaborative Arrangements

Relational governance arrangements across agencies and sectors have become prevalent as a means for government to become more responsive and effective in addressing complex, large scale or ‘wicked’ problems. The primary characteristic of such ‘collaborative’ arrangements is the utilisation of the joint capacities of multiple organisations to achieve collaborative advantage, which Huxham (1993) defines as the attainment of creative outcomes that are beyond the ability of single agencies to achieve. Attaining collaborative advantage requires organisations to develop collaborative capabilities that prepare organisations for collaborative practice (Huxham, 1993b). Further, collaborations require considerable investment of staff effort that could potentially be used beneficially elsewhere by both the government and non-government organisations involved in collaboration (Keast and Mandell, 2010). Collaborative arrangements to deliver services therefore requires a reconsideration of the way in which resources, including human resources, are conceptualised and deployed as well as changes to both the structure of public service agencies and the systems and processes by which they operate (Keast, forthcoming). A main aim of academic research and theorising has been to explore and define the requisite characteristics to achieve collaborative advantage. Such research has tended to focus on definitional, structural (Turrini, Cristofoli, Frosini, & Nasi, 2009) and organisational (Huxham, 1993) aspects and less on the roles government plays within cross-organisational or cross-sectoral arrangements. Ferlie and Steane (2002) note that there has been a general trend towards management led reforms of public agencies including the HRM practices utilised. Such trends have been significantly influenced by New Public Management (NPM) ideology with limited consideration to the implications for HRM practice in collaborative, rather than market contexts. Utilising case study data of a suite of collaborative efforts in Queensland, Australia, collected over a decade, this paper presents an examination of the network roles government agencies undertake. Implications for HRM in public sector agencies working within networked arrangements are drawn and implications for job design, recruitment, deployment and staff development are presented. The paper also makes theoretical advances in our understanding of Strategic Human Resource Management (SHRM) in network settings. While networks form part of the strategic armoury of government, networks operate to achieve collaborative advantage. SHRM with its focus on competitive advantage is argued to be appropriate in market situations, however is not an ideal conceptualisation in network situations. Commencing with an overview of literature on networks and network effectiveness, the paper presents the case studies and methodology; provides findings from the case studies in regard to the roles of government to achieve collaborative advantage and implications for HRM practice are presented. Implications for SHRM are considered.

A suspicious behaviour detection using a context space model for smart surveillance systems

Video surveillance systems using Closed Circuit Television (CCTV) cameras, is one of the fastest growing areas in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. This work attempts to address these problems by proposing an automatic suspicious behaviour detection which utilises contextual information. The utilisation of contextual information is done via three main components: a context space model, a data stream clustering algorithm, and an inference algorithm. The utilisation of contextual information is still limited in the domain of suspicious behaviour detection. Furthermore, it is nearly impossible to correctly understand human behaviour without considering the context where it is observed. This work presents experiments using video feeds taken from CAVIAR dataset and a camera mounted on one of the buildings Z-Block) at the Queensland University of Technology, Australia. From these experiments, it is shown that by exploiting contextual information, the proposed system is able to make more accurate detections, especially of those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information gives critical feedback to the system designers to refine the system.

Visualising invisible networks as collaborative arts practice

This paper examines approaches to the visualisation of ‘invisible’ communications networks. It situates network visualisation as a critical design exercise, and explores how community artists might use such a practice to develop telematic art projects – works that use communications networks as their medium. The paper’s hypotheses are grounded in the Australian community media arts field, but could be applied to other collaborative contexts.