CUMCM_2012_D_Chinese
2012年第五届数学中国数学建模网络挑战赛报名系统注册说明
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郜莉 陈蕊 曹敏 曹敏 秦彩玲 王俊英 崔茜 江华 张宏涛 王静 常呈霞 梁萍 白桂芬 常长海 闫莉鸽 张引弟 余阳 刘悦 张宏涛 王静 王静 卜佑娟 吴敏 杨瑞玲 杨瑞玲 郭臻臻 李西新 于阳晓 陈蕊 江华 杨洋 江华 王春香 王翠芳 李西新 刘悦 张引弟 耿延
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热动10-4 安管10-1 信管09-3 机设10-1 测绘09-6 电信10-1 国贸09-1 化学10-1 电气11-A1 机制09-2 地质10-3 地科11-2 生物10-2 水文09-2 信管09-3 电气09-1 自动化09-1 消防09-1 软件09-2 电气本11-4 材控09-3 工业09-01 电气09-3 材控10—1 工管10-1 财管11-3 建环10-1 会计10-1班 会计10-3班 财管09-1 化工10-3 国贸09-1 电气11-A1 电气11-A1 地质10-3 地质10-3 测绘城管10-2 地信09—1 采矿09-04 计算机11-1
吴敏 刘冰 李玲玲 吴敏 刘悦 刘悦 刘冰 支光辉 白桂芬 耿延 陈蕊 张引弟 张宏涛 余阳 张宏涛 耿延 杨瑞玲 刘庭华 刘冰 尹士花 刘悦 张引娣 刘冰 吴敏 张苏燕 王伟 王红英 张小雨 周长军 马闻生 曹月坤 张引弟 王静 刘悦 张宏涛 代倩 于阳晓 王俊英
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98 97.5 97.5 97 97 97 97 97 97 97 97 97 97 97 97 97 97 97 97 96.5 96.5 96.5 96.5 96.5 96.5 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 第 3 页
Projective Nonnegative Matrix Factorization for Image Compression and Feature Extraction
Projective Nonnegative Matrix Factorization for Image Compression and Feature ExtractionZhijian Yuan and Erkki OjaNeural Networks Research Centre,Helsinki University of Technology,P.O.Box5400,02015HUT,Finland{zhijian.yuan,erkki.oja}@hut.fiAbstract.In image compression and feature extraction,linear expan-sions are standardly used.It was recently pointed out by Lee and Seungthat the positivity or non-negativity of a linear expansion is a very power-ful constraint,that seems to lead to sparse representations for the images.Their technique,called Non-negative Matrix Factorization(NMF),wasshown to be a useful technique in approximating high dimensional datawhere the data are comprised of non-negative components.We proposehere a new variant of the NMF method for learning spatially localized,sparse,part-based subspace representations of visual patterns.The algo-rithm is based on positively constrained projections and is related bothto NMF and to the conventional SVD or PCA decomposition.Two it-erative positive projection algorithms are suggested,one based on mini-mizing Euclidean distance and the other on minimizing the divergence ofthe original data matrix and its non-negative approximation.Experimen-tal results show that P-NMF derives bases which are somewhat bettersuitable for a localized representation than NMF.1IntroductionFor compressing,denoising and feature extraction of digital image windows, one of the classical approaches is Principal Component Analysis(PCA)and its extensions and approximations such as the Discrete Cosine Transform.In PCA or the related Singular Value Decomposition(SVD),the image is projected on the eigenvectors of the image covariance matrix,each of which provides one linear feature.The representation of an image in this basis is distributed in the sense that typically all the features are used at least to some extent in the reconstruction.Another possibility is a sparse representation,in which any given image win-dow is spanned by just a small subset of the available features[1,2,6,10].This kind of representations have some biological significance,as the sparse features seem to correspond to the receptivefields of simple cells in the area V1of the mammalian visual cortex.This approach is related to the technique of Indepen-dent Component Analysis[3]which can be seen as a nongaussian extension of PCA and Factor Analysis.H.Kalviainen et al.(Eds.):SCIA2005,LNCS3540,pp.333–342,2005.c Springer-Verlag Berlin Heidelberg2005334Z.Yuan and E.OjaRecently,it was shown by Lee and Seung[4]that positivity or non-negativity of a linear expansion is a very powerful constraint that also seems to yield sparse representations.Their technique,called Non-negative Matrix Factoriza-tion(NMF),was shown to be a useful technique in approximating high di-mensional data where the data are comprised of non-negative components.The authors proposed the idea of using NMF techniques tofind a set of basis func-tions to represent image data where the basis functions enable the identification and classification of intrinsic“parts”that make up the object being imaged by multiple observations.NMF has been typically applied to image and text data [4,9],but has also been used to deconstruct music tones[8].NMF imposes the non-negativity constraints in learning the basis images. Both the values of the basis images and the coefficients for reconstruction are all non-negative.The additive property ensures that the components are combined to form a whole in the non-negative way,which has been shown to be the part-based representation of the original data.However,the additive parts learned by NMF are not necessarily localized.In this paper,we start from the ideas of SVD and NMF and propose a novel method which we call Projective Non-negative Matrix Factorization(P-NMF), for learning spatially localized,parts-based representations of visual patterns. First,in Section2,we take a look at a simple way to produce a positive SVD by truncating away negative parts.Section3briefly reviews Lee’s and Seung’s ing this as a baseline,we present our P-NMF method in Section4. Section5gives some experiments and comparisons,and Section6concludes the paper.2Truncated Singular Value DecompositionSuppose that our data1is given in the form of an m×n matrix V.Its n columns are the data items,for example,a set of images that have been vectorized by row-by-row scanning.Then m is the number of pixels in any given image.Typically, n>m.The Singular Value Decomposition(SVD)for matrix V isV=UDˆU T,(1) where U(m×m)andˆU(n×m)are orthogonal matrices consisting of the eigenvectors of VV T and V T V,respectively,and D is a diagonal m×m matrix where the diagonal elements are the ordered singular values of V.Choosing the r largest singular values of matrix V to form a new diagonal r×r matrixˆD,with r<m,we get the compressive SVD matrix X with given rank r,X=UˆDˆU T.(2)1For clarity,we use here the same notation as in the original NMF theory by Lee and SeungProjective Nonnegative Matrix Factorization335 Now both matrices U andˆU have only r columns corresponding to the r largest eigenvalues.The compressive SVD gives the best approximation X of the matrix V with the given compressive rank r.In many real-world cases,for example,for images,spectra etc.,the original data matrix V is non-negative.Then the above compressive SVD matrix X fails to keep the nonnegative property.In order to further approximate it by a non-negative matrix,the following truncated SVD(tSVD)is suggested.We simply truncate away the negative elements byˆX=12(X+abs(X)).(3)However,it turns out that typically the matrixˆX in(3)has higher rank than X. Truncation destroys the linear dependences that are the reason for the low rank. In order to get an equal rank,we have to start from a compressive SVD matrix X with lower rank than the given r.Therefore,tofind the truncated matrix ˆX with the compressive rank r,we search all the compressive SVD matrices X with the rank from1to r and form the corresponding truncated matrices.The one with the largest rank that is less than or equal to the given rank r is the truncated matrixˆX what we choose as thefinal non-negative approximation. This matrix can be used as a baseline in comparisons,and also as a starting point in iterative improvements.We call this method truncated SVD(t-SVD).Note that the tSVD only produces the non-negative low-rank approximation ˆX to the data matrix V,but does not give a separable expansion for basis vectors and weights as the usual SVD expansion.3Non-negative Matrix FactorizationGiven the nonnegative m×n matrix V and the constant r,the Nonnegative Matrix Factorization algorithm(NMF)[4]finds a nonnegative m×r matrix W and another nonnegative r×n matrix H such that they minimize the following optimality problem:minW,H≥0||V−WH||.(4) This can be interpreted as follows:each column of matrix W contains a basis vector while each column of H contains the weights needed to approximate the corresponding column in V using the basis from W.So the product WH can be regarded as a compressed form of the data in V.The rank r is usually chosen so that(n+m)r<nm.In order to estimate the factorization matrices,an objective function defined by the authors as Kullback-Leibler divergence isF=mi=1nµ=1[V iµlog(WH)iµ−(WH)iµ].(5)This objective function can be related to the likelihood of generating the images in V from the basis W and encodings H.An iterative approach to336Z.Yuan and E.Ojareach a local maximum of this objective function is given by the following rules[4,5]:W ia←W iaµV iµ(WH)iµH aµ,W ia←W iajW ja(6)H aµ←H aµi W iaV iµ(WH)iµ.(7)The convergence of the process is ensured2.The initialization is performed using positive random initial conditions for matrices W and H.4The Projective NMF Method4.1Definition of the ProblemThe compressive SVD is a projection method.It projects the data matrix V onto the subspace of the eigenvectors of the data covariance matrix.Although the truncated method t-SVD outlined above works and keeps nonnegativity, it is not accurate enough for most cases.To improve it,for the given m×n nonnegative matrix V,m<n,let us try tofind a subspace B of R m,and an m×m projection matrix P with given rank r such that P projects the nonnegative matrix V onto the subspace B and keeps the nonnegative property, that is,PV is a nonnegative matrix.Finally,it should minimize the difference ||V−PV||.This is the basic idea of the Projective NMF method.We can write any symmetrical projection matrix of rank r in the formP=WW T(8) with W an orthogonal(m×r)matrix3.Thus,we can solve the problem by searching for a nonnegative(m×r)matrix W.Based on this,we now introduce a novel method which we call Projective Non-negative Matrix Factorization(P-NMF)as the solution to the following optimality problemminW≥0||V−WW T V||,(9)where||·||is a matrix norm.The most useful norms are the Euclidean dis-tance and the divergence of matrix A from B,defined as follows:The Euclidean distance between two matrices A and B is2The matlab program for the above update rules is available at under the”Computational Neuroscience”discussion category.3This is just notation for a generic basis matrix;the solution will not be the same as the W matrix in NMF.Projective Nonnegative Matrix Factorization337||A−B||2=i,j(A ij−B ij)2,(10) and the divergence of A from BD(A||B)=i,j (A ij logA ijB ij−A ij+B ij).(11)Both are lower bounded by zero,and vanish if and only if A=B.4.2AlgorithmsWefirst consider the Euclidean distance(10).Define the functionF=12||V−WW T V||2.(12)Then the unconstrained gradient of F for W,∂F∂w ij,is given by∂F∂w ij=−2(VV T W)ij+(WWT VV T W)ij+(VVT WW T W)ij.(13)Using the gradient we can construct the additive update rule for minimization,W ij←W ij−ηij∂F∂w ij(14)whereηij is the positive step size.However,there is nothing to guarantee that the elements W ij would stay non-negative.In order to ensure this,we choose the step size as follows,ηij=W ij(WW T VV T W)ij+(VV T WW T W)ij.(15)Then the additive update rule(14)can be formulated as a multiplicative update rule,W ij←W ij(VV T W)ij(WW T VV T W)ij+(VV T WW T W)ij.(16)Now it is guaranteed that the W ij will stay nonnegative,as everything on the right-hand side is nonnegative.For the divergence measure(11),we follow the same process.First we calcu-late the gradient∂D(V||WW T V)∂w ij=k(W T V)jk+lW lj V ik(17)−kV ik(W T V)jk/(WW T V)ik(18)−k V iklW lj V lk/(WW T V)lk.(19)338Z.Yuan and E.OjaUsing the gradient,the additive update rule becomesW ij ←W ij +ζij ∂D (V ||WW T V )∂w ij(20)where ζij is the step size.Choosing this step size as following,ζij =W ij k V ik [(W T V )jk /(WW T V )ik + l W lj V lk /(WW T V )lk ].(21)we obtain the multiplicative update ruleW ij ←W ij k (W T V )jk + l W lj V ik k V ik ((W T V )jk /(WW T V )ik + l W lj V lk /(WW T V )lk ).(22)It is easy to see that both multiplicative update rules (16)and (22)can ensure that the matrix W is non-negative.4.3The Relationship Between NMF and P-NMFThere is a very obvious relationship between our P-NMF algorithms and the original paring the two optimality problems,P-NMF (9)and the original NMF (4),we see that the weight matrix H in NMF is simply replaced by W T V in our algorithms.Both multiplicative update rules (16)and (22)are obtained similar to Lee and Seung’s algorithms [5].Therefore,the convergence of these two algorithms can also be proved following Lee and Seung [5]by noticing that the coefficient matrix H is replaced by WV .4.4The Relationship Between SVD and P-NMFThere is also a relationship between the P-NMF algorithm and the SVD.For the Euclidean norm,note the similarity of the problem (9)with the conventional PCA for the columns of V .Removing the positivity constraint,this would be-come the usual finite-sample PCA problem,whose solution is known to be an orthogonal matrix consisting of the eigenvectors of VV T .But this is the matrix U in the SVD of eq.(1).However,now with the positivity constraint in place,the solution will be something quite different.5Simulations 5.1Data PreparationAs experimental data,we used face images from the MIT-CBCL database and derived the NMF and P-NMF expansions for them.The training data set con-tains 2429faces.Each face has 19×19=361pixels and has been histogram-equalized and normalized so that all pixel values are between 0and 1.ThusProjective Nonnegative Matrix Factorization339 the data matrix V which now has the faces as columns is361×2429.This matrix was compressed to rank r=49using either t-SVD,NMF,or P-NMF expansions.5.2Learning Basis ComponentsThe basis images of tSVD,NMF,and P-NMF with dimension49are shown in Figure1.For NMF and P-NMF,these are the49columns of the corresponding matrices W.For t-SVD,we show the49basis vectors of the range space of the rank-49nonnegative matrixˆX,obtained by ordinary SVD of this matrix.Thus the basis images for NMF and P-NMF are truly non-negative,while the t-SVD only produces a non-negative overall approximation to the data but does not give a separable expansion for basis vectors and weights.All the images are displayed with the matlab command”imagesc”without any extra scale.Both NMF and P-NMF bases are holistic for the training set. For this problem,the P-NMF algorithm converges about5times faster than NMF.Fig.1.NMF(top,left),t-SVD(bottom,left)and the two versions of the new P-NMF method(right)bases of dimension49.Each basis component consists of19×19pixels340Z.Yuan and E.OjaFig.2.The original face image(left)and its reconstructions by NMF(top row),the two versions of the new P-NMF method under100iterative steps(second and third rows),and t-SVD(bottom row).The dimensions in columns2,3,and4are25,49and 81,respectively5.3Reconstruction AccuracyWe repeated the above computations for ranks r=25,49and81.Figure2 shows the reconstructions for one of the face images in the t-SVD,NMF,and P-NMF subspaces of corresponding dimensions.For comparison,also the original face image is shown.As the dimension increases,more details are recovered. Visually,the P-NMF method is comparable to NMF.The recognition accuracy,defined as the Euclidean distance between the orig-inal data matrix and the recognition matrix,can be used to measure the perfor-mance quantitatively.Figure3shows the recognition accuracy curves of P-NMF and NMF under different iterative steps.NMF converges faster,but when the number of steps increases,P-NMF works very similarly to NMF.One thing to be noticed is that the accuracy of P-NMF depends on the initial values.Al-though the number of iteration steps is larger in P-NMF for comparable error with NMF,this is compensated by the fact that the computational complexity for one iteration step is considerably lower for P-NMF,as only one matrix has to be updated instead of two.Projective Nonnegative Matrix Factorization341Fig.3.Recognition accuracies(unit:108)versus iterative steps using t-SVD,NMF and P-NMF with compressive dimension496ConclusionWe proposed a new variant of the well-known Non-negative Matrix Factorization (NMF)method for learning spatially localized,sparse,part-based subspace rep-resentations of visual patterns.The algorithm is based on positively constrained projections and is related both to NMF and to the conventional SVD decompo-sition.Two iterative positive projection algorithms were suggested,one based on minimizing Euclidean distance and the other on minimizing the divergence of the original data matrix and its pared to the NMF method, the iterations are somewhat simpler as only one matrix is updated instead of two as in NMF.The tradeoffis that the convergence,counted in iteration steps, is slower than in NMF.One purpose of these approaches is to learn localized features which would be suitable not only for image compression,but also for object recognition. Experimental results show that P-NMF derives bases which are better suitable for a localized representation than NMF.It remains to be seen whether they would be better in pattern recognition,too.342Z.Yuan and E.OjaReferences1. A.Bell and T.Sejnowski.The”independent components”of images are edgefilters.Vision Research,37:3327–3338,1997.2. A.Hyv¨a rinen and P.Hoyer.Emergence of phase and shift invariant features bydecomposition of natural images into independent feature subspaces.Neural Com-putation,13:1527–1558,2001.3. A.Hyv¨a rinen,J.Karhunen,and E.Oja.Independent Component Analysis.Wiley,New York,2001.4. D.D.Lee and H.S.Seung.Learning the parts of objects by non-negative matrixfactorization.Nature,401:788–791,1999.5. D.D.Lee and H.S.Seung.Algorithms for non-negative matrix factorization.InNIPS,pages556–562,2000.6. B.A.Olshausen and D.J.Field.Natural image statistics and efficient coding.Network,7:333–339,1996.7.P.Paatero and U.Tapper.Positive Matrix Factorization:A non-negative factormodel with optimal utilization of error estimations of data values.Environmetrics, 5,111-126,1997.8.T.Kawamoto,K.Hotta,T.Mishima,J.Fujiki,M.Tanaka and T.Kurita.Esti-mation of single tones from chord sounds using non-negative matrix factorization.Neural Network World,3,429-436,July2000.9.L.K.Saul and D.D.Lee.Multiplicative updates for classification by mixture mod-ela.In Advances in Neural Information Processing Systems14,2002.10.J.H.van Hateren and A.van der Schaaf.Independent componentfilters of natu-ral images compared with simple cells in primary visual cortex.Proc.Royal Soc.London B,265:2315–2320,1998.。
【2012高教社杯全国大学生数学建模竞赛赛题B】cumcm2012B附件7_小屋的建筑要求
全国大学生数学建模竞赛真题试卷复习材料附件7：小屋的建筑要求
限定小屋使用空间高度为：建筑屋顶最高点距地面高度≤5.4m, 室内使用空间最低净空高度距地面高度为≥2.8m；建筑总投影面积（包括挑檐、挑雨棚的投影面积）为≤74m2；建筑平面体型长边应≤15m，最短边应≥3m；建筑采光要求至少应满足窗地比（开窗面积与房间地板面积的比值，可不分朝向）≥0.2的要求；建筑节能要求应满足窗墙比（开窗面积与所在朝向墙面积的比值）南墙≤0.50、东西墙≤0.35、北墙≤0.30。
建筑设计朝向可以根据需要设计，允许偏离正南朝向。
2012年全国大学生数学建模竞赛西安交通大学获奖学生、指导教师名单
刘玉琪
3
计算机01
10055013
雷力明
计算机01
10055008
陈世阳
信计01
10092002
郭巧真
4
应数01
10091018
毛磊
电气03
10041075
李高扬
电气03
10041077
林岑
5
信息03
10052060
蔡勇新
机械（硕）01
10014016
张朝辉
信息93
09052054
陈曦
10041215
赵谡
电气07
10041193
陈志威
电气07
10041187
霍小晶
13
省级一等奖
电气13
2110401093
朱明良
电气13
2110401082
王春林
电气13
2110401068
陈铉
14
计算机03
10055068
刘阳
计算机03
10055075
徐彪根
计算机04
10055090
洪耀
15
能动04
10041024
武康宁
电气03
10041068
朱兆芳
34
物理试验班01
10099008
郭华世
数学试验班01
10098014
魏鑫
数学试验班01
10098020
朱炎文
35
医电11
2111201019
王安宇
能动15
2111201065
魏晓阳
物理试验班11
2110909003
12级名单(正稿)
经济学 3115003063 经济学 3115003068 经济学 3115003069 经济学 3115003081 经济学 3115003092 经济学 3115003094 经济学 3115003106 经济学 3115003111 经济学 3115003120 经济学 3115002001 经济学 3115002009 经济学 3115002019 经济学 3115004019 经济学 3115004011 经济学 3115004016 经济学 3115004017 经济学 3115004022 经济学 3115004025 经济学 3115004036 经济学 3115004038 经济学 3115004046 经济学 3115101002 经济学 3115101054 经济学 3115106014 经济学 3115104001 经济学 3115104007 经济学 3115102053 经济学 3115207035 经济学 3115201034 经济学 3115204052 经济学 3115204066 经济学 3115207016 经济学 3115207045 经济学 3115207049 经济学 3115207037 经济学 3115201019 经济学 3115201067 经济学 3115201073 经济学 3115201087 经济学 3115501030 经济学 3115501040 经济学 3115505052
经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 经济管理与旅游学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 人文学院 作物学院 作物学院
ProblemSet-Fzu2010-Online-Contest
Problem A
A math problem
Time Limit: 1 second Memory Limit: 32 megabytes
Problem C
How many stars
Time Limit: 3 seconds Memory Limit: 32 megabytes
John has a telescope and he always observes the stars. After each observation, John draws all stars on a paper. To simplify the problem, each star is described as a distinct point in two-dimensional space and no three points are in a line. There are N stars on the paper. John is so boring that he wants to find some ways to kill the time. He chooses three different points random to form a triangle, and then he wants to know the number of points inside this triangle. There is only one point inside the triangle in Figure 1.
2012年美国大学生数学建模竞赛通知(MCM
2012年美国大学生数学建模竞赛（MCM/ICM）参赛通知The Interdisciplinary Contest in Modeling (ICM),an international contest for high school students and college undergraduates, will hold its 14th annual competition in February 2012. Last year,735 teams from 150 institutions in four countries participated in the contest. ICM is designed to develop and advance interdisciplinary problem-solving skills as well as competence in written communication. The interdisciplinary problem will focus on a network science issue第14届国际数学建模竞赛（ICM）将于2012年2 月举行，该赛事是面向大学生和高中生的国际性赛事！去年，来自4个国家的150个院校共735个团队参加了竞赛。
ICM 旨在发展并提升学生的解决交叉学科问题的能力和写作论文的能力。
交叉学科问题主要以网络科学为主题！Your institution may take part in the ICM effort by encouraging a member of your department to serve as a faculty advisor and by promoting the participation of faculty and students from associated departments. Advisors help organize the teams,distribute contest materials, and return solution papers to COMAP.!各院校可以设定一个负责人作来组织学校的学生参加建模比赛，负责人主要起动员和组织学校的学生参加此项赛事，同时负责人帮助学生组队，分发竞赛资料及给竞赛官方发送比赛论文！Please take a moment to read this Contest Overview, thengo to for more information about registration, deadlines, and contest rules. All registration will take place online.If you have any questions please contact icm@.请先阅读本竞赛概要，有关登记注册，截止日，竞赛规则等信息可登We hope that you and your team(s) will enjoy this modeling challenge.Best Wishes,Chris Arney, Contest Director希望你和你们团队都能享受本次大赛带来的挑战。
2012全国数学建模竞赛A题附件2指标总表
样品编号氨基酸总量天门冬氨酸苏氨酸丝氨酸谷氨酸Glu(mg/100gSer(mg/100gThr(mg/100g红葡萄mg/100gfw Asp(mg/100g葡萄样品12027.96101.22393.4277.61266.60葡萄样品22128.8264.43140.6271.9439.26葡萄样品38397.28108.07222.35173.0867.54葡萄样品42144.6879.39133.83158.74156.72葡萄样品51844.0052.28145.09164.05102.43葡萄样品63434.1768.01102.4275.7880.60葡萄样品72391.1665.10267.76239.20208.97葡萄样品81950.7672.09345.8744.23176.02葡萄样品92262.7272.89113.94110.61110.53葡萄样品101364.1487.52114.29130.87126.71葡萄样品112355.6994.42111.67141.58186.52葡萄样品122556.7963.3271.6869.3547.89葡萄样品131416.1154.30110.6380.6572.32葡萄样品141237.8171.2756.41104.5064.28葡萄样品152177.9185.20223.12226.60172.69葡萄样品161553.5073.34110.72110.49112.05葡萄样品171713.65107.1995.6189.15100.97葡萄样品182398.3832.4571.4952.0638.22葡萄样品192463.6072.9496.1391.1168.70葡萄样品202273.6373.49157.32131.45177.91葡萄样品216346.8369.49180.03194.11107.73葡萄样品222566.61110.52207.26251.11237.70葡萄样品232380.81120.86138.15159.47131.54葡萄样品241638.8358.60160.81148.5859.23葡萄样品251409.7073.28130.81115.85150.57葡萄样品26851.1759.0095.6674.4747.83葡萄样品271116.6151.45132.5579.0648.36白葡萄氨基酸总量天门冬氨酸苏氨酸丝氨酸谷氨酸Glu(mg/100gSer(mg/100gmg/100gfw Asp(mg/100gThr(mg/100g葡萄样品11279.3062.31134.0777.1659.41葡萄样品21870.9371.87130.62149.98160.72葡萄样品35022.14121.28208.82286.88210.28葡萄样品42085.7689.06181.14186.92133.66葡萄样品52658.0437.24232.84158.2399.72葡萄样品61847.1283.99181.76181.56131.73葡萄样品71721.5861.08137.61134.50137.41葡萄样品81273.2266.58101.3396.3054.11葡萄样品91927.4265.89171.92156.4963.91葡萄样品102095.61120.88204.85141.15212.53葡萄样品111566.9751.4090.5084.3566.55葡萄样品121724.1697.1797.0183.8964.37葡萄样品13664.9657.6196.7632.9829.91葡萄样品141542.1773.92146.12140.7370.85葡萄样品152669.2254.43157.9797.8051.62葡萄样品16991.9248.13139.2959.7644.14葡萄样品171167.2974.69108.03124.6476.21葡萄样品181289.9362.9676.6784.2296.05葡萄样品19817.8157.09126.2258.8851.03葡萄样品202045.2475.08160.63161.0484.96葡萄样品211554.0284.50130.07133.52112.67葡萄样品221457.6767.78122.97161.6168.22葡萄样品231522.5268.04114.82153.2776.21葡萄样品243068.34129.69167.09274.40395.73葡萄样品252350.7953.17264.29162.64109.02葡萄样品262073.3362.26143.85176.2062.54葡萄样品272475.2198.35194.81186.03181.41葡萄样品283785.5796.41158.71403.13273.55表中：蓝色为一级指标，红色为二级指标;一个项目下有几列数据，表脯氨酸甘氨酸丙氨酸胱氨酸缬氨酸蛋氨酸异亮氨酸亮氨酸Ile(mg/100gLeumg/100gfVal(mg/100gMet(mg/100gCyr(mg/100gGly(mg/100gPro(mg/100gAla(mg/100g723.88177.3789.2824.8315.7417.14 6.5810.86 1560.9732.3811.1324.1120.69 4.609.4222.82 7472.2855.7975.3413.1819.607.847.8218.17 1182.2393.2389.3646.7021.94 6.5515.7920.75816.0886.8369.5418.6433.6716.4630.4821.69 2932.7618.0119.3923.1712.63 2.6510.527.901096.2874.0689.5618.1946.9811.3618.0333.84962.01150.7342.63 6.4320.849.8016.2219.38 1334.1995.1842.807.0733.4915.1524.8226.61477.5088.1460.50 5.7525.31 4.0010.5620.05 1150.09158.3683.1633.2123.937.1713.4418.07 2127.9136.8413.9817.0619.04 3.9616.0514.60621.2541.2528.3916.7637.6715.1121.5036.36677.7839.0951.4322.2315.73 1.7012.6711.66817.5796.02100.8634.2235.0810.7223.9141.26679.2546.9540.0827.0823.88 3.9714.3119.17806.5662.4244.8526.3832.11 5.4920.1428.52 2097.6124.3110.0111.4210.85 1.59 4.62 4.441438.08165.6668.0532.0741.57 4.448.7630.10754.899.89100.7012.6242.0417.1729.9644.74 5144.8162.2216.3653.6441.4613.6616.1234.28863.99197.3270.8643.7758.8011.8666.9366.98 1341.12106.7178.8735.5142.2711.4018.6135.54797.5588.5679.4034.0730.32 6.1911.228.60479.1788.0357.0241.9726.15 4.3815.9420.85147.7022.8128.7214.9419.49 4.3018.4223.33418.0129.2528.589.9628.74 5.4010.4732.75脯氨酸甘氨酸丙氨酸胱氨酸缬氨酸蛋氨酸异亮氨酸亮氨酸Met(mg/100gLeumg/100gfVal(mg/100gIle(mg/100gCyr(mg/100gGly(mg/100gPro(mg/100gAla(mg/100g589.1950.5415.20 5.2227.070.1510.9520.74742.05150.7470.1030.7444.79 2.8112.0019.06 3217.96143.1676.4816.7254.36 6.0620.1740.11678.0177.1159.589.3948.17 6.5723.2640.73 1559.1762.79110.2013.2651.49 5.6222.8832.78687.2674.9683.8031.4046.79 6.5726.7640.95701.8043.3349.2717.5730.29 2.538.3324.44399.4340.5742.1912.3846.00 4.8320.4735.36800.5959.5393.288.7339.03 6.6415.8025.52823.18106.4975.2916.0120.57 6.3825.609.79702.8826.2656.07 3.9935.23 4.1417.0225.44600.1438.0932.6511.9032.85 4.2515.7327.09238.50 6.8757.6511.3312.04 2.69 5.869.39586.7158.5971.7134.8331.05 3.6215.8930.651767.2742.2551.4138.6530.05 2.5617.1228.75380.3632.5617.44 6.3223.01 2.609.6213.17387.6545.4429.2810.5938.748.3920.5332.04551.6433.8335.4516.7516.05 2.147.8615.28287.2321.259.119.2122.88 2.5512.3525.10995.1256.5394.4316.9634.84 6.7925.8443.19638.2052.6877.927.5735.67 5.0714.6117.81538.1545.8635.1720.5237.51 3.6622.5232.01702.9460.3131.1227.1433.84 2.9113.8219.03812.46214.87263.1127.9062.80 4.5230.3663.881020.1864.5058.4832.0862.27 4.7421.5032.761259.7648.1546.0517.6843.13 4.1523.2435.01891.09127.74170.907.2169.498.5324.7859.432006.25152.42120.2016.5460.04 4.4815.9837.34一个项目下有几列数据，表示该项目测试几次。
数据挖掘与python实践_中央财经大学中国大学mooc课后章节答案期末考试题库2023年
数据挖掘与python实践_中央财经大学中国大学mooc课后章节答案期末考试题库2023年1.数据挖掘又称从数据中发现知识，后者英文简称为（）。
答案:KDD2.数据挖掘又称从数据中发现知识，前者英文简称为（）。
答案:DM3.一般数据挖掘的流程顺序，下列正确的是（）。
①选择数据挖掘的技术、功能和合适的算法②选择数据，数据清洗和预处理③了解应用领域，了解相关的知识和应用目标④寻找感兴趣的模式、模式评估、知识表示⑤创建目标数据集答案:③⑤②①④4.结构化的数据是指一些数据通过统一的（）的形式存储的，这类数据我们称为结构化的数据。
答案:二维表格5.数值预测用于连续变量的取值，常用的预测方法是（）。
答案:回归分析6.以下Python包中，绘图功能最强大的是（）。
答案:matplotlib7.以下Python包中，最适合用于机器学习的是（）。
答案:scikit-learn8.以下Python包中，提供了DataFrame数据类型的是（）。
答案:pandas9.下列关于数据规范化说法错误的是（）。
答案:数据规范化是为了给重要的属性赋予更大的权重10.使用python处理缺失值的方法中叙述错误的是（）。
答案:interpolate()使用中位数填充缺失值11.主成分分析方法PCA属于属于python中的哪个包（）。
答案:sklearn12.在numpy包中，计算中位数的函数为（）。
答案:numpy.median()13.运行以下代码“import matplotlib.pyplot as plt”引入plt后，要绘制直方图，需要利用的函数为（）。
答案:plt.hist()14.使用最小-最大法进行数据规范化，需要映射的目标区间为[0,100]，原来的取值范围是[-10,10]。
根据等比映射的原理，一个值8映射到新区间后的值是（）。
答案:9015.利用tree.DecisionTreeClassifier()训练模型时调用.fit()方法需要传递的第一个参数是（）。
黄河水院参加2012年全国大学生数学建模竞赛成绩优异
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ACM-GIS%202006-A%20Peer-to-Peer%20Spatial%20Cloaking%20Algorithm%20for%20Anonymous%20Location-based%
A Peer-to-Peer Spatial Cloaking Algorithm for AnonymousLocation-based Services∗Chi-Yin Chow Department of Computer Science and Engineering University of Minnesota Minneapolis,MN cchow@ Mohamed F.MokbelDepartment of ComputerScience and EngineeringUniversity of MinnesotaMinneapolis,MNmokbel@Xuan LiuIBM Thomas J.WatsonResearch CenterHawthorne,NYxuanliu@ABSTRACTThis paper tackles a major privacy threat in current location-based services where users have to report their ex-act locations to the database server in order to obtain their desired services.For example,a mobile user asking about her nearest restaurant has to report her exact location.With untrusted service providers,reporting private location in-formation may lead to several privacy threats.In this pa-per,we present a peer-to-peer(P2P)spatial cloaking algo-rithm in which mobile and stationary users can entertain location-based services without revealing their exact loca-tion information.The main idea is that before requesting any location-based service,the mobile user will form a group from her peers via single-hop communication and/or multi-hop routing.Then,the spatial cloaked area is computed as the region that covers the entire group of peers.Two modes of operations are supported within the proposed P2P spa-tial cloaking algorithm,namely,the on-demand mode and the proactive mode.Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode,but the on-demand incurs longer response time.Categories and Subject Descriptors:H.2.8[Database Applications]:Spatial databases and GISGeneral Terms:Algorithms and Experimentation. Keywords:Mobile computing,location-based services,lo-cation privacy and spatial cloaking.1.INTRODUCTIONThe emergence of state-of-the-art location-detection de-vices,e.g.,cellular phones,global positioning system(GPS) devices,and radio-frequency identification(RFID)chips re-sults in a location-dependent information access paradigm,∗This work is supported in part by the Grants-in-Aid of Re-search,Artistry,and Scholarship,University of Minnesota. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on thefirst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.ACM-GIS’06,November10-11,2006,Arlington,Virginia,USA. Copyright2006ACM1-59593-529-0/06/0011...$5.00.known as location-based services(LBS)[30].In LBS,mobile users have the ability to issue location-based queries to the location-based database server.Examples of such queries include“where is my nearest gas station”,“what are the restaurants within one mile of my location”,and“what is the traffic condition within ten minutes of my route”.To get the precise answer of these queries,the user has to pro-vide her exact location information to the database server. With untrustworthy servers,adversaries may access sensi-tive information about specific individuals based on their location information and issued queries.For example,an adversary may check a user’s habit and interest by knowing the places she visits and the time of each visit,or someone can track the locations of his ex-friends.In fact,in many cases,GPS devices have been used in stalking personal lo-cations[12,39].To tackle this major privacy concern,three centralized privacy-preserving frameworks are proposed for LBS[13,14,31],in which a trusted third party is used as a middleware to blur user locations into spatial regions to achieve k-anonymity,i.e.,a user is indistinguishable among other k−1users.The centralized privacy-preserving frame-work possesses the following shortcomings:1)The central-ized trusted third party could be the system bottleneck or single point of failure.2)Since the centralized third party has the complete knowledge of the location information and queries of all users,it may pose a serious privacy threat when the third party is attacked by adversaries.In this paper,we propose a peer-to-peer(P2P)spatial cloaking algorithm.Mobile users adopting the P2P spatial cloaking algorithm can protect their privacy without seeking help from any centralized third party.Other than the short-comings of the centralized approach,our work is also moti-vated by the following facts:1)The computation power and storage capacity of most mobile devices have been improv-ing at a fast pace.2)P2P communication technologies,such as IEEE802.11and Bluetooth,have been widely deployed.3)Many new applications based on P2P information shar-ing have rapidly taken shape,e.g.,cooperative information access[9,32]and P2P spatio-temporal query processing[20, 24].Figure1gives an illustrative example of P2P spatial cloak-ing.The mobile user A wants tofind her nearest gas station while beingfive anonymous,i.e.,the user is indistinguish-able amongfive users.Thus,the mobile user A has to look around andfind other four peers to collaborate as a group. In this example,the four peers are B,C,D,and E.Then, the mobile user A cloaks her exact location into a spatialA B CDEBase Stationregion that covers the entire group of mobile users A ,B ,C ,D ,and E .The mobile user A randomly selects one of the mobile users within the group as an agent .In the ex-ample given in Figure 1,the mobile user D is selected as an agent.Then,the mobile user A sends her query (i.e.,what is the nearest gas station)along with her cloaked spa-tial region to the agent.The agent forwards the query to the location-based database server through a base station.Since the location-based database server processes the query based on the cloaked spatial region,it can only give a list of candidate answers that includes the actual answers and some false positives.After the agent receives the candidate answers,it forwards the candidate answers to the mobile user A .Finally,the mobile user A gets the actual answer by filtering out all the false positives.The proposed P2P spatial cloaking algorithm can operate in two modes:on-demand and proactive .In the on-demand mode,mobile clients execute the cloaking algorithm when they need to access information from the location-based database server.On the other side,in the proactive mode,mobile clients periodically look around to find the desired number of peers.Thus,they can cloak their exact locations into spatial regions whenever they want to retrieve informa-tion from the location-based database server.In general,the contributions of this paper can be summarized as follows:1.We introduce a distributed system architecture for pro-viding anonymous location-based services (LBS)for mobile users.2.We propose the first P2P spatial cloaking algorithm for mobile users to entertain high quality location-based services without compromising their privacy.3.We provide experimental evidence that our proposed algorithm is efficient in terms of the response time,is scalable to large numbers of mobile clients,and is effective as it provides high-quality services for mobile clients without the need of exact location information.The rest of this paper is organized as follows.Section 2highlights the related work.The system model of the P2P spatial cloaking algorithm is presented in Section 3.The P2P spatial cloaking algorithm is described in Section 4.Section 5discusses the integration of the P2P spatial cloak-ing algorithm with privacy-aware location-based database servers.Section 6depicts the experimental evaluation of the P2P spatial cloaking algorithm.Finally,Section 7con-cludes this paper.2.RELATED WORKThe k -anonymity model [37,38]has been widely used in maintaining privacy in databases [5,26,27,28].The main idea is to have each tuple in the table as k -anonymous,i.e.,indistinguishable among other k −1tuples.Although we aim for the similar k -anonymity model for the P2P spatial cloaking algorithm,none of these techniques can be applied to protect user privacy for LBS,mainly for the following four reasons:1)These techniques preserve the privacy of the stored data.In our model,we aim not to store the data at all.Instead,we store perturbed versions of the data.Thus,data privacy is managed before storing the data.2)These approaches protect the data not the queries.In anonymous LBS,we aim to protect the user who issues the query to the location-based database server.For example,a mobile user who wants to ask about her nearest gas station needs to pro-tect her location while the location information of the gas station is not protected.3)These approaches guarantee the k -anonymity for a snapshot of the database.In LBS,the user location is continuously changing.Such dynamic be-havior calls for continuous maintenance of the k -anonymity model.(4)These approaches assume a unified k -anonymity requirement for all the stored records.In our P2P spatial cloaking algorithm,k -anonymity is a user-specified privacy requirement which may have a different value for each user.Motivated by the privacy threats of location-detection de-vices [1,4,6,40],several research efforts are dedicated to protect the locations of mobile users (e.g.,false dummies [23],landmark objects [18],and location perturbation [10,13,14]).The most closed approaches to ours are two centralized spatial cloaking algorithms,namely,the spatio-temporal cloaking [14]and the CliqueCloak algorithm [13],and one decentralized privacy-preserving algorithm [23].The spatio-temporal cloaking algorithm [14]assumes that all users have the same k -anonymity requirements.Furthermore,it lacks the scalability because it deals with each single request of each user individually.The CliqueCloak algorithm [13]as-sumes a different k -anonymity requirement for each user.However,since it has large computation overhead,it is lim-ited to a small k -anonymity requirement,i.e.,k is from 5to 10.A decentralized privacy-preserving algorithm is proposed for LBS [23].The main idea is that the mobile client sends a set of false locations,called dummies ,along with its true location to the location-based database server.However,the disadvantages of using dummies are threefold.First,the user has to generate realistic dummies to pre-vent the adversary from guessing its true location.Second,the location-based database server wastes a lot of resources to process the dummies.Finally,the adversary may esti-mate the user location by using cellular positioning tech-niques [34],e.g.,the time-of-arrival (TOA),the time differ-ence of arrival (TDOA)and the direction of arrival (DOA).Although several existing distributed group formation al-gorithms can be used to find peers in a mobile environment,they are not designed for privacy preserving in LBS.Some algorithms are limited to only finding the neighboring peers,e.g.,lowest-ID [11],largest-connectivity (degree)[33]and mobility-based clustering algorithms [2,25].When a mo-bile user with a strict privacy requirement,i.e.,the value of k −1is larger than the number of neighboring peers,it has to enlist other peers for help via multi-hop routing.Other algorithms do not have this limitation,but they are designed for grouping stable mobile clients together to facil-Location-based Database ServerDatabase ServerDatabase ServerFigure 2:The system architectureitate efficient data replica allocation,e.g.,dynamic connec-tivity based group algorithm [16]and mobility-based clus-tering algorithm,called DRAM [19].Our work is different from these approaches in that we propose a P2P spatial cloaking algorithm that is dedicated for mobile users to dis-cover other k −1peers via single-hop communication and/or via multi-hop routing,in order to preserve user privacy in LBS.3.SYSTEM MODELFigure 2depicts the system architecture for the pro-posed P2P spatial cloaking algorithm which contains two main components:mobile clients and location-based data-base server .Each mobile client has its own privacy profile that specifies its desired level of privacy.A privacy profile includes two parameters,k and A min ,k indicates that the user wants to be k -anonymous,i.e.,indistinguishable among k users,while A min specifies the minimum resolution of the cloaked spatial region.The larger the value of k and A min ,the more strict privacy requirements a user needs.Mobile users have the ability to change their privacy profile at any time.Our employed privacy profile matches the privacy re-quirements of mobiles users as depicted by several social science studies (e.g.,see [4,15,17,22,29]).In this architecture,each mobile user is equipped with two wireless network interface cards;one of them is dedicated to communicate with the location-based database server through the base station,while the other one is devoted to the communication with other peers.A similar multi-interface technique has been used to implement IP multi-homing for stream control transmission protocol (SCTP),in which a machine is installed with multiple network in-terface cards,and each assigned a different IP address [36].Similarly,in mobile P2P cooperation environment,mobile users have a network connection to access information from the server,e.g.,through a wireless modem or a base station,and the mobile users also have the ability to communicate with other peers via a wireless LAN,e.g.,IEEE 802.11or Bluetooth [9,24,32].Furthermore,each mobile client is equipped with a positioning device, e.g.,GPS or sensor-based local positioning systems,to determine its current lo-cation information.4.P2P SPATIAL CLOAKINGIn this section,we present the data structure and the P2P spatial cloaking algorithm.Then,we describe two operation modes of the algorithm:on-demand and proactive .4.1Data StructureThe entire system area is divided into grid.The mobile client communicates with each other to discover other k −1peers,in order to achieve the k -anonymity requirement.TheAlgorithm 1P2P Spatial Cloaking:Request Originator m 1:Function P2PCloaking-Originator (h ,k )2://Phase 1:Peer searching phase 3:The hop distance h is set to h4:The set of discovered peers T is set to {∅},and the number ofdiscovered peers k =|T |=05:while k <k −1do6:Broadcast a FORM GROUP request with the parameter h (Al-gorithm 2gives the response of each peer p that receives this request)7:T is the set of peers that respond back to m by executingAlgorithm 28:k =|T |;9:if k <k −1then 10:if T =T then 11:Suspend the request 12:end if 13:h ←h +1;14:T ←T ;15:end if 16:end while17://Phase 2:Location adjustment phase 18:for all T i ∈T do19:|mT i .p |←the greatest possible distance between m and T i .pby considering the timestamp of T i .p ’s reply and maximum speed20:end for21://Phase 3:Spatial cloaking phase22:Form a group with k −1peers having the smallest |mp |23:h ←the largest hop distance h p of the selected k −1peers 24:Determine a grid area A that covers the entire group 25:if A <A min then26:Extend the area of A till it covers A min 27:end if28:Randomly select a mobile client of the group as an agent 29:Forward the query and A to the agentmobile client can thus blur its exact location into a cloaked spatial region that is the minimum grid area covering the k −1peers and itself,and satisfies A min as well.The grid area is represented by the ID of the left-bottom and right-top cells,i.e.,(l,b )and (r,t ).In addition,each mobile client maintains a parameter h that is the required hop distance of the last peer searching.The initial value of h is equal to one.4.2AlgorithmFigure 3gives a running example for the P2P spatial cloaking algorithm.There are 15mobile clients,m 1to m 15,represented as solid circles.m 8is the request originator,other black circles represent the mobile clients received the request from m 8.The dotted circles represent the commu-nication range of the mobile client,and the arrow represents the movement direction.Algorithms 1and 2give the pseudo code for the request originator (denoted as m )and the re-quest receivers (denoted as p ),respectively.In general,the algorithm consists of the following three phases:Phase 1:Peer searching phase .The request origina-tor m wants to retrieve information from the location-based database server.m first sets h to h ,a set of discovered peers T to {∅}and the number of discovered peers k to zero,i.e.,|T |.(Lines 3to 4in Algorithm 1).Then,m broadcasts a FORM GROUP request along with a message sequence ID and the hop distance h to its neighboring peers (Line 6in Algorithm 1).m listens to the network and waits for the reply from its neighboring peers.Algorithm 2describes how a peer p responds to the FORM GROUP request along with a hop distance h and aFigure3:P2P spatial cloaking algorithm.Algorithm2P2P Spatial Cloaking:Request Receiver p1:Function P2PCloaking-Receiver(h)2://Let r be the request forwarder3:if the request is duplicate then4:Reply r with an ACK message5:return;6:end if7:h p←1;8:if h=1then9:Send the tuple T=<p,(x p,y p),v maxp ,t p,h p>to r10:else11:h←h−1;12:Broadcast a FORM GROUP request with the parameter h 13:T p is the set of peers that respond back to p14:for all T i∈T p do15:T i.h p←T i.h p+1;16:end for17:T p←T p∪{<p,(x p,y p),v maxp ,t p,h p>};18:Send T p back to r19:end ifmessage sequence ID from another peer(denoted as r)that is either the request originator or the forwarder of the re-quest.First,p checks if it is a duplicate request based on the message sequence ID.If it is a duplicate request,it sim-ply replies r with an ACK message without processing the request.Otherwise,p processes the request based on the value of h:Case1:h= 1.p turns in a tuple that contains its ID,current location,maximum movement speed,a timestamp and a hop distance(it is set to one),i.e.,< p,(x p,y p),v max p,t p,h p>,to r(Line9in Algorithm2). Case2:h> 1.p decrements h and broadcasts the FORM GROUP request with the updated h and the origi-nal message sequence ID to its neighboring peers.p keeps listening to the network,until it collects the replies from all its neighboring peers.After that,p increments the h p of each collected tuple,and then it appends its own tuple to the collected tuples T p.Finally,it sends T p back to r (Lines11to18in Algorithm2).After m collects the tuples T from its neighboring peers, if m cannotfind other k−1peers with a hop distance of h,it increments h and re-broadcasts the FORM GROUP request along with a new message sequence ID and h.m repeatedly increments h till itfinds other k−1peers(Lines6to14in Algorithm1).However,if mfinds the same set of peers in two consecutive broadcasts,i.e.,with hop distances h and h+1,there are not enough connected peers for m.Thus, m has to relax its privacy profile,i.e.,use a smaller value of k,or to be suspended for a period of time(Line11in Algorithm1).Figures3(a)and3(b)depict single-hop and multi-hop peer searching in our running example,respectively.In Fig-ure3(a),the request originator,m8,(e.g.,k=5)canfind k−1peers via single-hop communication,so m8sets h=1. Since h=1,its neighboring peers,m5,m6,m7,m9,m10, and m11,will not further broadcast the FORM GROUP re-quest.On the other hand,in Figure3(b),m8does not connect to k−1peers directly,so it has to set h>1.Thus, its neighboring peers,m7,m10,and m11,will broadcast the FORM GROUP request along with a decremented hop dis-tance,i.e.,h=h−1,and the original message sequence ID to their neighboring peers.Phase2:Location adjustment phase.Since the peer keeps moving,we have to capture the movement between the time when the peer sends its tuple and the current time. For each received tuple from a peer p,the request originator, m,determines the greatest possible distance between them by an equation,|mp |=|mp|+(t c−t p)×v max p,where |mp|is the Euclidean distance between m and p at time t p,i.e.,|mp|=(x m−x p)2+(y m−y p)2,t c is the currenttime,t p is the timestamp of the tuple and v maxpis the maximum speed of p(Lines18to20in Algorithm1).In this paper,a conservative approach is used to determine the distance,because we assume that the peer will move with the maximum speed in any direction.If p gives its movement direction,m has the ability to determine a more precise distance between them.Figure3(c)illustrates that,for each discovered peer,the circle represents the largest region where the peer can lo-cate at time t c.The greatest possible distance between the request originator m8and its discovered peer,m5,m6,m7, m9,m10,or m11is represented by a dotted line.For exam-ple,the distance of the line m8m 11is the greatest possible distance between m8and m11at time t c,i.e.,|m8m 11|. Phase3:Spatial cloaking phase.In this phase,the request originator,m,forms a virtual group with the k−1 nearest peers,based on the greatest possible distance be-tween them(Line22in Algorithm1).To adapt to the dynamic network topology and k-anonymity requirement, m sets h to the largest value of h p of the selected k−1 peers(Line15in Algorithm1).Then,m determines the minimum grid area A covering the entire group(Line24in Algorithm1).If the area of A is less than A min,m extends A,until it satisfies A min(Lines25to27in Algorithm1). Figure3(c)gives the k−1nearest peers,m6,m7,m10,and m11to the request originator,m8.For example,the privacy profile of m8is(k=5,A min=20cells),and the required cloaked spatial region of m8is represented by a bold rectan-gle,as depicted in Figure3(d).To issue the query to the location-based database server anonymously,m randomly selects a mobile client in the group as an agent(Line28in Algorithm1).Then,m sendsthe query along with the cloaked spatial region,i.e.,A,to the agent(Line29in Algorithm1).The agent forwards thequery to the location-based database server.After the serverprocesses the query with respect to the cloaked spatial re-gion,it sends a list of candidate answers back to the agent.The agent forwards the candidate answer to m,and then mfilters out the false positives from the candidate answers. 4.3Modes of OperationsThe P2P spatial cloaking algorithm can operate in twomodes,on-demand and proactive.The on-demand mode:The mobile client only executesthe algorithm when it needs to retrieve information from the location-based database server.The algorithm operatedin the on-demand mode generally incurs less communica-tion overhead than the proactive mode,because the mobileclient only executes the algorithm when necessary.However,it suffers from a longer response time than the algorithm op-erated in the proactive mode.The proactive mode:The mobile client adopting theproactive mode periodically executes the algorithm in back-ground.The mobile client can cloak its location into a spa-tial region immediately,once it wants to communicate withthe location-based database server.The proactive mode pro-vides a better response time than the on-demand mode,but it generally incurs higher communication overhead and giveslower quality of service than the on-demand mode.5.ANONYMOUS LOCATION-BASEDSERVICESHaving the spatial cloaked region as an output form Algo-rithm1,the mobile user m sends her request to the location-based server through an agent p that is randomly selected.Existing location-based database servers can support onlyexact point locations rather than cloaked regions.In or-der to be able to work with a spatial region,location-basedservers need to be equipped with a privacy-aware queryprocessor(e.g.,see[29,31]).The main idea of the privacy-aware query processor is to return a list of candidate answerrather than the exact query answer.Then,the mobile user m willfilter the candidate list to eliminate its false positives andfind its exact answer.The tighter the spatial cloaked re-gion,the lower is the size of the candidate answer,and hencethe better is the performance of the privacy-aware query processor.However,tight cloaked regions may represent re-laxed privacy constrained.Thus,a trade-offbetween the user privacy and the quality of service can be achieved[31]. Figure4(a)depicts such scenario by showing the data stored at the server side.There are32target objects,i.e., gas stations,T1to T32represented as black circles,the shaded area represents the spatial cloaked area of the mo-bile client who issued the query.For clarification,the actual mobile client location is plotted in Figure4(a)as a black square inside the cloaked area.However,such information is neither stored at the server side nor revealed to the server. The privacy-aware query processor determines a range that includes all target objects that are possibly contributing to the answer given that the actual location of the mobile client could be anywhere within the shaded area.The range is rep-resented as a bold rectangle,as depicted in Figure4(b).The server sends a list of candidate answers,i.e.,T8,T12,T13, T16,T17,T21,and T22,back to the agent.The agent next for-(a)Server Side(b)Client SideFigure4:Anonymous location-based services wards the candidate answers to the requesting mobile client either through single-hop communication or through multi-hop routing.Finally,the mobile client can get the actualanswer,i.e.,T13,byfiltering out the false positives from thecandidate answers.The algorithmic details of the privacy-aware query proces-sor is beyond the scope of this paper.Interested readers are referred to[31]for more details.6.EXPERIMENTAL RESULTSIn this section,we evaluate and compare the scalabilityand efficiency of the P2P spatial cloaking algorithm in boththe on-demand and proactive modes with respect to the av-erage response time per query,the average number of mes-sages per query,and the size of the returned candidate an-swers from the location-based database server.The queryresponse time in the on-demand mode is defined as the timeelapsed between a mobile client starting to search k−1peersand receiving the candidate answers from the agent.On theother hand,the query response time in the proactive mode is defined as the time elapsed between a mobile client startingto forward its query along with the cloaked spatial regionto the agent and receiving the candidate answers from theagent.The simulation model is implemented in C++usingCSIM[35].In all the experiments in this section,we consider an in-dividual random walk model that is based on“random way-point”model[7,8].At the beginning,the mobile clientsare randomly distributed in a spatial space of1,000×1,000square meters,in which a uniform grid structure of100×100cells is constructed.Each mobile client randomly chooses itsown destination in the space with a randomly determined speed s from a uniform distribution U(v min,v max).When the mobile client reaches the destination,it comes to a stand-still for one second to determine its next destination.Afterthat,the mobile client moves towards its new destinationwith another speed.All the mobile clients repeat this move-ment behavior during the simulation.The time interval be-tween two consecutive queries generated by a mobile client follows an exponential distribution with a mean of ten sec-onds.All the experiments consider one half-duplex wirelesschannel for a mobile client to communicate with its peers with a total bandwidth of2Mbps and a transmission range of250meters.When a mobile client wants to communicate with other peers or the location-based database server,it has to wait if the requested channel is busy.In the simulated mobile environment,there is a centralized location-based database server,and one wireless communication channel between the location-based database server and the mobile。
青岛理工大学-《Python》练习题及答案
一、填空题1.Python源代码程序编译后的文件扩展名为_________。
答案：pyc2.使用pip工具升级科学计算扩展库numpy的完整命令是_________________。
答案：pip install -- upgrade numpy3.使用pip工具查看当前已安装的Python扩展库的完整命令是_____________。
答案：pip list4.查看变量类型的Python内置函数是________________。
答案：type( )5.使用运算符测试集合包含集合A是否为集合B的真子集的表达式可以写作_______。
答案：A<B6.语句x = 3==3, 5执行结束后，变量x的值为_____________。
答案：(True, 5）7.已知x = 3，那么执行语句x += 6 之后，x的值为_______________。
答案：98.假设列表对象aList的值为[3, 4, 5, 6, 7, 9, 11, 13, 15, 17]，那么切片aList[3:7]得到的值是______________________。
答案：[6, 7, 9, 11]9.使用列表推导式生成包含10个数字5的列表，语句可以写为_______________。
答案：[5 for i in range(10)]10.假设有列表a = ['name', 'age', 'sex']和b = ['Dong', 38, 'Male']，请使用一个语句将这两个列表的内容转换为字典，并且以列表a中的元素为“键”，以列表b中的元素为“值”，这个语句可以写为_____________________。
答案：c = dict(zip(a, b))11.已知 a = [1, 2, 3]和 b = [1, 2, 4]，那么id(a[1])==id(b[1])的执行结果为___________。
2012年全球信息安全调查
尽管在安全技术支出保持乐观态度, 但中国许多机构在「人员」、「流 程」及「技术」三大主要因素的配合方面仍然滞后。
国资委强调要用更完善的数据安全措施来确保中国国有企业的信息安全。然而, 中国许多 机构在平衡「人员」、「流程」以及「技术」三个方面有所滞后。
• 目前中国政府要求更多的“中国创造”，要求各企业都加强自主研发；同时，随着中国 市场的不断成熟，很多国际公司也将研发中心搬到中国。但在人员经常发生变化，人员 信息安全意识不强的情况下，如何做好信息资产的保护，成为各企业的焦点，同时也成 为中国政府的关注点；
• 监管部门已经或正在引入各种信息保护措施，包括数据保护和数据隐私的规章制度，这 也刺激了中国企业对于信息安全，特别是数据安全保护措施的投资需求。
相比全球其他国家，调查显示更多中国受访者认为自己的企业在信息安全策略制 定和执行中，属于领跑者。
问题 26n11: 「那一个描述最好形容贵公司信息安全的做法?」 * 报告之数字可能未完全与原此数据一致, 因为四舍五入的影响。
对于信息安全的认知，这种感觉是否真实？是否存在自我感觉良好，但是 实际与感觉有差异的情况？
有关对个人资料私隐的保障 中国 2009
人员
58.0%
流程
46.4%
技术
60.2%
(加上之数不等于100%)
中国 2010 57.3% 45.9% 62.7%
中国 2011 59.7%
从2010年至2011年 之间发生的变化
+2.4%
49.7%
+3.8%
63.4%
+0.7%
问题15: 「贵机构有甚么数据私穏的保障?」
/sundae_meng PwC
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2012高教社杯全国大学生数学建模竞赛题目
（请先阅读“全国大学生数学建模竞赛论文格式规范”）
D 题 机器人避障问题
图1是一个800×800的平面场景图，在原点O(0, 0)点处有一个机器人，它只能在该平面场景范围内活动。
图中有12个不同形状的区域是机器人不能与之发生碰
标点与障碍物的距离至少超过10个单位）。
规定机器人的行走路径由直线段和圆弧组成，其中圆弧是机器人转弯路径。
机器人不能折线转弯，转弯路径由与直线路径相切的一段圆弧组成，也可以由两个或多个相切的圆弧路径组成，但每个圆弧的半径最小为10个单位。
为了不与障碍物发生碰撞，同时要求机器人行走线路与障碍物间的最近距离为10个单位，否则将发生碰撞，若碰撞发生，则机器人无法完成行走。
机器人直线行走的最大速度为50=v 个单位/秒。
机器人转弯时，最大转弯速
度为21.0100
e 1)(ρρ-+==v v v ，其中ρ是转弯半径。
如果超过该速度，机器人将发生侧
翻，无法完成行走。
请建立机器人从区域中一点到达另一点的避障最短路径和最短时间路径的数学模型。
对场景图中4个点O(0, 0)，A(300, 300)，B(100, 700)，C(700, 640)，具体计算：
(1) 机器人从O(0, 0)出发，O→A 、O→B 、O→C 和O→A→B→C→O 的最短路径。
(2) 机器人从O (0, 0)出发，到达A 的最短时间路径。
注：要给出路径中每段直线段或圆弧的起点和终点坐标、圆弧的圆心坐标以及机器人行走的总距离和总时间。
图1 800×800平面场景图。