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。     

Performance of Alcohol and Safer Sex Change Rulers Compared With Readiness to Change Questionnaires.

As part of a larger intervention study, the authors hypothesized that change rulers created for alcohol and safer sex would be equivalent to longer questionnaires. Ninety-six male college students completed rulers and questionnaires for assessing behavior change readiness. Participants' scores on the rulers significantly correlated with their scores on the questionnaires (r = .77 for alcohol; r = .77 for safer sex). In both domains, the rulers outperformed the questionnaires in predicting behavioral intentions, suggesting that the rulers had at least comparable concurrent criterion validity. This finding is the first of its kind in the safe sex literature and suggests that quick assessments of readiness to change are possible. Because the rulers are a continuous measure, the results are consistent with the idea that the change process is continuous rather than a series of discrete stages. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Inhibition of Return and Repetition Priming Effects in Localization and Discrimination Tasks.

Inhibition of return (IOR) refers to slower responding to a stimulus that is presented at the same, rather than a different location as a preceding, spatially nonpredictive, stimulus. Repetition priming refers to speeded responding to a stimulus that duplicates the visual characteristics of a stimulus that precedes it. IOR and repetition priming effects interact in nonspatial discrimination tasks but not in localization tasks; three experiments examined whether this is due to processing differences or due to response differences between tasks. Two stimuli, S1 and S2, occurred on each trial. In Experiment 1, S1 and S2 were both peripheral arrows; in Experiment 2, S1 was a central arrow and S2 was a peripheral nondirectional rectangle; in Experiment 3, S1 was a peripheral nondirectional rectangle and S2 was a peripheral arrow. S1 never required a response; S2 required a localization or a discrimination response. Despite evidence that form information was likely extracted from the arrow stimuli, the localization task revealed no repetition priming: IOR occurred regardless of shared visual identity of the S1 and S2 arrows. The discrimination task revealed IOR only when the visual identity changed from S1 to S2; otherwise, facilitation occurred. These results suggest that IOR is masked by repetition priming only when the response depends on the explicit processing of form information; repetition priming does not occur when such information is extracted automatically but is task (and response) irrelevant. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Individual Differences in Susceptibility to False Memory in the Deese-Roediger-McDermott Paradigm.

The authors addressed whether individual differences in the working memory capacity (WMC) of young adults influence susceptibility to false memories for nonpresented critical words in the Deese-Roediger-McDermott associative list paradigm. The results of 2 experiments indicated that individuals with greater WMC recalled fewer critical words than individuals with reduced WMC when participants were forewarned about the tendency of associative lists (e.g., bed, rest, . . .) to elicit illusory memories for critical words (e.g., sleep). In contrast, both high and low WMC participants used repeated study-test trials to reduce recall of critical words. These findings suggest that individual differences in WMC influence cognitive control and the ability to actively maintain task goals in the face of interfering information or habit. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An Examination of Indicators of Engineering Students' Success and Persistence

Student success and persistence within the major and university were examined through hierarchical linear and logistic regression analyses for two cohorts of engineering students. Indicators of success and persistence were based on theoretical and empirical evidence and included both cognitive and noncognitive variables. Cognitive variables included high school rank, SAT scores, and university cumulative grade point average. Noncognitive factors included academic motivation and institutional integration. Outcome variables included grade point average, enrollment at the university, and status as an engineering major. Gender differences also were evaluated. Several significant relationships among the variables were found. For instance, increased levels of motivation were significantly related to continuing in the major. Implications and directions for future research are discussed.