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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

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

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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

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Enhancing Intersection Identification for Autonomous Vehicles: A Hash-Based Approach 10

Enhancing Intersection Identification for Autonomous Vehicle...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Olivieri, Giuseppe Volpe, Gaetano Mangini, Agostino Marcello Fanti, Maria Pia Polytech Univ Bari Dept Elect & Informat Engn I-70126 Bari BA Italy

ISBN: (纸本)9798350373981;9798350373974

The rapid advancement and deployment of Autonomous Vehicles (AVs) necessitate innovative solutions for reliable and efficient navigation. In this context, a crucial aspect is the unequivocal identification of intersections. This paper proposes a novel methodology for uniquely identifying intersections by applying a hash algorithm that generates a distinct fingerprint of each intersection, inspired by the operational mechanisms within blockchain platforms, particularly mimicking the generation of Transaction Hashes. The solution's core is creating a hash tree to unequivocally identify the intersection for the AVs' navigation. The application to a real complex case study shows the applicability of the proposed approach.

关键词： hashing algorithms Autonomous Vehicles Intersections

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

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

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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

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A Hardware Implementation of DICE on a RISC-V Processor

A Hardware Implementation of DICE on a RISC-V Processor

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Naik, Vaibhavi Masur, Tushar Spandana, K. Sanyal, Wrileena Kumar, Sudeendra Pes University Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350365306

In the current era of technology, the use of IoT devices have become increasingly popular. On the contrary, there is a growing concern of security in these resource constrained devices as they don't have the computational power required to implement high end security modules like TPM (Trusted Platform Module). Hence, a simple solution for Trusted Computing on microcontrollers has been devised with the Device Identifier Composition Engine (DICE). The majority of current solutions make use of non-purpose-built hardware. These implementations rely on black box MPUs (Micro-Processor Units), and the use of hardware that wasn't tailored for DICE causes implementation process difficulties. This paper proposes a dedicated hardware for DICE with some additional novel security features. © 2024 IEEE.

关键词： hashing algorithms Root of Trust Security

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Towards Efficient hashing in Ethereum Smart Contracts 16

Towards Efficient Hashing in Ethereum Smart Contracts

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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

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Hash-Based Remote Sensing Image Retrieval

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Han, Lirong Paoletti, Mercedes E. Tao, Xuanwen Wu, Zhaoyue Haut, Juan M. Li, Peng Pastor-Vargas, R. Plaza, Antonio Univ Extremadura Dept Technol Comp & Commun Caceres 10001 Spain China Univ Petr East China Coll New Energy Qingdao 266580 Peoples R China Univ Nacl Educ Distancia UNED Escuela Tecn Super Ingn Informat Dept Sistemas Comunicac & Control Madrid 28040 Spain

In recent years, the rapid development of remote sensing (RS) technology has led to a drastic increase in the availability of RS images. This calls for the need to develop new methods able to effectively and efficiently retrieve the required instances from a massive amount of RS imagery. In retrieval tasks, finding the nearest-neighbor sample of the retrieval query is a fundamental research topic. Exhaustive comparison is the simplest method to accomplish this task. However, due to the involved computational complexity and memory limitations, this solution is no longer feasible in large data retrieval tasks. As an important branch of approximate nearest-neighbor retrieval (NNR), hash algorithms transform high-dimensional data into low-bit expressions (hash codes) with elements of 0 and 1 to reduce storage and computational costs. Hash algorithms aim to preserve the same nearest-neighbor relationship between the learned hash codes and the original data. Existing hash algorithms are divided into two classes: shallow and deep methods. Furthermore, deep hash algorithms can be divided into (semi-) supervised and unsupervised algorithms. In this article, representative hash-based RS image retrieval (HBRSIR) methods are reviewed, studying the application of hashing in other areas of the RS community and introducing available datasets and evaluation metrics for RS image retrieval (RSIR). The performance of representative and cross-modal hashing methods is validated using two common RSIR datasets (UCMerced and AID) and a cross-modal dataset (DSRSID). Prospects of future work summarizing HBRSIR are also provided.

关键词： Feature extraction Image retrieval Deep learning Satellites Task analysis Codes Radar imaging hashing algorithms hashing applications large-scale image retrieval remote sensing (RS) datasets RS image retrieval (RSIR)

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Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval

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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

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The Blockchain Art Simulation (BARTS) and Experiential Exercises 21

The Blockchain Art Simulation (BARTS) and Experiential Exerc...

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26th Conference on Innovation and Technology in Computer Science Education

作者： Sanders, Sean Patrick Sanders, G. Lawrence SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781450383974

This paper introduces the online Blockchain ART Simulation (BARTS) and a set of experiential online exercises that illustrate hashing concepts for validating blockchain transactions. Our premise is that understanding the role of secure hashing algorithms in the mining process is the primary conceptual impediment for students to gain an accurate picture of the validation process for blockchain transactions. The BARTS simulation helps develop the conceptual foundation for understanding hashing concepts, and it also leads to understanding blockchain-related concepts. In addition to assisting in understating blockchain concepts, the BARTS simulation and the accompanying exercises are useful in understanding and applying hashing concepts related to password verification, verifying software signatures, digital content signature verification, and averting man-in-the-middle threats.

关键词： hashing algorithms BARTS simulation blockchain concepts education

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

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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

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