Plagiarism 2.0 : the new economy of academic dishonesty

As a writer, teacher and scholar of ‘the knowledge economy’ in the broadest sense, plagiarism fascinates me. I first encountered plagiarism in my Year 12 English class. We had been working for weeks writing poems and had submitted them to our teacher Mr How for assessment. Mr How was generally a pleasant individual who I remember as one of my favourite school teachers; however, he did not suffer fools easily. The time arrived for each of us to read our work to the class. Year 12 poetry being what it usually is, most of our efforts tended to blur into an angsty, slightly pretentious, self-important mess (similar to staff meetings in many university departments). However, one student’s poem stood out. It was emotive, insightful and economical in its use of language … and best of all, it did not suck! The poem’s author was one of the class’ biggest jocks, and not usually one to display such sensitivity, so we were all a little taken aback by what we were hearing. Stunned silence! At the poem’s conclusion, Mr How congratulated the student on such an excellent effort and produced a copy of the collected works of Emily Dickenson (if I remember correctly) from under his desk. He asked the student to turn to a page he had marked and recite the poem printed there. It was, of course, the same one the student had passed off as his. This time, there was no stunned silence: just the sound of remorseful sobs from our jock-poet-plagiarist who had been exposed in front of his classmates.

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.

Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

A new Salmonella typhimurium NM5004 strain expressing rat glutathione S-transferase 5-5 : use in detection of genotoxicity of dihaloalkanes using an SOS/umu test system

The Escherichia coli mu operon was subcloned into a pKK233-2 vector containing rat glutathione S-transferase (GST) 5-5 cDNA and the plasmid thus obtained was introduced into Salmonella typhimurium TA1535. The newly developed strain S.typhimurium NM5004, was found to have 52-fold greater GST activity than the original umu strain S.typhimurium TA1535/pSK1002. We compared sensitivities of these two tester strains, NM5004 and TA1535/ pSK1002, for induction of umuC gene expression with several dihaloalkanes which are activated or inactivated by GST 5-5 activity. The induction of umuC gene expression by these chemicals was monitored by measuring the cellular P-galactosidase activity produced by umuC'lacZ fusion gene in these two tester strains. Ethylene dibromide, 1-bromo-2-chloroethane, 1,2-dichloroethane, and methylene dichloride induced umuC gene expression more strongly in the NM5004 strain than the original strain, 4-Nitroquinoline 1-oxide and N-methyl-N'-nitro-N-nitrosoguanidine were found to induce umuC gene expression to similar extents in both strains. In the case of 1-nitropyrene and 2-nitrofluorene, however, NM5004 strain showed weaker umuC gene expression responses than the original TA1535/ pSK1002 strain, 1,2-Epoxy-3-(4'-nitrophenoxy)propane, a known substrate for GST 5-5, was found to inhibit umuC induction caused by 1-bromo-2-chloroethane. These results indicate that this new tester NM5004 strain expressing a mammalian GST theta class enzyme may be useful for studies of environmental chemicals proposed to be activated or inactivated by GST activity.

Determination of urinary thymidine glycol using affinity chromatography, HPLC and post-column reaction detection : a biomarker of oxidative DNA damage upon kidney transplantation

Reactive oxygen species are generated during ischaemia-reperfusion of tissue. Oxidation of thymidine by hydroxyl radicals (HO) leads to the formation of 5,6-dihydroxy-5,6-dihydrothymidine (thymidine glycol). Thymidine glycol is excreted in urine and can be used as biomarker of oxidative DNA damage. Time dependent changes in urinary excretion rates of thymidine glycol were determined in six patients after kidney transplantation and in six healthy controls. A new analytical method was developed involving affinity chromatography and subsequent reverse-phase high-performance liquid chromatography (RP-HPLC) with a post-column chemical reaction detector and endpoint fluorescence detection. The detection limit of this fluorimetric assay was 1.6 ng thymidine glycol per ml urine, which corresponds to about half of the physiological excretion level in healthy control persons. After kidney transplantation the urinary excretion rate of thymidine glycol increased gradually reaching a maximum around 48 h. The excretion rate remained elevated until the end of the observation period of 10 days. Severe proteinuria with an excretion rate of up to 7.2 g of total protein per mmol creatinine was also observed immediately after transplantation and declined within the first 24 h of allograft function (0.35 + 0.26 g/mmol creatinine). The protein excretion pattern, based on separation of urinary proteins on sodium dodecyl sulphate-polyacrylamide gel electrophorosis (SDS-PAGE), as well as excretion of individual biomarker proteins, indicated nonselective glomerular and tubular damage. The increased excretion of thymidine glycol after kidney transplantation may be explained by ischaemia-reperfusion induced oxidative DNA damage of the transplanted kidney.

Near-miss event detection at 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 the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.

Differential effects of monotony versus fatigue on driving performance and the effectiveness of various detection methods : implications for road safety in the ACT

Background Situational driving factors, including fatigue, distraction, inattention and monotony, are recognised killers in Australia, contributing to an estimated 40% of fatal crashes and 34% of all crashes . More often than not the main contributing factor is identified as fatigue, yet poor driving performance has been found to emerge early in monotonous conditions, independent of fatigue symptoms and time on task. This early emergence suggests an important role for monotony. However, much road safety research suggests that monotony is solely a task characteristic that directly causes fatigue and associated symptoms and there remains an absence of consistent evidence explaining the relationship. Objectives We report an experimental study designed to disentangle the characteristics and effects of monotony from those associated with fatigue. Specifically, we examined whether poor driving performance associated with hypovigilance emerges as a consequence of monotony, independent of fatigue. We also examined whether monotony is a multidimensional construct, determined by environmental characteristics and/or task demands that independently moderate sustained attention and associated driving performance. Method Using a driving simulator, participants completed four, 40 minute driving scenarios. The scenarios varied in the degree of monotony as determined by the degree of variation in road design (e.g., straight roads vs. curves) and/or road side scenery. Fatigue, as well as a number of other factors known to moderate vigilance and driving performance, was controlled for. To track changes across time, driving performance was assessed in five minute time periods using a range of behavioural, subjective and physiological measures, including steering wheel movements, lane positioning, electroencephalograms, skin conductance, and oculomotor activity. Results Results indicate that driving performance is worse in monotonous driving conditions characterised by low variability in road design. Critically, performance decrements associated with monotony emerge very early, suggesting monotony effects operate independent of fatigue. Conclusion Monotony is a multi-dimensional construct where, in a driving context, roads containing low variability in design are monotonous and those high in variability are non-monotonous. Importantly, low variability in road side scenery does not appear to exacerbate monotony or associated poor performance. However, high variability in road side scenery can act as a distraction and impair sustained attention and poor performance when driving on monotonous roads. Furthermore, high sensation seekers seem to be more susceptible to distraction when driving on monotonous roads. Implications of our results for the relationship between monotony and fatigue, and the possible construct-specific detection methods in a road safety context, will be discussed.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).

Detection of DNA mutations using novel SERS (Surface-Enhanced Raman Spectroscopy) diagnostic platform

This article describes the detection of DNA mutations using novel Au-Ag coated GaN substrate as SERS (surface-enhanced Raman spectroscopy) diagnostic platform. Oligonucleotide sequences corresponding to the BCR-ABL (breakpoint cluster region-Abelson) gene responsible for development of chronic myelogenous leukemia were used as a model system to demonstrate the discrimination between the wild type and Met244Val mutations. The thiolated ssDNA (single-strand DNA) was immobilized on the SERS-active surface and then hybridized to a labeled target sequence from solution. An intense SERS signal of the reporter molecule MGITC was detected from the complementary target due to formation of double helix. The SERS signal was either not observed, or decreased dramatically for a negative control sample consisting of labeled DNA that was not complementary to the DNA probe. The results indicate that our SERS substrate offers an opportunity for the development of novel diagnostic assays.

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.