Hybrid system for plagiarism detection

The Internet boom in recent years has increased the interest in the field of plagiarism detection. A lot of documents are published on the Net everyday and anyone can access and plagiarize them. Of course, checking all cases of plagiarism manually is an unfeasible task. Therefore, it is necessary to create new systems that are able to automatically detect cases of plagiarism produced. In this paper, we introduce a new hybrid system for plagiarism detection which combines the advantages of the two main plagiarism detection techniques. This system consists of two analysis phases: the first phase uses an intrinsic detection technique which dismisses much of the text, and the second phase employs an external detection technique to identify the plagiarized text sections. With this combination we achieve a detection system which obtains accurate results and is also faster thanks to the prefiltering of the text.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

Assessment of the Performance of Acoustic and Mass Balance Methods for Leak Detection in Pipelines for Transporting Liquids

On-line leak detection is a main concern for the safe operation of pipelines. Acoustic and mass balance are the most important and extensively applied technologies in field problems. The objective of this work is to compare these leak detection methods with respect to a given reference situation, i.e., the same pipeline and monitoring signals acquired at the inlet and outlet ends. Experimental tests were conducted in a 749 m long laboratory pipeline transporting water as the working fluid. The instrumentation included pressure transducers and electromagnetic flowmeters. Leaks were simulated by opening solenoid valves placed at known positions and previously calibrated to produce known average leak flow rates. Results have clearly shown the limitations and advantages of each method. It is also quite clear that acoustics and mass balance technologies are, in fact, complementary. In general, an acoustic leak detection system sends out an alarm more rapidly and locates the leak more precisely, provided that the rupture of the pipeline occurs abruptly enough. On the other hand, a mass balance leak detection method is capable of quantifying the leak flow rate very accurately and of detecting progressive leaks.

Residence time distribution in holding tubes using generalized convection model and numerical convolution for non-ideal tracer detection

A procedure is proposed for the determination of the residence time distribution (RTD) of curved tubes taking into account the non-ideal detection of the tracer. The procedure was applied to two holding tubes used for milk pasteurization in laboratory scale. Experimental data was obtained using an ionic tracer. The signal distortion caused by the detection system was considerable because of the short residence time. Four RTD models, namely axial dispersion, extended tanks in series, generalized convection and PER + CSTR association, were adjusted after convolution with the E-curve of the detection system. The generalized convection model provided the best fit because it could better represent the tail on the tracer concentration curve that is Caused by the laminar velocity profile and the recirculation regions. Adjusted model parameters were well cot-related with the now rate. (C) 2010 Elsevier Ltd. All rights reserved.

Field evaluation of a sentinel mosquito (Diptera : culicidae) trap system to detect Japanese encephalitis in remote Australia

Incursions of Japanese encephalitis (JE) virus into northern Queensland are currently monitored using sentinel pigs. However, the maintenance of these pigs is expensive, and because pigs are the major amplifying hosts of the virus, they may contribute to JE transmission. Therefore, we evaluated a mosquito-based detection system to potentially replace the sentinel pigs. Single, inactivated JE-infected Culex annulirostris Skuse and C. sitiens Wiedemann were placed into pools of uninfected mosquitoes that were housed in a Mosquito Magnet Pro (MM) trap set under wet season field conditions in Cairns, Queensland for 0, 7, or 14 d. JE viral RNA was detected (cycling threshold [CT] = 40) in 11/ 12, 10/14, and 2/5 pools containing 200, 1,000, and 5,000 mosquitoes, respectively, using a TaqMan real-time reverse transcription-polymerase chain reaction (RT-PCR). The ability to detect virus was not affected by the length of time pools were maintained under field conditions, although the CT score tended to increase with field exposure time. Furthermore, JE viral RNA was detected in three pools of 1,000 mosquitoes collected from Badu Island using a MM trap. These results indicated that a mosquito trap system employing self-powered traps, such as the MosquitoMagnet, and a real-time PCR system, could be used to monitor for JE in remote areas.

Detection of Cryptococcus neoformans capsular polysaccharide antigen in asymptomatic HIV-infected patients

Serum samples from 242 HIV-positive persons were studied for the detection of capsular polysaccha-ride antigen of Cryptococcus neoformans; 193 of these patients presented less than 300 CD4+ cells/µl of blood and 49 patients had more than 300 CD4+ cells/µl. None of them had symptoms or signs characteristic of cryptococcosis. The capsular antigen of C. neofarmans was detected by latex agglutination technique with pronase pre-treatment (IMMY, Crypto-Latex Antigen Detection System, Immunomycologics Inc., OK, USA); in 61% of the samples, ELISA technique was also used (Premier, Cryptococcal Antigen, Meridian Diagnostic Inc., Cincinatti, Oh, USA). The comparative study of both methods showed that the results obtained were similar in 96.9% of the cases. The capsular antigen was detected in 13 out of 193 (6.7%) patients with less than 300 CD4+ cells/µl. Cryptococcosis was confirmed mycologically in 3 of these 13 cases (23%) by the isolation of C. neoformans in CSF or blood cultures. Three patients, who had presented negative results of both tests for capsular antigen, suffered disseminated cryptococcosis 4 to 8 months later. The predictive diagnostic value of capsular antigen detection of C. neoformans seems tobe low and we believe that it should not be done routinely in asymptomatic HIV-positive persons.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

A low-voltage RF-CMOS receiver front-end for a wireless fall detection microsystem

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores, pela Universidade Nova de Ciências e Tecnologia

Compact portable biosensor for arsenic detection in aqueous samples with Escherichia coli bioreporter cells.

We present a compact portable biosensor to measure arsenic As(III) concentrations in water using Escherichia coli bioreporter cells. Escherichia coli expresses green fluorescent protein in a linearly dependent manner as a function of the arsenic concentration (between 0 and 100 μg/L). The device accommodates a small polydimethylsiloxane microfluidic chip that holds the agarose-encapsulated bacteria, and a complete optical illumination/collection/detection system for automated quantitative fluorescence measurements. The device is capable of sampling water autonomously, controlling the whole measurement, storing and transmitting data over GSM networks. We demonstrate highly reproducible measurements of arsenic in drinking water at 10 and 50 μg/L within 100 and 80 min, respectively.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Hand detection in cluttered scene images using Fourier-Mellin invariant features

This paper proposes an automatic hand detection system that combines the Fourier-Mellin Transform along with other computer vision techniques to achieve hand detection in cluttered scene color images. The proposed system uses the Fourier-Mellin Transform as an invariant feature extractor to perform RST invariant hand detection. In a first stage of the system a simple non-adaptive skin color-based image segmentation and an interest point detector based on corners are used in order to identify regions of interest that contains possible matches. A sliding window algorithm is then used to scan the image at different scales performing the FMT calculations only in the previously detected regions of interest and comparing the extracted FM descriptor of the windows with a hand descriptors database obtained from a train image set. The results of the performed experiments suggest the use of Fourier-Mellin invariant features as a promising approach for automatic hand detection.

Hand detection in cluttered scene images using Fourier-Mellin invariant features

This paper proposes an automatic hand detection system that combines the Fourier-Mellin Transform along with other computer vision techniques to achieve hand detection in cluttered scene color images. The proposed system uses the Fourier-Mellin Transform as an invariant feature extractor to perform RST invariant hand detection. In a first stage of the system a simple non-adaptive skin color-based image segmentation and an interest point detector based on corners are used in order to identify regions of interest that contains possible matches. A sliding window algorithm is then used to scan the image at different scales performing the FMT calculations only in the previously detected regions of interest and comparing the extracted FM descriptor of the windows with a hand descriptors database obtained from a train image set. The results of the performed experiments suggest the use of Fourier-Mellin invariant features as a promising approach for automatic hand detection.

Sensitive photonic system to measure oxidative potential of airborne nanoparticles and ROS levels in exhaled air

A photonic system has been developed that enables sensitive quantitative determination of reactive oxygen species (ROS) - mainly hydrogen peroxide (H2O2) - in aerosol samples such as airborne nanoparticles and exhaled air from patients. The detection principle relies on the amplification of the absorbance under multiple scattering conditions due to optical path lengthening [1] and [2]. In this study, the presence of cellulose membrane that acts as random medium into the glass optical cell considerably improved the sensitivity of the detection based on colorimetric FOX assay (FeII/orange xylenol). Despite the loss of assay volume (cellulose occupies 75% of cell volume) the limit of detection is enhanced by one order of magnitude reaching the value of 9 nM (H2O2 equivalents). Spectral analysis is performed automatically with a periodicity of 5 to 15 s, giving rise to real-time ROS measurements. Moreover, the elution of air sample into the collection chamber via a micro-diffuser (impinger) enables quantitative determination of ROS contained in or generated from airborne samples. As proof-of-concept the photonic ROS detection system was used in the determination of both ROS generated from traffic pollution and ROS contained in the exhaled breath as lung inflammation biomarkers.

Identification and characterisation of a novel adhesin of Streptococcus suis and its use as a target of adhesion inhibition and bacterial detection

Streptococcus suis is an important pig pathogen but it is also zoonotic, i.e. capable of causing diseases in humans. Human S. suis infections are quite uncommon but potentially life-threatening and the pathogen is an emerging public health concern. This Gram-positive bacterium possesses a galabiose-specific (Galalpha1−4Gal) adhesion activity, which has been studied for over 20 years. P-fimbriated Escherichia coli−bacteria also possess a similar adhesin activity targeting the same disaccharide. The galabiose-specific adhesin of S. suis was identified by an affinity proteomics method. No function of the protein identified was formerly known and it was designated streptococcal adhesin P (SadP). The peptide sequence of SadP contains an LPXTG-motif and the protein was proven to be cell wall−anchored. SadP may be multimeric since in SDS-PAGE gel it formed a protein ladder starting from about 200 kDa. The identification was confirmed by producing knockout strains lacking functional adhesin, which had lost their ability to bind to galabiose. The adhesin gene was cloned in a bacterial expression host and properties of the recombinant adhesin were studied. The galabiose-binding properties of the recombinant protein were found to be consistent with previous results obtained studying whole bacterial cells. A live-bacteria application of surface plasmon resonance was set up, and various carbohydrate inhibitors of the galabiose-specific adhesins were studied with this assay. The potencies of the inhibitors were highly dependent on multivalency. Compared with P-fimbriated E. coli, lower concentrations of galabiose derivatives were needed to inhibit the adhesion of S. suis. Multivalent inhibitors of S. suis adhesion were found to be effective at low nanomolar concentrations. To specifically detect galabiose adhesin−expressing S. suis bacteria, a technique utilising magnetic glycoparticles and an ATP bioluminescence bacterial detection system was also developed. The identification and characterisation of the SadP adhesin give valuable information on the adhesion mechanisms of S. suis, and the results of this study may be helpful for the development of novel inhibitors and specific detection methods of this pathogen.