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Process mining aims at extracting information from event logs to capture the business process as it is being executed. In spite of many researchers' persistent efforts, there are still some challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, where the process models are represented in Petri nets. In fact, there are totally two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. Implicit dependency is very hard to mine by current mining approaches. Thus we propose three theorems to detect implicit dependency between tasks and give their proofs. The experimental results show that our approach is powerful enough to mine process models with non-free-choice constructs. [ABSTRACT FROM AUTHOR]

Security schemes of pairwise key establishment, which enable sensors to communicate with each other securely, play a fundamental role in research on security issue in wireless sensor networks. A general framework for key predistribution is presented, based on the idea of Key Distribution Center and polynomial pool schemes. By utilizing nice properties of Hierarchical Hypercube model, a new security mechanism for key predistribution based on such model is also proposed. Theoretic analysis and experimental figures show that the new algorithm has better performance and provides higher possibilities for sensors to establish pairwise key, compared with previous related works. [ABSTRACT FROM AUTHOR]

The paper presents a novel probabilistic learning approach to state separation problem which occurs in the counterexample guided abstraction refinement. The method is based on the sample learning technique, evolutionary algorithm and effective probabilistic heuristics. Compared with the previous work by the sampling decision tree learning solver, the proposed method outperforms 2 to 4 orders of magnitude faster and the size of the separation set is 76% smaller on average. [ABSTRACT FROM AUTHOR]

With the wide implementations of Role-based access control (RBAC) models in the information systems, the access control for RBAC itself, administration of RBAC, becomes more and more important. In this paper, we propose a Domain Administration of RBAC Model, DARBAC, which defines an administrative domain for each administrative role. The administrative role can execute administrative operations on the users, roles, objects and child administrative roles within its administrative domain. Then we use π-calculus to formalize the elements of DARBAC model and their interactions. Although π-calculus has been successfully used in many security areas such as protocol analysis and information flow analysis, as we have known, our approach is the first attempt to use π-calculus to formalize RBAC and its administrative model. [ABSTRACT FROM AUTHOR]

We describe a novel technique called Analytic Boosted Cascade Detector (ABCD) to automatically locate features on the human face. ABCD extends the original Boosted Cascade Detector (BCD) in three ways: (i) a probabilistic model is included to connect the classifier responses with the facial features; (ii) a features location method based on the probabilistic model is formulated; (iii) a selection criterion for face candidates is presented. The new technique melts face detection and facial features location into a unified process. It outperforms Average Positions (AVG) and Boosted Classifiers + best response (BestHit). It also shows great speed superior to the methods based on nonlinear optimization, e.g. AAM and SOS. [ABSTRACT FROM AUTHOR]

A new anonymous secure communication protocol with conditional traceability is proposed to provide personal anonymity and privacy protection, in which a secure mapping function is introduced to provide anonymity and personal information protection, when necessary, only authority principal part can act as arbitrator for communication validation. The proposed protocol has 3 advantages: 1) mutual communication; 2) anonymity of communication. 3) conditional traceability. [ABSTRACT FROM AUTHOR]

In this paper, an Adaptive and Active Computing Paradigm (AACP) for personalized information service in heterogeneous environment is proposed to provide user-centered, push-based high quality information service timely in a proper way, the motivation of which is generalized as R4 Service: the Right information at the Right time in the Right way to the Right person, upon which formalized algorithms of adaptive user profile management, incremental information retrieval, information filtering, and active delivery mechanism are discussed in details. The AACP paradigm serves users in a push-based, event-driven, interest-related, adaptive and active information service mode, which is useful and promising for long-term user to gain fresh information instead of polling from kinds of information sources. Performance evaluations based on the AACP retrieval system that we have fully implemented manifest the proposed schema is effective, stable, feasible for adaptive and active information service in distributed heterogeneous environment. [ABSTRACT FROM AUTHOR]

While analyzing the previous methods for determining the number of probability-based clustering, this paper introduces an improved Monte Carlo Cross-Validation algorithm (iMCCV) and attempts to solve the posterior probabilities spread problem, which cannot be resolved by the Monte Carlo Cross-Validation algorithm. Furthermore, we present a hybrid approach to determine the number of probability-based clustering by combining the iMCCV algorithm and the parallel coordinates visual technology. The efficiency of our approach is discussed with experimental results. [ABSTRACT FROM AUTHOR]

Extraction and representation of the events in trajectory data enable us go beyond the primitive and quantitative values and focus on the high level knowledge. On the other hand, it enables the applications of vast off the shelf methods, which was originally designed for mining event sequences, to trajectory data. In this paper, the problem of symbolizing trajectory data is addressed. We first introduce a static symbolization method, in which typical sub-trajectories are generated automatically based on the data. For facilitating the data mining process on streaming trajectories, we also present an incremental method, which dynamically adjusts the typical sub-trajectories according to the most recent data characters. The performances of our approaches were evaluated on both real data and synthetic data. Experimental results justify the effectiveness of the proposed methods and the superiority of the incremental approach. Keywords: Motion data mining, spatial trajectory, symbolization [ABSTRACT FROM AUTHOR]