A first approach to combining ontologies and defeasible argumentation for the semantic web

The Semantic Web is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web through the use of ontology definitions. Problems for modelling common-sense reasoning (such as reasoning with uncertainty or with incomplete and potentially inconsistent information) are also present when defining ontologies. In recent years, defeasible argumentation has succeeded as an approach to formalize such common-sense reasoning. Agents operating in multi-agent systems in the context of the Semantic Web need to interact with each other in order to achieve the goals stated by their users. In this paper we propose a XML-based language named XDeLP for ontology interchange among agents in the web.Eje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Multi-agent system for credit scoring

The use of multi-agent systems to solve complex problems in today’s world is not a new approach. Nevertheless, there has been a growing interest in using its properties in conjunction with machine learning and data mining techniques in order to build smarter systems. A multi-agent system able to classify and recommend attribute values in an instance of a dataset is presented and intended to provide to the end user a better understanding of both the classification in the dataset and client possibilities to obtain a good classification. The multi-agent system presented will have the ability to classify a user credit application and suggest different values for its attributes under assessment

Credit-Scoring Methods (in English)

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.banking sector, credit scoring, discrimination analysis, pattern recognition, retail loans

ESPOONERBAC_{{ERBAC}}: Enforcing Security Policies In Outsourced Environments

Data outsourcing is a growing business model offering services to individuals and enterprises for processing and storing a huge amount of data. It is not only economical but also promises higher availability, scalability, and more effective quality of service than in-house solutions. Despite all its benefits, data outsourcing raises serious security concerns for preserving data confidentiality. There are solutions for preserving confidentiality of data while supporting search on the data stored in outsourced environments. However, such solutions do not support access policies to regulate access to a particular subset of the stored data. For complex user management, large enterprises employ Role-Based Access Controls (RBAC) models for making access decisions based on the role in which a user is active in. However, RBAC models cannot be deployed in outsourced environments as they rely on trusted infrastructure in order to regulate access to the data. The deployment of RBAC models may reveal private information about sensitive data they aim to protect. In this paper, we aim at filling this gap by proposing \textbf{$\mathit{ESPOON_{ERBAC}}$} for enforcing RBAC policies in outsourced environments. $\mathit{ESPOON_{ERBAC}}$ enforces RBAC policies in an encrypted manner where a curious service provider may learn a very limited information about RBAC policies. We have implemented $\mathit{ESPOON_{ERBAC}}$ and provided its performance evaluation showing a limited overhead, thus confirming viability of our approach.Comment: The final version of this paper has been accepted for publication in Elsevier Computers & Security 2013. arXiv admin note: text overlap with arXiv:1306.482

Board composition, monitoring and credit risk: evidence from the UK banking industry

This paper examines the effects of board composition and monitoring on the credit risk in the UK banking sector. The study finds CEO duality, pay and board independence to have a positive and significant effect on credit risk of the UK banks. However, board size and women on board have a negative and significant influence on credit risk. Further analysis using sub-samples divided into pre-financial crisis, during the financial crisis and post crisis reinforce the robustness of our findings. Overall, the paper sheds light on the effectiveness of the within-firm monitoring arrangement, particularly, the effects of CEO power and board independence on credit risk decisions thereby contributing to the agency theory

Data Mining for Discrimination Discovery

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this paper, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-bylaw groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, e.g., census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this paper, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset