Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

A Review of Image Steganography Based on Multiple Hashing Algorithm

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item.From ancient times to the present,the security of secret or vital information has always been a significant problem.The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest.Therefore,several approaches,including steganography,have been developed by researchers over time to enable safe data transit.In this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,etc.We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)approach.In this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this strategy.Before encoding the data and embedding it into a carry image,this review verifies that it has been encrypted.Usually,embedded text in photos conveys crucial signals about the content.This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport.If the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.

基于多重熵Hash及Box-Cox蓄电池续航时长分析与仿真

现代工业生产中,蓄电池是市电停电后通信网络业务能够持续运行的重要保障,是化解网络风险发生的重要一环。针对目前通信行业蓄电池智能化管理水平不高,尤其对于蓄电池的剩余寿命及续航时长无法测算,导致通信机房停电后无法准确预估蓄电池的续航时长。针对该问题,首先设计了一种基于信息熵的多重哈希(Hash)查询方法,然后提出了一种基于对数似然函数优化方法的Box-Cox算法,实现蓄电池时序运维数据续航时长的平稳化处理。实验结果表明对于大量的通信机房运维数据,通过基于熵的多重哈希(Hash)方法进行数据查询结构设计并使用最优化参数的Box-Cox算法可以有效计算得到蓄电池的续航时长。该方法能够将蓄电池机房运维数据转化为蓄电池续航时长生产知识数据,实现通信机房蓄电池能耗的有效管理运营,实际生产实践也证明了该方法的可行性。

基于Simhash算法的题库查重系统的设计与实现

Simhash算法是一种基于局部敏感哈希(LSH)的技术,以其快速的计算速度和高度的查重准确性而知名。该算法通过将文本特征转换为二进制码,进而通过计算这些二进制码之间的汉明距离来评估文本的相似度。在文本去重和重复文档检测等多个领域,Simhash算法已经展现出了显著的效果。鉴于此,将Simhash算法应用于题库查重具有很高的可行性和实际应用价值。

基于变色龙hash的区块链可扩展存储方案

区块链中的节点以副本形式保存数据,随着时间的推移,区块链中的区块数不断增加,导致节点承受的存储压力随之增大,存储压力成为区块链应用落地的瓶颈之一。为了解决区块链中存储压力问题,提出了基于变色龙hash的区块链可扩展存储方案,该方案利用节点被攻击成功的概率和改进的温度模型,将区块分为高低安全性的冷热区块;基于变色龙hash算法和改进的Merkle tree,对高安全性的冷区块进行部分节点存储。在存储过程中,除高安全性冷区块的区块体信息被重构外,其余数据保持不变。仿真实验表明,在不改变区块链结构和安全性能的情况下,所提出的方案可减少区块链中数据的存储总量,减少存储节点的存储压力;且区块数量越多,其优势越明显。

基于RS_Hash频繁项集的卫星载荷关联规则算法

遥测数据是反映卫星健康状态的重要依据,对遥测载荷数据进行关联性分析,在一定程度上能反映出卫星的整体运行情况的好坏。针对传统关联规则算法存在效率低下、占用内存过多的问题,提出一种基于RS_Hash频繁项集的卫星载荷关联规则算法。首先对事务数据库使用动态随机抽样的方法获取样本数据,设计抽样误差和抽样停止规则来确定最优的样本容量;其次将抽取出的样本使用哈希桶来存储频繁项集,进而减少占用的内存,提高算法的运行效率;最后使用3个与载荷数据相似的公开数据集和卫星载荷数据集进行实验,结果表明,在公共数据集上取得了良好的效果,尤其是在具有大数据量级的卫星载荷数据集上效果明显,在不同事务长度和支持度的情况下,相较于Apriori、PCY、SON、FP-Growth、RCM_Apriori和Hash_Cumulate算法,RS_Hash算法在平均时间效率上分别提高了75.81%、49.10%、59.38%、50.22%、40.16%和39.22%。

TOE Hash冲突处理设计与实现

随着以太网速率的不断提高,为了处理TCP/IP协议CPU承受了巨大的负担,因此使用单独的TCP卸载引擎处理TCP数据流显得尤为重要。TCP卸载引擎首先需要对链接进行辨认,而Hash函数作为一种发挥映射作用的函数常常被用于TCP链接的辨认。然而使用Hash函数后将不可避免地产生Hash冲突问题。设计实现的孔雀散列冲突解决方式相较传统一冗余与三冗余结构,分别节省4.1%空间与51.57%空间。在使用同种Hash且填充率为106%的情况下,孔雀散列的冲突解决率相较传统一冗余与三冗余结构分别提升21%与降低1%。在综合考虑使用空间与冲突解决率的情况下该方案有显著优势。

基于Hash和倒排项集的海上钻井平台隐患关.联分析

为了充分挖掘海上平台隐患案例中隐患属性与致因之间的关联性,提高对平台隐患风险预测的准确性和时效性,提出了一种基于Hash技术和倒排项集的关联规则挖掘模式。首先针对292条平台结构现场数据的每个隐患属性进行分析提取;其次在多支持度下,按照多维、多层关联规则挖掘模式挖掘隐患设备、隐患位置、隐患现象等属性的关联规则;然后提出用信息增益来衡量关联规则的有效性;最后,根据挖掘结果分析并总结海上钻井平台隐患特征。结果表明:春季油水分离器常出现缺少保养的隐患,护管常出现未封堵的隐患;一般腐蚀和锈蚀的致因为操作维保不当、防护层剥落,以及防护装置锈蚀严重、脱焊、不符合规范,提高海上平台管理水平可减小现场隐患的发生概率。

并行耦合动态整数帐篷Hash函数设计

提出一种基于耦合动态整数帐篷映射的并行Hash函数。针对数据量较大、处理速度较慢等问题,采用MD6算法框架,利用多核处理器技术并行处理数据。同时在压缩函数中,利用双向耦合映像格子模型进行扩散,用动态整数帐篷映射代替传统的逻辑函数。实验结果表明,该算法可根据需要获得不同长度的Hash值,使用较短时间即可完成对数据的杂凑处理,同时具有较为理想的混乱与扩散性质,符合Hash函数的各项性能要求。

Cross-Domain Authentication Scheme Based on Blockchain and Consistent Hash Algorithm for System-Wide Information Management

System-wide information management(SWIM)is a complex distributed information transfer and sharing system for the next generation of Air Transportation System(ATS).In response to the growing volume of civil aviation air operations,users accessing different authentication domains in the SWIM system have problems with the validity,security,and privacy of SWIM-shared data.In order to solve these problems,this paper proposes a SWIM crossdomain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication.The scheme uses a consistent hash algorithm with virtual nodes in combination with a cluster of authentication centers in the SWIM consortium blockchain architecture to synchronize the user’s authentication mapping relationships between authentication domains.The virtual authentication nodes are mapped separately using different services provided by SWIM to guarantee the partitioning of the consistent hash ring on the consortium blockchain.According to the dynamic change of user’s authentication requests,the nodes of virtual service authentication can be added and deleted to realize the dynamic load balancing of cross-domain authentication of different services.Security analysis shows that this protocol can resist network attacks such as man-in-the-middle attacks,replay attacks,and Sybil attacks.Experiments show that this scheme can reduce the redundant authentication operations of identity information and solve the problems of traditional cross-domain authentication with single-point collapse,difficulty in expansion,and uneven load.At the same time,it has better security of information storage and can realize the cross-domain authentication requirements of SWIM users with low communication costs and system overhead.KEYWORDS System-wide information management(SWIM);consortium blockchain;consistent hash;cross-domain authentication;load balancing.

基于hash表的华容道算法研究

目前,华容道算法是基于广度优先或深度优先搜索策略的改进,时间复杂为O(V+E)。为了提高效率,文章利用排列组合算法找到所有开局,再用广度优先算法找到开局的最优解,将所有开局及对应最优解保存在文件中。执行算法时最快可以以O(1)的时间复杂度找到最优解。理论分析和实验结果都表明,基于h as h表的求解华容道的算法能明显提高算法效率。

利用HASH方法反演美国三次中等地震的震源机制解

震源机制是了解震源信息及应力场的重要手段。在对HASH(Hardebeck and Shearer)方法的原理进行详细介绍的基础上,利用该方法反演得到美国3次4级以上天然地震的震源机制解。反演结果与美国地质调查局(USGS)结果基本一致,进一步证实该方法对于中等地震反演计算的可靠性。

Penetration Tests of Multi-Bucket Foundation in Sand for Offshore Wind Turbines

Suction foundations are generally installed with negative pressures to overcome the resistance of soils and complete the penetration,but excessive negative pressures are also avoided to cause seepage damages.In this paper,the model test method was used to analyze the movement characteristics of multi-bucket foundations in the process of sinking in sand,and the common calculation methods of sinking resistances are verified.The critical negative pressure corresponding to the seepage failure of foundation was determined under the action of increasing negative pressure step by step and the characteristics of soil failure were studied.The calculation formula of critical suction in sand was verified in application,and according to the test results,the value of seepage coefficient was modified,which provides an example for the study of suction foundation in sand soils.

基于Vision Transformer Hashing的民族布艺图案哈希检索算法研究

针对民族布艺图案复杂多样、语义提取、图像识别与检索困难等问题,以蜡染图案和织锦图案为例,提出一种图像检索算法,以提高匹配和检索民族布艺图案的准确度。结合民族布艺图案领域知识将民族布艺图案图像进行预处理,使用VIT为主干网络在哈希检索算法框架下进行图像检索。该方法优化了深度哈希检索算法,通过自身的自注意力机制提升了提取图案深层语义特征的能力,提高了深度哈希算法检索民族布艺图案的速度和精度。实验结果表明:提出的方法最佳检索精度可以达到95.32%。

An Efficient Encrypted Speech Retrieval Based on Unsupervised Hashing and B+ Tree Dynamic Index

Existing speech retrieval systems are frequently confronted with expanding volumes of speech data.The dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’real-time retrieval requirements.This study proposes an efficient method for retrieving encryption speech,using unsupervised deep hashing and B+ tree dynamic index,which avoid privacy leak-age of speech data and enhance the accuracy and efficiency of retrieval.The cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan(DGHV)Fully Homomorphic Encryption(FHE)technique,which encrypts the original speech.In addition,this research employs Residual Neural Network18-Gated Recurrent Unit(ResNet18-GRU),which is used to learn the compact binary hash codes,store binary hash codes in the designed B+tree index table,and create a mapping relation of one to one between the binary hash codes and the corresponding encrypted speech.External B+tree index technology is applied to achieve dynamic index updating of the B+tree index table,thereby satisfying users’needs for real-time retrieval.The experimental results on THCHS-30 and TIMIT showed that the retrieval accuracy of the proposed method is more than 95.84%compared to the existing unsupervised hashing methods.The retrieval efficiency is greatly improved.Compared to the method of using hash index tables,and the speech data’s security is effectively guaranteed.

TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage.Cross-modal retrieval technology can be applied to search engines,crossmodalmedical processing,etc.The existing main method is to use amulti-label matching paradigm to finish the retrieval tasks.However,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal results.To avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal data.First,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for processing.Second,this method uses the inference capabilities of the transformer encoder to generate global fine-grained features.Finally,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching datasets.This article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous classicalmethods.The experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

ViT2CMH:Vision Transformer Cross-Modal Hashing for Fine-Grained Vision-Text Retrieval

In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to process image and text information,respectively.This makes images or texts subject to local constraints,and inherent label matching cannot capture finegrained information,often leading to suboptimal results.Driven by the development of the transformer model,we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically,we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally,the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO(MS-COCO)and Flickr30K,comparing with baselines of some hashing methods and image-text matching methods,showing that our method has better performance.

Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption scheme.First,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep features.Secondly,the central similarity is used to quantify and construct the deep hash sequence of features.The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index.Finally,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted domain.The experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user privacy.The retrieval accuracy is improved by at least 37%compared to traditional hashing schemes.At the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre.The storage service is distributed as pay a service for accessing the size to collect the data.Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage space.The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis.It creates a complex nature to increase the storage consumption under cost.To resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud environment.Initially,preprocessing is done using the sparse augmentation technique.Further,the preprocessed files are segmented into blocks to make Hash-Index.The block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the file.Based on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a cluster.Finally,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case.This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).