ConstraintLisp: an object-oriented constraint programming language
ConstraintLisp is an object-oriented constraint programming language. It is based on the constraint satisfaction problem (CSP) model in Artificial Intelligence and the object-oriented technology. This integration provides both the declarative representation and efficiency of constraint solving of constraint programming, and the powerful problem modelling capability, maintainability, reusability and extensibility of the object-oriented technology. ConstraintLisp is aimed at providing practical solutions to real life constrained search problems such as scheduling, resource allocation, etc. It is also designed and implemented in an object-oriented approach.

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: 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: 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: 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: Distributed Intelligent Network Management Model for the Large-Scale Computer Network
abstract: Computer networks have become large-scale distributed systems and network management will develop towards the distributed management and the intelligent management. Multi-agent based Distributed Intelligent Network Management model (MDINM) for the large-scale computer network, which utilizes key characteristics of the agent, such as autonomy, reactivity, and mobility, is put forward in this paper. This model can not only avoid transmitting a large amount of original data on the network to realize the distributed network management but also provides network management with more intelligence by realizing the dynamic self-management for the network. The experimental results show that MDINM is better than the existing network management solutions on performance for the large-scale computer network.

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: Accelerated Bundle Adjustment in Multiple-View Reconstruction
abstract: Bundle adjustment is one of the main tools used for multi- ple view reconstruction. It seeks to refine the estimate of the 3D scene and the view parameter that minimize a certain cost function, e.g. the overall reprojection error. In this paper we propose a new algorithm to simplify the computation of the reprojection error for multiple views. With this algorithm, the bundle adjustment will be accelerated, whether the cameras are calibrated or un-calibrated. The proposed techniques for bundle adjustment are not only tolerant of missing data, but also allow the assignment of individual covariance to each image measure- ment. Experiments are conducted both on synthetic data and on real data to compare the proposed bundle adjustment techniques with the other existing methods. It is shown that the result obtained with the proposed techniques is comparable to that with the maximum likelihood estimation, however, it is more efficient.

title: Note on third-order boundary value problem for differential equations with deviating arguments
abstract: A third-order boundary value problem for differential equations with deviating arguments is considered in this note. An existence result is obtained by the help of the well-known Leray-Schauder Alternative. Also, a uniqueness result is given.

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.