To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algor...
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To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algorithms for fuzzy DLs is based on a combination of tableau algorithms and Operational Research (OR) problems, specifically using Mixed Integer Linear Programming (MILP). In this paper, we present a MILP-based tableau procedure that allows to reason within fuzzy ALCB, i.e., ALC with individual value restrictions. Interestingly, unlike classical tableau procedures, our tableau algorithm is deterministic, in the sense that it defers the inherent non-determinism in ALCB to a MILP solver.
The Business Process Modelling and Notation (BPMN) is a widely-accepted standard for process modelling, which can be used to model the clinical processes contained in guidelines. computer systems based on guidelines n...
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The Business Process Modelling and Notation (BPMN) is a widely-accepted standard for process modelling, which can be used to model the clinical processes contained in guidelines. computer systems based on guidelines need to embed these clinical processes, e.g. using a computer-Interpretable Guideline (CIG) language. However, encoding guidelines in a CIG language is a difficult task which is usually performed by technical staff. Building on our previous work, the transformation-based refinement of guideline models, in this paper we describe an algorithm to transform BPMN models into the SDA CIG language. The use of BPMN has the potential to empower clinicians in the modelling task. In combination with the transformation algorithm, this can lead to an increased adoption of CIG languages, SDA and others.
The goal of this paper is to bridge the paradigms of Linked Data and Conceptual Modeling, which have been developed from quite distinct concerns, although certain opportunities may stimulate the evolution of the Web t...
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The goal of this paper is to bridge the paradigms of Linked Data and Conceptual Modeling, which have been developed from quite distinct concerns, although certain opportunities may stimulate the evolution of the Web towards a new type of knowledge space driven by diagrammatic models. To this end, the work at hand investigates structural patterns in a multitude of modeling languages accumulated over time within the Open Model Initiative Laboratory and defines for each pattern transformation rules that produce graph-based model serializations, thus enabling the processing of diagrammatic models using the Web of Data tech-nological space and practices.
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining ...
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ISBN:
(纸本)9781450328630
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.
In public vs. private solutions (i.e. Cloud vs. In-house, or leased vs. Owned) for storage, both alternatives have their pros and cons. Cloud storage can easily adapt to the company needs, but exhibits a higher unit c...
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In public vs. private solutions (i.e. Cloud vs. In-house, or leased vs. Owned) for storage, both alternatives have their pros and cons. Cloud storage can easily adapt to the company needs, but exhibits a higher unit cost than in-house solutions. On the other hand, if the company relies on its own storage equipment, it must periodically purchase it on the basis of forecasts, which may prove imprecise and lead to idle equipment. In this paper, we propose a comparative evaluation tool for the two procurement approaches, where the cloud can play the role of either exclusive storage medium or supplement to in-house equipment (in the case of underestimation of storage needs). The tool considers the impact of equipment acquisition intervals and forecast accuracy over a long time horizon, adopting a Geometric Brownian Motion model for the evolution of storage capacity needs, it can be employed as a decision support tool for procurement decisions.
In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p p-priors are used to impose sparsity on the adaptive...
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Many real classification tasks are oriented to sequence (neighbor) labeling, that is, assigning a label to every sample of a signal while taking into account the sequentiality (or neighborhood) of the samples. This is...
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An interval estimate is provided for the Herfindahl-Hirschman Index (HHI) when the knowledge about the market is incomplete, and we know just the largest market shares. Two rigorous bounds are provided for the HHI, wi...
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An interval estimate is provided for the Herfindahl-Hirschman Index (HHI) when the knowledge about the market is incomplete, and we know just the largest market shares. Two rigorous bounds are provided for the HHI, without any further assumptions. Though the interval gets wider as the sum of the known market shares gets smaller, the estimate proves to be quite tight even when the fraction of the market that we do not know in detail is as high as 30%. This robustness is shown through three examples, considering respectively a set of real data and two sets of synthetic data, with the company sizes (a proxy for market shares) following respectively a Zipf law and a Pareto distribution.
In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p < 1, l_p-priors are used to impose sparsity on th...
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ISBN:
(纸本)9781479946037
In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p < 1, l_p-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between the prior model used and the independent Gaussian prior model is provided. The posterior probabilities are used to classify new samples and to define two Active Learning methods to improve classifier performance: Minimum Probability and Maximum Entropy. In the experimental section the proposed Bayesian framework is applied to Image Segmentation problems on both synthetic and real datasets, showing higher accuracy than state-of-the-art approaches.
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