PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

A survey of human motion analysis using depth imagery

Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, but recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions.

Performance evaluation of crowd image analysis using the PETS2009 dataset

This paper presents the PETS2009 outdoor crowd image analysis surveillance dataset and the performance evaluation of people counting, detection and tracking results using the dataset submitted to five IEEE Performance Evaluation of Tracking and Surveillance (PETS) workshops. The evaluation was carried out using well established metrics developed in the Video Analysis and Content Extraction (VACE) programme and the CLassification of Events, Activities, and Relationships (CLEAR) consortium. The comparative evaluation highlights the detection and tracking performance of the authors’ systems in areas such as precision, accuracy and robustness and provides a brief analysis of the metrics themselves to provide further insights into the performance of the authors’ systems.

Representing 3D shape and location

The 3D shape of an object and its 3D location have traditionally thought of as very separate entities, although both can be described within a single 3D coordinate frame. Here, 3D shape and location are considered as two aspects of a view-based approach to representing depth, avoiding the use of 3D coordinate frames.

Classification in e-procurement

Three coupled knowledge transfer partnerships used pattern recognition techniques to produce an e-procurement system which, the National Audit Office reports, could save the National Health Service £500 m per annum. An extension to the system, GreenInsight, allows the environmental impact of procurements to be assessed and savings made. Both systems require suitable products to be discovered and equivalent products recognised, for which classification is a key component. This paper describes the innovative work done for product classification, feature selection and reducing the impact of mislabelled data.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

Mannose-binding lectin complement pathway plays a key role in complement activation by Paracoccidioides brasiliensis

Paracoccidioides brasiliensis (Pb) is a dimorphic fungal pathogen that causes paracoccidioidomycosis the most severe deep mycosis from South America Although cell mediated immunity is considered the most efficient protective mechanism against Pb infection mechanisms of innate immunity are poorly defined Herein we investigated the interaction of the complement system with high and low virulence isolates of Pb We demonstrated that Pb18 a high virulence Pb Isolate when incubated with normal human serum (NHS) induces consumption of hemolytic complement and when immobilized promotes binding of C4b C3b and C5b-C9 Both low virulence (Pb265) and high virulence (Pb18) isolates consumed C4 C3 and mannose-binding learn (MBL) of MBL-sufficient but not of MBL-deficient serum as revealed by deposition of residual C4 C3 and MBL on immune complexes and mannan However higher complement components consumption was observed with Pb265 as compared with Pb18 The suggested relationship between low virulence and significant complement activation properties of Pb isolates was confirmed by the demonstration that virulence attenuation of Pb 18 results in acquisition of the ability to activate complement Conversely reactivation of attenuated Pb18 results in loss of the ability to activate complement Our results demonstrate for the first time that Pb yeasts activate the complement system by the lectin pathway and there is an Inverse correlation between complement activating ability and Pb virulence These differences could exert an influence on Innate immunity and severity of the disease developed by infected hosts (C) 2010 Elsevier Ltd All rights reserved

THE ROLE OF INNATE IMMUNITY IN SEPTIC ACUTE KIDNEY INJURIES

Acute kidney injury (AKI) is an important clinical syndrome characterized by abnormalities in the hydroelectrolytic balance. Because of high rates of morbidity and mortality (from 15% to 60%) associated with AKI, the study of its pathophysiology is critical in searching for clinical targets and therapeutic strategies. Severe sepsis is the major cause of AKI. The host response to sepsis involves an inflammatory response, whereby the pathogen is initially sensed by innate immune receptors (pattern recognition receptors [PRRs]). When it persists, this immune response leads to secretion of proinflammatory products that induce organ dysfunction such as renal failure and consequently increased mortality. Moreover, the injured tissue releases molecules resulting from extracellular matrix degradation or dying cells that function as alarmines, which are recognized by PRR in the absence of pathogens in a second wave of injury. Toll-like receptors (TLRs) and NOD-like receptors (NLRs) are the best characterized PRRs. They are expressed in many cell types and throughout the nephron. Their activation leads to translocation of nuclear factors and synthesis of proinflammatory cytokines and chemokines. TLRs` signaling primes the cells for a robust inflammatory response dependent on NLRs; the interaction of TLRs and NLRs gives rise to the multiprotein complex known as the inflammasome, which in turn activates secretion of mature interleukin 1 beta and interleukin 18. Experimental data show that innate immune receptors, the inflammasome components, and proinflammatory cytokines play crucial roles not only in sepsis, but also in organ-induced dysfunction, especially in the kidneys. In this review, we discuss the significance of the innate immune receptors in the development of acute renal injury secondary to sepsis.

Insulin temporal sensitivity and its signaling pathway in the rat pineal gland

Aims: In our previous work, we reported that the insulin potentiating effect on melatonin synthesis is regulated by a post-transcriptional mechanism. However, the major proteins of the insulin signaling pathway (ISP) and the possible pathway component recruited on the potentiating effect of insulin had not been characterized. A second question raised was whether windows of sensitivity to insulin exist in the pineal gland due to insulin rhythmic secretion pattern. Main methods: Melatonin content from norepinephrine(NE)-synchronized pineal gland cultures was quantified by high performance liquid chromatography with electrochemical detection and arylalkylamine-N-acetyltransferase (AANAT) activity was assayed by radiometry. Immunoblotting and immunoprecipitation techniques were performed to establish the ISP proteins expression and the formation of 14-3-3: AANAT complex, respectively. Key findings: The temporal insulin susceptibility protocol revealed two periods of insulin potentiating effect, one at the beginning and another one at the end of the in vitro induced ""night"". In some Timed-insulin Stimulation (TSs), insulin also promoted a reduction on melatonin synthesis, showing its dual action in cultured pineal glands. The major ISP components, such as IR beta, IGF-1R, IRS-1, IRS-2 and PI3K(p85), as well tyrosine phosphorylation of pp85 were characterized within pineal glands. Insulin is not involved in the 14-3-3:AANAT complex formation. The blockage of PI3K by LY 294002 reduced melatonin synthesis and AANAT activity. Significance: The present study demonstrated windows of differential insulin sensitivity, a functional ISP and the PI3K-dependent insulin potentiating effect on NE-mediated melatonin synthesis, supporting the hypothesis of a crosstalk between noradrenergic and insulin pathways in the rat pineal gland. (C) 2010 Elsevier Inc. All rights reserved.

Generalized external indexes for comparing data partitions with overlapping categories

There is a family of well-known external clustering validity indexes to measure the degree of compatibility or similarity between two hard partitions of a given data set, including partitions with different numbers of categories. A unified, fully equivalent set-theoretic formulation for an important class of such indexes was derived and extended to the fuzzy domain in a previous work by the author [Campello, R.J.G.B., 2007. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Lett., 28, 833-841]. However, the proposed fuzzy set-theoretic formulation is not valid as a general approach for comparing two fuzzy partitions of data. Instead, it is an approach for comparing a fuzzy partition against a hard referential partition of the data into mutually disjoint categories. In this paper, generalized external indexes for comparing two data partitions with overlapping categories are introduced. These indexes can be used as general measures for comparing two partitions of the same data set into overlapping categories. An important issue that is seldom touched in the literature is also addressed in the paper, namely, how to compare two partitions of different subsamples of data. A number of pedagogical examples and three simulation experiments are presented and analyzed in details. A review of recent related work compiled from the literature is also provided. (c) 2010 Elsevier B.V. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.