A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

Word length and lexical activation: Longer is better.

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a system, long words should produce stronger lexical activation than short words, for 2 reasons: Long words provide more bottom-up evidence than short words, and short words are subject to greater inhibition due to the existence of more similar words. Four experiments provide evidence for this view. In addition, reaction-time-based partitioning of the data shows that long words generate greater activation that is available both earlier and for a longer time than is the case for short words. As a result, lexical influences on phoneme identification are extremely robust for long words but are quite fragile and condition-dependent for short words. Models of word recognition must consider words of all lengths to capture the true dynamics of lexical activation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Tracking the mind during reading: The influence of past, present, and future words on fixation durations.

Reading requires the orchestration of visual, attentional, language-related, and oculomotor processing constraints. This study replicates previous effects of frequency, predictability, and length of fixated words on fixation durations in natural reading and demonstrates new effects of these variables related to 144 sentences. Such evidence for distributed processing of words across fixation durations challenges psycholinguistic immediacy-of-processing and eye-mind assumptions. Most of the time the mind processes several words in parallel at different perceptual and cognitive levels. Eye movements can help to unravel these processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

指纹考勤管理软件的研究与实现

针对企、事业单位原有的考勤系统，如打卡钟、磁卡、IC卡等考勤方式存在代打卡、卡丢失等不足和缺陷的实际，结合指纹识别技术，建立一套考勤、薪资、人事管理于一体的软件系统。给出了系统的体系结构和功能实现，能使企业考勤管理更加简便快捷、客观公正。并提出了进一步的发展方向和展望。     

针对不同地质目标的叠前时间偏移成像解释评价

随着计算机硬件和地震勘探成像技术的发展，叠前时间偏移正逐步替代常规的NMO加DMO加叠后时间偏移成为地震数据成像处理方法的主流。但对于不同的地质目标，叠前时间偏移的成像效果是否优于常规NMO加DMO加叠后时间偏移的成像效果呢？为此，从地震数据成像处理方法、处理流程和处理参数等方面进行了讨论，并基于某地区三维数据常规处理结果和叠前时间偏移处理结果，针对不同地质目标进行了剖析与评价。认为：叠前时间偏移成像的垂向分辨率较常规处理明显降低，但对于空间波阻抗变化明显的河流和断层，叠前时间偏移成像的空间分辨率要高于常规处理；对于小于1／4波长的叠置薄储层，叠前时间偏移成像的垂向和空间分辨率低于常规处理结果。     

论建立动态勘探目标评价体系的迫切必要性

从国际上通行的油气勘探评价工作重点,中国石油所面临的"已逐步进入一个不断在老区范围内开展精细勘探、在老区范围内开展新区块、新层系勘探以获得主要的储量增长"的新的勘探形势,现有地质研究中各专题研究与具体勘探目标评价脱节等多个方面,论述了在中国石油内部建立一套动态勘探目标评价体系的迫切必要性;并在此基础上阐述了一个完整的动态勘探目标评价体系应包括的主要内容;最后对该体系的建设进行了可行性分析。     

应用时频分析方法辨识时变系统的模态参数

应用Gabor展开及Hilbert变换进行时变线性系统模态参数的辨识。利用非平稳信号的Gabor变换将结构响应信号表示在时频面上，并通过Gabor展开重构分离模态分量，建立每一阶模态的时变线性模型。对单模态时变线性模型应用Hilbert变换来辨识随时间变化的模态频率和阻尼。通过对刚度和阻尼慢变的两自由度系统模态参数的仿真辨识验证辨识方法的有效性。仿真结果表明：本文方法为时变线性系统的模态参数辨识提供了一条新的途径。     

基于扩展分形和CFAR特征融合的SAR图像目标识别

研究了多信息融合技术在SAR图像目标识别中的应用。将扩展分形特征(Extended Fractal)与双参数恒虚警特征(Double Parameter CFAR)形成的多信息进行融合处理。运用Dempster-Shafer证据理论，在决策层对SAR图像中的像素进行识别分类。实验结果表明通过融合对像素分类的准确性明显好于单特征的检测结果，减少了虚警概率，提高了系统的识别能力。     

Allocentric Versus Egocentric Spatial Memory After Unilateral Temporal Lobectomy in Humans.

Thirty patients who had undergone either a right or left unilateral temporal lobectomy (14 RTL; 16 LTL) and 16 control participants were tested on a computerized human analogue of the Morris Water Maze. The procedure was designed to compare allocentric and egocentric spatial memory. In the allocentric condition, participants searched for a target location on the screen, guided by object cues. Between trials, participants had to walk around the screen, which disrupted egocentric memory representation. In the egocentric condition, participants remained in the same position, but the object cues were shifted between searches to prevent them from using allocentric memory. Only the RTL group was impaired on the allocentric condition, and neither the LTL nor RTL group was impaired on additional tests of spatial working memory or spatial manipulation. The results support the notion that the right anterior temporal lobe stores long-term allocentric spatial memories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)