基于改进词嵌入表示和卷积神经网络的银行智能派单系统的研究

银行智能派单系统的实现和功能完善,对银行提升客户满意度、提高突发事件处理效率、降低人工处理成本等非常重要。针对现有的基于Word2vec和TextCNN模型的银行智能派单系统进行了改进,针对特征词权重表达性弱,特征词类别及位置区分性弱等问题,提出基于改进TF-IDF加权的Word2vec词嵌入表示和卷积神经网络结合的银行智能派单系统:首先利用Word2vec模型得到输入事件单的词嵌入向量;再针对经典TF-IDF方法不具备类别区分性、位置区分性,也没有考虑极端频率特征词代表性的情况,提出改进型TF-IDF算法,计算每个特征词的权重,得到基于改进TF-IDF加权的Word2vec词嵌入表示;最后在卷积神经网络模型中进行训练,通过迭代训练最终得到分类器,利用分类器可对输入事件单信息自动进行系统类别的判断。实验结果表明改进词嵌入表示的银行智能派单系统分类模型的宏查准率、宏查全率、准确率以及宏F1值都得到进一步的提高。     

基于BERT-Att-biLSTM模型的医学信息分类研究

目前,对互联网上虚假健康信息的研究多集中于谣言识别,而对医学信息自动分类的研究较少。采用基于双向编码的语言表征模型和注意力增强的双向长短时记忆模型(BERT-Att-BiLSTM模型),对健康信息文本进行分类,实现自动识别虚假健康信息。实验结果表明,BERT-Att-BiLSTM模型可以高效地对医学信息进行分类,其中BERT模型相较于BiLSTM模型,性能提升明显;与融合Word2Vec的BiLSTM模型相比,BERT-Att-BiLSTM模型效果更佳。     

面向可解释性的物体拓扑结构骨架表征方法

目的 模式识别中，通常使用大量标注数据和有效的机器学习算法训练分类器应对不确定性问题。然而，这一过程缺乏知识表征和可解释性。认知心理学和实验心理学的研究表明，人类往往不使用代价如此巨大的机制，而是使用表征、归纳、推理、解释和约束传播等与符号主义人工智能方法类似的手段来应对物体识别中的不确定性并提供可解释性。因此本文旨在从传统的符号计算出发，利用骨架拓扑结构表征提供一种可解释性的思路。方法 以骨架树为基本手段来形成物体拓扑结构特征和几何特征的形式化表征，并基于泛化框架对少量同类表征进行知识抽取来形成关于物体类别的知识概括显式化表征。结果 在形成物体类别的概括表征实验中，通过路径重建直观展示了同类属物体上得到的最一般表征的几何物理意义。在可解释性验证实验中，通过跨数据的拓扑应用展示了新测试样本相对于概括表征的特定差异，表明该表征具有良好的可解释性。最后在形状补全的不确定性推理实验中，不仅可以得到识别结论，而且清晰展示了识别背后做出的判断依据，进一步验证了该表征的可解释性。结论 实验表明一般化的形式表征能够应对尺寸、颜色和形状等不确定性问题，本文方法避免了基于纹理特征所带来的不确定性，适用于任意基于基元的表征方式，具有更好的鲁棒性、普适性和可解释性，计算代价更小。     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.     

Examining the role of regional culture and geographical distances on the representation of unfamiliar foods in a continental-size country

This study provides an experimental-exploratory investigation about the role of regional culture and Euclidean distances on the consumers’ representation of edible insects in Brazil, a country with an extensive geographical surface. Seven hundred and eighty participants were recruited on the streets of eight cities from different Brazilian states: Manaus in Amazonas; Porto Velho in Rondônia; Macapá in Amapá; Cuiabá in Mato Grosso; Aracaju in Sergipe; Rio de Janeiro in Rio de Janeiro; Campinas in São Paulo; and Santa Maria in Rio Grande do Sul. These participating cities were considered from their cultural identity differences and geographical distances. Through a continual restricted word association task, participants were instructed to promptly verbalize the first five terms that came to their minds when stimulated with the expression “food made with edible insects”. Following, they had to score the valence of each term they produced. The dictionaries produced in each city were compared and classified into groups using the Ellegård’s index. Each group presented distinct ways of expression and attitude with respect to the inductive expression. Basically, Brazil was divided into two main groups according to their representation of edible insects: one consisted by the cities situated near the shore of the Atlantic Ocean, which present a cultural formation influenced by the European immigrants; and the other comprised the cities from the continental region that have strong cultural influence from the Amerindians. Thus, the cultural formation was more decisive to explain the similar representations among the cities than their geographical proximity. Given that, to effectively introduce a novel food in a country with varied regional culture, the marketing strategy should be focused on the values and beliefs of their culture subgroups instead of a single strategy for the whole country.     

煤基多级孔炭纳米材料的优化制备及性能表征

以低变质粉煤为原料,采用热解活化技术制备煤基多级孔炭纳米材料(CCNM),利用统计学预测分析软件(JMP)设计优化制备实验的正交阵列,主要因素包括煤直接液化残渣(DCLR)的添加量(A)、热解过程升温速率(B)、热解终温(C)和原料粒度(D),每个因素选取三个水平。采用响应面分析法对CCNM的碘吸附值和抗压强度进行评估,确定最佳优化条件为A1B3C1D2,影响因素按显著性由大到小的顺序为CABD,碘吸附值的预测公式为m I=258.26-33.22 x 1-34.88x 2+28.12x 21+1.92x 1x 2+34.12x 22,预测碘吸附值最高为390.51 mg/g,三组平行验证实验测定的碘吸附值平均为394.69 mg/g,实验值与预测值吻合良好。对优化条件下制备的CCNM进行性能表征,材料呈多孔结构;通过图像处理,统计得到孔隙率在40%以上;碘吸附值为398.22 mg/g,抗压强度为4.12 MPa,比表面积为146.181 m 2/g,总孔容为0.0534 cm 3/g,中孔率为71.10%,孔径主要分布在1.5 nm^100 nm,平均孔径为5.254 nm,表明CCNM是一种包含微孔、中孔和大孔的多级孔炭纳米材料。     

Equivalent circuit representation of a vortex-induced vibration-based energy harvester using a semi-empirical lumped parameter approach

Small-scale wind energy harvesting from vortex-induced vibrations (VIV) has been introduced in recent years as a renewable power source for microelectronics and wireless sensors. Previous studies have focused on modeling and optimizing the VIV-based piezoelectric energy harvester (VIVPEH) structures and simplified the complicated interface circuits as pure resistors with an alternating current (AC) output. In practice, an AC output is required to be transformed into a direct current (DC) followed by further regulations before being used for real applications. Incorporating the rectification and regulation, traditional theoretical and numerical models will become extremely cumbersome and even impossible. To address this issue, this work proposes an equivalent circuit model (ECM) for a typical VIVPEH. The Scanlan-Ehsan aerodynamic force model is employed to describe the fluid-structure interaction. Wind tunnel experiments are carried out to validate the derived model. The performances of the VIVPEH with AC and DC interface circuits are subsequently analyzed and compared to understand the influences of these circuits on the operational wind speed bandwidth, power output, vibration amplitude, and electrical damping.     

The power average operator for unbalanced linguistic term sets

In order to aggregate linguistic values of unbalanced linguistic term sets, this paper introduces the linguistic proportional 2-tuple power average operator, which can reflect the relationship among the aggregated values by considering the support for each value from others. Its advantage regarding other linguistic power average operators enables it to be used in such cases in which the linguistic term sets are not necessarily to be balanced, and the membership functions of the linguistic terms are utilized in the computational processes. In this operator, a linguistic proportional 2-tuple is represented by a normalized numerical representation. Some properties of the operator are discussed. A group decision making model based on the proposed operator is introduced. Finally an illustrative example is presented.     

Sample re-weighting hyper box classifier for multi-class data classification

In this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers.Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies.     

面向多源数据融合的稀疏表示目标跟踪

传统的基于稀疏表示的目标跟踪方法主要利用目标的灰度特征构建稀疏表示模型。由于灰度特征对光照变化敏感，这会影响目标跟踪在复杂场景下的鲁棒性。基于多源数据融合的目标跟踪可以明显提升目标跟踪鲁棒性，但如何有效融合不同维度，不同类型的多源目标特征成为基于多源数据融合的目标跟踪所要解决的关键问题。提出了一个基于目标状态以及灰度特征的稀疏表示目标跟踪方法。所提出的方法可通过基于核函数表示的稀疏表示模型，在探究目标状态以及灰度特征相关性的基础上，将两种不同维度的特征进行有效融合，提升目标跟踪在复杂场景下的鲁棒性。