Linking of serially ordered lists by macaque monkeys (Macaca mulatta): List position influences.

Five monkeys (Macaca mulatta) were trained on 2 sets of 3 5-item serially ordered lists. Then, each set was either linked or not in a counterbalanced, within-subject design. Linking entailed training on the 2 pairs that ordered the 3 5-item fists into a single overall 15-item series. Choices on novel pairings after linking conditions attempted to define the unique contributions of knowledge of within-list ordinal position and between-lists link training. With linkage, the series was immediately treated as a 15-item ordered list. Without linkage, choices reflected list positions from initial learning, but continued testing with directional reward yielded gradual ordering into a 15-itern list. Apparently, monkeys remembered and used initial list-position information, but linkage allowed inference of an integrated serial relationship among items. Results supported primate list memory as an organizational process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

Gold码的因子图迭代捕获方法

为降低Gold码的捕获时间与复杂度,提出将基于因子图的迭代m序列捕获算法应用于Gold码捕获. 以全球定位系统(GPS)信号的C/A码为例,首先根据Gold码的生成多项式确定1组具有稀疏结构的校验关系;然后为降低迭代计算的复杂度,提出了不同校验关系顺次校验与混合校验的迭代捕获算法;最后通过仿真研究比较了不同的校验关系和校验方法对迭代捕获算法性能的影响. 仿真结果表明,顺次迭代且迭代次数递减的校验方法最优,该方法能在不损失检测概率的情况下降低Gold码捕获的复杂度,同时降低其捕获时间.     

一种新的实时智能汽车轨迹规划方法

针对智能汽车的驾驶决策和轨迹规划问题, 将轨迹表示为轨迹曲线和加速度变化两部分, 以优化轨迹的行驶效率、安全性、舒适性和经济性为目标建立非线性规划模型. 基于序优化思想, 提出混合智能优化算法OODE, 分内、外两层分别优化加速度变化和轨迹曲线, 通过“粗糙” 评价轨迹曲线实现轨迹曲线的快速择优. 仿真结果表明, 所提出的方法能够处理包含多动态障碍物的复杂交通场景, 且具备实时应用能力, 模型的精度和求解速度均优于传统方法.

     

基于局部排序的视频拷贝检测

排序法是一种常用的视频拷贝检测方法.为获得更佳的检测性能,提出一种基于排序特征的视频拷贝检测方案.该方案将帧进行分块,并按照Hilbert曲线顺序分别计算曲线上相邻块的灰度关系排序特征,生成用于检测的哈希码;为了准确地在目标视频中定位可疑内容,提H{了哈希匹配方案,将序列相似度作为匹配的依据,并引入动态规划的方法提高匹配精度;最后构造了拷贝测试样本,并与传统的排序签名检测方案进行性能对比实验.结果表明,文中方案拥有较好的检测性能,适用于视频内容的拷贝检测.     

基于内容的视频拷贝检测研究

提出一种新的基于内容的视频拷贝检测方法,根据视频连续帧之间信息的冗余,提出关键帧提取算法,对提取的关键帧做DCT变换,并将AC系数的有序测度作为关键帧的特征,计算视频关键帧之间的L1距离作为视频之间的相似度度量。实验表明该方法对抗几种不同的几何攻击有较好的效果。     

A Ramsey theorem in Boyer-Moore logic

We use the Boyer-Moore Prover, Nqthm, to verify the Paris-Harrington version of Ramsey's theorem. The proof we verify is a modification of the one given by Ketonen and Solovay. The theorem is not provable in Peano Arithmetic, and one key step in the proof requires ₀ induction.Author supported by NSF Grant DMS-9100665.     

基于相似度的多粒度不确定语言群决策方法

针对具有多粒度不确定语言评价信息的多属性群决策问题,提出了一种基于区间二元语义信息处理和矢量相似度的群决策方法,弥补了基于距离测度的决策方法易造成信息混淆的不足。该方法首先使用二元语义转换函数对多粒度区间语言评价信息进行一致化处理;然后通过建立使备选方案对正理想解相似度最大、负理想解相似度最小的最优化模型来获得相应的属性权重;最后利用区间二元语义的集结算子对评价信息进行加权集成,并通过优序数排序法实现对各方案的排序。实例分析说明了该方法的可行性和有效性。     

Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

基于Logistic回归服装品牌忠诚度影响因素分析

基于服装行业特点、企业层面和消费者层面,提出了服装品牌忠诚度影响因素模型。根据研究假设形成调查问卷指标体系,利用回收的数据,运用因子分析和聚类分析等多元统计方法提取了11个主因子,并用序次Logistic回归模型进行处理分析,结果显示,影响服装品牌忠诚度的显著因子从大到小依次为：风险规避态度、服装属性、多样性寻求心态、自我形象概念态度、购买便利性、服务完整性、促销与推广,并分别从服装品牌忠诚的两个维度,即行为忠诚和态度忠诚对这些主因子的作用进行分析,研究结果表明,南于影响行为忠诚和态度忠诚的因素不完全相同,所以影响程度也存在较大差异。