Subjective Relevance: Implications on Interface Design for Information Retrieval Systems
Information retrieval (IR) systems are traditionally developed using the objective relevance approach based on the “best match” principle assuming that users can specify their needs in queries and that the documents retrieved are relevant to them. This paper advocates a subjective relevance (SR) approach to value-add objective relevance and address its limitations by considering relevance in terms of users' needs and contexts. A pilot study was conducted to elicit features on SR from experts and novices. Elicited features were then analyzed using characteristics of SR types and stages in information seeking to inform the design of an IR interface supporting SR. The paper presents initial work towards the design and development of user-centered IR systems that prompt features supporting the four main types of SR.

title: Creative Interface Design for Information Seeking
abstract: Inspired by Weisberg's argument that everyone is creative, and exists in varying degrees, this PhD proposal aims to investigate the relationship between creativity and information seeking, and explores implications for interface design with design features that might support users' information seeking behaviours. A holistic model of creativity was proposed synthesizing established models in creativity. Based on the model, five approaches investigating relationships between creativity and information seeking were highlighted. Initial work carried out attempted to understand users' information seeking behaviours to elicit supportive features. Discussion of on-going work is also presented to indicate approaches proposed to design, implement, and evaluate a prototype with design features to support information seeking.

title: Creativity in Digital Libraries and Information Retrieval Environments
abstract: Inspired by Weisberg’s [20] argument that creativity exists in everyone and on the assumption that every information seeker is creative and undergoes a creative process in information seeking, this paper synthesizes a holistic “creativity” model and “information seeking” model to identify stages in a creative process for information seeking. A pilot study was conducted to verify these stages and also examine if present digital libraries and information retrieval environments provide “creative” features to support creative information seeking. We discussed these initial findings and made recommendations for further work to better support creative information seeking in digital libraries and information retrieval environments.

title: Subjective Relevance: Implications on Digital Libraries for Experts and Novices
abstract: Traditional information retrieval (IR) systems are developed based on the “best match” principle which assumes that users can specify their needs in a query and documents retrieved are relevant to users. However, this objective measure of relevance is limited as it does not consider differences in experts' and novices' knowledge and context. This paper presents initial work towards addressing this limitation by investigating subjective relevance (that can include topical, pertinence, situational, and motivational relevance) features that can be incorporated into digital library interfaces to help experts and novices search and judge relevance more effectively. A pilot study was conducted to elicit initial subjective relevance features from experts and novices. The paper concludes with a discussion of elicited design features and their implications for user-centered digital libraries.

title: Cooperative peer groups in NICE
abstract: A distributed scheme for trust inference in peer-to-peer networks is presented. Our work is in context of the NICE system, which is a platform for implementing cooperative applications over the Internet. We describe a technique for efficiently storing user reputation information in a completely decentralized manner, and show how this information can be used to efficiently identify noncooperative users in NICE. We present a simulation based study of our algorithms, in which we show our scheme scales to thousands of users using modest amounts of storage, processing, and bandwidth at any individual node. Lastly, we show that our scheme is robust and can form cooperative groups in systems where the vast majority of users are malicious.

title: SmartSeer: Using a DHT to Process Continuous Queries Over Peer-to-Peer Networks
abstract: As the academic world moves away from physical journals and proceedings towards online document repositories, the ability to efficiently locate work of interest among the torrent of newly-generated papers will become increasingly important. To aid in this endeavor, we designed SmartSeer, a system that allows users to register personalized continuous queries over the CiteSeer database of technical documents. Users are then alerted whenever papers that match their queries are put online. SmartSeer has two main design requirements. First, to allow effective information retrieval, it should support rich continuous queries (as opposed to simple keyword searches). Second, to make effective use of donated infrastructure, it should be capable of running on a loosely maintained group of unreliable machines spread across multiple organizations (as opposed to assuming a reliable and tightly coupled distributed system). Existing work on distributed continuous query systems fails at least one of these requirements. Our design for SmartSeer is based on Distributed Hash Tables (DHTs), and thereby leverages previous work on DHT-based query systems. A prototype of SmartSeer has been implemented and evaluated on Planetlab. Though we evaluate our design only for the SmartSeer application, we believe it also provides useful insights into other distributed and rich continuous query systems (web alerts, news alerts etc).

title: Multisensor raster and vector data fusion based on uncertainty modeling
abstract: We propose a new methodology for fusing temporally changing multisensor raster and vector data by developing a spatially and temporally varying uncertainty model of acquired and transformed multisensor measurements. The proposed uncertainty model includes errors due to (1) each sensor by itself, e.g., sensor noise; (2) transformations of measured values to obtain comparable physical entities for data fusion and/or to calibrate sensor measurements; (3) vector data spatial interpolation that is needed to match different spatial resolutions of multisensor data; and (4) temporal interpolation that has to take place if multisensor acquisitions are not accurately synchronized. The proposed methodology was tested using simulated data with varying (a) amount of sensor noise, (b) spatial offset of point sensors generating vector data, and (c) model complexity of the underlying physical phenomenon. We demonstrated the multisensor fusion approach with a data set from a structural health monitoring application domain.

title: Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables
abstract: Recently, it is recognized that nonlinear relationships among latent variables in a structural equation model are important. In this article, maximum likelihood (ML) analysis of a general nonlinear structural equation model that contains fixed covariates in the measurement equation and the nonlinear structural equation is investigated. A MCEM algorithm is implemented to obtain the ML estimates, in which the E-step is completed with the help of a hybrid algorithm that combines the Gibbs sampler and the Metropolis–Hastings algorithm whilst the M-step is completed by conditional maximization. The importance sampling is employed to compute the observed-data likelihood in the Bayesian Information Criterion for model comparison. The methodology is illustrated with a simulation study and a real example.

title: Merging Back-propagation and Hebbian Learning Rules for Robust Classifications
abstract: By imposing saturation requirements on hidden-layer neural activations, a new learning algorithm is developed to improve robustness on classification performance of a multi-layer Perceptron. Derivatives of the sigmoid functions at hidden-layers are added to the standard output error with relative significance factors, and the total error is minimized by the steepest-descent method. The additional gradient-descent terms become Hebbian, and this new algorithm merges two popular learning algorithms, i.e., error back-propagation and Hebbian learning rules. Only slight modifications are needed for the standard back-propagation algorithm, and additional computational requirements are negligible. This saturation requirement effectively reduces output sensitivity to the input, which results in improved robustness and better generalization for classifier networks. Also distributed representations at hidden-layers are successfully suppressed to accomplish efficient utilization of hidden neurons. Computer simulations demonstrates much faster learning convergence as well as improved robustness for classifications and hetero-associations of binary patterns.