Data Mining - Concepts and Techniques.

物件導向軟體之架構(Object-Oriented Software Construction)探討

On robust estimation of the location parameter

An Introduction to MultiAgent Systems.

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

With the development of the web, large numbers of documents are available on the Internet. Digital libraries, news sources and inner data of companies surge more and more. Automatic text categorization becomes more and more important for dealing with massive data. However the major problem of text categorization is the high dimensionality of the feature space. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present another method of dealing with text feature selection. Our study is based on Gini index theory and we design a novel Gini index algorithm to reduce the high dimensionality of the feature space. A new measure function of Gini index is constructed and made to fit text categorization. The results of experiments show that our improvements of Gini index behave better than other methods of feature selection.

http://ccc.inaoep.mx/~villasen/bib/A%20novel%20feature%20selection%20algorithm%20for%20text%20categorization.pdf

A novel feature selection algorithm for text categorization

Computer-supported cooperative work (CSCW) systems are computer-based tools that support the collaborative activities of human users. They should not only support virtual face-to-face collaborative environments but also improve face-to-face collaboration by providing more mechanisms to overcome the drawbacks of usual face-to-face collaboration. Introducing roles into CSCW systems is important in achieving this. This paper's contributions include establishing the requirements for a role-based collaboration, presenting the concept, requirements, and principles of role-based collaboration, proposing a model E-CARGO for role-based collaboration, and describing the kernel mechanisms and their implementation to facilitate the development of role-based collaborative systems for industrial applications

Role-based collaboration and its kernel mechanisms

Role assignment is a critical task in role-based collaboration. It has three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the group role assignment problem (GRAP), describes a general assignment problem (GAP), converts a GRAP to a GAP, proposes an efficient algorithm based on the Kuhn-Munkres (K-M) algorithm, conducts numerical experiments, and analyzes the solutions' performances. The results show that the proposed algorithm significantly improves the algorithm based on exhaustive search. The major contributions of this paper include formally defining the GRAPs, giving a general efficient solution for them, and expanding the application scope of the K-M algorithm. This paper offers an efficient enough solution based on the K-M algorithm that outperforms significantly the exhaustive search approach.

Group Role Assignment via a Kuhn–Munkres Algorithm-Based Solution

With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF’s recommendation performance obviously outperforms nine state-of-the-art service recommendation methods.

Location-Aware Deep Collaborative Filtering for Service Recommendation

As a primary defense technique, intrusion detection becomes more and more significant since the security of the networks is one of the most critical issues in the world. We present an adaptive collaboration intrusion detection method to improve the safety of a network. A self-adaptive and collaborative intrusion detection model is built by applying the Environmentsclasses, agents, roles, groups, and objects U+0028 E-CARGO U+0029 model. The objects, roles, agents, and groups are designed by using decision trees U+0028 DTs U+0029 and support vector machines U+0028 SVMs U+0029, and adaptive scheduling mechanisms are set up. The KDD CUP 1999 data set is used to verify the effectiveness of the method. The experimental results demonstrate the feasibility and efficiency of the proposed collaborative and adaptive intrusion detection method. Also, the proposed method is shown to be more predominant than the methods that use a set of single type support vector machine U+0028 SVM U+0029 in terms of detection precision rate and recall rate.

Haibin Zhu

Papers

A novel feature selection algorithm for text categorization

Role-based collaboration and its kernel mechanisms

Group Role Assignment via a Kuhn–Munkres Algorithm-Based Solution

Location-Aware Deep Collaborative Filtering for Service Recommendation

SVM-DT-based adaptive and collaborative intrusion detection