检索结果-内蒙古大学图书馆

Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions

COMMUNICATIONS OF THE ACM 2008年第1期51卷 117-122页

作者： Andoni, Alexandr Indyk, Piotr MIT Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Theory Computat Grp Cambridge MA 02139 USA

In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query The problem is of significant interest in a wide variety of areas.

关键词： .data points Euclidean distance data structure query point Approximate Nearest nearest neighbor problem hashing algorithms Neighbor in High

来源：评论

学校读者我要写书评

暂无评论

Improving hashing algorithms for Similarity Search via MLE and the Control Variates Trick 13

Improving Hashing Algorithms for Similarity Search via MLE a...

引用

13th Asian Conference on Machine Learning (ACML)

作者： Kang, Keegan Kushnarev, Sergey Wong, Wei Pin Pratap, Rameshwar Yeo, Haikal Chen, Yijia Singapore Univ Technol & Design Engn Syst & Design Pillar Singapore Singapore Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Singapore Univ Technol & Design Sci Math & Technol Cluster Singapore Singapore IIT Mandi Sch Comp & Elect Engn Suran India Singapore Univ Technol & Design Singapore Singapore

hashing algorithms are continually used for large-scale learning and similarity search, with computationally cheap and better algorithms being proposed every year. In this paper we focus on hashing algorithms which involve estimating a distance measure d(x(i);x(j)) between two vectors xi;xj. Such hashing algorithms require generation of random variables, and we propose two approaches to reduce the variance of our hashed estimates: control variates and maximum likelihood estimates. We explain how these approaches can be immediately applied to a wide subset of hashing algorithms. Further, we evaluate the impact of these methods on various datasets. We finally run empirical simulations to verify our results.

关键词： control variates hashing algorithms maximum likelihood estimation statistical techniques

来源：评论

学校读者我要写书评

暂无评论

Light-weight hashing method for user authentication in Internet-of-Things

引用

AD HOC NETWORKS 2019年第0期89卷 97-106页

作者： Rao, Vidya Prema, K., V Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India

The goal of Internet-of-Things (IoT) is that every object across the globe be interconnected under the Internet Infrastructure. loT is expanding its application domain to range from environmental monitoring to industrial automation thereby leading to vast research challenges. Vast presence of devices in the Internet has increased the possible challenges faced by devices and data. The devices communicate on a public channel that is more likely to be accessed by unauthorized users and disturb the privacy of genuine users. The existing solutions that ensure data authenticity and user privacy, use MD5 and SHA-family of hashing algorithms under digital signature schemes. These algorithms create a trade-off between the security concern and energy consumption of loT devices. To provide an energy efficient authentication method, we propose a customized BLAKE2b hashing algorithm with modified elliptic curve digital signature scheme (ECDSA). The parameters considered for the evaluation of the proposed methods are signature generation time, signature verification time and hashing time. The experiments are conducted under client server model using Raspberry Pi-3. The proposed method has shown about 0.7-1.91% improvement in the signature generation time and 7.67-9.13 % improvement in signature verification time when compared with BLAKE2b based signature generation/verification. The proposed method is resistant to Man-in-the-Middle attack, Distributed DoS attack (DDoS), pre-image resistance, second pre-image resistance and collision resistance. Based on the performance obtained by the experiments, it can be inferred that the proposed scheme is feasible for resource-constrained IoT devices. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Authentication BLAKE2b ECDSA hashing algorithms Internet-of-Things Raspberry Pi-3

来源：评论

学校读者我要写书评

暂无评论

STORAGE UTILIZATION IN A MEMORY HIERARCHY WHEN STORAGE ASSIGNMENT IS PERFORMED BY A hashing ALGORITHM

引用

COMMUNICATIONS OF THE ACM 1971年第3期14卷 172-&页

作者： WILLIAMS, JG RCA Labs. Princeton NJ

The utilization of storage is studied in a two-level memory hierarchy. The first storage level, which is the fast store, is divided into a number of storage areas. When an entry is to be filed in the hierarchy, a hashing algorithm will attempt to place the entry into one of these areas. If this particular area is full, then the entry will be placed into the slower second-level store, even though other areas in the first-level store may have space available. Given that N entries have been filed in the entire hierarchy, an expression is derived for the expected number of entries filed in the first-level store. This expression gives a measure of how effectively the first-level store is being used. By means of examples, storage utilization is then studied as a function of the hashing algorithm, the number of storage areas into which the first-level store is divided and the total size of the first-level store.

关键词： hashing algorithms memory allocation memory hierarchy memory utilization storage allocation storage hierarchy storage utilization

来源：评论

学校读者我要写书评

暂无评论

The Complexity of hashing with Lazy Deletion

引用

ALGORITHMICA 1986年第1-4期1卷 17-29页

作者： Van Wyk, Christopher J. Vitter, Jeffrey Scott AT&T Bell Labs Murray Hill NJ 07974 USA Brown Univ Dept Comp Sci Providence RI 02912 USA

We examine a version of the dynamic dictionary problem in which stored items have expiration times and can be removed from the dictionary once they have expired. We show that under several reasonable assumptions about the distribution of the items, hashing with lazy deletion uses little more space than methods that use eager deletion. The simple algorithm suggested by this observation was used in a program for analyzing integrated circuit artwork.

关键词： hashing algorithms Complexity Lazy deletion strategies Birth-death process Laplace's method

来源：评论

学校读者我要写书评

暂无评论

An Innovative Attention-based Triplet Deep hashing Approach to Retrieve Histopathology Images

引用

JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年 1-18页

作者： Alizadeh, Seyed Mohammad Helfroush, Mohammad Sadegh Celebi, M. Emre Shiraz Univ Technol Dept Elect Engn Shiraz Iran Univ Cent Arkansas Dept Comp Sci & Engn Conway AR 72035 USA

Content-based histopathology image retrieval (CBHIR) can assist in the diagnosis of different diseases. The retrieval procedure can be complex and time-consuming if high-dimensional features are required. Thus, hashing techniques are employed to address these issues by mapping the feature space into binary values of varying lengths. The performance of deep hashing approaches in image retrieval is often superior to that of traditional hashing methods. Among deep hashing approaches, triplet-based models are typically more effective than pairwise ones. Recent studies have demonstrated that incorporating the attention mechanism into a deep hashing approach can improve its effectiveness in retrieving images. This paper presents an innovative triplet deep hashing strategy based on the attention mechanism for retrieving histopathology images, called histopathology attention triplet deep hashing (HATDH). Three deep attention-based hashing models with identical architectures and weights are employed to produce binary values. The proposed attention module can aid the models in extracting features more efficiently. Moreover, we introduce an improved triplet loss function considering pair inputs separately in addition to triplet inputs for increasing efficiency during the training and retrieval steps. Based on experiments conducted on two public histopathology datasets, BreakHis and Kather, HATDH significantly outperforms state-of-the-art hashing algorithms.

关键词： Attention mechanism hashing algorithms Content-based histopathology image retrieval Neural networks Triplet models

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient hashing in Ethereum Smart Contracts 16

Towards Efficient Hashing in Ethereum Smart Contracts

引用

16th International Conference on Software Technologies (ICSOFT)

作者： Onica, Emanuel Schifirnet, Cosmin-Ionut Alexandru Ioan Cuza Univ Fac Comp Sci Iasi Romania

ISBN: (纸本)9789897585234

Ethereum is a popular public blockchain platform and currently the most significant featuring smart contract functionality. Smart contracts are small programs that are executed on the blockchain nodes, which can be used to implement complex transaction logic. Several high-level programming languages are available for writing Ethereum smart contracts, the most used being Solidity. The high-level code is further translated into a bytecode executed by a dedicated runtime environment, the Ethereum Virtual Machine (EVM). A few operations are, however, externalized as precompiled contracts, and run by the native implementation of the Ethereum node. These are typically computationally intensive operations such as cryptographic hash functions. Various smart contract patterns require hash computations. In such contexts, the current hash functions supported by Ethereum have a direct impact in both the performance and cost inflicted on the blockchain users. In this paper we investigate the available options for hashing in smart contracts, we discuss the implications regarding some patterns and we evaluate possible improvements. In particular we focus on the recent Blake family of cryptographic hash functions, which show promising performance results, but has yet limited support in the Ethereum platform.

关键词： Blockchain hashing algorithms Ethereum

来源：评论

学校读者我要写书评

暂无评论

Cross-Scale Context Extracted hashing for Fine-Grained Image Binary Encoding 14

Cross-Scale Context Extracted Hashing for Fine-Grained Image...

引用

Asian Conference on Machine Learning (ACML)

作者： Xue, Xuetong Shi, Jiaying He, Xinxue Xu, Shenghui Pan, Zhaoming 7 Bldg Zhongguancun Software Pk West Beijing Peoples R China

Deep hashing has been widely applied to large-scale image retrieval tasks owing to efficient computation and low storage cost by encoding high-dimensional image data into binary codes. Since binary codes do not contain as much information as float features, the essence of binary encoding is preserving the main context to guarantee retrieval quality. However, the existing hashing methods have great limitations on suppressing redundant background information and accurately encoding from Euclidean space to Hamming space by a simple sign function. In order to solve these problems, a Cross-Scale Context Extracted hashing Network (CSCE-Net) is proposed in this paper. Firstly, we design a two-branch framework to capture fine-grained local information while maintaining high-level global semantic information. Besides, Attention guided Information Extraction module (AIE) is introduced between two branches, which suppresses areas of low context information cooperated with global sliding windows. Unlike previous methods, our CSCE-Net learns a content-related Dynamic Sign Function (DSF) to replace the original simple sign function. Therefore, the proposed CSCE-Net is context-sensitive and able to perform well on accurate image binary encoding. We further demonstrate that our CSCE-Net is superior to the existing hashing methods, which improves retrieval performance on standard benchmarks.

关键词： hashing algorithms Image retrieval Cross-scale Binary encoding

来源：评论

学校读者我要写书评

暂无评论

Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3907-3918页

作者： Cao, Yuan Qi, Heng Gui, Jie Li, Keqiu Tang, Yuan Yan Kwok, James Tin-Yau Ocean Univ China Sch Informat Sci & Engn Qingdao 266100 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116023 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Univ Macao Dept Comp & Informat Sci Macau 999078 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong 999077 Peoples R China

The last few years have witnessed the rise of the big data era, in which approximate nearest neighbor search is a fundamental problem in many applications, such as large-scale image retrieval. Recently, many research results demonstrate that hashing can achieve promising performance due to its appealing storage and search efficiency. Since the complex optimization problems for loss functions are difficult to solve, most hashing methods decompose the hash codes learning problem into two steps: projection and quantization. In the quantization step, binary codes are widely used because ranking them by Hamming distance is very efficient. However, the huge information loss produced by the quantization step should be reduced in applications, such as image retrieval where high search accuracy is required. Since many two-step hashing methods produce uneven projected dimensions in the projection step, in this paper, we propose a novel dimension analysis based quantization method (DAQ) on two-step hashing methods for image retrieval. We first perform an importance analysis of the projected dimensions and select a subset of them that are more informative than the others, then we divide the selected projected dimensions into several regions with our quantizer. Every region is quantized with its corresponding codebook. Finally, the similarity between two hash codes is estimated by Manhattan distance between their corresponding codebooks, which is also efficient. We conduct experiments on three public benchmarks containing up to one million descriptors and show that the proposed DAQ method consistently leads to significant accuracy improvements over state-of-the-art quantization methods.

关键词： Quantization (signal) Data acquisition Image retrieval Nearest neighbor methods Hamming distance Symmetric matrices Binary codes Approximate nearest neighbor search hashing algorithms image retrieval quantization

来源：评论

学校读者我要写书评

暂无评论

rDBF: A r-Dimensional Bloom Filter for massive scale membership query

引用

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2019年 136卷 100-113页

作者： Patgiri, Ripon Nayak, Sabuzima Borgohain, Samir Kumar Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 Assam India

Bloom Filter is a data structure for membership filtering that is deployed in various domains to boost up the lookup performance and to lower the memory consumption. Bloom Filter has gained a massive popularity nowadays, and thus, it is deployed in diverse domains, namely, Big Data, Cloud Computing, Networking and Security, Bioinformatics, and IoT. Large scale computing uses a huge memory space, on the contrary, Bloom Filter uses a tiny amount of memory space. However, contemporary solution uses large amounts of memory per input item, and uses very complex arithmetic operations. Therefore, in this article, we propose a novel algorithm, called r-Dimensional Bloom Filter which comprises of Two Dimensional Bloom Filter (2DBF), Three Dimensional Bloom Filter (3DBF), Four Dimensional Bloom Filter (4DBF), and Five Dimensional Bloom Filter (5DBF), and it features a) a very fast filtering system, b) less false positive, c) low extra space consumption, d) free from false negative, e) high adaptability, and f) high scalability. Also, multidimensional Bloom Filter is not found in our literature search. But, many solutions claim the development of multidimensional Bloom Filter. However, these are hierarchical Bloom Filters. To evaluate our proposed data structure, we conduct has carried out an extensive experimentation. 2DBF, 3DBF, 4DBF, and 5DBF outperforms Cuckoo Filter in every aspect. We have experimented using Microsoft trace data and Twitter tweets data.

关键词： Bloom filter Membership filter Membership query Metadata Twitter hashtag hashing algorithms rDBF Multidimensional bloom filter

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：