38145107
Measuring the quality of network visualization
A quantitative method is developed for measuring the quality of network visualizations in terms of log-likelihood metrics resulted from expectation maximization (EM) clustering intrinsic and extrinsic attributes of network nodes
206109439
How did university departments interweave the Web: A study of connectivity and underlying factors
This paper presents two studies of the use of the WWW in Scottish universities and American land-grant universities. First, we investigated the relationship between the organisational profile of a university department in Scotland and its structural connectivity on the WWW. A Spearman rank order correlation analysis revealed a number of strong correlation relationships between structural connectivity measures and the organisational profile based on research assessment exercise ratings, teaching quality assessments, student‐staff ratios and funding levels. Linkage patterns from 13 Scottish academic sites to commercial sites in Britain and America highlighted the impact of culture and the appropriateness of information technologies on the acceptance of the WWW. The second study is a content survey of WWW-based education activities in American land-grant universities to investigate successful applications of these enabling techniques in education. The two studies together highlighted cultural, political and technological interactions in the use of the WWW. q 1998 Elsevier Science B.V. All rights reserved
45076304
Spatial Semantic Virtual Worlds: The Structural Duality and Beyond
15424500
Semantically modified diffusion limited aggregation for visualizing large-scale networks
Diffusion-limited aggregation (DLA) is a model of fractal growth. Computer models can simulate the fast aggregation of millions of particles. We propose a modified version of DLA, called semantically modified DLA (SM-DLA), for visualizing large-scale networks. SM-DLA introduces similarity measures between particles so that instead of attaching to the nearest particle in the aggregation, a new particle is stochastically directed to attach to particles that are similar to it. The results of our initial experiment with a co-citation network using SM-DLA are encouraging, suggesting that the algorithm has the potential as an alternative paradigm for visualizing large-scale networks. Further studies in this direction are recommended.
46129433
Behavioural Patterns of Collaborative Writing with Hypertext - A State Transition Approach
This study investigates behavioural patterns of collaborative writing with a hypertext system by using a state-transition approach. State-transition models are empirically developed for capturing the dynamic nature of collaborative writing. Users were frequently engaged in tasks such as exploration, organization, and editing, whereas the use of collaborative support functions was transient in nature. The study shows that state-transition analysis is an important approach to task analysis, requirements engineering, and human-computer interaction studies. This study has produced some valuable experiences and lessons for researchers and practitioners in collaborative writing.
468820
Tracing knowledge diffusion
Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy.
14219803
A new method to measure the semantic similarity of GO terms
MOTIVATION
Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources.


RESULTS
To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery.


AVAILABILITY
The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME.


SUPPLEMENTARY INFORMATION
http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.
2980352
Global Memory Net Offers New Innovative Access to Tsurumi's Old Japanese Waka Poems and Tales, and Maps
This paper describes how Global Memory Net (GMNet) has been able to provide new kind of innovative access to the invaluable content of the classical Japanese ancient poems and maps at Tsurumi University that was not available for public access before. The collaboration began with the development of a prototype collection, based on images included in two publications of the Tsurumi University Library – the Eighty Selections of Waka Poems and Tales from the Classical Japanese Literature and the Japanese Maps in the Old Age. As the project developed, coinciding with the technology development of GMNet in bilingual retrieval as well as with sound presentations, the inclusion of sound files for each of the Waka selection was considered a very desirable feature since Waka poems are generally only readable by very small number of specialists. 
 
The paper will present a bird's eye view of how Tsurumi's rare collection is organized, presented with much enhanced access in GMNetsystem. Through this project, the Tsurumi team has gained considerable important experiences. The overall process for them was very time consuming even though the technology of GMNet was already in place. These valuable experiences will be discussed and shared.
18159266
A Distributed Simulation-Based Computational Intelligence Algorithm for Nanoscale Semiconductor Device Inverse Problem
In this paper, a distributed simulation-based computational intelligence algorithm for inverse problem of nanoscale semiconductor device is presented. This approach features a simulation-based optimization strategy, and mainly integrates the semiconductor process simulation, semiconductor device simulation, evolutionary strategy, and empirical knowledge on a distributed computing environment. For a set of given target current-voltage (I-V) curves of metal-oxide-semiconductor field effect transistors (MOSFETs) devices, the developed prototype executes evolutionary tasks to solve an inverse doping profile problem, and therefore optimize fabrication recipes. In the evolutionary loop, the established management server allocates the jobs of process simulation and device simulation on a PC-based Linux cluster with message passing interface (MPI) libraries. Good benchmark results including the speed-up, the load balancing, and the parallel efficiency are presented. Computed results, compared with the realistic measured data of 65 nm n-type MOSFET, show the accuracy and robustness of the method.
6671949
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach
Gene expression profiling by microarray technology is usually difficult to interpret into a simpler pattern. One approach to resolve the complexity of gene expression profiles is the application of artificial neural networks (ANNs). A potential difficulty in this strategy, however, is that the non-linear nature of ANN makes it essentially a 'black-box' computation process. Addition of a fuzzy logic approach is useful because it can complement ANN by explicitly specifying membership function during computation. We employed a hybrid approach of neural network and fuzzy logic to further analyze a published microarray study of gene responses to eight bacteria in human macrophages. The original analysis by hierarchical clustering found common gene responses to all bacteria but did not address individual responses. Our method allowed exploration of the gene response of the host to individual bacterium. We implemented a two-layer, feed-forward neural network containing the principle of 'competitive learning' (i.e. 'winner-take-all'). The weights of the trained neural network were fed into a fuzzy logic inference system. A new measurement, called the impact rating (IR) was also introduced to explore the degree of importance of each gene. To assess the reliability of the IR value, a bootstrap re-sampling method was applied to the dataset and a confidence level for each IR was obtained. Our approach has successfully uncovered the unique features of host response to individual bacterium. Further, application of gene ontology (GO) annotation to the genes of high IR values in each response has suggested new biological pathways for individual host-pathogen interactions.
2319896
Range Image Registration Based on Circular Features
Range sensing technology allows the photo-realistic modeling of large-scale scenes, such as urban structures. A major bottleneck in the process of 3D scene acquisition is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. The generated 3D representations, after automated registration, are useful for urban planning, historical preservation, or virtual reality applications. Man-made urban scenes provide an abundance of linear features that can be used for the solution of the problem. Many scenes though require the utilization of non-linear primitives. This paper provides a solution of the registration problem based on the robust detection and matching of circular features from the range data sets. We present results from experiments with complex range scans from the interior of a large-scale landmark urban structure (Grand Central Terminal, NYC), where traditional methods would fail. This work is part of a larger range registration system that is based on extracted features of multiple geometric types.