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This paper describes an approach to the design of a wrapper for self-managing COTS (commercial off-the-shelf) components. Each wrapper for COTS components encapsulates the properties of self-management — detection, reconfiguration, and repair. A COTS component deals with the application perspectives, whereas the wrapper handles the self-management perspectives, separately from the application perspectives. Each wrapper for self-managing COTS components is structured into several objects in support of detection, reconfiguration, and repair of the anomalous COTS components. The approach suggested in this paper is applied to the distributed elevator system consisting of multiple COTS components.

In the last few years, several works in the literature have addressed the problem of data extraction from web pages. The importance of this problem derives from the fact that, once extracted, data can be handled in a way similar to instances of a traditional database, which in turn can facilitate application of web data integration and various other domain specific problems. In this paper, we propose a novel table extraction technique that works on web pages generated dynamically from a back-end database. The proposed system can automatically discover table structure by relevant pattern mining from web pages in an efficient way, and can generate regular expression for the extraction process. Moreover, the proposed system can assign intuitive column names to the columns of the extracted table by leveraging Wikipedia knowledge base for the purpose of table annotation. To improve accuracy of the assignment, we exploit the structural homogeneity of the column values and their co-location information to weed out less likely candidates. This approach requires no human intervention and experimental results have shown its accuracy to be promising. Moreover, the wrapper generation algorithm works in linear time.

This paper describes a domain independent approach for automatically constructing information extraction patterns for semi-structured web pages. Given a randomly chosen page from a web site of similarly structured pages, the system identifies a region of the page that has a regular "tabular" structure, and then infers an extraction pattern that will match the "rows" of the region and identify the data elements. The approach was tested on three corpora containing a series of tabular web sites from different domains and achieved a success rate of at least 80%. A significant strength of the system is that it can infer extraction patterns from a single training page and does not require any manual labeling of the training page.

Due to the current technological and economical context, there is an increasing need for cooperating information systems based on federated databases. Though the technical issues of these architectures have been studied for long, the way to build them has not triggered as much effort. This paper describes a general architecture, a methodology and a CASE environment intended to address the problem of providing users and programmers with an abstract interface to independent heterogeneous and distributed databases. The architecture comprise a hierarchy of mediators and a repository that dynamically transform actual data into a virtual homogeneous database and allow client applications to query it. The InterDB approach provides a complete methodology to define this architecture, including schema recovery through reverse engineering, database integration and mapping building. The methodology is supported by the DB-MAIN CASE tool that helps developers generate the mediators and their repository.

This work designs, develops and implements a virtual laboratory for digital circuit design in Macro Universities that can create a rich learning environment for students from all over the world. Our system consists of four parts: agent execution environments, mobile agents, wrapper agents and experimental platforms. Mobile agents are received and executed in the agent execution environment, which provides locations and other resources for the mobile agents. The wrapper agent acts as a middleware for the mobile agent and the experimental platform. The experimental platform is an existing application program that could be a learning program, such as a CAI tool or a design tool, such as a CAD system. The major merit of our system is that it enables network capability for a standalone program without the need for the source code of the original program. The prototype of our virtual laboratory is targeted at a digital circuit laboratory. Experimental results demonstrate the feasibility of our approach.

Feature selection is an important research field for pattern classification, data mining, etc. Population-based optimization algorithms (POA) have high parallelism and are widely used as search algorithm for feature selection. Population-based feature selection algorithms (PFSA) involve compromise between precision and time cost. In order to optimize the PFSA, the feature selection models need to be improved. Feature selection algorithms broadly fall into two categories: the filter model and the wrapper model. The filter model is fast but less precise; while the wrapper model is more precise but generally computationally more intensive. In this paper, we proposed a new mechanism — proportional hybrid mechanism (PHM) to combine the advantages of filter and wrapper models. The mechanism can be applied in PFSA to improve their performance. Genetic algorithm (GA) has been applied in many kinds of feature selection problems as search algorithm because of its high efficiency and implicit parallelism. Therefore, GAs are used in this paper. In order to validate the mechanism, seven datasets from university of California Irvine (UCI) database and artificial toy datasets are tested. The experiments are carried out for different GAs, classifiers, and evaluation criteria, the results show that with the introduction of PHM, the GA-based feature selection algorithm can be improved in both time cost and classification accuracy. Moreover, the comparison of GA-based, PSO-based and some other feature selection algorithms demonstrate that the PHM can be used in other population-based feature selection algorithms and obtain satisfying results.

Body surface potential maps were investigated to identify a set of optimal recording sites required to discriminate between several diseases. Specifically, recordings captured from subjects exhibiting myocardial infarction or left ventricular hypertrophy, as well as a control group consisting of healthy subjects, were investigated. Owing to the fact that multi-class problems are inherently difficult to solve we divided the problem into several two-class scenarios. Six data sets were generated from the available 744 records, each viewing the available data differently, to form several two-class problems. A data-driven selection algorithm was applied to each of the generated data sets to produce six classification models, each utilizing as features those recording sites offering most to the discrimination task being investigated. Subsequently, a framework was introduced to facilitate the combination of outputs from each classifier. Essentially, the framework used the outputs from half of the classification models to determine which of the remaining models would be employed to form a final decision. A benchmark, in the form of a multi-group classifier, was introduced to evaluate the perceived benefits of the proposed approach. An improvement of approximately 10% upon the benchmark was observed resulting in an overall accuracy of 79.19%.