A bayesian approach to deformed pattern matching of iris images

We describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: it normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications     

Dynamic SOA Framework to Support Ad Hoc Enterprise Alliance Formation

Collaboration among businesses is needed to successfully fulfill a given task and goal. Service-Oriented Architecture (SOA) has been regarded as an efficient platform to support flexible interoperability among various enterprises by discovering, selecting and composing services. However, since a large number of enterprises have been participating in this SOA platform, relationships among these enterprises are getting too complicated to obtain flexibility and scalability for efficient collaboration. Thereby, in this paper, we propose a dynamic SOA platform to discover service chains for building ad hoc enterprise alliances where the only relevant enterprises are sorted out and merged. As a result, given an event, decision makers can find out which enterprises (and services from the enterprises) might be selected for their collaboration. The proposed SOA platform has been applied to mobile advertisement application as a case study. With respect to two indicators (i.e., precision and agility), we have shown that the proposed SOA outperforms traditional enterprise collaboration schemes.     

The effects of personality traits,self-esteem,loneliness, and narcissism on Facebook use among university students

This study examined the relationship between three of the “Big Five” traits (neuroticism, extraversion, and openness), self-esteem, loneliness and narcissism, and Facebook use. Participants were 393 first year undergraduate psychology students from a medium-sized Australian university who completed an online questionnaire. Negative binomial regression models showed that students with higher openness levels reported spending more time on Facebook and having more friends on Facebook. Interestingly, students with higher levels of loneliness reported having more Facebook friends. Extraversion, neuroticism, self-esteem and narcissism did not have significant associations with Facebook use. It was concluded that students who are high in openness use Facebook to connect with others in order to discuss a wide range of interests, whereas students who are high in loneliness use the site to compensate for their lack of offline relationships.     

Evolutionary approach for semantic-based query sampling in large-scale information sources

Metadata about information sources (e.g., databases and repositories) can be collected by Query Sampling (QS). Such metadata can include topics and statistics (e.g., term frequencies) about the information sources. This provides important evidence for determining which sources in the distributed information space should be selected for a given user query. The aim of this paper is to find out the semantic relationships between the information sources in order to distribute user queries to a large number of sources. Thereby, we propose an evolutionary approach for automatically conducting QS using multiple crawlers and obtaining the optimized semantic network from the sources. The aim of combining QS and evolutionary methods is to collaboratively extract metadata about target sources and optimally integrate the metadata, respectively. For evaluating the performance of contextualized QS on 122 information sources, we have compared the ranking lists recommended by the proposed method with user feedback (i.e., ideal ranks), and also computed the precision of the discovered subsumptions in terms of the semantic relationships between the target sources.     

Throughput analysis for order picking system with multiple pickers and aisle congestion considerations

Most of previous studies in picker-to-parts warehousing systems investigated only single-picker operations and are therefore adequate to evaluate order picking efficiency by travel distance as aisle congestion never takes place in such systems. In real world applications, the congestion inevitably occurs when a system has multiple pickers working together within the same region. This paper presents an approximation method based on a GI/G/1 closed queueing network by using self-correcting approximation technique algorithm to evaluate the throughput time of an order picking system with multiple pickers and aisle congestion considerations for different routing policies. The results generated by the proposed method are compared and validated via simulation model using eM-plant simulator for different sizes of warehouses. The results indicate that the approximation method appears to be sufficiently accurate for practical purposes. The sensitivity analysis of the throughput time with respect to order sizes, number of pickers and number of aisles are conducted and the performance of different item storage policies are also evaluated using the proposed approximation model.     

Constraint graph-based frequent pattern updating from temporal databases

There have been many kinds of association rule mining (ARM) algorithms, e.g., Apriori and FP-tree, to discover meaningful frequent patterns from a large dataset. Particularly, it is more difficult for such ARM algorithms to be applied for temporal databases which are continuously changing over time. Such algorithms are generally based on repeating time-consuming tasks, e.g., scanning databases. To deal with this problem, in this paper, we propose a constraint graph-based method for maintaining frequent patterns (FP) discovered from the temporal databases. Particularly, the constraint graph, which is represented as a set of constraint between two items, can be established by temporal persistency of the patterns. It means that some patterns can be used to build the constraint graph, when the patterns have been shown in a set of the FP. Two types of constraints can be generated by users and adaptation. Based on our scheme, we find that a large number of dataset has been efficiently reduced during mining process and the gathering information while updating.     

Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the Psi Sigma Neural Network (PSI) and the Gene Expression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USD exchange rate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models.     

设计与制造之间的界面优化

分辨率增强技术（RET）设计流程中聚集了各式各样的点工具和方法。当前对深亚波长可制造性（DFM）设计的要求迫使设计与制造行业必须建立十分密切的合作关系。同时也促使EDA行业提供一种平台，以促进这些行业间的密切交流。合理利用这一平台将会对产能和品质产生直接的影响。