. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts

learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any

Service-Oriented Computing (SOC) is the computing paradigm that utilizes services as fundamental elements for developing applications/solutions. To build the service model, SOC relies on the Service Oriented Architecture (SOA), which is a way of reorganizing software applications and infrastructure

The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a “generic ” error-processing system associated with the anterior cingulate cortex. The existence

present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented

that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive

are not preplanned by top-down composition models. Consequently, the management of service-oriented applications is difficult due to changing interaction and behavior patterns that possibly contradict and result in faults from varying conditions and misbehavior in the network. In this paper we present a self-adaptation

with aspect-oriented techniques towards coping with dynamic adaptability. As a core further step, we semantically govern this aspectual service composition modeling with a tailored rewriting logic theory. More precisely, we extend to Maude language to faithfully specify and validate this model.

This paper presents a Context-Aware Dynamic Software Product Line (DSPL) for building service oriented applications and adapting them at runtime in accordance with their using context. This DSPL, named CAPucine for Context-Aware Service-Oriented Product Line is based on two different processes

We propose uncertainty relations for the different coordinates of spacetime events, motivated by Heisenberg’s principle and by Einstein’s theory of classical gravity. A model of Quantum Spacetime is then discussed where the commutation relations exactly implement our uncertainty relations. We