Cache management is an important component in any network and it has even more importance in the Future Internet Architectures (FIAs) including Named Data Networking (NDN), because the caches play the key role in redu...
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With the increasing focus on healthcare, especially real-time analytics and self-diagnosis, the interest in capturing real-time patient data has increased significantly for both physicians and patients. The developmen...
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The industry that designs and promotes advertising products in television channels is constantly growing. For effective market analysis and contract validation, various commercial tracker systems are employed. However...
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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 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.
General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialise...
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General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialised inference rules, resulting in an important performance gain. In this work, we develop a first absorption algorithm for fuzzy DLs, and evaluate it over some ontologies.
Recently, the Internet of Things (IoT) has gained widespread popularity, yet its security remains a critical challenge due to the massive amount of information generated by connected devices. While encryption algorith...
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Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge that is imprecise or vague by nature. In fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful...
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Most energy management system applications are based on the positive sequence network model. Since most systems do not operate under fully-balanced operating conditions, methods to minimize the impact of the balanced ...
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ISBN:
(纸本)9781424480463
Most energy management system applications are based on the positive sequence network model. Since most systems do not operate under fully-balanced operating conditions, methods to minimize the impact of the balanced operation assumption on the network applications must be developed. This paper studies the impact of load imbalances on state estimation results by comparing state estimates using different measurement assumptions. In particular, the use of PMUs and systematic tuning of measurement weights are studied as practical ways of addressing this issue. Several scenarios are simulated using IEEE test systems with different measurement configurations and performance improvement of the state estimator in response to the proposed changes is illustrated by simulations.
Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. In the setting of fuzzy DLs, restricting to a finite set of degrees of truth has prov...
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Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. In the setting of fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful. In this paper, we propose finite fuzzy DLs as a generalization of existing approaches. We assume a finite totally ordered set of linguistic terms or labels, which is very useful in practice since expert knowledge is usually expressed using linguistic terms. Then, we consider any smooth t-norm defined over this set of degrees of truth. In particular, we focus on the finite fuzzy DL ALCH, studying some logical properties, and showing the decidability of the logic by presenting a reasoning preserving reduction to the non-fuzzy case.
We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment i...
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ISBN:
(纸本)9781586034528
We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment is used to derive an initial set of collocations, these are used in turn to improve the alignment and this new alignment is used to generate new collocations. This process is repeated until no more collocations are found. The final alignment and the set of collocations are used to train a translation model. We use a model that is based on finite state transducers and word clusters and has been modified to work with collocations in addition to single words. We present experiments in which we show that automatic collocations improve translation quality without prior linguistic information.
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