Identifying major contributing sources to odour annoyance using a non-specific gas sensor array

Identification of major contributors to odour annoyance in areas with multiple emission sources is necessary to address and resolve odour disputes. In an effort to develop an appropriate tool for this task, odour samples were collected on-site at a piggery and an abattoir (the major odour sources in the area) and at surrounding off-site areas, then analysed using a commercial non-specific chemical sensor array to develop an odour fingerprint database. The developed odour fingerprint database was analysed using two pattern recognition algorithms including a partial least squares-discriminant analysis (PLS-DA) and a Kohonen self-organising map (KSOM). The KSOM model could identify odour samples sourced from the piggery shed 15, piggery pond 8, piggery pond 9, abattoir, motel and others with mean percentage values of 77.5, 65.0, 90.2, 75.7, 44.8 and 64.6%, respectively.

Learning Optimal Discriminant Functions through a Cooperative Game of Automata

The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.

Syntactic Approach to ECG Rhythm Analysis

A diagnostic system for ECG rhythm monitoring based on syntactic approaches to pattern recognition is presented here. The method proposed exploits the difference in shape and structure between arrhythmic and normal ECG patterns to generate distinctly different descriptions in terms of a chosen set of primitives. A given frame of signal is first approximated piecewise linearly into a set of line segments which are completely specified in terms of their length and slope values. The slope values are quantized into seven distinct levels and a unit-length line segment with a slope value in each of these levels is coded as a slope symbol. Seven such slope symbols constitute the set of primitives. The given signal is represented as a string of such symbols based on the length and angle of the line segments approximating the signal. Context-free languages are used for describing the classes of abnormal and normal ECG patterns considered here. Analysis of actual ECG data shows efficiency comparable with that of existing methods and a saving in processing time.

Uropathogenic Escherichia Coli engages CD14-dependent signaling to enable bladder-macrophage-dependent control of acute urinary tract infection

Background CD14, a coreceptor for several pattern recognition receptors and a widely used monocyte/macrophage marker, plays a key role in host responses to gram-negative bacteria. Despite the central role of CD14 in the inflammatory response to lipopolysaccharide and other microbial products and in the dissemination of bacteria in some infections, the signaling networks controlled by CD14 during urinary tract infection (UTI) are unknown. Methods We used uropathogenic Escherichia coli (UPEC) infection of wild-type (WT) C57BL/6 and Cd14−/− mice and RNA sequencing to define the CD14-dependent transcriptional signature and the role of CD14 in host defense against UTI in the bladder. Results UPEC induced the upregulation of Cd14 and the monocyte/macrophage-related genes Emr1/F4/80 and Csf1r/c-fms, which was associated with lower UPEC burdens in WT mice, compared with Cd14−/− mice. Exacerbation of infection in Cd14−/− mice was associated with the absence of a 491-gene transcriptional signature in the bladder that encompassed multiple host networks not previously associated with this receptor. CD14-dependent pathways included immune cell trafficking, differential cytokine production in macrophages, and interleukin 17 signaling. Depletion of monocytes/macrophages in the bladder by administration of liposomal clodronate led to higher UPEC burdens. Conclusions This study identifies new host protective and signaling roles for CD14 in the bladder during UPEC UTI.

A computationally efficient technique for data-clustering

A computationally efficient agglomerative clustering algorithm based on multilevel theory is presented. Here, the data set is divided randomly into a number of partitions. The samples of each such partition are clustered separately using hierarchical agglomerative clustering algorithm to form sub-clusters. These are merged at higher levels to get the final classification. This algorithm leads to the same classification as that of hierarchical agglomerative clustering algorithm when the clusters are well separated. The advantages of this algorithm are short run time and small storage requirement. It is observed that the savings, in storage space and computation time, increase nonlinearly with the sample size.

Estimation of fuzzy memberships from histograms

Based on the conclusions drawn in the bijective transformation between possibility and probability, a method is proposed to estimate the fuzzy membership function for pattern recognition purposes. A rational function approximation to the probability density function is obtained from the histogram of a finite (and sometimes very small) number of samples. This function is normalized such that the highest ordinate is one. The parameters representing the rational function are used for classifying the pattern samples based on a max-min decision rule. The method is illustrated with examples.

Infection-induced IL-10 and Jak-Stat: A review of the molecular circuitry controlling immune hyperactivity in response to pathogenic microbes

Generation of effective immune responses against pathogenic microbes depends on a fine balance between pro- and anti-inflammatory responses. Interleukin-10 (IL-10) is essential in regulating this balance and has garnered renewed interest recently as a modulator of the response to infection at the JAK-STAT signaling axis of host responses. Here, we examine how IL-10 functions as the “master regulator” of immune responses through JAK-STAT, and provide a perspective from recent insights on bacterial, protozoan, and viral infection model systems. Pattern recognition and subsequent molecular events that drive activation of IL-10-associated JAK-STAT circuitry are reviewed and the implications for microbial pathogenesis are discussed.

A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.

Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach

Video surveillance infrastructure has been widely installed in public places for security purposes. However, live video feeds are typically monitored by human staff, making the detection of important events as they occur difficult. As such, an expert system that can automatically detect events of interest in surveillance footage is highly desirable. Although a number of approaches have been proposed, they have significant limitations: supervised approaches, which can detect a specific event, ideally require a large number of samples with the event spatially and temporally localised; while unsupervised approaches, which do not require this demanding annotation, can only detect whether an event is abnormal and not specific event types. To overcome these problems, we formulate a weakly-supervised approach using Kullback-Leibler (KL) divergence to detect rare events. The proposed approach leverages the sparse nature of the target events to its advantage, and we show that this data imbalance guarantees the existence of a decision boundary to separate samples that contain the target event from those that do not. This trait, combined with the coarse annotation used by weakly supervised learning (that only indicates approximately when an event occurs), greatly reduces the annotation burden while retaining the ability to detect specific events. Furthermore, the proposed classifier requires only a decision threshold, simplifying its use compared to other weakly supervised approaches. We show that the proposed approach outperforms state-of-the-art methods on a popular real-world traffic surveillance dataset, while preserving real time performance.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Formal description, compression and transformation of digital pictures II. Picture algebra and geometry

In an earlier paper (Part I) we described the construction of Hermite code for multiple grey-level pictures using the concepts of vector spaces over Galois Fields. In this paper a new algebra is worked out for Hermite codes to devise algorithms for various transformations such as translation, reflection, rotation, expansion and replication of the original picture. Also other operations such as concatenation, complementation, superposition, Jordan-sum and selective segmentation are considered. It is shown that the Hermite code of a picture is very powerful and serves as a mathematical signature of the picture. The Hermite code will have extensive applications in picture processing, pattern recognition and artificial intelligence.

Formal description, compression and transformation of digital pictures

This paper describes the application of vector spaces over Galois fields, for obtaining a formal description of a picture in the form of a very compact, non-redundant, unique syntactic code. Two different methods of encoding are described. Both these methods consist in identifying the given picture as a matrix (called picture matrix) over a finite field. In the first method, the eigenvalues and eigenvectors of this matrix are obtained. The eigenvector expansion theorem is then used to reconstruct the original matrix. If several of the eigenvalues happen to be zero this scheme results in a considerable compression. In the second method, the picture matrix is reduced to a primitive diagonal form (Hermite canonical form) by elementary row and column transformations. These sequences of elementary transformations constitute a unique and unambiguous syntactic code-called Hermite code—for reconstructing the picture from the primitive diagonal matrix. A good compression of the picture results, if the rank of the matrix is considerably lower than its order. An important aspect of this code is that it preserves the neighbourhood relations in the picture and the primitive remains invariant under translation, rotation, reflection, enlargement and replication. It is also possible to derive the codes for these transformed pictures from the Hermite code of the original picture by simple algebraic manipulation. This code will find extensive applications in picture compression, storage, retrieval, transmission and in designing pattern recognition and artificial intelligence systems.

Generating product feature hierarchy from product reviews

User generated information such as product reviews have been booming due to the advent of web 2.0. In particular, rich information associated with reviewed products has been buried in such big data. In order to facilitate identifying useful information from product (e.g., cameras) reviews, opinion mining has been proposed and widely used in recent years. In detail, as the most critical step of opinion mining, feature extraction aims to extract significant product features from review texts. However, most existing approaches only find individual features rather than identifying the hierarchical relationships between the product features. In this paper, we propose an approach which finds both features and feature relationships, structured as a feature hierarchy which is referred to as feature taxonomy in the remainder of the paper. Specifically, by making use of frequent patterns and association rules, we construct the feature taxonomy to profile the product at multiple levels instead of single level, which provides more detailed information about the product. The experiment which has been conducted based upon some real world review datasets shows that our proposed method is capable of identifying product features and relations effectively.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.