融合全局和局部方向特征的掌纹识别方法

摘 要：掌纹识别是受到较多关注的生物特征识别技术之一。在各类掌纹识别的方法中, 基于方向特征的方法取得了很好的效果。为了进一步提升识别精度,提出一种融合全局和局部 方向特征的掌纹识别算法,主要融合了基于方向编码的方法、基于方向特征局部描述子的方法 和结合方向特征和相关滤波器的方法。其中前 2 种方法属于空间域方法,可很好地提取掌纹的 局部方向特征;而第 3 种方法属于频域方法,能有效地提取全局方向特征。在匹配值层对该 3 种方法的识别结果进行融合。本文算法在 2 个掌纹数据库上进行了验证,实验结果表明,本文 方法的识别性能明显优于其他几种掌纹识别方法。     

Paillier-Based Fuzzy Multi-Keyword Searchable Encryption Scheme with Order-Preserving

Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage. However, the current searchable encryption solutions still have deficiencies in search efficiency, accuracy and multiple data owner support. In this paper, we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving (PMS). First, a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction) is proposed to correct user input errors, so that fuzzy keywords input can be supported. Second, Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving. Then, a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern. Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency. The theoretical analysis and experiment results show that our scheme is secure, accurate, error-tolerant and very efficient.     

A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems

The dynamic cellular facility layout problem (DCFLP) is a well-known NP-hard problem. It has been estimated that the efficient design of DCFLP reduces the manufacturing cost of products by maintaining the minimum material flow among all machines in all cells, as the material flow contributes around 10–30% of the total product cost. However, being NP hard, solving the DCFLP optimally is very difficult in reasonable time. Therefore, this article proposes a novel similarity score-based two-phase heuristic approach to solve the DCFLP optimally considering multiple products in multiple times to be manufactured in the manufacturing layout. In the first phase of the proposed heuristic, a machine–cell cluster is created based on similarity scores between machines. This is provided as an input to the second phase to minimize inter/intracell material handling costs and rearrangement costs over the entire planning period. The solution methodology of the proposed approach is demonstrated. To show the efficiency of the two-phase heuristic approach, 21 instances are generated and solved using the optimization software package LINGO. The results show that the proposed approach can optimally solve the DCFLP in reasonable time.     

New Approaches in Monitoring Multivariate Categorical Processes based on Contingency Tables in Phase II

In some statistical process control (SPC) applications, quality of a process or product is characterized by contingency table. Contingency tables describe the relation between two or more categorical quality characteristics. In this paper, two new control charts based on the WALD and Stuart score test statistics are designed for monitoring of contingency table‐based processes in Phase‐II. The performances of the proposed control charts are compared with the generalized linear test (GLT) control chart proposed in the literature. The results show the better performance of the proposed control charts under small and moderate shifts. Moreover, new schemes are proposed to diagnose which cell corresponding to different levels of categorical variables is responsible for out‐of‐control signal. In addition, we propose EWMA–WALD and EWMA–Stuart score test control charts to improve the performance of Shewhart‐based control charts in detecting small and moderate shifts in contingency table parameters. Meanwhile, we compare the performances of two proposed EWMA‐based control charts with the ones of three existing control charts called EWMA–GLT, EWMA–GLRT and an EWMA‐type control chart for multivariate binomial/multinomial processes along with the ones of the corresponding Shewhart‐based control charts. A numerical example is given to show the efficiency of the proposed methods. Finally, the effect of parameter estimation in Phase I based on m historical contingency table on the performance of the Shewhart‐based control charts is studied. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

课程推荐预测模型优化方案及数据离散化算法

本研究基于k-NN算法建立了课程推荐预测模型.由于原始样本数据的局部不均衡和数据叠交性,预测模型在不进行任何参数调整和数据优化的情况下,模型预测评分并不理想.针对上述问题,本研究设计了一套预测模型参数优化方案和样本数据优化方案,包括最优k值选择算法设计、距离公式优化、数据离散化算法设计.本研究提出的“数据离散化算法”驱使kd树的分类空间排序按照我们期望的特征向量的权重排序,该算法对提升模型预测评分起到了积极作用.上述优化方案和算法设计使课程推荐预测模型的评分从0.67提升到0.85,预测结果的准确度提高了27个百分点,学生对课程推荐的满意度得到显著提升.     

移动互联网下视频时延的用户体验质量研究

随着移动互联网的发展,移动视频业务的迅速爆发,良好的用户体验质量(QoE) 成为运营商挽留用户的关键因素。从用户体验角度,建立不同时延下移动视频业务的评价量化 标准。通过情景实验模拟的方法,以平均主观意见分(mean opinion score,MOS)量表建立用户 视频体验质量的实数映射关系。针对用户对视频观看时延的有效反应进行分级研究,得到以下 结果：单次和多次的初始缓冲时延和卡顿时延对用户观看移动视频的影响并建立用户体验质量 评价等级。在短视频中出现单次时延在4 s 以上,用户会出现负面情绪;在相同延迟时长下, 单次的初始缓冲时延的用户体验质量略优于卡顿时延;对于在一定播放时长的移动视频业务中 出现多次卡顿,卡顿时延短但是间隔频繁的体验质量比卡顿时延长但是间隔时间长的体验质量 要更差。     

Enjoy the most beautiful scene now: a memetic algorithm to solve two-fold time-dependent arc orienteering problem

Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks.However,in real cases,especially in the era of smart cities where many kinds of transportation-related data become easily available,recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria,e.g.,finding the route with the highest accumulated scenic score along(utility)while not exceeding the given travel time budget(cost).Such problem can be viewed as a variant of arc orienteering problem(AOP),which is well-known as an NP-hard problem.In this paper,targeting a more realistic AOP,we allow both scenic score(utility)and travel time(cost)values on each arc of the road network are time-dependent(2TD-AOP),and propose a memetic algorithm to solve it.To be more specific,within the given travel time budget,in the phase of initiation,for each population,we iteratively add suitable arcs with high scenic score and build a path from the origin to the destination via a complicate procedure consisting of search region narrowing,chromosome encoding and decoding.In the phase of the local search,each path is improved via chromosome selection,local-improvement-based mutation and crossover operations.Finally,we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively,and the experimental results demonstrate that it outperforms the baselines.     

Efficiency assessment using a multidirectional DDF approach

This paper proposes a new efficiency measure that aims at capturing the overall distance to the efficient frontier and not the distance corresponding to a specific projection, as most oriented and non‐oriented methods compute. The proposed approach uses a grid‐based method to sample all possible improvement directions from a given operating point and for each direction it computes a directional efficiency score solving a directional distance function model. This provides information about the distribution of the distance to the efficient frontier. Therefore, the proposed multidirectional efficiency measure does not provide a single efficiency score but a distribution of efficiency scores. The minimum, the median, the Q1 and Q3 quartiles and the maximum can be singled out as representatives of that distribution. The latter corresponds to the closest among the computed efficient targets. In addition to the efficiency score distribution, the associated efficient targets are also provided. The proposed approach can take into account integer and nondiscretionary variables and a preference structure. An application of the method to assess the efficiency of material recovery facilities is presented.     

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%。