Automatic Wrapper Generation Using Tree Matching and Partial Tree Alignment
This paper is concerned with the problem of structured data extraction from Web pages. The objective of the research is to automatically segment data records in a page, extract data items/fields from these records and store the extracted data in a database. In this paper, we first introduce the extraction problem, and then discuss the main existing approaches and their limitations. After that, we introduce a novel technique (called DEPTA) to automatically perform Web data extraction. The method consists of three steps: (1) identifying data records with similar patterns in a page, (2) aligning and extracting data items from the identified data records and (3) generating tree-based regular expressions to facilitate later extraction from other similar pages. The key innovation is the proposal of a new multiple tree alignment algorithm called partial tree alignment, which was found to be particularly suitable for Web data extraction. This paper is based on our work published in KDD-03 and WWW-05.

title: Finding Actionable Knowledge via Automated Comparison
abstract: The problem of finding interesting and actionable patterns is a major challenge in data mining. It has been studied by many data mining researchers. The issue is that data mining algorithms often generate too many patterns, which make it very hard for the user to find those truly useful ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. At the end of 2005, we built a data mining system for Motorola (called Opportunity Map) to enable the user to explore the space of a large number of rules in order to find actionable knowledge. The approach is based on the concept of rule cubes and operations on rule cubes. A rule cube is similar to a data cube, but stores rules. Since its deployment, some issues have also been identified during the regular use of the system in Motorola. One of the key issues is that although the operations on rule cubes are flexible, each operation is primitive and has to be initiated by the user. Finding a piece of actionable knowledge typically involves many operations and intense visual inspections, which are labor-intensive and time-consuming. From interactions with our users, we identified a generic problem that is crucial for finding actionable knowledge. The problem involves extensive comparison of sub-populations and identification of the cause of their differences. This paper first defines the problem and then proposes an effective method to solve the problem automatically. To the best of our knowledge, there is no reported study of this problem. The new method has been added to the Opportunity Map system and is now in daily use in Motorola.

title: Entity discovery and assignment for opinion mining applications
abstract: Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.

title: Opinion Mining
abstract: Описанл процедуру формування інтегрального показника об’єкта згідно з відгуками користувачів під час застосування методів оцінювання опінії текстової інформації у web-документах. Запропоновано використання лінгвістичних змінних та застосування вагових коефіцієнтів для достовірнішого результату оцінювання емоційного забарвлення текстової інформації. Ключові слова: опінія, емоційне забарвлення, об’єкт, інтегральний показник, лінгвістична змінна, ваговий коефіцієнт.

title: Dealing with different distributions in learning from
abstract: In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experimental results with product page classification demonstrate the effectiveness of the proposed technique.

title: Speed-up iterative frequent itemset mining with constraint changes
abstract: Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for this purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before being satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in an enormous waste of time and computation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the context of two existing frequent itemset mining algorithms, FP-tree and tree projection. Experiment results on both synthetic and real-life datasets show that the proposed approach achieves a dramatic saving of computation.

title: CoDevFrame: An Event-drive Grid Oriented Cooperative Software Development System
abstract: Though twenty years have passed since the birth of CSCW, the original goal of it is not reached as well as people expected. This situation is mostly due to the supporting technology especially the infrastructure. Because of Grid computing and Web services, the globalization of software development has become a reality. Many software projects are now distributed in diverse sites across the globe. The distance between these sites creates several problems that did not exist for previously co-located teams. Problems with the coordination of the activities, as well as with the communication between team members emerge. This paper describes how event-drive servers that are built on grid and web services are a useful technology for supporting global software development. They provide some general yet flexible cooperation related services and organizes them into different layers. In general, advantages of using event-drive servers are described as well as specific problems that they

title: Twi-Map Support Vector Machine for Multi-classification Problems
abstract: In this paper, a novel method called Twi-Map Support Vector Machines (TMSVM) for multi-classification problems is presented. Our ideas are as follows: Firstly, the training data set is mapped into a high-dimensional feature space. Secondly, we calculate the distances between the training data points and hyperplanes. Thirdly, we view the new vector consisting of the distances as new training data point. Finally, we map the new training data points into another high-dimensional feature space with the same kernel function and construct the optimal hyperplanes. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, Fuzzy Least Square Support Vector Machine (FLS-SVM) and the proposed algorithm are applied to five UCI data sets. Comparison results obtained by using three algorithms are given. The results show that the training accuracy and the testing one of the proposed algorithm are higher than those of One-against-One and FLS-SVM.

title: Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
abstract: A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.

title: REGULARIZED SENSE RECONSTRUCTION USING ITERATIVELY REFINED TOTAL VARIATION METHOD
abstract: SENSE has been widely accepted as one of the standard reconstruction algorithms for parallel MRI. When large acceleration factors are employed, the SENSE reconstruction becomes very ill-conditioned. For Cartesian SENSE, Tikhonov regularization has been commonly used. However, the Tikhonov regularized image usually tends to be overly smooth, and a high-quality regularization image is desirable to alleviate this problem but is not available. In this paper, we propose a new SENSE regularization technique that is based on total variation with iterated refinement using Bregman iteration. It penalizes highly oscillatory noise but allows sharp edges in reconstruction without the need for prior information. In addition, the Bregman iteration refines the image details iteratively. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction

title: A Study on Information Extraction from PDF Files
abstract: Portable Document Format (PDF) is increasingly being recognized as a common format of electronic documents. The prerequisite to management and indexing of PDF files is to extract information from them. This paper describes an approach for extracting information from PDF files. The key idea is to transform the text information parsed from PDF files into semi-structured information by injecting additional uniform tags. An extensible rule set is built on tags and other knowledge. Guided by the rules, one pattern matching algorithm based on a tree model is applied to obtain the necessary information. A further experiment proved that this method was effective.