Creativity in Digital Libraries and Information Retrieval Environments
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: 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: A new neuro-fuzzy identification model of nonlinear dynamic systems
abstract: Multilayer neural networks with error back-propagation learning algorithms have the capability of learning an arbitrary continuous nonlinear function with examples of input and output sample pairs and a great potential for identifying nonlinear dynamic systems with unknown characteristics. A fuzzy system is composed of fuzzification of input, reasoning (or inference) by fuzzy rules, and defuzzification of fuzzy output. In general, there are some difficulties in finding suitable fuzzification and defuzzification methods and fuzzy rules. Formation of fuzzy rules with complex input-output relationships can be replaced by building neural networks with input and output sample pairs. A neuro-fuzzy identifier is proposed to have a cascaded structure of fuzzification, neural network, and defuzzification and additionally is shown to be able to compensate for fuzzification and defuzzification error of fuzzy logic. Computer simulation shows that neuro-fuzzy identification is very effective in modeling the fuzzy system whose fuzzy rules can not be obtained easily.

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: Scalability study of the ad hoc on-demand distance vector routing protocol
abstract: As mobile networking continues to experience increasing popularity, the need to connect large numbers of wireless devices will become more prevalent. Many recent proposals for ad hoc routing have certain characteristics that may limit their scalability to large networks. This paper examines five different combinations of modifications that may be incorporated into virtually any on-demand protocol in order to improve its scalability. The scalability of current on-demand routing protocols is evaluated through the selection of a representative from this class of protocols. The performance of the un-modified on-demand protocol is compared against that of it combined with each of the scalability modifications. Each scheme's behavior is analyzed in networks as large as 10,000 nodes through detailed simulation. Based on the observations, conclusions are drawn as to the expected scalability improvement that can be achieved by each modification.

title: Cooperative peer groups in NICE
abstract: We present a distributed scheme for trust inference in peer-to-peer networks. Our work is in the 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 non-cooperative 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: Training Algorithm with Incomplete Data for Feed-Forward Neural Networks
abstract: A new algorithm is developed to train feed-forward neural networks for non-linear input-to-output mappings with small incomplete data in arbitrary distributions. The developed Training-EStimation-Training (TEST) algorithm consists of 3 steps, i.e., (1) training with the complete portion of the training data set, (2) estimation of the missing attributes with the trained neural networks, and (3) re-training the neural networks with the whole data set. Error back propagation is still applicable to estimate the missing attributes. Unlike other training methods with missing data, it does not assume data distribution models which may not be appropriate for small training data. The developed TEST algorithm is first tested for the Iris benchmark data. By randomly removing some attributes from the complete data set and estimating the values latter, accuracy of the TEST algorithm is demonstrated. Then it is applied to the Diabetes benchmark data, of which about 50% contains missing attributes. Compared with other existing algorithms, the proposed TEST algorithm results in much better recognition accuracy for test data.