Search results

The purpose of this article is to explore some of the biases that affect the classification of Welsh art materials and to examine how they are being perpetuated both in library classification systems and beyond.

A discourse analysis, in the loosest sense, was used to explore the research topic. Using a hermeneutic and interpretative approach facilitated an examination of some of the tacit assumptions and conceptions that shape the way in which Welsh art is spoken about, thought about, and generally represented.

The paper explores biases in the classification of Welsh art in relation to the analytical categories of dispersion, dilettantism, and depreciation. Evidence is drawn from three examples of discursive practice: the application of Library of Congress subject headings in the library in Howard Gardens; the Salisbury Collection classification scheme at Cardiff University; and the descriptive text taken from the web site of the National Museum, Cardiff. The paper concludes with a discussion of the nature of classification, and the role of the information professional as active player in the practice of representation in and through various methods of classification.

The paper contributes to the literature of classification bias. The focus on the specific rather than the more general biases both adds to Olsen and Schlegl's work and reflects a sensitivity to the subject matter itself. The paper also contributes to the literature at a methodological level in its use of a hermeneutic and interpretative analytical framework to explore representation in classification.

The purpose of this paper is to establish pluralism as the basis for bibliographic classification theory and practice and examine the possibility of establishing trustworthy classifications.

The paper examines several key notions in classification and extends previous frameworks by combining an explanation‐based approach to classification with the concepts of cognitive authority and trust.

The paper presents an understanding of classification that allows designers and editors to establish trust through the principle of transparency. It demonstrates that modern classification theory and practice are tied to users' activities and domains of knowledge and that trustworthy classification systems are in close dialogue with users to handle bias responsible and establish trust.

The paper establishes a foundation for exploring trust and authority for classification systems.

The purpose of this paper is to explore the representation of Kanaka Maoli (Hawaiian) Hula Dance in traditional systems of representation and organization.

This exploratory study analyzes the controlled and natural language vocabularies employed for the representation and organization of Hawaiian culture, in particular Hawaiian hula. The most widely accepted and used systems were examined: classification systems (Library of Congress Classification and Dewey Decimal Classification), subject heading systems (Library of Congress Subject Headings and authority files (Library of Congress and OCLC Authority Files), and citation indexing systems (Web of Science Social Sciences and Art and Humanities databases).

Analysis of various tools of representation and organization revealed biases and diasporization in depictions of Hawaiian culture. The study emphasizes the need to acknowledge the aesthetic perspective of indigenous people in their organization and presentation of their own cultural knowledge and advocates a decolonizing methodology to promote alternative information structures in indigenous communities.

This study contributes to the relatively limited scholarship on representation and organization for indigenous knowledge organization systems, in particular Hawaiian culture. Research suggests that access to Native Hawaiian cultural heritage will raise awareness among information professionals in Hawai’i to the beauty of Native Hawaiian epistemology.

Unconscious biases are often ignored in organizations; thus, it becomes more important to identify them so that we can build strong and competent organizations. In present dynamic and competitive business situations, you need to be well aware about those concerns of the organizations where these biases exist.

This paper identifies various types of unconscious biases prevailing at workplace. It also identifies different strategies which can be used to avoid them so that we can gain a competitive edge over others in market.

Unconscious biases are a fact of life; no one can deny them. Thus, it is important to identify them so that they can be eliminated, and our businesses can avoid their detrimental effects.

Unconscious biases narrow down the pool of people in an organization and ultimately destabilize an organization’s base.

Unconscious biases narrow down the pool of people in the organization and ultimately destabilize an organization’s base.

This paper can help managers and executives to highlight the areas where these biases lie so that they can be removed easily.

Takes a random sample of 156 respondents from municipal and rural police forces in Canada to examine the relationship between various factors concerning their careers. Finds that a significant number of officers perceive their career orientation to have changed over time. Presents findings on social activists, enforcers, careerists, specialists and self investors. Differs from previous research linking career orientation to personality type by seeing career orientation as changing with time, stages of career and circumstances. Remarks that policing needs to be technically sophisticated, cost‐effective, community‐based and sensitive to the realities of a multicultural society. Recommends that police departments consider the career orientation of recruits and establish a reward structure suited to the varied career types

Prior analyses of racial bias in the New York City’s Stop-and-Frisk program implicitly assumed that potential bias of police officers did not vary by crime type and that their decision of which type of crime to report as the basis for the stop did not exhibit any bias. In this paper, we first extend the hit rates model to consider crime type heterogeneity in racial bias and police officer decisions of reported crime type. Second, we reevaluate the program while accounting for heterogeneity in bias along crime types and for the sample selection which may arise from conditioning on crime type. We present evidence that differences in biases across crime types are substantial and specification tests support incorporating corrections for selective crime reporting. However, the main findings on racial bias do not differ sharply once accounting for this choice-based selection.

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

In this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.

The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.

The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training.

The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

The paper provides a comprehensive overview of the biases in inventory decisions, under the umbrella of behavioural operations, considering research findings that used experimental methods. Research in this field has gained traction but, to the best of our knowledge, review articles that summarize these research findings are scarce. Hence, there is a need to synthesize the anomalies and biases reported in inventory decision-making literature to gain a more integrated understanding.

This study reviews themes relevant to inventory and behavioural operations management from the articles published in recognized top-tier journals during the period between 2000 and 2020 with an aim to build a classification framework. Further, using a systematic review procedure, the relevant research studies are divided into sub-sections and appropriate search strategies are adopted to pleat more information on inventory ordering biases.

This study presents a classification framework by highlighting the factors influencing the biases in inventory decisions and describes the effects of utility preferences on the decision-making behaviour. It highlights the inventory ordering pattern under unconventional settings and also for different supply chain settings. This systematic review helps in identifying the research gap and in giving directions for future researchers.

The study presents a systematic review and detailed analysis of the research in inventory decision making through a behavioural lens. The study shows a clear direction of progress over the years, and implies new directions for looking beyond placing orders and for moving towards a more integrated approach while making supply chain decisions. It will be useful for researchers and practitioners working on newsvendor decisions, supply chain contracts, behavioural economics, behavioural operations management, bounded rationality theory and experimental economics.

Financial reporting standards on foreign currency translation in many countries such as New Zealand, US, Australia, and Canada and the international standard issued by the International Accounting Standards Committee require the classification of foreign operations for translation purposes into two mutually exclusive types: integrated or independent. This classification determines the translation method. In judging whether a foreign operation is either integrated or independent, the accounting standard requires the evaluation of five qualitative factors. The standard neither describes the judgement process nor identifies the relative importance of the determining factors. It has been asserted that this lack of clarity may yield dissimilar results for firms whose circumstances are similar and consequently may reduce the comparability of financial statements across firms. Using a repeated measures design, this paper examines the judgement of preparers of financial statements (financial controllers) in determining the designation of foreign operations for translation purposes. The results indicate that the relative importance of the determining factors is about equal. No support is found for the assertion that the use of qualitative factors in accounting standards results in dissimilar judgements (lack of consensus) across respondents. Further, the results show that the subjects demonstrated consistency and self‐insight in their judgements. The results also indicate that the judgements of respondents are not biased toward either classification of foreign operation. This suggests that the observed bias may be motivated by economic factors rather than the outcome of using the qualitative cues in the accounting standard. When the respondents were debriefed, several of them identified ‘managerial independence’ as another determining factor that has not been included in the standard.