Xiaofeng Wang

Technical Institute of Physics and Chemistry, Peping, Beijing, China

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Publications (9)4.02 Total impact

  • Xiaofeng Wang · Gang Li · Guang Jiang · Zhongzhi Shi
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    ABSTRACT: Video event detection is an effective way to automatically understand the semantic content of the video. However, due to the mismatch between low-level visual features and high-level semantics, the research of video event detection encounters a number of challenges, such as how to extract the suitable information from video, how to represent the event, how to build up reasoning mechanism to infer the event according to video information. In this paper, we propose a novel event detection method. The method detects the video event based on the semantic trajectory, which is a high-level semantic description of the moving object’s trajectory in the video. The proposed method consists of three phases to transform low-level visual features to middle-level raw trajectory information and then to high-level semantic trajectory information. Event reasoning is then carried out with the assistance of semantic trajectory information and background knowledge. Additionally, to release the users’ burden in manual event definition, a method is further proposed to automatically discover the event-related semantic trajectory pattern from the sample semantic trajectories. Furthermore, in order to effectively use the discovered semantic trajectory patterns, the associative classification-based event detection framework is adopted to discover the possibly occurred event. Empirical studies show our methods can effectively and efficiently detect video events.
    Knowledge and Information Systems 11/2013; 37(2). DOI:10.1007/s10115-011-0471-8 · 1.78 Impact Factor
  • Zhuxiao Wang · Hui Peng · Jing Guo · Ying Zhang · Kehe Wu · Huan Xu · Xiaofeng Wang
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    ABSTRACT: ADML is an architectural description language based on Dynamic Description Logic for defining and simulating the behavior of system architecture. ADML is being developed as a new formal language and/or conceptual model for representing the architectures of concurrent and distributed systems, both hardware and software. ADML embraces dynamic change as a fundamental consideration, supports a broad class of adaptive changes at the architectural level, and offers a uniform way to represent and reason about both static and dynamic aspects of systems. Because the ADML is based on the Dynamic Description Logic DDL(\(\mathcal{SHON}\)(D)), which can represent both dynamic semantics and static semantics under a unified logical framework, architectural ontology entailment for the ADML languages can be reduced to knowledge base satisfiability in DDL(\(\mathcal{SHON}\)(D)), and dynamic description logic algorithms and implementations can be used to provide reasoning services for ADML. In this article, we present the syntax of ADML, explain its underlying semantics using the Dynamic Description Logic DDL(\(\mathcal{SHON}\)(D)), and describe the core architecture description features of ADML.
    Intelligent Information Processing VI, 01/2012: pages 157-166;
  • Xiaofeng Wang · Kun Yue · Wenjia Niu · Zhongzhi Shi
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    ABSTRACT: As a branch of classification, associative classification combines the basic ideas of association rule mining and general classification. Previous studies show that associative classification can achieve a higher classification accuracy comparing with traditional classification methods, such as C4.5. It is known that new frequent patterns may emerge from the classified resources during classification, and these newly emerging frequent patterns can be used to build new classification rules. However, this dynamic characteristics in associative classification has not been well reflected in traditional methods. In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ co-training to refine the discovered emerging frequent patterns for classification rule extension and utilize the maximum entropy model for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.
    Expert Systems with Applications 09/2011; 38(9):11873-11883. DOI:10.1016/j.eswa.2011.03.079 · 2.24 Impact Factor
  • Liang Chang · Zhongzhi Shi · Tianlong Gu · Xiaofeng Wang
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    ABSTRACT: The dynamic description logic DDL (dynamic description logic) provides a kind of action theory based on description logics. It is a useful representation of the dynamic application domains in the environment of the Semantic Web. In order to bring the representation capability of the branching temporal logic into the dynamic description logic, this paper treats the time slices of temporal logics as the executions of atomic actions, so that the temporal dimension and the dynamic dimension can be unified. Based on this idea, constructed over the description logic ALCQIO, a temporal dynamic description logic, named TDALCQIO, is presented. Tableau decision algorithm is provided for TDALCQIO. Both the termination and the correctness of this algorithm have been proved. The logic TDALCQIO not only inherits the representation capability provided by the dynamic description logic constructed over ALCQIO (attributive language with complements, qualified number restrictions, inverse roles and nominals), but it also has the ability to describe and reason about some temporal features such as the reachability property and the safety property of the whole dynamic application domains. Therefore, TDALCQIO provides further support for knowledge representation and reasoning in the environment of the Semantic Web. © Copyright 2011, Institute of Software, the Chinese Academy of Sciences.
    Journal of Software 07/2011; 22(7). DOI:10.3724/SP.J.1001.2011.03869
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    Zhongzhi Shi · Xiaofeng Wang
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    ABSTRACT: Intelligence science is an interdisciplinary subject mainly including brain science, cognitive science, artificial intelligence and others. Mind model is a basic issue of intelligence science. It is the internal sign or representation for external realistic world. If the neural network is a hardware of the brain system, then the mind model is the software of the brain system. We have proposed a mind model named Consciousness And Memory model (CAM). This paper will focus on the computational model of consciousness and episodic memory.
  • Xiaofeng Wang · Wenjia Niu · Gang Li · Xinghua Yang · Zhongzhi Shi
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    ABSTRACT: The dynamic description logic ( DDL ) is utilized as one emerging AI planning -related solution for automatic Web service composition. However, reasoning utilization when facing the real world service applications in such DDL -related solutions is still an open problem. In this paper, we propose the cooperative reasoning-based multi-agent model ( CREMA ) which can systematically incorporate DDL action reasoning with data mining, together with a support -based planning method for task decomposition in order to improve the overall throughput of the Web service execution. The case study and experimental analysis demonstrates the capability of the proposed approach.
    Agents and Data Mining Interaction - 7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers; 01/2011
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    Zhongzhi Shi · Xiaofeng Wang · Jinpeng Yue
    International Journal of Intelligence Science 01/2011; 1(02):25-34. DOI:10.4236/ijis.2011.12004
  • Zhongzhi Shi · Xiaofeng Wang · Zhiping Shi · Limin Chen · Zhuxiao Wang
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    ABSTRACT: Mind is all mankind's spiritual activities, including emotion, will, perception, consciousness, representation, learning, memory, thinking, intuition, etc. Mind model is for explaining what individuals operate in the cognitive process for something in the real world. It is the internal sign or representation for external realistic world. If the neural network is a hardware of the brain system, then the mind model is the software of the brain system. The key issue in intelligence science is to construct the mind model of the brain system, which will guide the development of brain-like computer in engineering through structure, dynamics, function and behavioral reverse engineering of the brain. This paper will discuss the computational model of memory and consciousness in the mind model named Consciousness And Memory model(CAM).
    Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on; 08/2010
  • Xiaofeng Wang · Liang Chang · Zhixin Li · Zhongzhi Shi
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    ABSTRACT: Video event detection is an important research area nowadays. Modeling the video event is a key problem in video event detection. In this paper, we combine dynamic description logic with linear time temporal logic to build a logic system for video event detection. The proposed logic system is named as LTDALCO which can represent and inference the static, dynamic and temporal knowledge in one uniform logic system. Based on the LTDALCO, a framework for video event detection is proposed. The video event detection framework can automatically obtain the logic description of video content with the help of ontology-based computer vision techniques and detect the specified video event based on satisfiability checking on LTDALCO formulas. Keywordsvideo event-semantics-dynamic description logics-reasoning-ontology
    Frontiers of Electrical and Electronic Engineering in China 06/2010; 5(2):137-142. DOI:10.1007/s11460-009-0078-y