Evolvable Fuzzy System for ATM Cell Scheduling
In this paper, we propose an evolvable fuzzy system for ATM cell scheduling. When the scenarios of cell flows in an ATM network change dramatically, traditional scheduling algorithms, first-in-first-out (FIFO) and static priority, which employ static switching scheme may see their efficiency deteriorate. An alternative is to use dynamically weighted priority, which changes the priority based on the cell delay. Still, the algorithm per se is fixed and on the hardware level, nothing changes. Our goal in this paper is to achieve hardware adaptability and demonstrate its applicability in ATM switching. To do this, we proposed an evolvable fuzzy system . With evolvable fuzzy system (EFS), the system undergoes transformation in response to changes in the cell patterns in order to maintain operational efficiency. We envisage that such a system is potentially useful for demanding real-time dynamic control applications.

title: An intrinsic evolvable and online adaptive evolvable fuzzy hardware scheme for packet switching network
abstract: Most published works usually focused on extrinsic evolvable hardware (EHW), and in some instances, intrinsic but offline adaptive EHW. In this paper, we describe an intrinsic evolvable and online adaptive EHW to solve packet switching problem. Based on the coding and evolution scheme, we refer to it as evolvable fuzzy hardware (EFH), an extension of our proposed evolvable fuzzy system (EFS) by Li and Lim (2003) and reconfigurable fuzzy inference chip (RFIC) by Cao et. al. EFH is a good framework for intrinsic evolvability and online adaptation.

title: OntSum: A Semantic Query Routing Scheme in P2P Networks Based on Concise Ontology Indexing
abstract: Locating desirable resources is very important for a large distributed system. However, the distributed, heterogeneous, and unstructured nature of the system makes this issue very challenging. The discovering mechanism has to be not only semantically rich, in order to cope with complex queries, but also scalable to handle large numbers of information sources. In this paper, we address these problems by proposing OntSum, an efficient peer-to-peer query routing scheme based on concise ontology indexing. Unlike most existing systems, our system does not assume a global ontology but heterogeneous ontologies. Peers in the system use their own ontologies to describe their resource knowledge and the network topology is adjusted according to peers' ontological properties. A novel indexing strategy enables forwarding queries only to semantically related nodes. The architecture improves interoperability among network participants and aids efficient resource discovery through an expressive query language.

title: Stochastic modeling of volume images with a 3-D hidden Markov model
abstract: Over the years, researchers in the image analysis community have successfully used various statistical modeling methods to segment, classify and annotate digital images. In this paper, we propose a 3-D hidden Markov model (HMM) for volume image modeling. A computationally efficient algorithm is developed to estimate the model. The 3-D HMM is applied to volume image segmentation and tested using synthetic images with ground truth. Experiments have demonstrated that 3-D HMM outperforms Gaussian mixture model based clustering by an order of magnitude in accuracy.

title: Direct interesting rule generation
abstract: An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation efficiency. We discuss properties of informative rule set and conclude that the informative rule set includes all interesting rules measured by many commonly used interestingness criteria, and that rules excluded by the informative rule set are forwardly prunable, i.e. they can be removed in the rule generation process instead of post pruning. Based on these properties, we propose a direct interesting rule generation algorithm, DIG, to directly generate interesting rules defined by any of 12 interestingness criteria. We further show experimentally that DIG is faster and uses less memory than Apriori.

title: Discovering and ranking important rules
abstract: Decision rules generated from reducts can fully describe a data set. We introduce a new method of evaluating rules by taking advantage of rough sets theory. We consider rules generated from the original data set as attributes in the new constructed decision table. Reducts generated from this new decision table contain essential attributes, which are the rules. Only important rules are contained in the reducts. Experiments on an artificial data set and a medical data set show that the "reduct rules" are more important, and this new method provides an automatic and effective way of ranking rules.

title: The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
abstract: Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.