Extracting Web Data Using Instance-Based Learning
This paper studies structured data extraction from Web pages, e.g., online product description pages. Existing approaches to data extraction include wrapper induction and automatic methods. In this paper, we propose an instance-based learning method, which performs extraction by comparing each new instance (or page) to be extracted with labeled instances (or pages). The key advantage of our method is that it does not need an initial set of labeled pages to learn extraction rules as in wrapper induction. Instead, the algorithm is able to start extraction from a single labeled instance (or page). Only when a new page cannot be extracted does the page need labeling. This avoids unnecessary page labeling, which solves a major problem with inductive learning (or wrapper induction), i.e., the set of labeled pages may not be representative of all other pages. The instance-based approach is very natural because structured data on the Web usually follow some fixed templates and pages of the same template usually can be extracted using a single page instance of the template. The key issue is the similarity or distance measure. Traditional measures based on the Euclidean distance or text similarity are not easily applicable in this context because items to be extracted from different pages can be entirely different. This paper proposes a novel similarity measure for the purpose, which is suitable for templated Web pages. Experimental results with product data extraction from 1200 pages in 24 diverse Web sites show that the approach is surprisingly effective. It outperforms the state-of-the-art existing systems significantly.

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: 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: 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: 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: Discovering the set of fundamental rule changes
abstract: The world around us changes constantly. Knowing what has changed is an important part of our lives. For businesses, recognizing changes is also crucial. It allows businesses to adapt themselves to the changing market needs. In this paper, we study changes of association rules from one time period to another. One approach is to compare the supports and/or confidences of each rule in the two time periods and report the differences. This technique, however, is too simplistic as it tends to report a huge number of rule changes, and many of them are, in fact, simply the snowball effect of a small subset of fundamental changes. Here, we present a technique to highlight the small subset of fundamental changes. A change is fundamental if it cannot be explained by some other changes. The proposed technique has been applied to a number of real-life datasets. Experiments results show that the number of rules whose changes are unexplainable is quite small (about 20% of the total number of changes discovered), and many of these unexplainable changes reflect some fundamental shifts in the application domain.

title: Filtering Spam in Social Tagging System with Dynamic Behavior Analysis
abstract: Spam in social tagging systems introduced by some malicious participants has become a serious problem for its global popularizing. Some studies which can be deduced to static user data analysis have been presented to combat tag spam, but either they do not give an exact evaluation or the algorithms’ performances are not good enough. In this paper, we proposed a novel method based on analysis of dynamic user behavior data for the notion that users’ behaviors in social tagging system can reflect the quality of tags more accurately. Through modeling the different categories of participants’ behaviors, we extract tag-associated actions which can be used to estimate whether tag is spam, and then present our algorithm that can filter the tag spam in the results of social search. The experiment results show that our method indeed outperforms the existing methods based on static data and effectively defends against the tag spam in various spam attacks.

title: A Neural Network Approach To Shape From Shading
abstract: In this paper, we propose a method of recovering shape from shading that solves directly for the surface height using neural networks. The main motivation of this paper is to provide an answer to the open problem proposed by Zhou and Chellappa [11]. We first formulate the shape from shading problem by combining a triangular element surface model with a linearized reflectance map. Then, we use a linear feed-forward network architecture with six layers to compute the surface height with a singular value decomposition. The weights in the model initialized using eigenvectors and eigen-values of the stiffness matrix of objective functional. Experimental results show that our solution is very effective.

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: Adaptive video coding using mixed-domain filter banks having optimal-shaped subbands
abstract: This contribution presents a mixed-domain filter bank. This filter bank can realize arbitrary (and thereby optimal) spectral decomposition and therefore is very suitable for the efficient subband coding of video signals. A coding scheme using the proposed filter bank is considered, which takes advantage of the spectral distribution of the signal and is especially selective to motion.<<ETX>>

title: WBEM Based Distributed Network Monitoring
abstract: In the paper we identify the needs in efficient management of distributed networks and some problematic areas in the field. Then we introduce Web Based Enterprise Management (WBEM) to address the problem of providing a unified way to model all kinds of managed elements in a single information model in heterogeneous network environments. The advantages brought about by the use of WBEM in network management solve some critical problems existing in current network management. Based on the brief description of components of WBEM, we discuss in depth the basic WBEM instrumentation and multi-tiered WBEM enabled management infrastructure. We also apply this multi-tiered management infrastructure to a network management application scenario to monitor network activities for unexpected behaviors.