VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Query Optimization Framework for Graph Database in Cloud Dew Environment

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

Application of Recursive Query on Structured Query Language Server

The advantage of recursive programming is that it is very easy to write and it only requires very few lines of code if done correctly.Structured query language(SQL)is a database language and is used to manipulate data.In Microsoft SQL Server 2000,recursive queries are implemented to retrieve data which is presented in a hierarchical format,but this way has its disadvantages.Common table expression(CTE)construction introduced in Microsoft SQL Server 2005 provides the significant advantage of being able to reference itself to create a recursive CTE.Hierarchical data structures,organizational charts and other parent-child table relationship reports can easily benefit from the use of recursive CTEs.The recursive query is illustrated and implemented on some simple hierarchical data.In addition,one business case study is brought forward and the solution using recursive query based on CTE is shown.At the same time,stored procedures are programmed to do the recursion in SQL.Test results show that recursive queries based on CTEs bring us the chance to create much more complex queries while retaining a much simpler syntax.

Visible-infrared person re-identification using query related cluster

Visible-infrared person re-identification(VIPR), is a cross-modal retrieval task that searches a target from a gallery captured by cameras of different spectrums.The severe challenge for VIPR is the large intra-class variation caused by the modal discrepancy between visible and infrared images.For that, this paper proposes a query related cluster(QRC) method for VIPR.Firstly, this paper uses an attention mechanism to calculate the similarity relation between a visible query and infrared images with the same identity in the gallery.Secondly, those infrared images with the same query images are aggregated by using the similarity relation to form a dynamic clustering center corresponding to the query image.Thirdly, QRC loss function is designed to enlarge the similarity between the query image and its dynamic cluster center to achieve query related clustering, so as to compact the intra-class variations.Consequently, in the proposed QRC method, each query has its own dynamic clustering center, which can well characterize intra-class variations in VIPR.Experimental results demonstrate that the proposed QRC method is superior to many state-of-the-art approaches, acquiring a 90.77% rank-1 identification rate on the RegDB dataset.

基于改进Deformable DETR的无人机视频流车辆目标检测算法

针对无人机视频流检测中小目标数量多、因图像传输质量较低而导致的上下文语义信息不充分、传统算法融合特征推理速度慢、数据集类别样本不均衡导致的训练效果差等问题,提出一种基于改进Deformable DETR的无人机视频流车辆目标检测算法。在模型结构方面,该算法设计了跨尺度特征融合模块以增大感受野,提升小目标检测能力,并采用针对object_query的挤压-激励模块提升关键目标的响应值,减少重要目标的漏检与错检率;在数据处理方面,使用了在线困难样本挖掘技术,改善数据集中类别样本分布不均的问题。在UAVDT数据集上进行了实验,实验结果表明,改进后的算法相较于基线算法在平均检测精度上提升了1.5%,在小目标检测精度上提升了0.8%,并在保持参数量较少增长的情况下,维持了原有的检测速度。

用Java来设计组件重用的Query方法

文章所探讨的组件重用的Query方法是利用Java反射技术和Java数据库连接技术来完成叶数据库的查询操作。它将查询数据库的公共操作封装于一个组件中,使编程工作者在具体编程工作中能方便地重复使用它们,以求简化编程工作。

基于JQuery的自动完成功能的实现

当客户在利用Goolge或Baidu搜索引擎在网上搜索信息时,自动完成功能给大家带来了很大的便利。随着Ajax技术的迅速发展,自动完成功能在不同的框架中都得到了一定程度的实现。以某精密机械有限公司ERP系统的制定年业务计划模块为例,详细介绍通过JQuery-aucomplete插件和基于文本的数据交换格式JSON实现基于JQuery的自动完成功能的方法。

Microsoft Query技术在医院统计分析中的应用

目的介绍Microsoft Query技术在医院统计工作中的应用。方法利用Microsoft Query自定义查询条件,从医院数据库提取数据导出到Excel。结果无须编程,即可及时、准确、全面、动态地获取数据库中的数据,结合Excel进行分析,能更快、更好地提供有价值的统计信息。结论Microsoft Query技术值得在医院统计工作中进一步推广应用。

Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

在Excel中用Microsoft Query导入外部数据

Excel拥有强大的数据管理、分析能力,借助Microsoft Query能够访问多种类型的外部数据源,可从外部数据源选择数据,将该数据导入到工作表中,以及根据需要刷新数据。本文借助于罗斯文数据库进行分析,讲述Excel与外部Access数据源连接的操作方法及技巧。

基于SSH2和jQuery技术的代码生成框架研究

采用Struts2、Spring3、Hibernate4和j Query技术开发了基于Web形式的代码生成框架,该框架以XML/XSLT、Velocity引擎、知识库模型为构建标准,在实现MVC模式代码生成的基础上,生成验证规则框架,具有良好的可移植特性跨平台特性,为软件开发模式提供了良好的参考模式。

Microsoft Query技术在医院统计分析中的应用

目的介绍Microsoft Query技术在医院统计工作中的应用。方法利用Microsoft Query自定义查询条件，从医院数据库提取数据产出到Excel。结果无须编程，即可及时、准确、全面、动态的获取数据库中的数据，结合Excel进行分析，能更快、更好的提供有价值的统计信息。结论Microsoft Query技术值得在医院统计工作中得到进一步推广应用。

浅谈CSS3 Media Query的使用方法

网页设计人员都希望开发的网页可以适用于各种终端设备上,使浏览者可以完整、友好地浏览网页。响应式布局的提出恰恰可以满足这一需求。而响应式布局的关键就是要通过Media Query媒介查询对终端设备的显示尺寸进行检测。该文分别对Media Query媒介查询、Media Query使用及其优、缺点进行阐述,希望对网页设计人员在实现响应式布局时有所帮助。

基于Skyline Query的高声誉用户识别方法研究

用户声誉的研究对于互联网金融和电子商务的健康发展具有重要意义,是在线用户行为分析中一个重要的研究方向。在线用户评分系统中研究学者提出了许多声誉度量算法,然而不同方法度量用户声誉的思想和角度是不同的。为了在海量数据中对用户声誉有一个总体的认识,提出一种基于Skyline Query的高声誉用户识别方法。将已有的几种声誉度量方法进行分类,综合选取代表性的算法得到的用户声誉用Skyline查询方法找到的集合Skyline中不被其他用户所支配的用户,即为高声誉用户。同时分析不同时间段上得到的集合Skyline中高声誉用户的规律。本文综合多种声誉度量方法从定性角度对声誉进行应用研究,拓宽了用户声誉研究的广度。

如何利用Microsoft Query查询数据库表结构

目的:研究Microsoft Query在读取数据库表结构中的应用。方法:使用数据库接口工具ODBC以及查询工具Mi-crosoft Query读取数据库的表结构。结果:使用Microsoft Query读取数据库的表结构,对于非计算机专业人员而言,清除了数据挖掘的入门障碍。结论:Microsoft Query读取数据库表结构,使用简单,功能强大,是数据挖掘的好工具。

一种基于jQuery的跨模块通信机制的改进方案

随着互联网技术的迅速发展,互联网产品在不断迭代的过程中也向着复杂化发展。目前,Web前端产品已经成为互联网产品的一个重要分支,本文研究了Web前端的开发模式。在模块化开发的基础上,提出了传统的基于j Query的跨模块通信机制的不足,给出了一种基于j Query的改进的跨模块通信机制的解决方案,为开发人员提供了跨模块通信机制的一种设计思路。

基于Node.js,Socket.io和jQuery Mobile的周界入侵检测报警控制系统设计

为解决当前周界入侵检测报警控制系统同底层硬件耦合紧密,硬件控制器功能单一,智能化不足的问题,提出了一种基于云架构和物联网的整体设计方案。依托于云架构,以安卓系统作为硬件控制器,利用web服务器Node.js结合即时通信技术Socket.io实现报警信息的即时推送,通过No SQL数据库Mongo DB进行相关数据储存,前端采用j Query Moble框架实现控制与显示功能。详细阐述了系统的架构、功能和设计方案,为建设新型周界入侵检测报警控制和显示系统提供了新的思路与方向。

Query Expansion Based on Semantics and Statistics in Chinese Question Answering System

In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.

Oceanographic ontology-based spatial knowledge query

The construction of oceanographic ontologies is fundamental to the ＂digital ocean＂. Therefore, on the basis of introduction of new concept of oceanographic ontology, an oceanographic ontology-based spatial knowledge query （OOBSKQ） method was proposed and developed. Because the method uses a natural language to describe query conditions and the query result is highly integrated knowledge, it can provide users with direct answers while hiding the complicated computation and reasoning processes, and achieves intelligent, automatic oceanographic spatial information query on the level of knowledge and semantics. A case study of resource and environmental application in bay has shown the implementation process of the method and its feasibility and usefulness.