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Elements of a person's biometrics are typically stable over the duration of a lifetime, and thus, it is highly important to protect biometric data while supporting recognition (it is also called secure biometric recognition). However, the biometric data that are derived from a person usually vary slightly due to a variety of reasons, such as distortion during picture capture, and it is difficult to use traditional techniques, such as classical encryption algorithms, in secure biometric recognition. The negative database (NDB) is a new technique for privacy preservation. Reversing the NDB has been demonstrated to be an NP-hard problem, and several algorithms for generating hard-to-reverse NDBs have been proposed. In this paper, first, we propose negative iris recognition, which is a novel secure iris recognition scheme that is based on the NDB. We show that negative iris recognition supports several important strategies in iris recognition, e.g., shifting and masking. Next, we analyze the security and efficiency of negative iris recognition. Experimental results show that negative iris recognition is an effective and secure iris recognition scheme. Specifically, negative iris recognition can achieve a highly promising recognition performance (i.e., GAR = 98.94% at FAR = 0.01%, EER = 0.60%) on the typical database CASIA-IrisV3-Interval.

Local patterns record the gray-level differences between a referenced pixel in an image and its surrounding pixels, which have been commonly used to describe the image features. However, traditional local patterns ignore the spatial distribution feature of texture information in images. We group the gray-level variations along three directions, i.e., horizontal, vertical, and diagonal directions. Each group is then merged into a Local spatial distribution pattern (LSDP) to represent the spatial distribution image feature. We also construct the LSDP patterns for gradient and filtered images, and finally form the Complete local spatial distribution pattern (CLSDP) descriptor to completely describe the texture image feature. Experiments on textural and natural image sets were conducted to compare our CLSDP-based image retrieval algorithm with four previous competitors. The results show that our method is superior to existing algorithms considering both average precision and recall.

Flash memory is widely used in embedded devices and enterprise storage systems. Currently, flash-based storage devices usually use a flash translation layer (FTL) to cope with the special features of flash memory. Many methods for the design and implementation of the FTL have been proposed, such as BAST (block-associative sector translation), FAST (fully associative sector translation), and IPL (inpage logging), of which IPL has been demonstrated to have the best performance. However, IPL offers little consideration to reducing merge operations that consequently result in the degradation of the overall performance of flash-memory storage systems. We propose an improvement to IPL, called adaptive IPL (AIPL). The idea of adaptive IPL is to make the log region in a block resizable, therefore a hot block (i.e., a write-intensive block) will use a large log region so as to absorb more page updates and in turn reduce the merge operations, while a cold block, i.e., a block rarely written to, will use a small log region. This is realized by first detecting the update pattern of a block and then presenting an updatepattern-based algorithm to dynamically adjust the log region size of a newly allocated block. We conduct experiments on TPC-C traces and synthetic traces and compare the performance of AIPL with other competitors in terms of merge count, write count and elapsed time. The results demonstrate that compared with IPL, AIPL can reduce merge operations by 65% and write operations by 54% on average.

Recently, negative databases (NDBs) are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a knearest neighbor (kNN) classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novelmethod for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.

Polymorphic gates are special kinds of logic gates that can exhibit different functions under the control of environmental parameters, such as light, temperature, and VDD. These polymorphic gates can be used to build polymorphic circuits that perform different functions under different environments. Because polymorphic gates are different from traditional logic gates, the existent completeness theory for the traditional logic gate set is not suitable for the polymorphic gate set. So far, only the definition of the complete polymorphic gate set is given. There is no approach to judging whether a given polymorphic gate set is complete. The contributions of this article include three aspects. First, the impact of logic-1 and logic-0 on the completeness of the polymorphic gate set is discussed. Second, the theory and two related algorithms for judging the completeness of polymorphic gate sets with two modes are given. Finally, the theory and related algorithms for complete polymorphic gate sets with more than two modes are proposed.

Introduction:Novel therapies have improved the median overall survival (OS) for newly diagnosed multiple myeloma (NDMM). In an unselected population of NDMM patients, the estimated rates of 5-year OS were 77%, 53%, and 19% for revised international staging system (R-ISS) Stage I, II, and III, respectively (Kastritis, 2017). However, the improvements have not been uniform, and prognosis varies considerably based on several baseline characteristics. Because of this clinical heterogeneity it is helpful to have a clear idea of risk factors involved in OS of patients with NDMM. Connect MM is the first and largest US-based, multicenter, prospective observational registry of patients with NDMM. Using data from the Connect MM registry, an unselected patient population reflective of the heterogeneity seen in routine clinical practice, we developed a prognostic tool to identify factors predictive of 3- and 5-year OS.