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.

New indices to balanceα-diversity against tree size inequality

The number and composition of species in a community can be quantified withα-diversity indices,including species richness(R),Simpson’s index(D),and the Shannon-Wiener index(H΄).In forest communities,there are large variations in tree size among species and individu-als of the same species,which result in differences in eco-logical processes and ecosystem functions.However,tree size inequality(TSI)has been largely neglected in studies using the available diversity indices.The TSI in the diameter at breast height(DBH)data for each of 99920 m×20 m forest census quadrats was quantified using the Gini index(GI),a measure of the inequality of size distribution.The generalized performance equation was used to describe the rotated and right-shifted Lorenz curve of the cumulative proportion of DBH and the cumulative proportion of number of trees per quadrat.We also examined the relationships ofα-diversity indices with the GI using correlation tests.The generalized performance equation effectively described the rotated and right-shifted Lorenz curve of DBH distributions,with most root-mean-square errors(990 out of 999 quadrats)being<0.0030.There were significant positive correlations between each of threeα-diversity indices(i.e.,R,D,and H’)and the GI.Nevertheless,the total abundance of trees in each quadrat did not significantly influence the GI.This means that the TSI increased with increasing spe-cies diversity.Thus,two new indices are proposed that can balanceα-diversity against the extent of TSI in the com-munity:(1−GI)×D,and(1−GI)×H’.These new indices were significantly correlated with the original D and H΄,and did not increase the extent of variation within each group of indices.This study presents a useful tool for quantifying both species diversity and the variation in tree sizes in forest communities,especially in the face of cumulative species loss under global climate change.

Learned Index和B-Tree在不同分布数据上的性能对比及优化

Learned Index是一种通过训练模型来建立输入数据和存储位置之间映射关系的索引,它能学习到数据间分布的信息,而不同的数据分布将影响模型训练准确率和模型复杂度之间的平衡。为了探索Learned Index适用的场景,使用不同分布、不同数据量的数据对它和加以优化的可更新的自适应学习索引(ALEX)进行性能测试,并与B-Tree进行对比,最终发现Learned Index构建大批量数据的索引时间比B-Tree短,读操作性能、存储空间大小有明显的优势,但写操作性能较差,因此得出Learned Index更适用于大数据情景下的在线分析处理(OLAP)数据库,用于静态数据的存储和查询操作的结论。基于B-Tree的索引结构,对初版Learned Index的结构进行了优化和调整,最终使优化后Learned Index在大批量数据的读写操作性能上有明显提高,其中读操作最高达到原版Learned Index的2倍,写操作最高达到原版的3倍。

The Maximum and Minimum Value of Exponential RandićIndices of Quasi-Tree Graph

The exponential Randić index has important applications in the fields of biology and chemistry. The exponential Randić index of a graph G is defined as the sum of the weights e 1 d( u )d( v ) of all edges uv of G, where d( u ) denotes the degree of a vertex u in G. The paper mainly provides the upper and lower bounds of the exponential Randić index in quasi-tree graphs, and characterizes the extremal graphs when the bounds are achieved.

Trees with Given Diameter Minimizing the Augmented Zagreb Index and Maximizing the ABC Index

Let G be a simple connected graph with vertex set V（G） and edge set E（G）.The augmented Zagreb index of a graph G is defined asAZI（G） =∑uv∈E（G）(d;d;/（d;+ d;-2）);,and the atom-bond connectivity index（ABC index for short） of a graph G is defined asABC（G） =∑uv∈E（G）(（d;+ d;-2）/d;d;),where d;and d;denote the degree of vertices u and v in G,respectively.In this paper,trees with given diameter minimizing the augmented Zagreb index and maximizing the ABC index are determined,respectively.

A new approach for effectively determining fracture network connec- tions in fractured rocks using R tree indexing

Determinations of fracture network connections would help the investigators remove those ＂meaningless＂ no-flow-passing fractures, providing an updated and more effective fracture network that could considerably improve the computation efficiency in the pertinent numerical simulations of fluid flow and solute transport. The effective algorithms with higher computational efficiency are needed to accomplish this task in large-scale fractured rock masses. A new approach using R tree indexing was proposed for determining fracture connection in 3D stochastically distributed fracture network. By com- paring with the traditional exhaustion algorithm, it was observed that from the simulation results, this approach was much more effective; and the more the fractures were investigated, the more obvious the advantages of the approach were. Furthermore, it was indicated that the runtime used for creating the R tree indexing has a major part in the total of the runtime used for calculating Minimum Bounding Rectangles （MBRs）, creating the R tree indexing, precisely finding out fracture intersections, and identifying flow paths, which are four important steps to determine fracture connections. This proposed approach for the determination of fracture connections in three-dimensional fractured rocks are expected to provide efficient preprocessing and critical database for practically accomplishing numerical computation of fluid flow and solute transport in large-scale fractured rock masses.

ZB^(+)-tree:一种ZNS SSD感知的新型索引结构

ZNS SSD是近年来提出的一种新型固态硬盘(solid state drive,SSD),它以分区(Zone)的方式管理和存取SSD内的数据.相比于传统SSD,ZNS SSD可以有效提升SSD的读写吞吐,降低写放大,减少SSD的预留空间.但是,ZNS SSD要求Zone内必须采用顺序写模式,并且Zone上的空间分配、垃圾回收等任务都需要用户自行控制.ZNS SSD的这些特性对于传统数据库系统的存储管理、索引、缓存等技术均提出了新的挑战.针对如何使传统的B^(+)-tree索引结构适配ZNS SSD的问题,提出了一种ZNS SSD感知的新型索引结构——ZB^(+)-tree(ZNS-aware B^(+)-tree).ZB^(+)-tree是目前已知的首个ZNS SSD感知的索引,它以B^(+)-tree为基础,利用ZNS SSD内部支持少量随机写的常规Zone(conventional zone,Cov-Zone)和只支持顺序写的顺序Zone(sequential zone,Seq-Zone),通过常规Zone来吸收对ZNS SSD的随机写操作.ZB^(+)-tree将索引节点分散存储在常规Zone和顺序Zone中,并为2种Zone内的节点分别设计了节点结构,使ZB^(+)-tree不仅能够吸收对索引的随机写操作,而且又可以保证顺序Zone内的顺序写要求.在实验中利用null_blk和libzbd模拟ZNS SSD设备,并将现有的CoW B^(+)-tree修改后作为对比索引.结果表明,ZB^(+)-tree在运行时间、空间利用率等多个指标上均优于CoW B^(+)-tree.

Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data

Variations in vegetation are closely related to climate change, but understanding of their characteristics and causes remains limited. As a typical semi-humid and semi-arid cold plateau region, it is important to understand the knowledge of long term Normalized Difference Vegetation Index(NDVI) variations and find the potential causes in the source region of the Yangtze River. Based on four tree-ring width chronologies, the regional mean NDVI for July and August spanning the period 1665–2013 was reconstructed using a regression model, and it explained 43.9% of the total variance during the period 1981–2013. In decadal, the reconstructed NDVI showed eight growth stages(1754–1764, 1766–1783, 1794–1811, 1828–1838, 1843–1855, 1862–1873, 1897–1909, and 1932–1945)and four degradation stages(1679–1698, 1726–1753, 1910–1923, and 1988–2000). And based on wavelet analysis, significant cycles of2–3 yr and 3–8 yr were identified. In additional, there was a significant positive correlation between the NDVI and the Palmer Drought Severity Index(PDSI) during the past 349 yr, and they were mainly in phase. However, according to the results of correlation analysis between different grades of drought/wet and NDVI, there was significant asymmetry in extreme drought years and extreme wet years. In extreme drought years, NDVI was positively correlated with PDSI, and in extreme wet years they were negatively correlated.

Infl uence of altitude and tree class on climate-growth relationships in a larch plantation in subtropical China

Precise quantifi cation of climate-growth relationships can make a major contribution to scientifi c forest management.However,whether diff erences in the response of growth to climate at diff erent altitudes remains unclear.To answer this,264 trees of Larix kaempferi from 88 plots,representing diff erent altitudinal ranges(1000-2100 m)and tree classes were sampled and used to develop tree-ring chronologies.Tree-ring growth(TRG)was either positively(dominant)or negatively(intermediate and suppressed)correlated with climate in diff erent tree classes at diff erent altitudes.TRG was strongly correlated with growing season at low altitudes,but was less sensitive to climate at middle altitudes.It was mainly limited by precipitation and was highly sensitive to climate at low altitudes.Climate-growth relationships at high altitudes were opposite compared to those at low altitudes.TRG of dominant trees was more sensitive to climate change compared to intermediate and suppressed trees.Climate factors(annual temperatures;moisture,the number of frost-free days)had diff erent eff ects on tree-ring growth of diff erent tree classes along altitudinal gradients.It was concluded that the increase in summer temperatures decreased water availability,resulting in a signifi cant decline in growth rates after 2005 at lower altitudes.L.kaempferi is suitable for planting in middle altitudes and dominant trees were the best sampling choice for accurately assessing climate-growth relationships.

An Improved Indexing and Matching Method for Mathematical Expressions Based on Inter-Relevant Successive Tree

In recent years, a growing number of math contents are available on the Web. When conventional search engines deal with mathematical expressions, the two-dimen- sion-al structure of mathematical expressions is lost, which results in a low performance of math retrieval. While the retrieval technology specifically designed for mathematical expressions is not mature currently. Aiming at these problems, an improved mathematical expression indexing and matching method was proposed through employing full text index method to deal with the two-dimensional structure of mathematical expressions. Firstly, through the fully consideration of LaTeX formulae’ characteristics, a feature representation method of mathematical expressions and a clustering method of feature keywords were put forward. Then, an improved inter-relevant successive trees index model was applied to the construction of the mathematical expression index, in which the cluster algorithm of mathematical expression features was employed to solve the problem of the quantity growth of the trees in processing large amount of formulae. Finally, the matching algorithms of mathematical expressions were given which provide four query modes called exact matching, compatible matching, sub-expression matching and fuzzy matching. In browser/server mode, 110027 formulae were used as experimental samples. The index file size was 29.02 Mb. The average time of retrieval was 1.092 seconds. The experimental result shows the effectiveness of the method.

LI-Tree:一个基于非易失性内存和轻量级B+树的学习索引

大数据背景下剧增的数据给经典的内存索引技术带来了巨大挑战,为了实现对海量数据的高性能索引,工业界和学术界分别从设备和结构角度推出了高性能大容量的非易失型内存(Non-Volatile Memory,NVM)和受机器学习启发的学习索引(Learned Index,LI).然而目前基于NVM的学习索引结构的相关研究非常稀少,在如何结合NVM和LI来高效地索引海量数据方面还有许多问题需要解决.本文提出了一种基于NVM的新型智能索引结构LI-Tree,充分发挥了两者的优势.具体的,LI-Tree可分为三层:由机器学习模型组成的能够提高LI-Tree单点性能的模型层、由静态数组构成的减少NVM写的数据索引层和由一系列轻量级B+树组成以避免模型层插入时频繁重训练的数据层.在真实设备上评估表明,LI-Tree相比传统B+树,插入、查询和删除性能分别提高了70%、30%和130%.另外,LI-Tree与学习索引结构ALEX,PGM-Index和XIndex对比,插入性能分别提升了80%,130%和150%.

Graph Similarity Join with K-Hop Tree Indexing

Graph similarity join has become imperative for integrating noisy and inconsistent data from multiple data sources. The edit distance is commonly used to measure the similarity between graphs. To accelerate the similarity join based on graph edit distance, in the paper, we make use of a preprocessing strategy to remove the mismatching graph pairs with significant differences. Then a novel method of building indexes for each graph is proposed by grouping the nodes which can be reached in k hops for each key node with structure conservation, which is the k-hop-tree based indexing method. Experiments on real and synthetic graph databases also confirm that our method can achieve good join quality in graph similarity join. Besides, the join process can be finished in polynomial time.

橡胶树体胚苗质量调查与分析

天然橡胶是我国重要的战略物资和工业原料,主要来源于橡胶树。橡胶树生长周期长,其苗木质量直接影响天然橡胶产业的发展,橡胶树体胚苗在前期研究中表现一致性好,且具有速生、高产的优势。海南省天然橡胶新型种植材料创新基地是当前规模生产的橡胶树体胚苗的基地,现生产品种有热研73397和热研917。本研究分析比较了热研73397体胚试管苗在2018—2020年间的株高、茎粗、叶片数、主根长等指标的差异性,比较了2020年2个品种各指标的差异性,并对不同年份不同品种进行分级比较,以期阐明橡胶树体胚苗规模生产的情况。结果表明:(1)2018—2020年,热研73397的株高、茎粗、叶片数、主根长度在各区间的比例相当;株高、叶片数、主根长度在3 a间的差异不显著;茎粗在2018和2019年之间差异显著,且3 a均值均在2.00 mm以上,最大值和最小值的差值仅为0.09 mm。(2)2020年,热研73397和热研917体胚苗的株高一致,均值均为8.0 cm,且大部分集中在5.0~10.0 cm,分别占66.13%、66.91%;热研73397和热研917的茎粗范围分别为0.80~4.23 mm、1.13~3.73 mm,均值分别为2.11 mm和2.18 mm,2个品种的茎粗在1.50~3.00 mm之间高度集中,分别占85.94%、83.82%;热研73397和热研917叶片数范围分别为0~6片和1~4片,2个品种叶片数绝大多数为1~3片,分别占88.82%、96.32%,其中热研917叶片数为2片的占比高达52.94%;热研73397的主根长度均值为9.5 cm,而热研917的为9.1 cm,2个品种的主根长度多数集中在8.0~12.0 cm之间,分别占74.12%、79.41%;方差分析结果表明,2020年2个品种的株高、茎粗、叶片数差异不显著,主根长度差异显著(P<0.05)。(3)2018—2020年调查的橡胶树体胚苗中,不同年份不同品种的橡胶树体胚苗,一级苗比例为19%~22%,二级苗比例为56%~62%。据此,规模化生产橡胶树体胚试管苗质量稳定,为橡胶树体胚苗生产工艺的推广应用提供参考。

邻近木多样性与竞争对天然云冷杉林树木生长的影响

【目的】探究天然云冷杉林邻近木多样性、竞争强度对林木生长的影响,为云冷杉林结构化经营和管理提供科学依据。【方法】选取长白山天然云冷杉林2015、2018年两期固定样地调查数据,基于结构方程模型分析邻近木多样性、竞争与树木生长的关系。【结果】(1)研究区内天然云冷杉林的邻近木树种、径阶、树高多样性指数均集中在1.04处,3个多样性指数整体上分布均匀,树种混交度高,林分结构复杂。(2)结构方程模型中,树种、径阶、树高3个邻近木多样性指数和竞争指数对材积生长量的总影响系数分别为-0.001、0.166、0.073和-0.489,结果表明竞争是影响林木生长的关键因素。(3)径阶和树高多样性的增加对生长量均为正面影响,其中径阶多样性为直接影响,树高多样性为间接影响;树种多样性表现为直接的负面影响与间接的正面影响,总体为负面影响;此外,树种多样性的提高可以减少林木间的竞争强度,树高多样性的提高可能会导致林分结构的分化,进而促进林分中林木个体的生长。(4)研究区内林木的生长压力可能多来自于同径级林木,小径级林木生长状况较差且竞争压力较大,大中径级林木与之相反。【结论】择伐同径级或相近径级林木,同时提高林分内的径阶、树高多样性水平,可以降低林木竞争水平,促进林木个体生长,进而提高云冷杉林林分生产力。

面向岗位能力匹配的多关键词编码MPB-Tree索引技术

单一关键词筛选的求职者信息与对应岗位能力的匹配度具有较大偏差,为帮助企业人力资源管理部门准确、全面地完成人才筛选,提出了一种多关键词编码MPB-Tree索引技术。扩展处理核心关键词,并构建岗位能力需求关键词轮廓集合。结合PB-Tree与Patricia索引建立支持多关键词索引的MPB-Tree索引结构。基于MPB-Tree索引结果与关键词轮廓集合的相关度,索引岗位能力需求关键词,实现岗位能力匹配。结果表明,所提技术的查全率和查准率均高于单一核心关键词索引下的查全率和查准率,由此说明所提技术的索引结果更为全面和准确。

基于树分解的时序最短路径计数查询算法

最短路径计数是图计算中的一个重要研究问题,旨在查询顶点间的最短路径数,在路径规划与推荐、社交网络分析、介数中心性计算等领域中具有广泛应用。目前越来越多的网络可以建模为时序图,但少有针对时序图最短路径计数查询问题的研究工作。与静态图相比,时序图增加了时间信息,结构更复杂,在查询顶点间的路径数时必须考虑边的激活时间,因此静态图中最短路径计数方法不再适用于时序图,并且在大规模时序图上查询更具有挑战性。针对时序图最短路径计数问题,提出一种基于树分解构建TG-TL(Temporal Graph-Tree Label)索引的方法。该方法包含构建索引和在线查询两个阶段,构建索引阶段根据时序图的属性设计时序树分解算法,将时序图转化为树结构;然后根据树分解的结构信息以及凸路径定义提出高效构建索引算法;在线查询阶段基于TG-TL索引提出了高效的时序最短路径计数查询算法。在4个真实数据集上的实验结果表明,与基于TG-base(Temporal Graph-base)索引的查询算法相比,所提算法在查询效率上至少提升了61%,因此所提算法在时序图最短路径计数问题上具有高效性和有效性。

“油-稻-稻”三熟制早熟冬油菜氮高效基因型及鉴定指标筛选

【目的】旨在筛选适宜“油-稻-稻”三熟制的氮高效早熟冬油菜基因型及其简易筛选指标。【方法】利用田间小区试验,选用了生育期185 d以下的的适宜“油-稻-稻”三熟制种植的14种早熟冬油菜基因型,进行正常施氮(N_(1))和不施氮(N_(0))处理。根据不同冬油菜基因型氮肥利用率(NUE)的差异,利用树状聚类分析进行分类,形成高、中、低3种氮效率基因型。同时通过逆向溯源的方法,观察分析3种氮效率基因型在不同氮水平条件下的产量构成、主要农艺性状、干物质积累及氮素吸收等方面的表现特征。总结分析出氮高效冬油菜基因型的一些简易判断的指标。【结果】不同氮效率基因型冬油菜在产量形成、主要农艺性状等方面存在显著差异。N1处理下成熟期早熟冬油菜的主要农艺性状的表型值和变异系数基本表现大于N0处理。因此,以N1处理下不同基因型的农艺性状表现作为不同氮效率基因型的鉴别标准。N1处理下,氮高效基因型比氮中效和氮低效基因型产量高、单株角果数多、每角粒数多、株高高、主花序长、一级分枝数多、角果密度高、各部位干物质积累量高、籽粒中氮含量高、氮肥偏生产力、氮肥农学效率、氮肥生理利用率和氮收获指数均较高。【结论】沣油737是适合南方“油-稻-稻”三熟制的氮高效早熟冬油菜;正常施氮条件下,成熟期较多的单株角果数与每角粒数,以及较高株高是判断早熟冬油菜氮高效基因型的简易指标。

面向科技文献多维语义组织的混合倒排索引构建方法

[目的/意义]为满足科研人员对科技文献内部细粒度语义信息进行高效查询的迫切需求,前期研究提出了面向科技文献的多维语义索引体系,然而基于HashMap的常见倒排索引会导致查询效率低下。本文旨在通过面向不同维度语义特征建立混合倒排索引,以改进语义查询性能。[方法/过程]本文以Treap、B+树等多种数据结构探索适合不同语义维度的倒排索引构建方法,并将其组合形成多种适用于科技文献多维语义组织的混合倒排索引构建方法,并通过对比实验,在排序查询和布尔查询条件下分析验证不同类型倒排索引构建方法的查询性能。[结果/结论]实验结果表明,组合形成的8种混合倒排索引构建方法中,表2所示的C3(HHHB)被证明在排序查询条件下具有最高的效率,而C4(TTTB)则在布尔查询条件下被证明最为高效。本文的方法能有效解决单一索引结构导致的查询效率问题。

基于差分隐私的路网环境skyline查询

路网中的skyline查询在智慧交通、兴趣点发现和位置服务等领域具有重要的应用价值,但存在查询效率较低、未考虑查询结果的隐私性等问题。有鉴于此,文中提出了一种基于差分隐私的路网环境下skyline查询方法。首先,针对路网环境下的初始数据集数据量大和数据复杂的特点,对数据集进行预处理,利用基于距离属性划分的skyline层和路网Voronoi图的性质提出了3个剪枝规则,基于剪枝规则给出了路网环境下的数据集剪枝算法,从而有效地过滤掉大量冗余数据;其次,针对过滤后的数据集,利用网格索引的存储方式来节省存储空间,并设计了基于网格索引的skyline扩展树,基于扩展树和相应的剪枝规则提出了查询全局候选skyline点集的算法;最后,针对查询结果集,利用差分隐私预算分配模型来分配隐私预算,并基于信息散度进行结果集发布,有效提高了数据信息的隐私性。实验结果表明:所提出的查询方法的准确率在99%以上;其在数据集规模较大情况下的查询效率相较于传统skyline查询方法提升10%以上;在总差分隐私预算为0.01、0.10、0.50和1.00时,所提出的隐私预算分配方法的相对误差均低于等差分配和等比分配方法。

基于Sentinel-2的青铜峡灌区水稻和玉米种植分布早期识别

及时准确地掌握灌区内作物种植分布对于灌溉水资源高效配置、农田精准管理具有重要指导意义。以宁夏青铜峡灌区为研究对象,利用多时相Sentinel-2卫星数据,通过水稻和玉米早期特征分析,提取关键的“水淹”信号和“植被”信号,构建时序归一化差异水体指数(MNDWI)和归一化植被指数(NDVI)特征值数据集,并通过样本分析关键特征阈值,构建水稻和玉米早期种植分布决策树模型,提取2022年宁夏青铜峡灌区水稻和玉米种植的空间分布。结果表明(:1)玉米和水稻苗期的后半段5月15—31日,水淹信号和植被信号是区分二者关键时期。(2)基于早期作物物候特征的方法,在5月16—31日获取的水稻和玉米图像制图精度高于90%,用户精度超过91%,总体精度超过90%,Kappa系数高于0.88,明显高于同时期随机森林方法的分类精度。(3)本研究提出的方法在早期水稻和玉米种植分布提取方面具有较强的适用性,并且能够在时空尺度上以较少的实地样本进行延展,同时在时间上也更有优势。因此,该方法为青铜峡灌区水稻和玉米种植分布早期调查提供了重要的方法支撑。