Gasping for AIR Why we need Linked Rules and Justifications on the Semantic Web

The Semantic Web is a distributed model for publishing, utilizing and extending structured information using Web protocols. One of the main goals of this technology is to automate the retrieval and integration of data and to enable the inference of interesting results. This automation requires logics and rule languages that make inferences, choose courses of action, and answer questions. The openness of the Web, however, leads to several issues including the handling of inconsistencies, integration of diverse information, and the determination of the quality and trustworthiness of the data. AIR is a Semantic Web-based rule language that provides this functionality while focusing on generating and tracking explanations for its inferences and actions as well as conforming to Linked Data principles. AIR supports Linked Rules, which allow rules to be combined, re-used and extended in a manner similar to Linked Data. Additionally, AIR explanations themselves are Semantic Web data so they can be used for further reasoning. In this paper we present an overview of AIR, discuss its potential as a Web rule language by providing examples of how its features can be leveraged for different inference requirements, and describe how justifications are represented and generated.This material is based upon work supported by the National Science Foundation under Award No. CNS-0831442, by the Air Force Office of Scientific Research under Award No. FA9550-09-1-0152, and by Intelligence Advanced Research Projects Activity under Award No. FA8750-07-2-0031

DPD-InfoGAN: Differentially Private Distributed InfoGAN

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy

Data Modeling for Ambient Home Care Systems

Ambient assisted living (AAL) services are usually designed to work on the assumption that real-time context information about the user and his environment is available. Systems handling acquisition and context inference need to use a versatile data model, expressive and scalable enough to handle complex context and heterogeneous data sources. In this paper, we describe an ontology to be used in a system providing AAL services. The ontology reuses previous ontologies and models the partners in the value chain and their service offering. With our proposal, we aim at having an effective AAL data model, easily adaptable to specific domain needs and services