The TREC Federated Web Search track is intended to pro-mote research related to federated search in a realistic web setting, and hereto provides a large data collection gathered from a series of online search engines. This overview paper discusses the results of the first edition of the track, FedWeb

The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions

Information retrieval (IR) has changed considerably in the last years with the expansion of the Web (World Wide Web) and the advent of modern and inexpensive graphical user interfaces and mass storage devices. As a result, traditional IR textbooks have become quite out-of-date which has led

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Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far removed from that vision. This is due to three main problems: (1) the notion of context is still ill defined; (2) there is a lack of conceptual models and methods to help drive the design of context-aware applications; and (3) no tools are available to jump-start the development of context-aware applications. In this paper, we address these three problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a contextaware application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of context-aware applications. We illustrate the usefulness of the conceptual framework by describing a number of contextaware applications that h...

Grid technologies enable large-scale sharing of resources within formal or informal consortia of individuals and/or institutions: what are sometimes called virtual organizations. In these settings, the discovery, characterization, and monitoring of resources, services, and computations are challenging problems due to the considerable diversity, large numbers, dynamic behavior, and geographical distribution of the entities in which a user might be interested. Consequently, information services are a vital part of any Grid software infrastructure, providing fundamental mechanisms for discovery and monitoring, and hence for planning and adapting application behavior. We present here an information services architecture that addresses performance, security, scalability, and robustness requirements. Our architecture defines simple low-level enquiry and registration protocols that make it easy to incorporate individual entities into various information structures, such as aggregate directories that support a variety of different query languages and discovery strategies. These protocols can also be combined with other Grid protocols to construct additional higher-level services and capabilities such as brokering, monitoring, fault detection, and troubleshooting. Our architecture has been implemented as MDS-2, which forms part of the Globus Grid toolkit and has been widely deployed and applied.

This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations

... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.