Effect of personal characteristics on relationships between attitudes and job performance.

Using 376 airmen, Whitlock and Cureton (see 35: 7252) collected attitude score and biographical data, which is reanalysed in the present study, using multiple regression techniques. "… much of the criterion variance associated with attitude measures is also related to more easily obtained biographical characteristics of workers and biographical variables should be considered when relating attitudes to productivity." (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

指横纹:一种新的生物身份特征

本文提出一种新的基于指横纹的生物特征认证方法. 指横纹图像具有抗噪性强、纹路简单、可分性强的优点, 并易于与其它手部特征如手形、掌纹等特征形成融合认证系统. 本文采用基于 Gabor 滤波的方法提取指横纹特征点, 并基于互相关点匹配与决策级分数融合完成在线认证系统. 评估系统建立在包含 98 个人、1971 幅图像的数据库上, 平均错误率仅为 0.57\%, 验证了指横纹作为一种生物特征的可靠性与可行性, 同时也证实了认证方法的有效性.     

Combining propensity score matching and group-based trajectory analysis in an observational study.

In a nonrandomized or observational study, propensity scores may be used to balance observed covariates and trajectory groups may be used to control baseline or pretreatment measures of outcome. The trajectory groups also aid in characterizing classes of subjects for whom no good matches are available and to define substantively interesting groups between which treatment effects may vary. These and related methods are illustrated using data from a Montreal-based study. The effects on subsequent violence of gang joining at age 14 are studied while controlling for measured characteristics of boys prior to age 14. The boys are divided into trajectory groups based on violence from ages 11 to 13. Within trajectory group, joiners are optimally matched to a variable number of controls using propensity scores, Mahalanobis distances, and a combinatorial optimization algorithm. Use of variable ratio matching results in greater efficiency than pair matching and also greater bias reduction than matching at a fixed ratio. The possible impact of failing to adjust for an important but unmeasured covariate is examined using sensitivity analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

学分制下成绩管理的探讨

本文总结了我校成绩管理在实行学分制并更换教务管理平台后产生的各种问题,分析了相关问题的产生原因,并提出了相应的解决方案。     

INTEGRATION OF A VOICE RECOGNITION SYSTEM IN A SOCIAL ROBOT

Human–robot interaction (HRI) ^{1 1}HRI can be defined as the study of humans, robots, and the ways in which they influence each other. is one of the main fields in the study and research of robotics. Within this field, dialogue systems and interaction by voice play an important role. When speaking about human–robot natural dialogue we assume that the robot has the capability to accurately recognize what the human wants to transmit verbally and even its semantic meaning, but this is not always achieved. In this article we describe the steps and requirements that we went through in order to endow the personal social robot Maggie, developed at the University Carlos III of Madrid, with the capability of understanding the natural language spoken by any human. We have analyzed the different possibilities offered by current software/hardware alternatives by testing them in real environments. We have obtained accurate data related to the speech recognition capabilities in different environments, using the most modern audio acquisition systems and analyzing not so typical parameters such as user age, gender, intonation, volume, and language. Finally, we propose a new model to classify recognition results as accepted or rejected, based on a second automatic speech recognition (ASR) opinion. This new approach takes into account the precalculated success rate in noise intervals for each recognition framework, decreasing the rate of false positives and false negatives.     

Multi-criteria decision-making methods with optimism and pessimism based on Atanassov's intuitionistic fuzzy sets

The theory of Atanassov's intuitionistic fuzzy sets (A-IFSs) developed over the last several decades has found useful application in fields requiring multiple-criteria decision analysis. Since the membership–nonmembership pair in A-IFSs belongs to the bivariate unipolarity type, this article describes an approach that relates optimism and pessimism to multi-criteria decision analysis in an intuitionistic fuzzy-decision environment. First, several optimistic and pessimistic point operators were defined to alter the estimation of decision outcomes. Next, based on the core of the estimations, optimistic and pessimistic score functions were developed to evaluate each alternative with respect to each criterion. The suitability function was then established to determine the degree to which each an alternative satisfies the decision maker's requirement. Because the information on multiple criteria corresponding to decision importance is often incomplete, this study included suitability functions in the optimisation models to account for poorly known membership grades. Using a linear equal-weighted summation method, these models were transformed into a single objective optimisation model to generate the optimal weights for criteria. The feasibility and effectiveness of the proposed methods were illustrated through a practical example. Finally, computational experiments with enormous amounts of simulation data were designed to conduct a comparative analysis on the ranking orders yielded by different optimistic/pessimistic point operators.     

基于拉普拉斯评分的多标记特征选择算法

针对传统的拉普拉斯评分特征选择算法只适应单标记学习,无法直接应用于多标记学习的问题,提出一种应用于多标记任务的拉普拉斯评分特征选择算法。首先,考虑样本在整体标记空间中共同关联和共同不关联的相关性,重新构建样本相似度矩阵;然后,将特征之间的相关性及冗余性判定引入拉普拉斯评分算法中,采用前向贪心搜索策略依次评价候选特征与已选特征的联合作用能力,用于评价特征的重要性;最后,在5个不同评价指标和6个多标记数据集上实验。实验结果表明：相比基于最大依赖的多标记维数约简方法（MDDM）、基于贝叶斯分类器的多标记特征选择算法（MLNB）及基于多元互信息的多标记分类特征选择算法（PMU）,所提算法不仅分类性能最优,且存在显著性优异达65%。     

Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMM and KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus.     

基于多信息融合的视觉目标类识别算法研究

视觉目标类识别是计算机视觉研究领域中的最具挑战性的难题之一,目前仍有许多问题没有得到很好的解决.近年来提出的空域金字塔直方图特征表示,在描述特征点集分布属性方面取得了比较好的实验效果.但是由于其描述的信息不全面,在性能上仍有较大改进余地.从信息互补性角度出发,提出了基于多信息融合的集成策略,将空域金字塔直方图表示与费舍分数表示各自描述的优势相结合,用于视觉目标类识别.实验证明该策略是有效的,在所进行测试的所有类别上相比单信息识别的性能均取得了一致性提高.     

论音乐空间与建筑空间的对应性

本文论述了音乐空间与建筑空间的对应性关系,同时以此为基础对将乐谱这一视觉化了的音乐空间转组并移注于建筑空间的实验理论和操作方法进行了探讨和提示。