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Open authorization (OAuth) is an open protocol, which allows secure authorization in a simple and standardized way from third-party applications accessing online services, based on the representational state transfer (REST) web architecture. OAuth has been designed to provide an authorization layer, typically on top of a secure transport layer such as HTTPS. The Internet of Things (IoTs) refers to the interconnection of billions of resource-constrained devices, denoted as smart objects, in an Internet-like structure. Smart objects have limited processing/memory capabilities and operate in challenging environments, such as low-power and lossy networks. IP has been foreseen as the standard communication protocol for smart object interoperability. The Internet engineering task force constrainedRESTful environments working group has defined the constrained application protocol (CoAP) as a generic web protocol for RESTful-constrained environments, targeting machine-tomachine applications, which maps to HTTP for integration with the existing web. In this paper, we propose an architecture targeting HTTP/CoAP services to provide an authorization framework, which can be integrated by invoking an external oauth-based authorization service (OAS). The overall architecture is denoted as IoT-OAS. We also present an overview of significant IoT application scenarios. The IoT-OAS architecture is meant to be flexible, highly configurable, and easy to integrate with existing services. Among the advantages achieved by delegating the authorization functionality, IoT scenarios benefit by: 1) lower processing load with respect to solutions, where access control is implemented on the smart object; 2) fine-grained (remote) customization of access policies; and 3) scalability, without the need to operate directly on the device.

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The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect “multi-modal” data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering complex biology questions, its value for predicting clinical phenotypes and contributions of each type of input remain unknown. We obtained 47 datasets/predictive tasks that in total span over 9 data modalities and executed analytic experiments for predicting various clinical phenotypes and outcomes. First, we analyzed each modality separately using uni-modal approaches based on several state-of-the-art supervised classification and feature selection methods. Then, we applied integrative multi-modal classification techniques. We have found that gene expression is the most predictively informative modality. Other modalities such as protein expression, miRNA expression, and DNA methylation also provide highly predictive results, which are often statistically comparable but not superior to gene expression data. Integrative multi-modal analyses generally do not increase predictive signal compared to gene expression data.

Abstract. The increasing popularity of location based social services such as Facebook Places, Foursquare and Google Latitude, solicits a new trend in fusing social networking with real world sensing. The availability of a wide range of sensing technologies in our everyday environment presents an opportunity to further enrich social networking systems with fine-grained real-world sensing. However, the introduction of passive sensing into a social networking application disrupts the traditional, user-initiated input to social services, raising both privacy and acceptability concerns. In this work we present an empirical study of the introduction of a sensor-driven social sharing application within the working environment of a research institution. Our study is based on a real deployment of a system that involves location tracking, conversation monitoring, and interaction with physical objects. By utilizing surveys, interviews and experience sampling techniques, we report on our findings regarding privacy and user experience issues, and significant factors that can affect acceptability of such services by the users. Our results suggest that such systems deliver significant value in the form of self reflection and comparison with others, while privacy concerns are raised primarily by the limited control over the way individuals are projected to their peers.

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The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included.