Adaptive query-based sampling of distributed collections

As part of a Distributed Information Retrieval system a de-scription of each remote information resource, archive or repository is usually stored centrally in order to facilitate resource selection. The ac-quisition ofprecise resourcedescriptionsistherefore animportantphase in Distributed Information Retrieval, as the quality of such represen-tations will impact on selection accuracy, and ultimately retrieval per-formance. While Query-Based Sampling is currently used for content discovery of uncooperative resources, the application of this technique is dependent upon heuristic guidelines to determine when a sufficiently accurate representation of each remote resource has been obtained. In this paper we address this shortcoming by using the Predictive Likelihood to provide both an indication of thequality of an acquired resource description estimate, and when a sufficiently good representation of a resource hasbeen obtained during Query-Based Sampling

The arguments of associations

This chapter considers associative solutions to “non‐linear” discrimination problems, such as negative patterning (A+ and B+ vs AB‐) and the biconditional discrimination (AB+ and CD+ vs AC‐ and BD‐). It is commonly assumed that the solution to these discriminations requires “configural” elements that are added to the compound of two stimuli. However, these discriminations can be solved by assuming that some elements of each stimulus are suppressed when two stimuli are presented in compound. Each of these approaches can solve patterning and biconditional discriminations because they allow some elements, as the arguments of associations, to have differential “presence” on reinforced versus nonreinforced trials, and thus differential associability and control over responding. The chapter then presents a more specific version of one of these models, describing how interactions between stimuli, particularly the competition for attention, provide a mechanism whereby some elements are more suppressed than others when stimuli are presented simultaneously as a compound

Agents, simulated users and humans : an analysis of performance and behaviour

Most of the current models that are used to simulate users in Interactive Information Retrieval (IIR) lack realism and agency. Such models generally make decisions in a stochastic manner, without recourse to the actual information encountered or the underlying information need. In this paper, we develop a more sophisticated model of the user that includes their cognitive state within the simulation. The cognitive state maintains data about what the simulated user knows, has done and has seen, along with representations of what it considers attractive and relevant. Decisions to inspect or judge are then made based upon the simulated user's current state, rather than stochastically. In the context of ad-hoc topic retrieval, we evaluate the quality of the simulated users and agents by comparing their behaviour and performance against 48 human subjects under the same conditions, topics, time constraints, costs and search engine. Our findings show that while naive configurations of simulated users and agents substantially outperform our human subjects, their search behaviour is notably different from actual searchers. However, more sophisticated search agents can be tuned to act more like actual searchers providing greater realism. This innovation advances the state of the art in simulation, from simulated users towards autonomous agents. It provides a much needed step forward enabling the creation of more realistic simulations, while also motivating the development of more advanced cognitive agents and tools to help support and augment human searchers. Future work will focus not only on the pragmatics of tuning and training such agents for topic retrieval, but will also look at developing agents for other tasks and contexts such as collaborative search and slow search

Understanding constraint expressions in large conceptual schemas by automatic filtering

Human understanding of constraint expressions (also called schema rules) in large conceptual schemas is very di cult. This is due to the fact that the elements (entity types, attributes, relationship types) involved in an expression are de ned in di fferent places in the schema, which may be very distant from each other and embedded in an intricate web of irrelevant elements. The problem is insignifi cant when the conceptual schema is small, but very signi cant when it is large. In this paper we describe a novel method that, given a set of constraint expressions and a large conceptual schema, automatically filters the conceptual schema, obtaining a smaller one that contains the elements of interest for the understanding of the expressions. We also show the application of the method to the important case of understanding the specication of event types, whose constraint expressions consists of a set of pre and postconditions. We have evaluated the method by means of its application to a set of large conceptual schemas. The results show that the method is eff ective and e cient. We deal with conceptual schemas in UML/OCL, but the method can be adapted to other languages.Peer ReviewedPreprin

Particle separation by horizontal deflection in paramagnetic fluid

This paper describes the horizontal deflection behaviour of the streams of particles in paramagnetic fluids under a high-gradient superconducting magnetic field, which is the continued work on the exploration of particle magneto-Archimedes levitation. Based on the previous work on the horizontal deflection of a single particle, a glass box and collector had been designed to observe the movement of particle group in paramagnetic fluids. To get the exact separation efficiency, the method of "sink-float" involved the high density fluid polytungstate (dense medium separation) and MLA (Mineral Liberation Analyser) was performed. It was found that the particles were deflected and settled at certain positions on the container floor due to the combined forces of gravity and magneto-Archimedes forces as well as a lateral buoyancy (displacement) force. Mineral particles with different densities and susceptibilities could be deflected to different positions, thus producing groups of similar types of particles. The work described here, although in its infancy, could form the basis of new approach of separating particles based on a combination of susceptibility and density

A Generative Programming Approach to Interactive Information Retrieval:Insights and Experiences

Abstract. We describe the application of generative programming to a problem in interactive information retrieval. The particular interactive information retrieval problem we study is the support for ‘out of turn interaction ’ with a website – how a user can communicate input to a website when the site is not soliciting such information on the current page, but will do so on a subsequent page. Our solution approach makes generous use of program transformations (partial evaluation, currying, and slicing) to delay the site’s current solicitation for input until after the user’s out-of-turn input is processed. We illustrate how studying out-of-turn interaction through a generative lens leads to several valuable in-sights: (i) the concept of a web dialog, (ii) an improved understanding of web taxonomies, and (iii) new web interaction techniques and interfaces. These notions allow us to cast the design of interactive (and responsive) websites in terms of the underlying dialog structure and, further, suggest a simple implementation strategy with a clean separation of concerns. We also highlight new research directions opened up by the generative pro-gramming approach to interactive information retrieval such as the idea of web interaction axioms.

Moving towards adaptive search in digital libraries

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour. © 2011 Springer-Verlag