Visualizing association rules for text mining
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: Organic data memory using the DNA approach
abstract: For very long-term storage and retrieval, encode information as artificial DNA strands and insert into living hosts. As vectors, bacteria, even some bugs and weeds, might be good for hundreds of millions of years.

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: Discovering Knowledge Through Visual Analysis
abstract: This paper describes a vision for the near future in digital content analysis as it relates to the creation, verification, and presentation of knowledge. This paper focuses on how visualization enables humans to gain knowledge. Visualization, in this context, is not just the picture representing the data, but a two-way interaction between the human and their information resources for the purposes of knowledge discovery, verification, and the sharing of knowledge with others. We present PNNL-developed software to demonstrate how current technology can use visualization tools to analyze large diverse collections of text. This will be followed by lessons learned and the presentation of a core concept for a new human information discourse.

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: Generating Graphs for Visual Analytics through Interactive Sketching
abstract: We introduce an interactive graph generator, GreenSketch, designed to facilitate the creation of descriptive graphs required for different visual analytics tasks. The human-centric design approach of GreenSketch enables users to master the creation process without specific training or prior knowledge of graph model theory. The customized user interface encourages users to gain insight into the connection between the compact matrix representation and the topology of a graph layout when they sketch their graphs. Both the human-enforced and machine-generated randomnesses supported by GreenSketch provide the flexibility needed to address the uncertainty factor in many analytical tasks. This paper describes more than two dozen examples that cover a wide variety of graph creations from a single line of nodes to a real-life small-world network that describes a snapshot of telephone connections. While the discussion focuses mainly on the design of GreenSketch, we include a case study that applies the technology in a visual analytics environment and a usability study that evaluates the strengths and weaknesses of our design approach

title: A blind watermarking technique in JPEG compressed domain
abstract: We propose a blind watermarking technique to embed a watermark in the JPEG compressed domain. Low frequency DCT coefficients are extracted to form an M-dimensional vector. Watermarking is achieved by modifying this vector in order to point to the centroid of a particular cell. This cell is determined according to the extracted vector, private keys and the watermark. A dual-key system is used to reduce the chance of the removal of watermark. An iterative approach is used to prevent the removal of the watermark by JPEG re-quantization. Experimental results show that the watermark can be detected when the watermarked image is further compressed using a larger scaling factor.

title: Performance Analysis of a Pull-Based Parallel Video Server
abstract: In conventional video-on-demand systems, video data are stored in a video server for delivery to multiple receivers over a communications network. The video server's hardware limits the maximum storage capacity as well as the maximum number of video sessions that can concurrently be delivered. Clearly, these limits will eventually be exceeded by the growing need for better video quality and larger user population. This paper studies a parallel video server architecture that exploits server parallelism to achieve incremental scalability. First, unlike data partition and replication, the architecture employs data striping at the server level to achieve fine-grain load balancing across multiple servers. Second, a client-pull service model is employed to eliminate the need for interserver synchronization. Third, an admission-scheduling algorithm is proposed to further control the instantaneous load at each server so that linear scalability can be achieved. This paper analyzes the performance of the architecture by deriving bounds for server service delay, client buffer requirement, prefetch delay, and scheduling delay. These performance metrics and design tradeoffs are further evaluated using numerical examples. Our results show that the proposed parallel video server architecture can be linearly scaled up to more concurrent users simply by adding more servers and redistributing the video data among the servers.

title: ICT production and diffusion in Asia Digital dividends or digital divide?
abstract: This paper examines the empirical evidence to determine whether Asian countries, despite having captured a disproportionately high share of global production of ICT goods, have as a group been laggard in the adoption of ICT in comparison to non-Asian countries. Using regression analysis, it is shown that as a group Asian countries have indeed had generally lower rates of ICT adoption relative to their levels of potential as predicted on the basis of their current level of development (GDP/capita) and competitiveness (world competitiveness index). In addition, disparities in ICT diffusion are found to be significantly higher among Asian countries than among non-Asian countries. In particular, a significant ‘digital divide’ is found to exist between the five more advanced countries of the region (Japan and the four Asian NIEs) and the other seven developing Asian countries. Policy implications of the findings for the Asian countries are highlighted.

title: A capacity estimation technique for JPEG-to-JPEG image watermarking
abstract: In JPEG-to-JPEG image watermarking (J2J), the input is a JPEG image file. After watermark embedding, the image is JPEG-compressed such that the output file is also a JPEG file. We use the human visual system (HVS) model to estimate the J2J data hiding capacity of JPEG images, or the maximum number of bits that can be embedded in JPEG-compressed images.  Watson's HVS model (Proc. SPIE Human Vision, Visual Process., and Digital Display IV, p.202-16, 1993) is modified to estimate the just noticeable difference (JND) for DCT coefficients. The number of modifications to DCT coefficients is limited by JND in order to guarantee the invisibility of the watermark. Our capacity estimation method does not assume any specific watermarking method and thus would apply to any watermarking method in the J2J framework.

title: Rate estimation for H.264/AVC spatial resolution reduction
abstract: The emergence of wireless networks has posed new challenges for multimedia content providers. In particular, many existing video contents have bit-rates and resolutions far exceeding the capability of wireless networks and mobile devices and they need to be adapted before transmission. In this paper, we propose a technique using /spl rho/-domain information to estimate the bit rate of down-sampled video in the H.264/AVC format. The bit rate is estimated in two stages. In the initialization stage, we use a low-complexity algorithm to estimate the /spl rho/-domain parameters for half and quarter resolutions, respectively. In the refinement stage, the estimation error is feed-forwarded to improve the estimation accuracy. Experimental results show that the proposed technique can estimate the bit rate accurately at a wide range of down-sampling ratios.