The Story Picturing Engine---a system for automatic text illustration
We present an unsupervised approach to automated story picturing. Semantic keywords are extracted from the story, an annotated image database is searched. Thereafter, a novel image ranking scheme automatically determines the importance of each image. Both lexical annotations and visual content play a role in determining the ranks. Annotations are processed using the Wordnet. A mutual reinforcement-based rank is calculated for each image. We have implemented the methods in our Story Picturing Engine (SPE) system. Experiments on large-scale image databases are reported. A user study has been performed and statistical analysis of the results has been presented.

title: Real-Time Computerized Annotation of Pictures
abstract: Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the automatic linguistic indexing of pictures - real time (ALIPR) system of fully automatic and high-speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as K-Means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for nonvector data is developed using the novel concept of hypothetical local mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demonstration site, where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real time and with good accuracy.

title: A source coding approach to classification by vector quantization and the principle of minimum description length
abstract: An algorithm for supervised classification using vector quantization and entropy coding is presented. The classification rule is formed from a set of training data {(X/sub i/, Y/sub i/)}/sub i=1//sup n/, which are independent samples from a joint distribution P/sub XY/. Based on the principle of minimum description length (MDL), a statistical model that approximates the distribution P/sub XY/ ought to enable efficient coding of X and Y. On the other hand, we expect a system that encodes (X, Y) efficiently to provide ample information on the distribution P/sub XY/. This information can then be used to classify X, i.e., to predict the corresponding Y based on X. To encode both X and Y, a two-stage vector quantizer is applied to X and a Huffman code is formed for Y conditioned on each quantized value of X. The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function reflecting the joint penalty of quantization error and misclassification rate. This vector quantizer provides an estimation of the conditional distribution of Y given X, which in turn yields an approximation to the Bayes classification rule. This algorithm, namely discriminant vector quantization (DVQ), is compared with learning vector quantization (LVQ) and CART/sup R/ on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed.

title: Text and picture segmentation by the distribution analysis of wavelet coefficients
abstract: This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.

title: On Zador's entropy-constrained quantization theorem
abstract: Zador's classic result for the asymptotic high-rate behavior of entropy-constrained vector quantization is recast in a Lagrangian form which better matches the Lloyd algorithm used to optimize such quantizers. A proof that the result holds for a general class of distributions is sketched.

title: Significance Of inter-species matches when evolutionary rate varies
abstract: We develop techniques to estimate the statistical significance of gap-free alignments between two genomic DNA sequences, using human-mouse alignments as an example. The sequences are assumed to be sufficiently similar that some but not all of the neutrally evolving regions (i.e., those under no evolutionary constraint) can be reliably aligned. Our goal is to model the situation in which the neutral rate of evolution, and hence the extent of the aligning intervals, varies across the genome. In some cases, this permits the weaker of two matches to be judged as less likely to have arisen by chance, provided it lies in a genomic interval with a high level of background divergence. We employ a Hidden Markov Model to capture variations in divergence rates, and assign probability values to gap-free alignments using techniques related to those used for the same purpose by Blast. Our methods are illustrated in detail using a 1.49 Mb genomic region. Preliminary results using all of human chromosome 22 indicate that these techniques will work for the entire human genome.

title: Incorporating Legacy System into Semantic Grid Framework
abstract: The semantic grid is an extension of the current grid. Resources description framework (RDF) can be used to extend the current grid into semantic grid in which information and services are given well-defined meaning. This paper proposed an approach to recognizing the reusable components from the legacy system and representing these components by using XML and RDF, which could be performed for incorporating legacy system components into the semantic grid framework

title: Influence and conditional influence-new interestingness measures in association rule mining
abstract: Discusses the issues of interestingness in association rule mining. First, a rule is possibly redundant or misleading even if it possesses high degrees of confidence and support. Second, association rules do not reflect the effect of negatively influential facts. Such problems are related to confidence deviation. In the paper, therefore, two new measures of interestingness, namely influence and conditional influence, are introduced to represent the effect of the antecedent on the consequent. Furthermore, the mining algorithms are extended accordingly such that certain redundant rules can be eliminated and negatively influential rules may be discovered.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.

title: Bottlenecks with respect to due-time performance in pull serial production lines
abstract: The problem of satisfying customers demand in pull serial production lines is addressed. The level of demand satisfaction is quantified by the due-time performance (DTP), which is defined as the probability to ship to the customer a required number of parts during a fixed time interval. The definitions of DTP bottlenecks are introduced and a method for their identification is developed.

title: An Empirical Study on Off-the-Shelf Component Usage in Industrial Projects
abstract: Using OTS (Off-The-Shelf) components in software projects has become increasing popular in the IT industry. After project managers opt for OTS components, they can decide to use COTS (Commercial-Off-The-Shelf) components or OSS (Open Source Software) components instead of building these themselves. This paper describes an empirical study on why project decision-makers use COTS components instead of OSS components, or vice versa. The study was performed in form of an international survey on motivation and risks of using OTS components, conducted in Norway, Italy and Germany. We have currently gathered data on 71 projects using only COTS components and 39 projects using only OSS components, and 5 using both COTS and OSS components. Results show that both COTS and OSS components were used in small, medium and large software houses and IT consulting companies. The overall software system also covers several application domains. Both COTS and OSS were expected to contribute to shorter time-to-market, less development effort and the application of newest technology. However, COTS users believe that COTS component should have good quality, technical support, and will follow the market trend. OSS users care more about the free ownership and openness of the source code. Projects using COTS components had more difficulties in estimating selection effort, following customer requirement changes, and controlling the component's negative effect on system security. On the other hand, OSS user had more difficulties in getting the support reputation of OSS component providers.