1822288
Incremental model building of polyhedral objects using structured light
Abstract A vision method using active sensing to construct the complete geometric model of a polyhedral object is presented. The model is constructed incrementally from multiple views. Each view produces a partial object model. Three different methods are described for integrating all the partial models into a final complete model. Theorems regarding the sufficient number of views required and how these views can be set up during the reconstruction process are also given. From theoretical analysis and experimental results, our method is shown to produce an accurate object geometric model, not just an approximate model. Furthermore, it does not rely on a precise object-to-image space transformation. Moreover, the method is fast and robust, since the basic geometric entity it derives is a face instead of a point.
42618487
Handwritten Chinese Character Recognition Using Stroke Structural Sequence Code
9213286
Modeling the spread of active worms
Active worms spread in an automated fashion and can flood the Internet in a very short time. Modeling the spread of active worms can help us understand how active worms spread, and how we can monitor and defend against the propagation of worms effectively. In this paper, we present a mathematical model, referred to as the Analytical Active Worm Propagation (AAWP) model, which characterizes the propagation of worms that employ random scanning. We compare our model with the Epidemiological model and Weaver's simulator. Our results show that our model can characterize the spread of worms effectively. Taking the Code Red v2 worm as an example, we give a quantitative analysis for monitoring, detecting and defending against worms. Furthermore, we extend our AAWP model to understand the spread of worms that employ local subnet scanning. To the best of our knowledge, there is no model for the spread of a worm that employs the localized scanning strategy and we believe that this is the first attempt on understanding local subnet scanning quantitatively.
16533233
Improving text classification using local latent semantic indexing
Latent semantic indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "local relevancy weighted LSI" to improve text classification by performing a separate single value decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.
8890999
A unified framework for clustering heterogeneous Web objects
We introduce a novel framework for clustering Web data which is often heterogeneous in nature. As most existing methods often integrate heterogeneous data into a unified feature space, their flexibilities to explore and adjust contributing effects from different heterogeneous information are compromised. In contrast, our framework enables separate clustering of homogeneous data in the entire process based on their respective features, and a layered structure with link information is used to iteratively project and propagate the clustered results between layers until it converges. Our experimental results show that such a scheme not only effectively overcomes the problem of data sparseness caused by the high dimensional link space but also improves the clustering accuracy significantly. We achieve 19% and 41% performance increases when clustering Web-pages and users based on a semi-synthetic Web log. Finally, we show a real clustering result based on UC Berkeley's Web log.
28060413
The Architecture of Yarrow: A Real-Time Intelligent Meta-Search Engine
15657776
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Web link analysis has proven to be a significant enhancement for quality based web search. Most existing links can be classified into two categories: intra-type links (e.g., web hyperlinks), which represent the relationship of data objects within a homogeneous data type (web pages), and inter-type links (e.g., user browsing log) which represent the relationship of data objects across different data types (users and web pages). Unfortunately, most link analysis research only considers one type of link. In this paper, we propose a unified link analysis framework, called "link fusion", which considers both the inter- and intra- type link structure among multiple-type inter-related data objects and brings order to objects in each data type at the same time. The PageRank and HITS algorithms are shown to be special cases of our unified link analysis framework. Experiments on an instantiation of the framework that makes use of the user data and web pages extracted from a proxy log show that our proposed algorithm could improve the search effectiveness over the HITS and DirectHit algorithms by 24.6% and 38.2% respectively.