39230310
SSOA: a Semantic Service-Oriented Architecture Based on Fuzzy Assertion System
15536271
Encouraging Good Interactions in Open Agent Systems
In the open agent systems, agents have to meet how, when and with who interact. Trust and reputation play an important role in reduces the uncertain. However, existing trust metric is not effective when an agent strategically behaviors, such as sudden behavior or alter 'name'. This paper presents a novel trust metric, which can penalize behavior of the suddenly falling behavior and change 'name'.
43513684
A representation theorem for recovering contraction relations satisfying wci
A notion of an image structure associated with the canonical epistemic state is introduced. Based on it, we get a representation result for recovering contraction inference relations satisfying the condition weak conjunctive inclusion (wci) in terms of F-standard epistemic AGM states. In effect, this result establishes a representation theorem for belief contraction functions satisfying AGM postulates (k-1) - (k-7), and Rott's (wci) and (k-8c), and hence generalizes Rott's corresponding result in the finite framework.
16775013
A Dynamic Trust Model for the Multi-agent Systems
Internet provides a fertile medium for new breeds of computer viruses. Many people who have access to a wealth of information via Internet are attacked by more computer viruses than they can effectively process. We present a dynamic computer virus detection model that can detect known viruses and previously unknown viruses to prevent information systems from damage. This model is inspired by biological immune systems that protect the body against damage from pathogens. The architecture of this model, the formal definitions of self nonself antigen, antibody, and vaccine gene library are introduced. Furthermore, the evolution of self and nonself, the generation of the antibody, the evolution of the virus vaccine gene, and the detection of the antigen are depicted. Experiment results show that this model has better capacity of self-adaptability and self-learning in detecting unknown viruses than traditional models.
16164261
Relevance Feedback for Sketch Retrieval Based on Linear Programming Classification
Relevance feedback plays as an important role in sketch retrieval as it does in existing content-based retrieval. This paper presents a method of relevance feedback for sketch retrieval by means of Linear Programming (LP) classification. A LP classifier is designed to do online training and feature selection simultaneously. Combined with feature selection, it can select a set of user-sensitive features and perform classification well facing a small number of training samples. Experiments prove the proposed method both effective and efficient for relevance feedback in sketch retrieval.
8899940
Sketch retrieval based on spatial relations
In this paper, we propose an effective approach for sketch retrieval. Given sketches are firstly decomposed into basic geometric primitives and represented as a topological graph that encodes both the intrinsic attributes of the primitives and their relationships. The graph spectrum descriptor is then adopted to translate the graph-match into the computation of vector distances. Sketches retrieval is finally done by means of similarity calculation of feature vectors based on dimensionality reduction. The relevance feedback is also introduced to refine the retrieval results. Experiments prove the effectiveness and efficiency of the proposed method in sketch content retrieval and user independent.
10259496
An E-Government Platform Based on Multi-Tier Architecture
With the development of dynamic e-government and the network, the application system becomes more complicated. Traditional e-government system will have an inherent scalability problem as the number of applications increases continuously. To solve this problem, an e-government platform based on multi-tier architecture is presented. By integrating applications, resources and system modules, the platform provides an effective mechanism to reduce the development and communication cost. This paper intensively discusses the design rationale and practical solutions of the platform, including structured data models, a secure data exchange model, uniform interface specifications, a topic based publish/subscribe communication mechanism and an asynchronous execution mechanism. The security of the platform is also evaluated by formal logic method.
26704614
A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network
A new oil price forecasting model based on fuzzy neural network, which combings RBF neural network, Markov chain based semi parametric model and wavelet analysis is proposed in this paper. The high degree of prediction accuracy confirms the performance of the model in accurate forecasts, reinforcement learning properties and mapping capabilities.
35160170
RBF Neural Network Based Shape Control of Hyper-redundant Manipulator with Constrained End-Effector
Hyper-redundant manipulator has more degrees of freedom than the least necessary to perform a given task, thus it has the features of overcoming conventional industrial robot’s limitation to carry out a designated difficult task. When the manipulator carries out the missions such as brushing or writing on a surface, drilling or inspection in a hole, the end-effector of the manipulator usually has both position and orientation requirement. Effective control of the hyper-redundant manipulator with such constrained end-effector is difficult for its redundancy. In this paper, a novel approach based on RBF neural network has been proposed to kinematically control the hyper-redundant manipulator. This technique, using variable regular polygon and RBF neural networks models, is completely capable of solving the control problem of a planar hyper-redundant manipulator with any number of links following any desired direction and path. With the shape transformation of variable regular polygon, the manipulator’s configuration changes accordingly and moves actively to perform the tasks. Compared with other methods to our knowledge, this technique has such superiorities as fewer control parameters and higher precision. Simulations have demonstrated that this control technique is available and effective.
34644832
On-Line Signature Verification Based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)
On-line signature verification is still an active topic in the field of biometrics. This paper proposes a novel method based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). Different from the application of PCA in other fields, both principal and minor components are used to signature verification, and MC plays a very important role. Comparing with DTW and the discriminance of Euclidean distance, the method based on PCA and MCA is better. With 1215 signatures contributed by 81 signers of which numbers of reference signatures, genuine signatures and forgeries (skilled) are 5 respectively, the EER is about 5%.
35117974
Online signature verification based on null component analysis and principal component analysis
This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.