Recognition And Top-Down Generation Of Beta-Acyclic Database Schemes

Database schemes can be viewed as hypergraphs with individual relation schemes corresponding to the edges of a hypergraph. Under this setting, a new class of "acyclic" database schemes was recently introduced and was shown to have a claim to a number of desirable properties. However, unlike the case of ordinary undirected graphs, there are several unequivalent notions of acyclicity of hypergraphs. Of special interest among these are agr-, beta-, and gamma-, degrees of acyclicity, each characterizing an equivalence class of desirable properties for database schemes, represented as hypergraphs. In this paper, two complementary approaches to designing beta-acyclic database schemes have been presented. For the first part, a new notion called "independent cycle" is introduced. Based on this, a criterion for beta-acyclicity is developed and is shown equivalent to the existing definitions of beta-acyclicity. From this and the concept of the dual of a hypergraph, an efficient algorithm for testing beta-acyclicity is developed. As for the second part, a procedure is evolved for top-down generation of beta-acyclic schemes and its correctness is established. Finally, extensions and applications of ideas are described.

Differential recognition by tick-resistant cattle of the recombinantly expressed Rhipicephalus microplus serine protease inhibitor-3 (RMS-3)

Rhipicephalus micro plus is an important bovine ectoparasite, widely distributed in tropical and subtropical regions of the world causing large economic losses to the cattle industry. Its success as an ectoparasite is associated with its capacity to disarm the antihemostatic and anti-inflammatory reactions of the host. Serpins are protease inhibitors with an important role in the modulation of host-parasite interactions. The cDNA that encodes for a R. microplus serpin was isolated by RACE and subsequently cloned into the pPICZ alpha A vector. Sequence analysis of the cDNA and predicted amino acid showed that this cDNA has a conserved serpin domain. B- and T-cell epitopes were predicted using bioinformatics tools. The recombinant R. microplus serpin (rRMS-3) was secreted into the culture media of Pichia pastoris after methanol induction at 0.2 mg l(-1) qRT-PCR expression analysis of tissues and life cycle stages demonstrated that RMS-3 was mainly expressed in the salivary glands of female adult ticks. Immunological recognition of the rRMS-3 and predicted B-cell epitopes was tested using tick-resistant and susceptible cattle sera. Only sera from tick-resistant bovines recognized the B-cell epitope AHYNPPPPIEFT (Seq7). The recombinant RMS-3 was expressed in P. pastoris, and ELISA screening also showed higher recognition by tick-resistant bovine sera. The results obtained suggest that RMS-3 is highly and specifically secreted into the bite site of R. microplus feeding on tick-resistant bovines. Capillary feeding of semi-engorged ticks with anti-AHYNPPPPIEFT sheep sera led to an 81.16% reduction in the reproduction capacity of R. microplus. Therefore, it is possible to conclude that R. microplus serpin (RMS-3) has an important role in the host-parasite interaction to overcome the immune responses in resistant cattle. (C) 2012 Elsevier GmbH. All rights reserved.

Improved optical character recognition by matched filtering

The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

Improved optical character recognition by matched filtering

The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

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.

Online identification guides for Australian smut fungi (Ustilaginomycotina) and rust fungi (Pucciniales)

Interactive identification keys for Australian smut fungi (Ustilaginomycotina and Pucciniomycotina, Microbotryales) and rust fungi (Pucciniomycotina, Pucciniales) are available online at http://collections.daff.qld.gov.au. The keys were built using Lucid software, and facilitate the identification of all known Australian smut fungi (317 species in 37 genera) and 100 rust fungi (from approximately 360 species in 37 genera). The smut and rust keys are illustrated with over 1,600 and 570 images respectively. The keys are designed to assist a wide range of end-users including mycologists, plant health diagnosticians, biosecurity scientists, plant pathologists, and university students. The keys are dynamic and will be regularly updated to include taxonomic changes and incorporate new detections, taxa, distributions and images. Researchers working with Australian smut and rust fungi are encouraged to participate in the on-going development and improvement of these keys.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

A model for visual recognition of alphanumerics

In this paper we propose a hypothetical scheme for recognizing the alphanumerics. The scheme is based on the known physiological structure of the visual cortex and the concept of a short Lino extractor nouron (SLEN). We assumo four basic typca of such units for extracting vertical, horizontal, right and left inclined straight line segments. The patterns reconstructed from the scheme show perfect agreement with the test patterns. The model indicates that the recognition of letters T and H requires extraction of the largest number of features.

Online learning an effective means for developing small business owner-managers’ capabilities?

Purpose If owner-managers engage in management development activities then chances of success may be improved for small businesses. But small business owner-managers (SBOMs) are a difficult group to engage in management development activities. While practitioners worry about timing, content and location of development activities, the purpose of this paper is to examine what drives SBOMs to participate in an online discussion forum (ODF) as a form of management development. An ODF was run with SBOMs and the factors affecting their participation are reported from this exploratory study. Design/methodology/approach A qualitative methodology was used where data gathered from three sources, the ODF posts, in-depth interviews with participants and a focus group with non-participants. These were analysed to evaluate factors affecting participation of SBOMs in an ODF. Findings The findings point to the importance of owner-managers’ attitudes. Attitudes that positively affected SBOMs participation in the ODF included; appreciating that learning leads to business success; positive self-efficacy developed through prior online experience; and an occupational identity as a business manager. Research limitations/implications Few SBOMs participated in the ODF, which is consistent with research finding that they are a difficult group to engage in management development learning activities. Three forms of data were analysed to strengthen results. Practical implications Caution should be exercised when considering investment in e-learning to develop the managerial capabilities of SBOMs. Originality/value Evidence of the factors important for participation in an informal voluntary ODF. The findings suggest greater emphasis should be placed on changing attitudes if SBOMs are to be encouraged to participate in management development activities.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

Portion of a manuscript of daily prayers in the handwriting of Abraham Isaacs

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Boys and girls in religious school, handwriting of Reverend Gershom Seixas

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Opinion on Cohanim, in handwriting of Abraham H. Cohen, son of hazan Cohen of Philadelphia

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Clustering Users of Online Content Service

Online content services can greatly benefit from personalisation features that enable delivery of content that is suited to each user's specific interests. This thesis presents a system that applies text analysis and user modeling techniques in an online news service for the purpose of personalisation and user interest analysis. The system creates a detailed thematic profile for each content item and observes user's actions towards content items to learn user's preferences. A handcrafted taxonomy of concepts, or ontology, is used in profile formation to extract relevant concepts from the text. User preference learning is automatic and there is no need for explicit preference settings or ratings from the user. Learned user profiles are segmented into interest groups using clustering techniques with the objective of providing a source of information for the service provider. Some theoretical background for chosen techniques is presented while the main focus is in finding practical solutions to some of the current information needs, which are not optimally served with traditional techniques.