Protection for software in measuring instruments

A protection of measuring instrument control programs and control system data against unauthorized change is necessary for provision of functionality of entire measuring instrument. Cyclic codes or hashing functions can be used as such protection. Application of the MD5 hashing function is illustrated on practical example.     

Linear spiral hashing for expansible files

The goal of dynamic hashing is to design a function and a file structure that allow the address space allocated to the file to be increased and reduced without reorganizing the whole file. We propose a new scheme for dynamic hashing in which the growth of a file occurs at a rate of n+k/n per full expansion, where n is the number of pages of the file and k is a given integer constant which is smaller than n, as compared to a rate of two in linear hashing. Like linear hashing, the proposed scheme (called linear spiral hashing) requires no index; however, the proposed scheme may or may not add one more physical page, instead of always adding one more page in linear hashing, when a split occurs. Therefore, linear spiral hashing can maintain a more stable performance through the file expansions and have much better storage utilization than linear hashing. From our performance analysis, linear spiral hashing can achieve nearly 97 percent storage utilization as compared to 78 percent storage utilization by using linear hashing, which is also verified by a simulation study     

Hashing methods for temporal data

External dynamic hashing has been used in traditional database systems as a fast method for answering membership queries. Given a dynamic set S of objects, a membership query asks whether an object with identity k is in (the most current state of) S. This paper addresses the more general problem of temporal hashing. In this setting, changes to the dynamic set are time-stamped and the membership query has a temporal predicate, as in: "Find whether object with identity k was in set S at time t". We present an efficient solution for this problem that takes an ephemeral hashing scheme and makes it partially persistent. Our solution, also termed partially persistent hashing, uses a space that is linear on the total number of changes in the evolution of set S and has a small {O[log_B(n/B)]} query overhead. An experimental comparison of partially persistent hashing with various straightforward approaches (like external linear hashing, the multi-version B-tree and the R*-tree) shows that it provides the faster membership query response time. Partially persistent hashing should be seen as an extension of traditional external dynamic hashing in a temporal environment. It is independent of the ephemeral dynamic hashing scheme used; while this paper concentrates on linear hashing, the methodology applies to other dynamic hashing schemes as well     

Using tries to eliminate pattern collisions in perfect hashing

Many current perfect hashing algorithms suffer from the problem of pattern collisions. In this paper, a perfect hashing technique that uses array-based tries and a simple sparse matrix packing algorithm is introduced. This technique eliminates all pattern collisions, and, because of this, it can be used to form ordered minimal perfect hashing functions on extremely large word lists. This algorithm is superior to other known perfect hashing functions for large word lists in terms of function building efficiency, pattern collision avoidance, and retrieval function complexity. It has been successfully used to form an ordered minimal perfect hashing function for the entire 24481 element Unix word list without resorting to segmentation. The item lists addressed by the perfect hashing function formed can be ordered in any manner, including alphabetically, to easily allow other forms of access to the same list     

编码选择哈希算法解决大规模图像检索问题

哈希算法已被广泛应用于解决大规模图像检索的问题. 在已有的哈希算法中, 无监督哈希算法因为不需要数据库中图片的语义信息而被广泛应用. 平移不变核局部敏感哈希(SKLSH)算法就是一种较为代表性的无监督哈希算法.该算法随机的产生哈希函数, 并没有考虑所产生的哈希函数的具体检索效果. 因此, SKLSH算法可能产生一些检索效果表现较差的哈希函数. 在本文中, 提出了编码选择哈希算法(BSH). BSH算法根据SKLSH算法产生的哈希函数的具体检索效果来进行挑选. 挑选的标准主要根据哈希函数在3个方面的表现: 相似性符合度, 信息包含量, 和编码独立性. 然后,BSH算法还使用了一种基于贪心的选择方法来找到哈希函数的最优组合. BSH算法和其他代表性的哈希算法在两个真实图像库上进行了检索效果的对比实验. 实验结果表明, 相比于最初的SKLSH算法和其他哈希算法, BSH算法在检索准确度上有着明显的提高.     

Multilevel extendible hashing: a file structure for very largedatabases

A dynamic hashing scheme based on extendible hashing is proposed whose directory can grow into a multilevel directory. The scheme is compared to the extendible hashing and the extendible hashing tree schemes. The simulation results reveal that the proposed scheme is superior than the other two with respect to directory space utilization, especially for files with nonuniform record distribution. This scheme can be easily extended to multikey file systems and also has good performance     

视频哈希的性能分析与测试

针对视频哈希中的单向性、唯一性以及鲁棒性等问题,对基于三维离散小波变换(3D-DWT)的视频哈希算法进行数学建模,利用密码学中的无条件安全性,证明该哈希算法具有单向性。计算参考哈希序列与所求哈希序列的汉明距离的概率密度,证明基于3D-DWT和基于三维离散余弦变换的视频哈希算法,在同等条件下均具有唯一性。结合理论和实验结果,分析2种算法在不同攻击下的鲁棒性,得出视频哈希的唯一性和鲁棒性是相互制约的结论。     

Geometric distortion-resilient image hashing scheme and its applications on copy detection and authentication

Media hashing is an alternative approach to many applications previously accomplished with watermarking. The major disadvantage of the existing media hashing technologies is their limited resistance to geometric attacks. In this paper, a novel geometric distortion-invariant image hashing scheme, which can be employed to perform copy detection and content authentication of digital images, is proposed. Our major contributions are threefold: (i) a mesh-based robust hashing function is proposed; (ii) a sophisticated hash database for error-resilient and fast matching is constructed; and (iii) the application scalability of our scheme for content copy tracing and authentication is studied. In addition, we further investigate several media hashing issues, including robustness and discrimination, error analysis, and complexity, with respect to the proposed image hashing system. Exhaustive experimental results obtained from benchmark attacks confirm the excellent performance of the proposed method.     

Supervised Coarse-to-Fine Semantic Hashing for cross-media retrieval

Due to its storage efficiency and fast query speed, cross-media hashing methods have attracted much attention for retrieving semantically similar data over heterogeneous datasets. Supervised hashing methods, which utilize the labeled information to promote the quality of hashing functions, achieve promising performance. However, the existing supervised methods generally focus on utilizing coarse semantic information between samples (e.g. similar or dissimilar), and ignore fine semantic information between samples which may degrade the quality of hashing functions. Accordingly, in this paper, we propose a supervised hashing method for cross-media retrieval which utilizes the coarse-to-fine semantic similarity to learn a sharing space. The inter-category and intra-category semantic similarity are effectively preserved in the sharing space. Then an iterative descent scheme is proposed to achieve an optimal relaxed solution, and hashing codes can be generated by quantizing the relaxed solution. At last, to further improve the discrimination of hashing codes, an orthogonal rotation matrix is learned by minimizing the quantization loss while preserving the optimality of the relaxed solution. Extensive experiments on widely used Wiki and NUS-WIDE datasets demonstrate that the proposed method outperforms the existing methods.     

基于感知哈希的图像内容鉴别性能分析

感知哈希能有效地区分不同内容的图像,常用于检测盗版或重复的图像内容鉴别应用中.针对已有的对感知哈希的图像内容鉴别应用研究主要集中在算法设计上,缺少对其性能评价的理论方法的问题,提出从理论上评价内容鉴别性能的方法.该方法基于一类图像感知哈希算法的抗碰撞特性建立了内容鉴别的判定模型,并定义内容鉴别的性能公式,最后推导出统计学上的函数作为性能评价指标.实验结果表明,文中方法能很好地估计感知哈希在内容鉴别应用中的性能.     

一个实用的针对URL的哈希函数

在Web信息处理的研究中，不少情况下需要对很大的URL序列进行散列（hashing）操作．本文提出了一个针对URL数据集合的均匀哈希函数，它是ELFhash函数的变型．通过对天网搜索引擎采集的1亿多个URL集合的抽样实验表明：它能有效使得URL集在哈希表中均匀散布．并通过与MD5和SHA-1的对比，认为它是实用的．最后，指出了进一步的研究方向．     

Multiple feature kernel hashing for large-scale visual search

Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.     

Locality preserving hashing for fast image search: theory and applications

Abstract

State-of-the-art hashing methods, such as the kernelised locality-sensitive hashing and spectral hashing, have high algorithmic complexities to build the hash codes and tables. Our observation from the existing hashing method is that, putting two dissimilar data points into the same hash bucket only reduces the efficiency of the hash table, but it does not hurt the query accuracy. Whereas putting two similar data points into different hash buckets will reduce the correctness (i.e. query accuracy) of a hashing method. Therefore, it is much more important for a good hashing method to ensure that similar data points have high probabilities to be put to the same bucket, than considering those dissimilar data-point relations. On the other side, attracting similar data points to the same hash bucket will naturally suppress dissimilar data points to be put into the same hash bucket. With this locality-preserving observation, we naturally propose a new hashing method called the locality-preserving hashing, which builds the hash codes and tables with much lower algorithmic complexity. Experimental results show that the proposed method is very competitive in terms of the training time spent for large data-sets among the state of the arts, and with reasonable or even better query accuracy.     

An ordered minimal perfect hashing scheme based upon Euler's theorem

We propose a new scheme for hashing. Contrary to the usual hashing, ours stores the records in order. Furthermore, our hashing functions allow single probe retrieval from minimal sized memory space. Our scheme is based upon the Fermat number and Euler's theorem. Many theorems concerning numbers are also discussed.     

一种改进的动态散列方法

线性散列方法是一种有效的动态散列方法。但是，一般的线性散列方法都不能保证查询时只要一次读磁盘操作。本文提出一种采用签名技术的改进方法，可以保证上述要求，同时保持线性散列方法的优点。此外，本文还对线性散列技术作了一般性扩充，并通过模拟实验研究了方法的性能。     

Key-dependent 3D model hashing for authentication using heat kernel signature

Multimedia-based hashing is considered an important technique for achieving authentication and copy detection in digital contents. However, 3D model hashing has not been as widely used as image or video hashing. In this study, we develop a robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications. We further discuss the robustness, uniqueness, security, and spaciousness of the method for 3D model hashing. In the proposed hashing scheme, we calculate the local and global HKS coefficients of vertices through time scales and 2D cell coefficients by clustering HKS coefficients with variable bin sizes based on an estimated L² risk function, and generate the binary hash through binarization of the intermediate hash values by combining the cell values and the random values. In addition, we use two parameters, bin center points and cell amplitudes, which are obtained through an iterative refinement process, to improve the robustness, uniqueness, security, and spaciousness further, and combine them in a hash with a key. By evaluating the robustness, uniqueness, and spaciousness experimentally, and through a security analysis based on the differential entropy, we verify that our hashing scheme outperforms conventional hashing schemes.     

Symbolic Gray Code as a Perfect Multiattribute Hashing Scheme for Partial Match Queries

In this paper, we shall show that the symbolic Gray code hashing mechanism is not only good for best matching, but also good for partial match queries. Essentially, we shall propose a new hashing scheme, called bucket-oriented symbolic Gray code, which can be used to produce any arbitrary Cartesian product file, which has been shown to be good for partial match queries. Many interesting properties of this new multiattribute hashing scheme, including the property that it is a perfect hashing scheme, have been discussed and proved.