An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Complete genomic sequence of a Rubus yellow net virus isolate and detection of genome-wide pararetrovirus-derived small RNAs

Rubus yellow net virus (RYNV) was cloned and sequenced from a red raspberry (Rubus idaeus L.) plant exhibiting symptoms of mosaic and mottling in the leaves. Its genomic sequence indicates that it is a distinct member of the genus Badnavirus, with 7932. bp and seven ORFs, the first three corresponding in size and location to the ORFs found in the type member Commelina yellow mottle virus. Bioinformatic analysis of the genomic sequence detected several features including nucleic acid binding motifs, multiple zinc finger-like sequences and domains associated with cellular signaling. Subsequent sequencing of the small RNAs (sRNAs) from RYNV-infected R. idaeus leaf tissue was used to determine any RYNV sequences targeted by RNA silencing and identified abundant virus-derived small RNAs (vsRNAs). The majority of the vsRNAs were 22-nt in length. We observed a highly uneven genome-wide distribution of vsRNAs with strong clustering to small defined regions distributed over both strands of the RYNV genome. Together, our data show that sequences of the aphid-transmitted pararetrovirus RYNV are targeted in red raspberry by the interfering RNA pathway, a predominant antiviral defense mechanism in plants. © 2013.

Screening for youth depression in schools : a review of the literature

This paper provides a review of the literature pertaining to screening for youth depression in schools. It provides the rationale regarding this topic – why this topic is important and needs to be explored. This justification is followed by an investigation of the importance of mental health intervention in general – including depression. Analysis will then shift to the current literature surrounding screening for youth depression in schools, followed by additional information relating to this topic.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

Practical Modbus flooding attack and detection

The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.

The association between socioeconomic status and depression in Vietnamese adults : pilot study

This study examined the prevalence of depressive symptoms and elucidated the causal pathway between socioeconomic status and depression in a community in the central region of Vietnam. The study used a combination of qualitative and quantitative research methods. Indepth interviews were applied with two local psychiatric experts and ten residents for qualitative research. A cross sectional survey with structured interview technique was implemented with 100 residents in the pilot quantitative survey. The Center for Epidemiological Studies-Depression Scale (CES-D) was applied to valuate depressive symptoms ( CES-D score over 21) and depression ( CESD core over 25). Ordinary Least Squares Regression following the three steps of Baron and Kenny’s framework was employed for testing mediation models. There was a strong social gradient with respect to depressive symptoms. People with higher education levels reported fewer depressive symptoms (lower CES-D scores). Incomes were also inversely associated with depressive symptoms, but only the ones at the bottom of the quartile income. Low level and unstable individuals in terms of occupation were associated with higher depressive symptoms compared with the highest occupation group. Employment status showed the strongest gradient with respect to its impact on the burden of depressive symptoms compared with other indicators of SES. Findings from this pilot study suggest a pattern on the negative association between socioeconomic status and depression in Vietnamese adults.

Exploring postnatal depression in Thua Thien Hue Province, Central Vietnam

Introduction Postnatal depression (PND) is an important public health issue due to its impact on maternal wellbeing, infant development, and family cohesion. The estimated prevalence of PND during the first 12 months post-partum ranges between10-20% worldwide. Whilst PND used to be considered a syndrome only occurring in western countries, there is now evidence that it occurs throughout the world, and often at higher rates in low and middleincome countries. To date, there has been little research into PND in South East Asia and only two community-based surveys in Vietnam, one in Ho Chi Minh City in 1999 and one in Hanoi and Ha Nam in 2009. This study will investigate health worker attitudes about risk and protective factors for PND among women in Thua Thien Hue province in central Vietnam. Methodology In 2009, 23 health professionals participated in qualitative exploratory research of postnatal depression in Hue. This included two focus groups with 12 health professionals who completed a concept mapping process, and in-depth interviews with another 11 health professionals. Results Many factors relating to postnatal depression were identified including socio-economic status, son preference, mother’s health, infant health, social support from family and the community, and health promoting behaviours. In-depth interviews highlighted community knowledge and attitudes surrounding PND such as traditional confinement practices and fear of experiencing stigma. Conclusion The findings of this research will be used to plan a substantial community-based quantitative survey in order to establish prevalence of PND and surrounding social determinants in central Vietnam.

Hydraulic instability onset detection in Kaplan turbines by monitoring shaft vibrations

Design of hydraulic turbines has often to deal with hydraulic instability. It is well-known that Francis and Kaplan types present hydraulic instability in their design power range. Even if modern CFD tools may help to define these dangerous operating conditions and optimize runner design, hydraulic instabilities may fortuitously arise during the turbine life and should be timely detected in order to assure a long-lasting operating life. In a previous paper, the authors have considered the phenomenon of helical vortex rope, which happens at low flow rates when a swirling flow, in the draft tube conical inlet, occupies a large portion of the inlet. In this condition, a strong helical vortex rope appears. The vortex rope causes mechanical effects on the runner, on the whole turbine and on the draft tube, which may eventually produce severe damages on the turbine unit and whose most evident symptoms are vibrations. The authors have already shown that vibration analysis is suitable for detecting vortex rope onset, thanks to an experimental test campaign performed during the commissioning of a 23 MW Kaplan hydraulic turbine unit. In this paper, the authors propose a sophisticated data driven approach to detect vortex rope onset at different power load, based on the analysis of the vibration signals in the order domain and introducing the so-called "residual order spectrogram", i.e. an order-rotation representation of the vibration signal. Some experimental test runs are presented and the possibility to detect instability onset, especially in real-time, is discussed.

Brisbane Macroscopic Fundamental Diagram : empirical findings on network partitioning and incident detection

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow, and the existence with dynamic features was confirmed in congested urban network in downtown Yokohama with real data set. Since the MFD represents the area-wide network traffic performances, studies on perimeter control strategies and an area traffic state estimation utilizing the MFD concept has been reported. However, limited works have been reported on real world example from signalised arterial network. This paper fuses data from multiple sources (Bluetooth, Loops and Signals) and develops a framework for the development of the MFD for Brisbane, Australia. Existence of the MFD in Brisbane arterial network is confirmed. Different MFDs (from whole network and several sub regions) are evaluated to discover the spatial partitioning in network performance representation. The findings confirmed the usefulness of appropriate network partitioning for traffic monitoring and incident detections. The discussion addressed future research directions

Modal flexibility method for structural damage detection in suspension bridges

Suspension bridges meet the steadily growing demand for lighter and longer bridges in today’s infrastructure systems. These bridges are designed to have long life spans, but with age, their main cables and hangers could suffer from corrosion and fatigue. There is a need for a simple and reliable procedure to detect and locate such damage, so that appropriate retrofitting can be carried out to prevent bridge failure. Damage in a structure causes changes in its properties (mass, damping and stiffness) which in turn will cause changes in its vibration characteristics (natural frequencies, modal damping and mode shapes). Methods based on modal flexibility, which depends on both the natural frequencies and mode shapes, have the potential for damage detection. They have been applied successfully to beam and plate elements, trusses and simple structures in reinforced concrete and steel. However very limited applications for damage detection in suspension bridges have been identified to date. This paper examines the potential of modal flexibility methods for damage detection and localization of a suspension bridge under different damage scenarios in the main cables and hangers using numerical simulation techniques. Validated finite element model (FEM) of a suspension bridge is used to acquire mass normalized mode shape vectors and natural frequencies at intact and damaged states. Damage scenarios will be simulated in the validated FE models by varying stiffness of the damaged structural members. The capability of damage index based on modal flexibility to detect and locate damage is evaluated. Results confirm that modal flexibility based methods have the ability to successfully identify damage in suspension bridge main cables and hangers.

Damage detection in cable structures using vibration characteristics

Cable structures find many applications such as in power transmission, in anchors and especially in bridges. They serve as major load bearing elements in suspension bridges, which are capable of spanning long distances. All bridges, including suspension bridges, are designed to have long service lives. However, during this long life, they become vulnerable to damage due to changes in loadings, deterioration with age and random action such as impacts. The main cables are more vulnerable to corrosion and fatigue, compared to the other bridge components, and consequently reduces the serviceability and ultimate capacity of the bridge. Detecting and locating such damage at the earliest stage is challenging in the current structural health monitoring (SHM) systems of long span suspension bridges. Damage or deterioration of a structure alters its stiffness, mass and damping properties which in turn modify its vibration characteristics. This phenomenon can therefore be used to detect damage in a structure. The modal flexibility, which depends on the vibration characteristics of a structure, has been identified as a successful damage indicator in beam and plate elements, trusses and simple structures in reinforced concrete and steel. Successful application of the modal flexibility phenomenon to detect and locate the damage in suspension bridge main cables has received limited attention in recent research work. This paper, therefore examines the potential of the modal flexibility based Damage Index (DI) for detecting and locating damage in the main cable of a suspension bridge under four different damage scenarios. Towards this end, a numerical model of a suspension bridge cable was developed to extract the modal parameters at both damaged and undamaged states. Damage scenarios considered in this study with varied location and severity were simulated by changing stiffness at particular locations of the cable model. Results confirm that the DI has the potential to successfully detect and locate damage in suspension bridge main cables. This simple method can therefore enable bridge engineers and managers to detect and locate damage in suspension bridges at an early stage, minimize expensive retrofitting and prevent bridge collapse.

Social event detection at MediaEval 2013 : challenges, datasets and evaluation

In this paper, we provide an overview of the Social Event Detection (SED) task that is part of the MediaEval Bench mark for Multimedia Evaluation 2013. This task requires participants to discover social events and organize the re- lated media items in event-specific clusters within a collection of Web multimedia. Social events are events that are planned by people, attended by people and for which the social multimedia are also captured by people. We describe the challenges, datasets, and the evaluation methodology.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

The CLIMATE schools combined study : a cluster randomised controlled trial of a universal Internet-based prevention program for youth substance misuse, depression and anxiety

Background Anxiety, depressive and substance use disorders account for three quarters of the disability attributed to mental disorders and frequently co-occur. While programs for the prevention and reduction of symptoms associated with (i) substance use and (ii) mental health disorders exist, research is yet to determine if a combined approach is more effective. This paper describes the study protocol of a cluster randomised controlled trial to evaluate the effectiveness of the CLIMATE Schools Combined intervention, a universal approach to preventing substance use and mental health problems among adolescents. Methods/design Participants will consist of approximately 8400 students aged 13 to 14-years-old from 84 secondary schools in New South Wales, Western Australia and Queensland, Australia. The schools will be cluster randomised to one of four groups; (i) CLIMATE Schools Combined intervention; (ii) CLIMATE Schools - Substance Use; (iii) CLIMATE Schools - Mental Health, or (iv) Control (Health and Physical Education as usual). The primary outcomes of the trial will be the uptake and harmful use of alcohol and other drugs, mental health symptomatology and anxiety, depression and substance use knowledge. Secondary outcomes include substance use related harms, self-efficacy to resist peer pressure, general disability, and truancy. The link between personality and substance use will also be examined. Discussion Compared to students who receive the universal CLIMATE Schools - Substance Use, or CLIMATE Schools - Mental Health or the Control condition (who received usual Health and Physical Education), we expect students who receive the CLIMATE Schools Combined intervention to show greater delays to the initiation of substance use, reductions in substance use and mental health symptoms, and increased substance use and mental health knowledge