The social life of categories : an empirical study of term categorization

Most classification schemes in common use are actually categorization schemes which fall somewhere on a continuum between unstructured, uncontrolled lists of terms and formal classifications. Over time, terms change meaning and acquire new definitions. This paper presents the results of an approach that used the librarianship principle of consensus to form categories of terms and to relate those categories using a domain reference group. Boisot's Social Learning Cycle (SLC) was then used as a model with which to explain category variations. The single study undertaken in this investigation demonstrated the value of the SLC for explaining the variations between reference group members, and showed the potential for explaining category changes over time. This identifies areas in which consensus is breaking down or emerging, allowing for focused maintenance of categorical schemes.

Object categorization via sparse representation of local features

Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation and classification into a unified framework. Therefore, it does not need a further classifier. Experimental results show that the proposed method achieved better or comparable accuracy than the well known bag-of-features representation with various classifiers.

A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

Does implicit motor imagery ability predict reaching correction efficiency? A test of recent models of human motor control

Neurocomputational models of reaching indicate that efficient purposive correction of movement midflight (e.g., online control) depends on one's ability to generate and monitor an accurate internal (neural) movement representation. In the first study to test this empirically, the authors investigated the relationship between healthy young adults’ implicit motor imagery performance and their capacity to correct their reaching trajectory. As expected, after controlling for general reaching speed, hierarchical regression demonstrated that imagery ability was a significant predictor of hand correction speed; that is, faster and more accurate imagery performance associated with faster corrections to reaching following target displacement at movement onset. They argue that these findings provide preliminary support for the view that a link exists between an individual's ability to represent movement mentally and correct movement online efficiently.

Multi-label classification with clustering for image and text categorization

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.

Categorization of disaster decision support needs for the development of an integrated model for DMDSS

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.

Discovering implicit criteria preferences reflected in tourism journal ranking lists using the Choquet Integral

Variations between journal rankings may cause confusion. As such, prior attempts were made to compare and evaluate journal ranking criteria for obtaining insightful knowledge on how different research communities have ranked journals. However, existing approaches are unable to model the journal ranking process closely enough as they are incapable of considering the relationship between multiple criteria simultaneously. In this paper, we address the challenges by introducing the Choquet Integral (CI) for evaluating journal ranking criteria. The new approach is able to account for interactions between criteria in relation to overall ranking score, using a fuzzy measure in its computation. Its properties, the Shapley value and the Interaction index, allow for good representations of importance and interactions between criteria. We demonstrate the efficiency of the CI through a case study of journal ranking lists in tourism and service journals.

Children's implicit recall of junk food, alcohol and gambling sponsorship in Australian sport

BACKGROUND: In Australia, sport is saturated by the promotion of junk food, alcohol and gambling products. This is particularly evident on player jerseys. The effect of this advertising on children, who are exposed to these messages while watching sport, has not been thoroughly investigated. The aim of this research study was to investigate: (1) the extent to which children implicitly recalled shirt sponsors with the correct sporting team; (2) whether children associated some types of sponsors with certain sporting codes more than others; and (3) whether age of the children influenced the correct recall of sponsoring brands and teams. METHOD: This experimental study conducted in New South Wales, Australia used projective techniques to measure the implicit recall of team sponsorship relationships of 85 children aged 5-12 years. Participants were asked to arrange two sets of magnets - one which contained sporting teams and one which contained brand logos - in the manner deemed most appropriate by them. Children were not given any prompts relating to sporting sponsorship relationships. RESULTS: Three quarters (77 %) of the children were able to identify at least one correct shirt sponsor. Children associated alcohol and gambling brands more highly with the more popular sporting code, the National Rugby League compared to the Australian Football League sporting code. Results showed that age had an effect on number of shirt sponsors correctly recalled with 9-12 year olds being significantly more likely than 5-8 year olds to correctly identify team sponsors. CONCLUSIONS: Given children's ability to implicitly recall shirt sponsors in a sporting context, Australian sporting codes should examine their current sponsorship relationships to reduce the number of unhealthy commodity shirt sponsors. While there is some regulation that protects children from the marketing of unhealthy commodity products, these findings suggest that children are still exposed to and recall these sponsorship relationships. Results suggest that the promotion of unhealthy commodity products during sporting matches is contributing to increased awareness amongst children of unhealthy commodity brands. Further investigation is required to examine the extent and impact of marketing initiatives during televised sporting matches on children.

Towards a conceptual framework on the categorization of stereotypical perceptions in accounting

The stereotypical image of the profession is poor with accountants appearing in the popular media as either the object of satire or the criminally inclined expert who deceives the public for self-gain. Extant research on the portrayal of the stereotypic accountant is limited in two ways: (1) existing research assumes a unitary concept by inferring a dominant image when the accountant stereotype is multifaceted; and (2) it is unclear from existing research whether the dominant image results from perceived character traits or the duties undertaken by accountants. This paper relies on qualitative methods of data analysis to unpack the elements that underpin stereotypical images in accounting to develop a framework of external perceptions that distinguishes one image from another. The framework is constructed on two broad criteria that comprise accountants (personality traits and physical characteristics) and accounting (task functionality). The interplay of these two criteria creates four subtypes representing positive (Scorekeeper and Guardian) and negative (Beancounter and Entrepreneur) interpretations of the two basic categorizations: bookkeeper and business professional. Further analysis revealed four primary dimensions (Ethics and Sociable, Skill and Service) that underlie the construction of the subtypes. In general, the 'Scorekeeper' rates more highly than the 'Beancounter' on 'Ethics and Sociable' and the 'Guardian' rates more highly than the 'Entrepreneur' on 'Ethics'. Accounting researchers and the profession could benefit from understanding how stereotypical perceptions are constructed and managed.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

Implicit corporate political activity and elite formation

Nesta tese, analisamos como a elite empresarial progressista criou a organização da sociedade civil Rede Nossa São Paulo (RNSP), alcançando mudanças institucionais significativas, permitindo assim a consolidação da elite na esfera política. A pesquisa resultou em três artigos. O primeiro artigo discute como a RNSP se tornou um forte ator político na cidade de São Paulo e também no Brasil. Para abordar esta questão, mostramos como a RNSP usou a história retórica para se tornar um ator central na esfera política. No segundo artigo, propomos o conceito de atividade política corporativa implícita (ICPA), complementar a atividade política corporativa. Conceituamos ICPA como elites empresariais em conjunto com organizações da sociedade civil agindo para influenciar o governo. Com os limites entre o governo, as empresas e organizações da sociedade civil difusos; entendemos que este conceito é extremamente importante para chamar a atenção e criar novos caminhos para a pesquisa sobre a influência das empresas no governo. No último artigo, mostramos os micro fundamentos da ICPA. Especificamente, como as elites empresariais e corporações influenciam a RNSP e, indiretamente, o governo. Concluindo, contribuímos para a literatura sobre a influência das empresas no governo e na esfera pública indiretamente, por meio de organizações da sociedade civil. Teoricamente, estendemos a literatura de teoria institucional, história e poder

Optimal provision of implicit and explicit incentives in asset management contracts

This paper investigates the importance of the fiow of funds as an implicit incetive provided by investors to portfolio managers in a two-period relationship. We show that the fiow of funds is a powerful incentive in an asset management contract. We build a binomial moral hazard model to explain the main trade-ofIs in the relationship between fiow, fees and performance. The main assumption is that efIort depend" on the combination of implicit and explicit incentives while the probability distrioutioll function of returns depends on efIort. In the case of full commitment, the investor's relevant trade-ofI is to give up expected return in the second period vis-à-vis to induce efIort in the first período The more concerned the investor is with today's payoff. the more willing he will be to give up expected return in the following periods. That is. in the second period, the investor penalizes observed low returns by withdrawing resources from non-performing portfolio managers. Besides, he pays performance fee when the observed excess return is positive. When commitment is not a plausible hypothesis, we consider that the investor also learns some symmetríc and imperfect information about the ability of the manager to generate positive excess returno In this case, observed returns reveal ability as well as efIort choices exerted by the portfolio manager. We show that implicit incentives can explain the fiow-performance relationship and, conversely, endogenous expected return determines incentives provision and define their optimal leveIs. We provide a numerical solution in Matlab that characterize these results.

Opening ILT blackbox: Exploring recognition-based leadership perceptions with conjoint analysis

Although research on Implicit Leadership Theories (ILT) has put great effort on determining what attributes define a leader prototype, little attention has been given to understanding the relative importance of each of these attributes in the categorization process by followers. Knowing that recognition-based leadership perceptions are the result of the match between followers’ ILTs and the perceived attributes in their actual leaders, understanding how specific prototypical leader attributes impact this impression formation process is particularly relevant. In this study, we draw upon socio-cognitive theories to explore how followers cognitively process the information about a leader’s attributes. By using Conjoint Analysis (CA), a technique that allows us to measure an individual’s trade-offs when making choices about multi-attributed options, we conducted a series of 4 studies with a total of 879 participants. Our results demonstrate that attributes’ importance for individuals’ leadership perceptions formation is rather heterogeneous, and that some attributes can enhance or spoil the importance of other prototypical attributes. Finally, by manipulating the leadership domain, we show that the weighting pattern of attributes is context dependent, as suggested by the connectionist approach to leadership categorization. Our findings also demonstrate that Conjoint Analysis can be a valuable tool for ILT research.