Graph Signatures for Visual Analytics
We present a visual analytics technique to explore graphs using the concept of a data signature. A data signature, in our context, is a multidimensional vector that captures the local topology information surrounding each graph node. Signature vectors extracted from a graph are projected onto a low-dimensional scatterplot through the use of scaling. The resultant scatterplot, which reflects the similarities of the vectors, allows analysts to examine the graph structures and their corresponding real-life interpretations through repeated use of brushing and linking between the two visualizations. The interpretation of the graph structures is based on the outcomes of multiple participatory analysis sessions with intelligence analysts conducted by the authors at the Pacific Northwest National Laboratory. The paper first uses three public domain data sets with either well-known or obvious features to explain the rationale of our design and illustrate its results. More advanced examples are then used in a customized usability study to evaluate the effectiveness and efficiency of our approach. The study results reveal not only the limitations and weaknesses of the traditional approach based solely on graph visualization, but also the advantages and strengths of our signature-guided approach presented in the paper

title: Visualizing sequential patterns for text mining
abstract: A sequential pattern in data mining is a finite series of elements such as A/spl rarr/B/spl rarr/C/spl rarr/D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment.

title: Walking the Path: A New Journey to Explore and Discover Through Visual Analytics
abstract: Under the leadership of the US Department of Homeland Security (DHS), researchers at the Pacific Northwest National Laboratory (PNNL) established a research center focusing on the discipline of visual analytics in 2004. A year later, the center led a multidisciplinary panel representing academia, industry, and government to formally define directions and priorities for future research and development (R&D) for visual analytics tools. The R&D agenda, Illuminating the Path, defines the term visual analytics as ‘the science of analytical reasoning facilitated by interactive visual interfaces’. This article describes our progress to date in walking that path. We briefly describe the background of the subject, present major professional activities and accomplishments of its community, and highlight some of the ongoing R&D efforts being carried out by researchers at PNNL to fulfill the requirements and missions of a new discipline that promises to change the way we deal with today's information.

title: Multivariate Visualization with Data Fusion
abstract: We discuss a fusion-based visualization method to analyze a multivariate climate dataset and its metadata. The primary difference between a conventional visualization and a fusion-based visualization is that the former draws on a single image whereas the latter draws on multiple see-through layers, which are then overlaid on each other to form the final visualization. We propose optimized colormaps to highlight subtle features that would not be shown with conventional colormaps. We present fusion techniques that integrate multiple single-purpose visualization techniques into the same viewing space. Our highly flexible fusion approach allows scientists to explore multiple parameters concurrently by mixing and matching images without frequently reconstructing new visualizations from the data for every possible combination. Although our primary visualization application is climate modeling, we show with examples that our fundamental design - fusing layers of data images for multivariate visualization - can be generalized for other information visualization applications.

title: Vector fields simplification-a case study of visualizing climate modeling and simulation data sets
abstract: In our study of regional climate modeling and simulation, we frequently encounter vector fields that are crowded with large numbers of critical points. A critical point in a flow is where the vector field vanishes. While these critical points accurately reflect the topology of the vector fields, in our study only a subset of them is worth further investigation. We present a filtering technique based on the vorticity of the vector fields to eliminate the less interesting and sometimes sporadic critical points in a multiresolution fashion. The neighboring regions of the preserved features, which are characterized by strong shear and circulation, are potential locations of weather instability. We apply our feature filtering technique to a regional climate modeling data set covering East Asia in the summer of 1991.

title: Visualizing association rules for text mining
abstract: An association rule in data mining is an implication of the form X/spl rarr/Y where X is a set of antecedent items and Y is the consequent item. For years researchers have developed many tools to visualize association rules. However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antecedents. Thus, it is extremely difficult to visualize and understand the association information of a large data set even when all the rules are available. This paper presents a novel visualization technique to tackle many of these problems. We apply the technology to a text mining study on large corpora. The results indicate that our design can easily handle hundreds of multiple antecedent association rules in a three-dimensional display with minimum human interaction, low occlusion percentage, and no screen swapping.

title: On improving the iterative watermark embedding technique for JPEG-to-JPEG watermarking
abstract: We previously proposed an iterative technique called iterative watermark embedding (IWE) for watermark embedding in JPEG compressed domains. The proposed technique embeds the watermark successfully when the quality factor of the JPEG image is high, but when the quality factor is low, not all the bits of the watermark can be embedded. This is due to uneven distribution of capacities in different regions of the image. Moreover, the computation complexity of the iterative loop is high. In this paper we improve the technique using shuffling to equalize the capacities of different regions. Also, the iterative loop is replaced by a direct approach to reduce the computational complexity. We call this direct approach Direct JPEG Watermarking Embedding (DJWE). A Human Visual System (HVS) model is used to prioritize the DCT coefficients to achieve good visual quality. Experimental results show that the watermarks are robust against Gaussian noise attack and JPEG transcoding attacks.

title: Fast multi-block selection for H.264 video coding
abstract: The JVT/H.264 is the new video coding standard which achieves higher coding efficiency than previous standards such as H.263 or MPEG-2. The improvement in the rate-distortion performance in H.264 is mainly due to accurate motion estimation with different mode decision including multiple block size and multiple reference frame motion estimation. However, full exhaustive search of all block sizes is computational intensive with complexity increasing linearly with the number of allowed block size. In this paper, a fast multi-block motion estimation (FMBME) is proposed for H.264 video coding. Experimental results show that the proposed FMBME can efficiently reduce the computational cost by 58% with similar visual quality and bit rate.

title: Towards a testing methodology for object-oriented systems

title: Phylogenetic Web Profiler
abstract: Phylogenetic Web Profiler (PWP) is a web-based service designed to perform phylogenetic profiling of proteins against genomes. The current version offers a selection of 63 completed genomes and available plasmids as annotated in the PEDANT genome database. Unlike currently available applications, this tool offers several choices of ortholog prediction parameters including E-value cutoff, percent length difference tolerance, and annotation similarity. Additional features include tight integration with the PEDANT database and tools to analyze properties of predicted proteins. PWP should prove very useful for the analysis of functional-linkage between proteins.

title: Novel blind multiple watermarking technique for images
abstract: Three novel blind watermarking techniques are proposed to embed watermarks into digital images for different purposes. The watermarks are designed to be decoded or detected without the original images. The first one, called single watermark embedding (SWE), is used to embed a watermark bit sequence into digital images using two secret keys. The second technique, called multiple watermark embedding (MWE), extends SWE to embed multiple watermarks simultaneously in the same watermark space while minimizing the watermark (distortion) energy. The third technique, called iterative watermark embedding (IWE), embeds watermarks into JPEG-compressed images. The iterative approach of IWE can prevent the potential removal of a watermark in the JPEG recompression process. Experimental results show that embedded watermarks using the proposed techniques can give good image quality and are robust in varying degree to JPEG compression, low-pass filtering, noise contamination, and print-and-scan.