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.

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.

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.

Development of a high-level gene expression system for the production of bioplastics in sugarcane

Despite various approaches, the production of biodegradable plastics such as polyhydroxybutyrate (PHB) in transgenic plants has met with limited success due largely to low expression levels. Even in the few instances where high levels of protein expression have been reported, the transgenic plants have been stunted indicating PHB is phytotoxic (Poirier 2002). This PhD describes the application of a novel virus-based gene expression technology, termed InPAct („In Plant Activation.), for the production of PHB in tobacco and sugarcane. InPAct is based on the rolling circle replication mechanism by which circular ssDNA viruses replicate and provides a system for controlled, high-level gene expression. Based on these features, InPAct was thought to represent an ideal system to enable the controlled, high-level expression of the three phb genes (phbA, phbB and phbC) required for PHB production in sugarcane at a preferred stage of plant growth. A Tobacco yellow dwarf virus (TbYDV)-based InPAct-phbA vector, as well as linear vectors constitutively expressing phbB and phbC were constructed and different combinations were used to transform tobacco leaf discs. A total of four, eight, three and three phenotypically normal tobacco lines were generated from discs transformed with InPAct-phbA, InPAct-phbA + p1300-TaBV P-phbB/phbC- 35S T, p1300-35S P-phbA-NOS T + p1300-TaBV P-phbB/phbC-35S T and InPAct-GUS, respectively. To determine whether the InPAct cassette could be activated in the presence of the TbYDV Rep, leaf samples from the eight InPActphbA + p1300-TaBV P-phbB/phbC-35S T plants were agroinfiltrated with p1300- TbYDV-Rep/RepA. Three days later, successful activation was indicated by the detection of episomes using both PCR and Southern analysis. Leaf discs from the eight InPAct-phbA + p1300-TaBV P-phbB/phbC-35S T transgenic plant lines were agroinfiltrated with p1300-TbYDV-Rep/RepA and leaf tissue was collected ten days post-infiltration and examined for the presence of PHB granules. Confocal microscopy and TEM revealed the presence of typical PHB granules in five of the eight lines, thus demonstrating the functionality of InPActbased PHB production in tobacco. However, analysis of leaf extracts by HPLC failed to detect the presence of PHB suggesting only very low level expression levels. Subsequent molecular analysis of three lines revealed low levels of correctly processed mRNA from the catalase intron contained within the InPAct cassette and also the presence of cryptic splice sites within the intron. In an attempt to increase expression levels, new InPAct-phb cassettes were generated in which the castorbean catalase intron was replaced with a synthetic intron (syntron). Further, in an attempt to both increase and better control Rep/RepA-mediated activation of InPAct cassettes, Rep/RepA expression was placed under the control of a stably integrated alc switch. Leaf discs from a transgenic tobacco line (Alc ML) containing 35S P-AlcR-AlcA P-Rep/RepA were supertransformed with InPAct-phbAsyn or InPAct-GUSsyn using Agrobacterium and three plants (lines) were regenerated for each construct. Analysis of the RNA processing of the InPAct-phbAsyn cassette revealed highly efficient and correct splicing of the syntron, thus supporting its inclusion within the InPAct system. To determine the efficiency of the alc switch to activate InPAct, leaf material from the three Alc ML + InPAct-phbAsyn lines was either agroinfiltrated with 35S P-Rep/RepA or treated with ethanol. Unexpectedly, episomes were detected not only in the infiltrated and ethanol treated samples, but also in non-treated samples. Subsequent analysis of transgenic Alc ML + InPAct-GUS lines, confirmed that the alc switch was leaky in tissue culture. Although this was shown to be reversible once plants were removed from the tissue culture environment, it made the regeneration of Alc ML + InPAct-phbsyn plant lines extremely difficult, due to unintentional Rep expression and therefore high levels of phb expression and phytotoxic PHB production. Two Alc ML + InPAct-phbAsyn + p1300-TaBV P-phbB/phbC-35S T transgenic lines were able to be regenerated, and these were acclimatised, alcohol-treated and analysed. Although episome formation was detected as late as 21 days post activation, no PHB was detected in the leaves of any plants using either microscopy or HPLC, suggesting the presence of a corrupt InPAct-phbA cassette in both lines. The final component of this thesis involved the application of both the alc switch and the InPAct systems to sugarcane in an attempt to produce PHB. Initial experiments using transgenic Alc ML + InPAct-GUS lines indicated that the alc system was not functional in sugarcane under the conditions tested. The functionality of the InPAct system, independent of the alc gene switch, was subsequently examined by bombarding the 35S Rep/RepA cassette into leaf and immature leaf whorl cells derived from InPAct-GUS transgenic sugarcane plants. No GUS expression was observed in leaf tissue, whereas weak and irregular GUS expression was observed in immature leaf whorl tissue derived from two InPAct- GUS lines and two InPAct-GUS + 35S P-AlcR-AlcA P-GUS lines. The most plausible reason to explain the inconsistent and low levels of GUS expression in leaf whorls is a combination of low numbers of sugarcane cells in the DNA replication-conducive S-phase and the irregular and random nature of sugarcane cells bombarded with Rep/RepA. This study details the first report to develop a TbYDV-based InPAct system under control of the alc switch to produce PHB in tobacco and sugarcane. Despite the inability to detect quantifiable levels of PHB levels in either tobacco or sugarcane, the findings of this study should nevertheless assist in the further development of both the InPAct system and the alc system, particularly for sugarcane and ultimately lead to an ethanol-inducible InPAct gene expression system for the production of bioplastics and other proteins of commercial value in plants.

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.

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.