A holistic lexicon-based approach to opinion mining
One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly

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: 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: Non-Almost-Derivable Frequent Itemsets Mining
abstract: The number of frequent itemsets is often too large to handle, so it is very necessary to work out a condensed representation of the collection of all frequent itemsets. In this paper, we propose a new condensed representation called frequent non-almost-derivable itemsets. This representation is a subset of the original collection of frequent itemsets. For any removed itemset X(which is called an frequent almost-derivable itemset), we can derive a lower and an upper bound of its support from this representation, and the lower bound and the upper bound is close enough(can be controlled by a userdefined parameter). We also propose an Apriori-like algorithm, which can extract all frequent nonderivable itemsets. Extensive empirical results on real datasets show the compactness and good approximation of this representation.

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: 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: Improved spiral sense reconstruction using a multiscale wavelet model
abstract: SENSE has been widely accepted and extensively studied in the community of parallel MRI. Although many regularization approaches have been developed to address the ill-conditioning problem for Cartesian SENSE, fewer efforts have been made to address this problem when the sampling trajectory is non-Cartesian. For non-Cartesian SENSE using the iterative conjugate gradient method, ill- conditioning can degrade not only the signal-to-noise ratio, but also the convergence behavior. This paper proposes a regularization technique for non-Cartesian SENSE using a multiscale wavelet model. The technique models the desired image as a random field whose wavelet transform coefficients obey a generalized Gaussian distribution. The effectiveness of the proposed method has been validated by in vivo experiments.

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: Development Of An Soi-Based Micro Check Valve
abstract: This paper presents a bulk micromachined check valve with very high frequency and extremely low leak rates. The valve is designed to have a hexagonal orifice, a hexagonal membrane flap and three flexible tethers. The three elbow-shaped flexible tethers are used both to secure the membrane flap to the valve seat and to abtain a large flap displacement in the forward flow direction. SOI wafer and DRIE technology are used to implement this micro valve. A very simple farbication process has been developed, and only two photolithographic masks are employed. Preliminary testing on a 1.5 milimeters size check valve shows that a maximum flow rate (DI water) of 35.6ml/min was obtained at pressure drop of 65.5kPa and negligible leakage rate in the reverse flow direction observed at pressure up to 600kPa.

title: Rate-Distortion Optimized Progressive Geometry Compression
abstract: During progressive transmission of 3D geometry models, the transmission order of details at different region has great effects on the quality of reconstructed models at low bit-rate. This work presents a ratedistortion (R-D) optimized progressive geometry compression scheme to improve the quality of reconstructed models by adjusting the transmission order of details. In this scheme, the input mesh is partitioned into parts, then each part is encoded into bit-stream independently, and the encoded bit-streams are truncated into segments while getting the R-D characteristics of every segment, at last all segments are assembled into a codestream based on R-D optimization, which ensure the region with rich detail will be transmitted early and make the reconstructed mesh achieve better quality as soon as possible. Experimental results show that, as compared with the well-known PGC method, the proposed one provides better R-D performance. Moreover, it provides a novel way to realize the region of interest (ROI) coding of 3D meshes. Rate-distortion optimization; Progressive compression; Mesh partition

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