Region duplication is one of the most common methods of video forgery. Existing forgery detection algorithms generally suffer from inefficiency and are not effective for the forged regions with mirroring. To address these problems, we present a fast forgery detection algorithm based on Exponential-Fourier moments (EFMs) for detecting region duplication in videos. The algorithm first extracts EFMs features from each block in the current frame, and performs a fast match to find potential matching pairs. Then, a postverification scheme is designed to eliminate falsely matched pairs and locate the altered regions in the current frame. Finally, an adaptive parameter-based fast compression tracking algorithm is used to track the tampered regions in the subsequent frames. The experimental results show that our proposed algorithm has higher detection accuracy and computational efficiency than those of previous algorithms.

Knowledge of the association information between the attributes in a data set provides insight into the underlying structure of the data and explains the relationships (independence, synergy, redundancy) between the attributes. Complex models learnt computationally from the data are more interpretable to a human analyst when such interdependencies are known. In this paper, we focus on mining two types of association information among the attributes – correlation information and interaction information which capture multivariate dependencies between the data attributes. Identifying the statistically significant attribute associations is a computationally challenging task – the number of possible associations increases exponentially and many associations contain redundant information when a number of correlated attributes are present. In this paper, we explore efficient data mining methods to discover non-redundant attribute sets that contain significant association information indicating the presence of informative patterns in the data.

Networks are well-suited model for representing complex systems and processes. This paper proposed a novel perspective on study of the evolvement of export market structure by combining network-based approach with data mining technology. Firstly, bipartite networks were constructed to describe the relationships that form between enterprises and their export markets based on China's Guangdong custom statistical data for the 2000-2005 periods. Network analysis techniques were used to analyze and describe the topology properties of the whole networks as well as their evolution over time. Secondly, data mining techniques were used to identify most frequent association relations and evolvement rule hidden in bipartite networks.

It is an important issue to obtain the consensus opinion under group decision making. In this paper, we propose a new approach to aggregate the experts’ opinions which is based on the area between the centroid points of fuzzy numbers and the original point. The opinions of experts are represented by positive fuzzy numbers which include the normal and the non-normal trapezoidal fuzzy numbers as well as interval numbers. A new index that the consensus of each expert to others is constructed by using a similarity measure under the area. We also take into consideration the importance of each expert in the process of aggregation. The operational procedure is simple and the numerical examples show its reliability.

Most existing dynamic competition studies focus on the rivalry between two firms, neglecting the truth that firms in some multimarket industry are competing over a network. By combining the competitive pressure network model and certain complex network approaches, the paper proposes a method to simulate the spread effect of tactical rivalry actions over a network. The method is helpful in understanding factors that may affect the spread effect of rivalry, and the relationship between a firm's network properties in the industry and its power in spreading the rivalry.

Prominent scale-free distributions of competitive relationships are observed in some empirical studies on some complex company competitive relationship networks (CRNs). To uncover the driving forces of these properties, the company-product bipartite network model, which is the prototype of CRN, is used to depict the dynamics of industries. The growth mechanisms of the network model based on the industry-specific traits are helpful in understanding the different properties of CRNs between industries. A simplified simulation implies that the structural properties of CRNs can be different among industries and may alter at different life cycle stages of an industry

The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently. Consequently, several new ideas have been proposed; among them a key technique is focused crawling which is able to crawl particular topical portions of the World Wide Web quickly without having to explore all Web pages. In this paper, we present an intelligent focused crawler algorithm in which we embed ontology to evaluate the page's relevance to the topic. Compared with other algorithms using domain knowledge, our algorithm can evolve the ontology automatically during crawl process. Considering the instinct characteristics of the ontology, propagation has also been imported to accelerate the evolution of the ontology. We applied our approaches in several tasks and provided an empirical evaluation which has shown promising results.

Based on recent progress in research of systems concept and systems methodology, including systems metaphor, systems of system methodology and total systems intervention, new conceptual frameworks and theoretical hypothesis on the synthesis and innovation of the enterprise strategy research schools were raised in this paper, so that to overcome difficulties which may occur in the practice of enterprise strategy research methodology.