Second-Order Belief Hidden Markov Models

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model

Designing a Belief Function-Based Accessibility Indicator to Improve Web Browsing for Disabled People

The purpose of this study is to provide an accessibility measure of web-pages, in order to draw disabled users to the pages that have been designed to be ac-cessible to them. Our approach is based on the theory of belief functions, using data which are supplied by reports produced by automatic web content assessors that test the validity of criteria defined by the WCAG 2.0 guidelines proposed by the World Wide Web Consortium (W3C) organization. These tools detect errors with gradual degrees of certainty and their results do not always converge. For these reasons, to fuse information coming from the reports, we choose to use an information fusion framework which can take into account the uncertainty and imprecision of infor-mation as well as divergences between sources. Our accessibility indicator covers four categories of deficiencies. To validate the theoretical approach in this context, we propose an evaluation completed on a corpus of 100 most visited French news websites, and 2 evaluation tools. The results obtained illustrate the interest of our accessibility indicator

Evidential-EM Algorithm Applied to Progressively Censored Observations

Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incom-plete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior infor-mation with the current imprecise knowledge conveyed by the observed data

Belief Hierarchical Clustering

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool

Post-grooming furunculose bij een hond

A five-year-old, intact, male labrador retriever was presented on emergency with general complaints of fever, lethargy and anorexia. During clinical examination, very painful skin lesions were noticed dorsally on the neck and back, and there was also a clear left apical systolic murmur with a degree of 4/6. On histopathological examination of the lesional skin, there was rupture of the follicle wall surrounded with a pronounced suppurative inflammation. The dog was diagnosed with post-grooming furunculosis and mitral valve endocardiosis ACVIM stage B2. Culture of the lesional skin and the shampoo used to wash the dog prior to the onset of the skin lesions revealed the presence of the same bacteria, evidencing a clear link between the bathing and development of the skin lesions

Charting new territory for organizational ethnography : Insights from a team-based video ethnography

Purpose: Increasing complexity, fragmentation, mobility, pace, and technological intermediation of organizational life make “being there” increasingly difficult. Where do ethnographers have to be, when, for how long, and with whom to “be there” and grasp the practices, norms, and values that make the situation meaningful to natives? These novel complexities call for new forms of organizational ethnography. The purpose of this paper is to discuss the above issues. Design/methodology/approach: In this paper, the authors respond to these calls for innovative ethnographic methods in two ways. First, the paper reports on the practices and ethnographic experiences of conducting a year-long team-based video ethnography of reinsurance trading in London. Findings: Second, drawing on these experiences, the paper proposes a framework for systematizing new approaches to organizational ethnography and visualizing the ways in which they are “expanding” ethnography as it was traditionally practiced. Originality/value: The paper contributes to the ethnographic literature in three ways: first, the paper develops a framework for charting new approaches to ethnography and highlight its different dimensions – site, instrument, and fieldworker. Second, the paper outlines the opportunities and challenges associated with these expansions, specifically with regard to research design, analytical rigour, and communication of results. Third, drawing on the previous two contributions, the paper highlights configurations of methodological expansions on the aforementioned dimensions that are more promising than others in leveraging new technologies and approaches to claim new territory for organizational ethnography and enhance its relevance for understanding today's multifarious organizational realities

Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

Evidential Communities for Complex Networks

Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure