基于Simhash的安全密文排序检索方案

针对密文检索中存在的计算量大、检索效率不高的问题，提出一种基于Simhash的安全密文排序检索方案。该方案基于Simhash的降维思想构建安全多关键词密文排序检索索引（SMRI），将文档处理成指纹和向量，利用分段指纹和加密向量构建B+树，并采用"过滤-精化"策略进行检索和排序，首先通过分段指纹的匹配进行快速检索，得到候选结果集；然后通过计算候选结果集与查询陷门的汉明距离和向量内积进行排序，带密钥的Simhash算法和安全k近邻（SkNN）算法保证了检索过程的安全性。实验结果表明，与基于向量空间模型（VSM）的方案相比，基于SMRI的排序检索方案计算量小，能节约时间和空间成本，检索效率高，适用于海量加密数据的快速安全检索。     

结合深度学习与相关反馈的遥感图像检索

目的 针对基于内容的图像检索存在低层视觉特征与用户对图像理解的高层语义不一致、图像检索的精度较低以及传统的分类方法准确度低等问题，提出一种基于卷积神经网络和相关反馈支持向量机的遥感图像检索方法。方法 通过对比度受限直方图均衡化算法对遥感图像进行预处理，限制遥感图像噪声的放大，采用自学习能力良好的卷积神经网络对遥感图像进行多层神经网络的监督学习提取丰富的图像特征，并将支持向量机作为基分类器，根据测试样本数据到分类超平面的距离进行排序得到检索结果，最后采用相关反馈策略对检索结果进行重新调整。结果 在UC Merced Land-Use遥感图像数据集上进行图像检索实验，在mAP（mean average precision）精度指标上，当检索返回图像数为100时，本文方法比LSH（locality sensitive Hashing）方法提高了29.4%，比DSH（density sensitive Hashing）方法提高了37.2%，比EMR（efficient manifold ranking）方法提高了68.8%，比未添加反馈和训练集筛选的SVM（support vector machine）方法提高了3.5%，对于平均检索速度，本文方法比对比方法中mAP精度最高的方法提高了4倍，针对复杂的遥感图像数据，本文方法的检索效果较其他方法表现出色。结论 本文提出了一种以距离评价标准为核心的反馈策略，以提高检索精度，并采用多距离结合的Top-k排序方法合理筛选训练集，以提高检索速度，本文方法可以广泛应用于人脸识别和目标跟踪等领域，对提升检索性能具有重要意义。     

Querying on large and complex databases by content: Challenges on variety and veracity regarding real applications

The amount and variety of digital data currently being generated, stored and analyzed, including images, videos, and time series, have brought challenges to data administrators, analysts and developers, who struggle to comply with the expectations of both data owners and end users. The majority of the applications demand searching complex data by taking advantage of queries that analyze different aspects of the data, and need the answers in a timely manner. Content-based similarity retrieval techniques are well-suited to handle large databases, because they enable performing queries and analyses using features automatically extracted from the data, without users’ intervention. In this paper, we review and discuss the challenges posed to the database and related communities in order to provide techniques and tools that can meet the variety and veracity characteristics of big and complex data, while also considering the aspects of semantical preservation and completeness of the data. Examples and results obtained over a two-decade-long experience with real applications are presented and discussed.     

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. ¹     

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

两种多层穿梭车系统的性能比较分析

物流配送中心订单响应速度的加快,对订单拣选系统提出了更高的要求。多层穿梭车系统因其柔性强、效率高等特点,近年来得到了广泛应用。多层穿梭车系统具有两种形式--基于转载车和基于环形输送线,二者均可实现货物的快速拣选,但在系统吞吐量和订单完成周期方面存在差异,致使企业决策者难以进行系统选择。基于上述原因,本文比较两种多层穿梭车系统的性能差异。建立两个系统的开环排队网络模型,然后求解系统吞吐量、订单完成周期,通过仿真验证了排队论模型的准确性,通过试验对两种系统的性能进行对比分析。结果表明,系统吞吐量与层数、巷道数相关,层数越多,巷道数越少,转载车系统相对于环线系统优势越显著;订单完成周期与层数、订单到达率相关,层数越多,订单到达率越低,转载车系统相对于环线系统越具优势。     

Structure of presented stimuli influences gazing behavior and choice

Well-structured stimuli presentation is essential in eye-tracking research to test predefined hypotheses reliably and to conduct relevant gazing behavior studies. Several bottom-up factors associated with stimuli presentation (such as stimuli orientation, size etc.) can influence gazing behavior. However, only a small number of scientific papers address these factors in a sensory and consumer science context and thus provide guidance to practitioners. The two presented eye-tracking studies on food images aimed at evaluating the effect of the bottom-up factors stimulus size, background of the picture, orientation of food product presentation, the evaluated products and the number of alternatives. Significant effects of product group were found in the case of all eye-movement parameters except time to first fixation and first fixation duration. In contrary, orientation significantly influenced only the time to first fixation and first fixation duration parameters. Stimulus size significantly increased fixation and dwell count, while background showed no significant effects. Furthermore, significant relationships were found between the number of presented images and eye-movement and decision time. Less time was needed in 2AFC (alternative forced choice test), 3AFC and 4AFC and significantly more time was needed to choose one alternative out of 7AFC and 8AFC. The results of the two studies show that the investigated bottom-up factors can significantly influence gazing behavior, and therefore need to be carefully considered when planning or comparing results of eye-tracking experiments.     

A comparison of pivot selection techniques for permutation-based indexing

Recently, permutation based indexes have attracted interest in the area of similarity search. The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots (or reference objects). Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query, following the assumption that similar objects are represented by similar permutations of the pivots. In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot selection techniques do not reveal better performance. However, to the best of our knowledge, no rigorous comparison has been performed yet. In this paper we compare five pivot selection techniques on three permutation-based similarity access methods. Among those, we propose a novel technique specifically designed for permutations. Two significant observations emerge from our tests. First, random selection is always outperformed by at least one of the tested techniques. Second, there is no technique that is universally the best for all permutation-based access methods; rather different techniques are optimal for different methods. This indicates that the pivot selection technique should be considered as an integrating and relevant part of any permutation-based access method.