Dongning Liu

Solving the Many to Many assignment problem by improving the Kuhn-Munkres algorithm with backtracking

The Many to Many (M-M) assignment problem is an important open problem where one task is assigned to many, but different, agents and one agent may undertake many, but different, tasks. The Kuhn-Munkres (K-M) algorithm is a famous and traditional process used in dealing with assignment problems. In this paper, we propose a solution to the M-M assignment problem by improving the K-M algorithm with backtracking (KMB). To demonstrate the solutions suitability, we prove that the proposed KMB algorithm is valid and that the worst time complexity of the KMB algorithm is O ( ( ? L a i ) 3 ) , where L a i denotes the maximum number of tasks that can be assigned to agent i. After that, we discuss several critical problems related to the algorithm and provide the necessary and sufficient conditions of solving the M-M assignment problem. Finally, we demonstrate, through experimentation, the validity, practicality and efficiency of the KMB algorithm. It improves the K-M algorithm to solve the M-M assignment problem.The improved algorithm (KMB) introduces backtracking.The KMB algorithm is valid and the worst time complexity is O ( ( ? L a i ) 3 ) .It provides the necessary and sufficient conditions for the solution.It illustrates the validity and efficiency of the KMB algorithm through simulations.

A Collaborative and Adaptive Intrusion Detection Based on SVMs and Decision Trees

Because network security has become one of the most serious problems in the world, intrusion detection is an important defence tool of network security. In this paper, A cooperative and adaptive intrusion detection method is proposed and a corresponding intrusion detection model is designed and implemented. The E-CARGO model is used to build the collaborative and adaptive intrusion detection model. The roles, agents and groups based on 2-class Support Vector Machines (SVMs) and Decision Trees (DTs) are described and built, and the adaptive scheduling mechanisms are designed. Finally, the KDD CUP 1999 data set is used to verify the effectiveness of our method. Experimental results show that the collaborative and adaptive intrusion detection method proposed in this paper is superior to the detection of the SVM in the detection accuracy and detection efficiency.

A Role-Permission Assignment Method of RBAC Involved Conflicting Constraints under E-CARGO

Multi-agent systems are historically one of the most crucial problems and now become an important topic in cognitive theory. In the view of ontology theory, multi-agent systems are usually related to roles and agents are the executors of roles. We investigate an improved multi-agent system by solving the RBAC role-permission assignment problem with conflicting constrains in this paper, under the E-CARGO model that provides the formalization, the description of conflicts and the algorithms used in this paper. Simulation experiments with comprehensive consideration can verify the correctness of our method.

Minimal role playing logic in Role-Based Collaboration

Role-Based Collaboration (RBC) is a computational thinking methodology where roles provide an underlying mechanism to facilitate abstraction, classification, separation of concerns, dynamics, and interactions. From a meta theoretical perspective, the specification of groups, roles and agents is a critical element of RBC. In consideration of the relationships and hierarchies faced by groups, roles and agents, we propose a minimal role playing logic system (MRPL) through substructural logic, which is polynomial in complexity, in support of RBC. From MRPL and RPLs extending from it, there are three levels of application, i.e., the global level governing how people organize agents to form a group; the concatenative level for role assignment with respect to logic and algebra, and the operational level governing properties, relations and structures that should appear in collaborative system design. From MRPL to RPLs, one can extend it to suit other appropriate applications.

A Cooperative Intrusion Detection Model Based on Granular Computing and Agent Technologies

We firstly analyze the method for four attack types, including Probing, DoS (Denial of Service), R2L (Remote to Local) and U2R (User to Root). Based on resource addresses and destination addresses of the network packages, attacks can be divided into four cases, which are respectively one host-one host, one host-many hosts, many hosts-one host and many hosts-many hosts. Specifically, the granular computing method is applied in intrusion detection. A cooperative intrusion detection model is proposed based on granular computing. The construction for an intrusion detection agent is presented.

Solving the Group Multirole Assignment Problem by Improving the ILOG Approach

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem.

When to Re-staff a Late Project – An E-CARGO Approach

Brooks’ law is popular in software development. It has been used as a reference for managing software projects for over four decades. However, not enough investigations express this law in a quantitative way that can provide specific project recommendations at critical times in the development process. This paper offers a quantitative way based on our research vehicle of Role-Based Collaboration and the related Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. The proposed approach is verified by simulations, experiments and a case study. The results produce insights into Brooks’ law, and quantitatively present the applicable scope of the law. This contribution is believed to be significant because it provides a quantitative measurement useful in the development of a software project.

A cooperative modeling of user experience based on the improved SVM

Nowadays, as the development of mobile communication, it is very important to serve users. Because of the subjectivity of user experience, the data of user experience has deviation. In the paper, a cooperative modeling method based on the improved Support Vector Machine is proposed, which can evaluate the quality of experience by using measurement report. The results of the experiments show that our method is effective to calculate the quality of experience.

RBAC role-permission assignment with conflicting constraints based on the E-CARGO model

The multi-agent is now an established research topic of epistemology, in the view of ontology, multiagent is usually related to role and agent is the executor of role, in this paper, we take research on multi-agent by solving the problem of RBAC role-permission assignment with conflicting constraints: the formalization, the description of conflict, and the algorithm are given based on the E-CARGO model, in the end, an simulation experiment is used to verify the correctness.

A cooperative multi-classifier method for local area meteorological data mining

Natural disasters can lead to severe losses in human life and property. Because many factors combine in a disaster, such events are difficult to forecast accurately. A cooperative multi-classifier method is proposed in this paper to mine local area meteorological data. The proposed method is verified by the implementation of both base and integration classifiers. Experimental results indicate that our proposed method has higher classification accuracy and faster grouping ability compared with conventional classifiers.