Recognition as a distinguishing criterion of IS journals

The number of journals publishing information systems (IS) research has grown dramatically over the past few decades. This has resulted in an environment where authors have a wider choice of journals in which to place articles. Electronic journals are now as readily recognised by authorities as print journals. This paper provides firm evidence in support of the assertion that the number of journals publishing IS research has increased. The paper also examines the Australian context where the selection of a journal in which to place an article is influenced by recognition from the Department of Education Science and Training (DEST). In Australia, obtaining DEST recognition as a recognised research journal is not an onerous task, and yet a significant number of IS journals have not done this. Publishing in a DEST recognised journal is essential for Australian researchers to contribute to their organisation’s research quantum and hence research funding. Attention is drawn to an increasing number of IS journals not recognised by DEST, and consequent action is recommended.

The rough sets feature selection for trees recognition in color aerial images using genetic algorithms

Selecting a set of features which is optimal for a given task is the problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The concept of reduction of the decision table based on the rough set is very useful for feature selection. In this paper, a genetic algorithm based approach is presented to search the relative reduct decision table of the rough set. This approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and finds the effective feature subset for texture classification . On the basis of the effective feature subset selected, this paper presents a method to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The experiments results show that the feature subset selected and the method of the object extraction presented in this paper are practical and effective.

What were they thinking? An exploration of child sexual offenders' beliefs using a lexical decision task

Cognitive distortions have been afforded a key role in the offending behaviour of child sexual offenders. While the mechanisms underlying cognitive distortions are not fully understood, they are generally thought to reflect entrenched beliefs that distinguish child sexual offenders from other individuals. We investigated this hypothesis using a robust experimental technique called the lexical decision task. Child sexual offenders, offender controls, and non-offender controls completed a lexical decision task in which they responded to words that completed sentences in either an offence-supportive or nonoffence-supportive manner. Contrary to predictions, child sexual offenders did not respond faster to words that were consistent with offence-supportive beliefs, relative to controls. However, they did show accelerated recognition for word stems supporting external locus of control beliefs. These results highlight the need to use cognitive experimental methods to study child sexual offenders' beliefs, and the importance of investigating potential alternative drivers of cognitive distortions.

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition

Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to a home video surveillance application and demonstrate its efficacy.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model

In this paper, we exploit the discrete Coxian distribution and propose a novel form of stochastic model, termed as the Coxian hidden semi-Makov model (Cox-HSMM), and apply it to the task of recognising activities of daily living (ADLs) in a smart house environment. The use of the Coxian has several advantages over traditional parameterization (e.g. multinomial or continuous distributions) including the low number of free parameters needed, its computational efficiency, and the existing of closed-form solution. To further enrich the model in real-world applications, we also address the problem of handling missing observation for the proposed Cox-HSMM. In the domain of ADLs, we emphasize the importance of the duration information and model it via the Cox-HSMM. Our experimental results have shown the superiority of the Cox-HSMM in all cases when compared with the standard HMM. Our results have further shown that outstanding recognition accuracy can be achieved with relatively low number of phases required in the Coxian, thus making the Cox-HSMM particularly suitable in recognizing ADLs whose movement trajectories are typically very long in nature.

Facial affect recognition and schizotypal personality characteristics

Aim: Deficits in facial affect recognition are well established in schizophrenia, yet relatively little research has examined facial affect recognition in hypothetically psychosis-prone or ‘schizotypal’ individuals. Those studies that have examined social cognition in psychosis-prone individuals have paid little attention to the association between facial emotion recognition and particular schizotypal personality features. The present study therefore sought to investigate relationships between facial emotion recognition and the different aspects of schizotypy.

Methods: Facial affect recognition accuracy was examined in 50 psychiatrically healthy individuals assessed for level of schizotypy using the Schizotypal Personality Questionnaire. This instrument provides a multidimensional measure of schizophrenia proneness, encompassing ‘cognitive-perceptual’, ‘interpersonal’ and ‘disorganized’ features of schizotypy. It was hypothesized that the cognitive-perceptual and interpersonal aspects of schizotypy would be associated with difficulties identifying facial expressions of emotion during a forced-choice recognition task using a standardized series of colour photographs.

Results: As predicted, interpersonal aspects of schizotypy (particularly social anxiety) were associated with reduced accuracy on the facial affect recognition task, but there was no association between affect recognition accuracy and cognitive-perceptual features of schizotypy.

Conclusions: These results suggest that subtle deficits in facial affect recognition in otherwise psychiatrically healthy individuals may be related to the vulnerability for interpersonal communication difficulties, as seen in schizophrenia.

Accurate and efficient face recognition from video

As a problem of high practical appeal but outstanding challenges, computer-based face recognition remains a topic of extensive research attention. In this paper we are specifically interested in the task of identifying a person from multiple training and query images. Thus, a novel method is proposed which advances the state-of-the-art in set based face recognition. Our method is based on a previously described invariant in the form of generic shape-illumination effects. The contributions include: (i) an analysis of computational demands of the original method and a demonstration of its practical limitations, (ii) a novel representation of personal appearance in the form of linked mixture models in image and pose-signature spaces, and (iii) an efficient (in terms of storage needs and matching time) manifold re-illumination algorithm based on the aforementioned representation. An evaluation and comparison of the proposed method with the original generic shape-illumination algorithm shows that comparably high recognition rates are achieved on a large data set (1.5% error on 700 face sets containing 100 individuals and extreme illumination variation) with a dramatic improvement in matching speed (over 700 times for sets containing 1600 faces) and storage requirements (independent of the number of training images).

Glucose administration and cognitive function: differential effects of age and effort during a dual task paradigm in younger and older adults

RATIONALE: Current research suggests that glucose facilitates performance on cognitive tasks which possess an episodic memory component and a relatively high level of cognitive demand. However, the extent to which this glucose facilitation effect is uniform across the lifespan is uncertain. METHODS: This study was a repeated measures, randomised, placebo-controlled, cross-over trial designed to assess the cognitive effects of glucose in younger and older adults under single and dual task conditions. Participants were 24 healthy younger (average age 20.6 years) and 24 healthy older adults (average age 72.5 years). They completed a recognition memory task after consuming drinks containing 25 g glucose and a placebo drink, both in the presence and absence of a secondary tracking task. RESULTS AND CONCLUSIONS: Glucose enhanced recognition memory response time and tracking precision during the secondary task, in older adults only. These findings do not support preferential targeting of hippocampal function by glucose, rather they suggest that glucose administration differentially increases the availability of attentional resources in older individuals.

Glucose enhancement of recognition memory: differential effects on effortful processing but not aspects of 'remember-know' responses

The administration of a glucose drink has been shown to enhance cognitive performance with effect sizes comparable with those from pharmaceutical interventions in human trials. In the memory domain, it is currently debated whether glucose facilitation of performance is due to differential targeting of hippocampal memory or whether task effort is a more important determinant. Using a placebo-controlled, double-blind, crossover 2(Drink: glucose/placebo) × 2(Effort: ± secondary task) design, 20 healthy young adults' recognition memory performance was measured using the 'remember-know' procedure. Two high effort conditions (one for each drink) included secondary hand movements during word presentation. A 25 g glucose or 30 mg saccharine (placebo) drink was consumed 10 min prior to the task. The presence of a secondary task resulted in a global impairment of memory function. There were significant Drink × Effort interactions for overall memory accuracy but no differential effects for 'remember' or 'know' responses. These data suggest that, in some circumstances, task effort may be a more important determinant of the glucose facilitation of memory effect than hippocampal mediation. This article is part of a Special Issue entitled 'Cognitive Enhancers'.

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Improving face recognition with multispectral fusion and support vector machines

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Separation of Scaptotrigona postica Workers into Defined Task Groups by the Chemical Profile on Their Epicuticle Wax Layer

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)