We introduce Dimple, a fully open-source API for probabilistic modeling. Dimple allows the user to specify probabilistic models in the form of graphical models, Bayesian networks, or factor graphs, and performs inference (by automatically deriving an inference engine from a variety of algorithms) on the model. Dimple also serves as a compiler for GP5, a hardware accelerator for inference.

A general overview of the system architecture for knowledge-based monitoring of in-tegrated networks is presented. A performance oriented model of integrated networksis introduced. A Tra c Control Architecture (TCA) for management and control ofnetwork resources is proposed. The proposed TCA ts into an integrated referencemodel for network management and control. A model for...

This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...

OWL 2 RL was standardized as a less expressive but scalable subset of OWL 2 that allows a forward-chaining implementation. However, building an enterprise-scale forward-chaining based inference engine that can 1) take advantage of modern multi-core computer architectures, and 2) efficiently update inference for additions remains a challenge. In this paper, we present an OWL 2 RL inference engin...

The main goal of this work is to introduce theoretical background of the extended forward inference algorithm. Proposed algorithm allow to continue inference after its failure. Inference failure means that the inference engine is unable to obtain the solutions — the new facts or goals confirmation. Two-phase extension of classical inference algorithm is considered. In the first phase, classical...