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.     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。     

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。     

湘中海相浅层湘冷1井酸压工艺研究及应用

湘冷1井具有气层埋藏浅（400m-500m）、纵向上裂隙发育的特点，属低孔、低渗的含泥质灰岩致密储层。大量室内实验和研究采用“前置液酸压＋闭合酸化”工艺技术，并优选了相应降阻酸和闭合酸配方。通过酸压实现了该井工业油气流的突破，取得了明显的增产效果，为新区海相浅层气的开发探索出了一套成熟的改造增产措施。     

低渗低温低压水敏性储气层压裂改造技术研究与应用

低渗低温低压水敏性储气层因压裂液快速破胶困难、黏土膨胀水敏伤害严重和压后压裂液返排动力不足等因素，使得压裂增产面临诸多难题。针对上述难题进行了技术攻关，形成了弱交联、低温活化剂与超量破胶剂的低温储层快速破胶技术、有机盐与无机盐双元体系复合防膨技术以及高比例液氮全程助排的排液技术。该压裂增产改造技术使牛101井头屯河组首次获得工业油气流，扩展了三塘湖盆地勘探空间。     

试油井选层压裂新工艺及现场试验

设计新型可洗井封隔器，保障油井有反洗井通道，有效防止封隔器以上砂埋所引发的管柱砂卡事故，并对新型封隔器、控制开关、密封接头、同心大通径压力计托筒等井下工具组合优化。解决了多层油井任意选层压裂、求产、测井温一体化的难题。     

川西新场气田JS_2~1气藏测井多参数评价方法

针对川西新场气田JS12气藏开展了多参数的综合评价研究,多参数包括储层参数、裂缝参数、储层分类厚度参数。首先,开展了储层参数及裂缝参数的计算,以岩心分析结果为依据,建立了孔隙度、渗透率、含水饱和度计算模型。利用铸体薄片进行了微裂缝识别,对样品的裂缝参数和测井曲线进行相关分析,建立了测井计算裂缝参数的数学模型;其次,对储层进行聚类分析,通过聚类将储层分为三类;最后,将计算的裂缝参数、储层分类中的Ⅰ类储层厚度、孔渗饱等参数进行正态化处理,拟和测试的无阻流量与这些参数建立多元回归计算模型,即综合含气指标的数学计算模型。以综合含气指标为主要依据,进行储层综合分类和评价,指出了有利的含气区域和可供挖潜井的建议。该方法在指导气藏开发中获得了良好效果。