Increasing Functional Constraints Need to Be Checked Only Once
Central to solving Constraint Satisfaction Problem (CSP) is the problem of consistency check. Past research has produced many general and specific consistency algorithms. Specific algorithms are efficient specializations of the general ones for specific constraints. Functional, anti-functional and monotonic constraints are three important classes of specific constraints. They form the basis of the current constraint programming languages. This paper proposes a more efficient method for checking an important subclass of functional constraints, increasing functional constraints. Rather than checking them many times as in a typical consistency check process, in the new method they (almost all of them) only need to be checked once. This results in a substantia] saving in computation.

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: 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: 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: 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: Theoretical study on the OH + CH3NHC(O)OCH3 reaction
abstract: The multiple-channel reactions OH + CH3NHC(O)OCH3 --> products are investigated by direct dynamics method. The optimized geometries, frequencies, and minimum energy path are all obtained at the MP2/6-311+G(d,p) level, and energetic information is further refined by the BMC-CCSD (single-point) method. The rate constants for every reaction channels, R1, R2, R3, and R4, are calculated by canonical variational transition state theory with small-curvature tunneling correction over the temperature range 200-1000 K. The total rate constants are in good agreement with the available experimental data and the two-parameter expression k(T) = 3.95 x 10(-12) exp(15.41/T) cm3 molecule(-1) s(-1) over the temperature range 200-1000 K is given. Our calculations indicate that hydrogen abstraction channels R1 and R2 are the major channels due to the smaller barrier height among four channels considered, and the other two channels to yield CH3NC(O)OCH3 + H2O and CH3NHC(O)(OH)OCH3 + H2O are minor channels over the whole temperature range.

title: Discovery of functional miRNA-mRNA regulatory modules with computational methods
abstract: The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures. This study proposes a computational method to discover the functional miRNA-mRNA regulatory modules (FMRMs), that is, groups of miRNAs and their target mRNAs that are believed to participate cooperatively in post-transcriptional gene regulation under specific conditions. The proposed method identifies negatively regulated patterns of miRNAs and mRNAs which associate with cancer and normal conditions, respectively, in a prostate cancer data set. GO and the literature also suggest that they may relate with prostate cancer. It can potentially identify the biologically relevant chains of 'miRNA-->target gene --> condition'.

title: Research on Service-Oriented Workflow and Performance Evaluation
abstract: The advent of SOA and Grid technology has brought new challenges to workflow operation and performance evaluation. In this paper, the characteristics of service-oriented workflow are presented, based on which the service-oriented workflow performance evaluation model is described and the performance analysis methods are depicted. Finally the design and implementation of our prototype system are introduced briefly.

title: Support Vector Machines for Text Categorization in Chinese Question Classification
abstract: Question classification plays a crucial important role in the question answering system because categorizing a given question is beneficial to identify an answer in the documents. The goal of question classification is to accurately assign labels to question based on expected answer type. Recently, many machine learning algorithms are used for question classification. However many research results show that SVM perform best in this task, because it is well known to work well for nonlinear, sparse, high dimensional problems. In this experiment, we perform the One-against-One SVM algorithm and a feature extraction method of Chinese questions to get high classification accuracy

title: Hybrid Algorithm Combining Ant Colony Algorithm with Genetic Algorithm for Continuous Domain
abstract: Ant colony algorithm is a kind of new heuristic biological modeling method which has the ability of parallel processing and global searching. By use of the properties of ant colony algorithm and genetic algorithm, the hybrid algorithm which adopts genetic algorithm to distribute the original pheromone is proposed to solve the continuous optimization problem. Several solutions are obtained using the ant colony algorithm through pheromone accumulation and renewal. Finally, by using crossover and mutation operation of genetic algorithm, some effective solutions are obtained. The results of experiments show better performances of the new algorithm based on six continuous test functions compared with the methods available in literature.