A Fuzzy Mining Algorithm for Association-Rule Knowledge Discovery

Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. On the other hand, using fuzzy classification in data mining has been developed in recent years. Hong and Lee proposed a general learning method that automatically derives fuzzy if-then rules and membership functions from a set of given training examples using a decision table. But it is complex if there are many attributes or if the predefined unit is small. Hong and Chen improve it by first selecting relevant attributes and building appropriate initial membership functions. Based on Hong’s heuristic algorithm of membership functions and Apriori approach, we propose a fuzzy mining algorithm to explore association rules from given quantitative transactions. Experimental results on Iris data show that the proposed algorithm effectively induces more association rules

Exploiting Spatial-temporal Correlations for Video Anomaly Detection

Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events. Existing deep learning-based VAD methods usually leverage proxy tasks to learn the normal patterns and discriminate the instances that deviate from such patterns as abnormal. However, most of them do not take full advantage of spatial-temporal correlations among video frames, which is critical for understanding normal patterns. In this paper, we address unsupervised VAD by learning the evolution regularity of appearance and motion in the long and short-term and exploit the spatial-temporal correlations among consecutive frames in normal videos more adequately. Specifically, we proposed to utilize the spatiotemporal long short-term memory (ST-LSTM) to extract and memorize spatial appearances and temporal variations in a unified memory cell. In addition, inspired by the generative adversarial network, we introduce a discriminator to perform adversarial learning with the ST-LSTM to enhance the learning capability. Experimental results on standard benchmarks demonstrate the effectiveness of spatial-temporal correlations for unsupervised VAD. Our method achieves competitive performance compared to the state-of-the-art methods with AUCs of 96.7%, 87.8%, and 73.1% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively.Comment: This paper is accepted at IEEE 26TH International Conference on Pattern Recognition (ICPR) 202

Numerical Investigation for the Bearing Performance of the Segmental Joint with Elastic Gasket

Detailed three-dimensional numerical models considering concrete indentation, bolts, elastic gasket (EG) and sealing gasket (SG) are established for the segmental joint with gaskets, and the load tests of the joints with EG and without EG are simulated and compared. The results reveal that the bearing performance of the joint with EG is very complex. In sagging moment scenarios, it can be divided into four stages by three key points “EG starts to open”, “joint external edge starts to contact” and “EG fully opened”. In hogging moment scenarios, it can be divided into three stages by two key points “SG opened” and “EG starts to open”. The EG has a significant effect on the joint bearing performance. It can soften the joint, which leads to the result that the average bending stiffness and ultimate bearing capacity of the joint with EG are evidently smaller and weaker than those of the joint without EG. With decreasing the joint axial force, this softening effect tends to be more obvious. Besides, for the two joints, the ultimate states of the joints subject to the bending moments are both that the concrete at the joint edges yields firstly, and it is necessary to protect or strengthen the corresponding concrete

Empirical Study on Consumer Perceived On-line Payment Risk

This paper takes the potential release of online transaction market size as the starting point of research, and discusses how Chinese consumer perceived risks influence online payment willingness. This study divides consumer perceived risks of online payment into eight dimensions: Economic risk, Functional risk, Private risk, Security risk, Time risk, Service risk, Psychological risk and Social risk. Furthermore, it explores the influence of multi-dimensional perceived risks on the willingness of consumers’ online payment on the basis of 616 samples from Shanghai. The empirical results show that there is a significant negative correlation between perceived economic risks and the willingness to pay online; perceived security risks and some other risks have significant positive effect on payment willingness, which shows that certain perceived risks are becoming systemic risks in accordance with the principle of finance. So, the paper imply that both Private and Government third payment platforms shall take certain measures to reduce consumers’ specific perceived risk for promoting the development of online transaction market in China