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Abstract A New Method for Similarity Indexing of Market Basket Data

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A New Method for Similarity Indexing of Market Basket DataCharu C.AggarwalIBM T.J.Watson Research Center Yorktown Heights,NY10598charu@Joel L.WolfIBM T.J.Watson Research Center Yorktown Heights,NY10598jlw@Philip S.YuIBM T.J.Watson Research Center Yorktown Heights,NY10598psyu@AbstractIn recent years,many data mining methods have been proposed forfinding useful and structured information from market basket data.The association rule model was recently proposed in order to discover useful patterns and dependencies in such data.This paper discusses a method for indexing market basket data efficiently for similarity search.The technique is likely to be very useful in applications which utilize the similarity in customer buying behavior in order to make peer recommendations.We propose an index called the signature table,which is very flexible in supporting a wide range of similarity functions. The construction of the index structure is independent of the similarity function,which can be specified at query time. The resulting similarity search algorithm shows excellent scalability with increasing memory availability and database size.1IntroductionThis paper addresses the problem of retrieval and similarity search for market basket data.In recent years,the progress of bar code technology has made it possible to collect information containing consumer transaction data effirmation about market baskets is collected in the form of sets of items which are bought together in a transaction.Let U denote the universal set of items.Thus,a transaction T is a set of items which is a subset of U,and represents those items from U which were bought in the corresponding transaction.Since the correlations in the buying behavior of the different items may be deduced from transactions,they may be used in order to make decisions in many target marketing applications.The market basket problem has been studied exten-sively with reference to the issue offinding association rules between the different items[2,3].In this paper, we will discuss this problem from the context of sim-ilarity search.For a given target transaction,we wish tofind the k transactions which are most similar to it. The straightforward solution of using a sequential scan may require considerable I/O for very large data collec-tions.This is especially true for transaction data,which is usually of the order of gigabytes or terabytes.The problem of indexing has been studied consid-erably in the database literature from the perspective of multidimensional quantitative data.Some examples of data structures for performing indexing are the R-Tree and its variants[1,5,12,22].These data struc-tures can support many kinds of queries,such as point queries,range queries,or similarity to a predefined tar-get.After the introduction of the R-Tree,substantial work was done on improving indexing techniques for high dimensional data[6,15,16].As the dimensional-ity increases,the performance of these methods dete-riorates rapidly because of the so called dimensional-ity curse.As a rule of thumb,when the dimensional-ity is more than10,none of the above methods work well.A variety of indexes and query processing tech-niques have been proposed in the database literature which are specially suited to the similarity search query [4,7,13,14,15,17,20,21,23].Recently,the pyra-mid technique was proposed for range queries,which reduces the performance deterioration caused by high dimensionality[6].It has also been observed[20],that the query performance is quite sensitive to the similar-ity(or distance)function which is used.A typical transaction contains only a few items out of a universe of hundreds or thousands of items [2].Furthermore,the items in a transaction are closely correlated.For example,in a database for a supermarket application,if a transaction contains the item“Milk”,then it may often also contain the items“Bread”or“Butter”.The total dimensionality of transaction data is clearly out of the range of the available indexing techniques for continuous valued attributes.On the other hand,the sparsity and correlations in transaction data provide an opportunitywhich can be exploited in order to design efficient index structures.It has been observed in earlier work[20],that indexstructures are often sensitive to the nature of thesimilarity function which is used.Two simple similarity functions between transactions are the number ofmatches and the hamming distance.The number ofmatches between two transactions is defined by the number of items which were bought in both.In otherwords,if T1and T2are two transactions,then the match function between T1and T2is defined by thecardinality of T1∩T2.The hamming distance between two transactions is defined to be the number of items which were bought in one of the transactions,butnot both.Thus,the hamming distance between thetransactions T1and T2is the sum of the cardinalities of the two sets(T1−T2)and(T2−T1).In reality,good similarity functions for transaction data are much more complex than the above mentioned functions.Often the similarity criterion may be complex function of the number of matches and the hamming distance.The inverted index is a data structure which cansupport queries based on the number of matches between the target and the database transactions. It relies on the sparsity of the data in order to provide efficient responses to similarity queries[18]and is widely used for information retrieval applications. Unfortunately,the inverted index cannot efficiently resolve queries in which the similarity criterion can be any general function of the number of matches and hamming distance between the target and database transactions.It also cannot easily support queries which are variations on the traditional similarity search problem(eg.range queries or multi-target variants of similarity queries).Furthermore,although transaction data is sparse,the degradation in performance of inverted indexes with increasing density is too steep to be of practical use for many real applications.We will introduce a data structure called the Signa-ture Table,and show how to use it effectively for sim-ilarity search.This data structure is quiteflexible in being able to support different kinds of similarity func-tions.Furthermore,it shows excellent scalability with increasing database size.This is a characteristic which is crucial to effective deployment for very large data-bases.This paper is organized as follows.In the next section,we will discuss the nature of the similarity function and the kinds of queries that we would like the index structure to support.In Section3,we will introduce the signature table,and discuss the details of its construction.In Section4,we will discuss the branch and bound method which uses the signature table for effective similarity search.Section5discusses the empirical results,whereas Section6is a conclusionand summary.1.1Contributions of this paperThis paper discusses aflexible indexing structure forsimilarity search which is applicable to a wide varietyof similarity functions.We would like to be able to perform similarity search when the similarity functionbetween two transactions is a general function ofthe number of matches and the hamming distance. We achieve this goal under certain very reasonableconstraints on the similarity function.The process of building the signature table is independent of thesimilarity function,which can be specified at querytime.Furthermore,the query processing techniques discussed in this paper have the potential to beextended to more complex variations of similaritysearch queries with multiple similarity functions and/or targets.The(percentage)pruning efficiency of ourtechnique increases with database size.This is a useful property,given the huge sizes of sales transactiondatabases.2The Similarity FunctionIn this section we will discuss the general propertiesof the similarity function which are supported by the techniques in this paper.Let x be the number of itemsin which the transactions T1and T2match,and let y be the number of items in which T1and T2differ. Then the similarity between T1and T2is a functionof both x and y.We shall denote this function by f(x,y).For the sake of convention,we will assume that the similarity function is such that higher values imply greater similarity.The index structure proposed in this paper is capable of handling maximization techniques on this general function f(x,y),provided that it satisfies the following two constraints:∆f(x,y)∆x≥0(1)∆f(x,y)∆y≤0(2)The conditions on the function ensure that it is an increasing function in terms of the number of matches and is decreasing in terms of the hamming distance. These are quite reasonable assumptions for similarity functions,since a higher number of matches imply greater similarity,whereas a greater hamming distance implies less similarity.Some examples of functions which satisfy this requirement are as follows:(1)Hamming distance(minimization):When re-stated in maximization form,we have f(x,y)=1/y.(2)Match to hamming distance ratio:f(x,y)=x/y.(3)Cosine:Let S and T denote two transactions,suchthat#S and#T are the number of items in S and T respectively.The cosine of the angle between S and T is defined as follows:cosine(S,T)=x√#S·√#T=x(2·x+y−#T)·√#TA variant of the above mentioned cosine functionis often used to measure similarity in information retrieval applications[11].A large number of other functions also satisfy the above property.In order to set the ground for further discussion,we shall prove a result which is key to the correctness of our similarity procedure for the function f(·,·).Lemma2.1Let f be a function satisfying:∆f(x,y)∆x≥0∆f(x,y)∆y≤0Letγbe an upper bound on the value of x andθbe a lower bound on the value of y.Then f(γ,θ)is an upper bound on the value of the function f(x,y).Proof:let(x0,y0)be any realization of the value of (x,y).Then,x0≤γand y0≥θ.It follows from the above conditions that:f(x0,y0)≤f(γ,y0)≤f(γ,θ)(3) The result follows.2.1The similarity search query and itsvariationsWhen stated in simple form,the similarity query is as follows:For a given target transaction T,find the k transactions which maximize the similarity function f(x,y),where x is the number of matches,and y is the hamming dis-tance with the target transaction.An interesting variation of this query is one in which we have multiple targets,and we wish to maximize some function of the similarity of the transactions to the dif-ferent targets.For example,we may wish to maximize the average similarity of the retrieved transactions to n different targets.Other variations on this query structure include range queries such as those in which we wish tofind all the transactions whose similarity to the target is at least equal to a predefined threshold t.More generally, let f1(·,·),...f n(·,·)be n similarity functions,and let t1,...t n be n threshold values.The range queryfinds all transactions which satisfy the constraints f1(·,·)≥t1,...f n(·,·)≥t n.An example of such a query is one in which we wish tofind all transactions which have at least p items in common and at most q items different from the target.In this paper,our primary focus is on the nearest neighbor query as stated in its purest form offinding the closest transaction to the target.However,since the branch and bound query processing technique is applicable to all optimization functions,the methods discussed in this paper can be easily extended to the above variations.In a later section of the paper,we will briefly discuss the slight modifications required in order to support these queries.3The Signature TableIn this section,we will discuss the definition and characterization of the signature table.We shallfirst discuss some notations and terminology in order to facilitate further discussion.A signature is a set of items.The items in the original data are partitioned into sets of signatures.Consider the case when the items in the original data are {1,...,20}.Then one possible partition of these items into signatures could be P={1,2,4,6,8,11,18},Q= {3,5,7,9,10,16,20},and R={12,13,14,15,17,19}.A transaction T is said to activate a signature A at level r if and only if|A∩T|≥r.This level r is referred to as the activation threshold.For example,consider the transaction T={2,6,17,20}.Then,the transaction T will activate the signatures P,Q,and R at level r=1, and will activate only the signature P at level r=2. The set of items are partitioned into K sets of signa-tures.We shall refer to this value of K as the signature cardinality.The supercoordinate of a transaction ex-ists in K-dimensional space such that each dimension of the supercoordinate has a unique correspondence with a particular signature and vice-versa.Each dimension in this K-dimensional supercoordinate is a0-1value which indicates whether or not the corresponding signature is activated by that transaction.Thus,if the items are partitioned into K signatures{S1,...S K},then there are2K possible supercoordinates.Each transaction maps on to a unique supercoordinate,though multi-ple transactions may map into the same supercoordi-nate.If S i1,S i2,...S ilbe the set of signatures which a transaction activates,then the supercoordinates of that transaction are defined by setting the l≤K dimen-sions{i1,i2,...i l}in this supercoordinate to1and the remaining to0.The signature table consists of a set of2K entries.DISK ---Supercoordinate 1Supercoordinate 2Supercoordinate 3MAIN MEMORY List of transactions List of transactions List of transactionsFigure 1:Illustration of the signature table One entry in the signature table corresponds to each possible supercoordinate.Thus,the entries in the signature table create a partition of the data.This partition will be used for the purpose of similarity search.The signature table is stored in main memory,whereas the actual transactions which are indexed by each entry of the signature table are stored on disk.This constrains the value of the signature cardinality since the 2K entries of the signature table need to be stored in main memory.Each entry in the signature table points to a list of pages which contain the transactions indexed by that supercoordinate.The signature table is illustrated in Figure 1.A signature can be understood as a small category of items from the universal set of items U .Thus,if the items in each signature are closely correlated,then a transaction is likely to activate a small number of sig-natures.These signatures provide an idea of the “ap-proximate”pattern of buying behavior for that transac-tion.The concept of using approximate representations in order to index data has been explored in earlier work [8,9,10].Our method of representation,data structure and query techniques are very different,being specially designed for the similarity search problem.3.1Details of signature table construction In order to explain the process of signature table con-struction we shall introduce some additional notation and terminology.An itemset I is defined to be a set of items satisfying I ⊆U .Here U is the universal set of items.A k-itemset is an itemset which has k items in it.The support of an itemset I is defined to be the per-centage of transactions in the database which contain I as a subset.The effective construction of the signature table requires us to partition the universal set of items U into subsets of items which form the signatures.We would like the set of items in each signature to be closely correlated.This is achieved by using the following procedure.We construct a graph such that each node of the graph corresponds to an item.For every pair of items such that the corresponding itemset has a predefined minimum support,we add an edge between the nodes.We also add a weight to each edge which is the inverse of the support of that pair of items.Thus,when the support of a pair of items is high,the weight of the corresponding edge is low,and vice-versa.We would like to find a partition of the items such that the sum of the supports of the items in each partition is similar and the cross-correlations among the items in the different partitions are as small as possible.In the transformation discussed above this translates to the problem of finding the graph partitioning such that the weight of the edges crossing the partitions of the graph is as small as possible.The desire to keep partitions evenly balanced arises from the need to create the signature table in such a way that the transactions are well distributed over the different entries.The desire to keep the cross-correlations between the different partitions small is motivated by the aim of keeping very closely correlated 1items in each signature.Unfortunately,the weighted graph partitioning prob-lem is notoriously difficult to solve.Thus,this approach is too time intensive and impractical for most problems.Instead,we choose to use a variant of the single link-age clustering algorithm [19]in order to find the opti-mal partitions.The disadvantage of single-linkage algo-rithms is that they are often likely to lead to long strag-gly clusters [19].However,space and time-optimal algo-rithms are available in order to perform single-linkage clustering,and this is clearly attractive when the to-tal number of items is very large.Briefly stated,the single-linkage clustering algorithm is as follows:(1)Construct a node for each item.(2)Let the distance between each pair of nodes be de-fined by the inverse of the support of the correspond-ing 2-itemset.(3)Apply the greedy minimum spanning tree algorithmto the resulting graph in order to generate the single linkage clusters.Add edges one by one to the graph,starting with the null graph which contains only nodes,but no edges.The edges are added in order of increasing distance.This strategy ensures that if the support of a pair of items is high,then they are1Anotherreason for desiring the correlation property is thatwe would like only a small number of signatures to be activated by a transaction.This is important in providing efficiency for our similarity search procedure.likely to belong to the same connected component. The mass of a connected component in this graph is defined as the sum of the supports of the individual items in that component.Remove any component which is such that its mass exceeds a predefined percentage of the sum of the supports of all the individual items.We shall refer to this thresholdvalue of the mass as the critical mass.This set of items which satisfies the critical mass criterion forms a signature set.We continue the process of adding edges and removing components of critical mass until each item belongs to some signature set. Note that the critical mass criterion in step(3)for determining when to remove a component from the graph determines the signature cardinality K.If the critical mass for a signature set is low,then the value of the signature cardinality K is high,and vice-versa. Higher values of K are desirable,since this results in afiner granularity of partitioning of the data.The empirical section will illustrate that partitions of higher granularity result in better pruning performance.On the other hand,it is also necessary to choose low enough values of K such that the signature table can be held in main memory.Once the signatures have been determined,the parti-tions of the data may be defined by using the superco-ordinates of the transactions.Each transaction belongs to the partition which is defined by its supercoordinate. The transaction lists are built for each supercoordinate (as illustrated in Figure1)based on this partition.4The Branch and Bound Technique The use of branch and bound methods for nearest neighbor search has been explored by Roussopoulos et.al.[17].This method was proposed for continuous valued attributes.The same principles are applicable to the market basket problem.The use of signature sets for organizing transactions is the key to determining good estimations on the similarity between the target transaction and the transactions indexed by the signature table entries.The branch and bound method is a classical technique in combinatorial optimization.It uses an ordered search method on a partitioning of the data in order to avoid searching many of the partitions.Pruning is done byfinding good optimistic bounds on the distance of a target point to each set representing a partition of the search space.This bound is an optimistic2bound (upper bound)on the value of the similarity function between the target and any transaction in this set.If 2In general,the term“optimistic bound”refers to an upper bound,when the similarity search problem is in maximization form,and a lower bound when the similarity search problem is in minimization form.Recall that our convention indicated that higher values of the function f(·,·)imply greater similarity.XData Subset 1Opt(i): OPTIMISTIC BOUND OF TARGET TO DATA SUBSET i.Data Subset 2Data Subset 3Data Subset 4Data Subset 5Opt(2)Opt(3)Opt(4)Opt(5)PessimisticBoundOpt(1)Figure2:The partitioning techniques in the branch and bound methodthis optimistic bound is less than than the similarity of the target to some solution R which has already been determined,then the corresponding set may be pruned from contention.This is because none of these transactions can provide better similarity to the target than R.This solution R defines a pessimistic bound (or lower bound)on the quality of the optimal solution. The overall idea of the branch and bound method is illustrated in Figure2.The process of pruning reduces the size of the accessed space substantially,and thereby leads to improved running times.We know that each entry in the signature table points to a set of transactions which corresponds to a particular supercoordinate.It is the partitioning created by these supercoordinates which may be used in order to perform the branch and bound method. Pseudocode for the branch and bound algorithm is given in Figure 3.The description pertains to the case when the single nearest neighbor needs to be found.The modifications for the more general problem are quite simple,and are discussed in Section 4.3.Thefirst step is to compute the optimistic bounds from the target transaction to each entry in the signature table.We perform a main memory sort of the entries in the signature table in decreasing order of the optimistic bounds.This sort provides the order in which the supercoordinates of the signature table should be scanned.The purpose of the sorting is to order the partitions created by the supercoordinates in such a way that the partitions which are most likely to contain the nearest transaction to the target are visited first.We scan the supercoordinates in the signature table one by one,and compute the similarity between the target transaction and the transactions indexed by that entry.The similarity computation is illustrated in the procedure EvaluateObjective(·,·)of Figure5.We always keep track of the best candidate which has been found so far.The similarity of this transaction to theAlgorithm BranchAndBound(TargetTransaction:T,SignatureTableEntries:{1,...2K})beginfor each table entry i dobeginOpt(i)=F indOptimisticBound(T,i);endSort the entries in the table in decreasing order of Opt(i); BestT ransaction=A randomly chosen transaction;{BestT ransaction keeps track of the transaction withthe highest similarity value among those scanned so far}P essimisticBound=EvaluateObjective(BestT ransaction,T); BestV alue=EvaluateObjective(BestT ransaction,T);for each table entry i,in decreasing order of Opt(i)do beginif Opt(i)≤P essimisticBound then prune entry i;elsebeginT(i)=Transactions indexed by entry i;for each transaction S∈T(i)doif EvaluateObjective(S,T)>BestV alue thenbeginBestV alue=EvaluateObjective(S,T);BestT ransaction=S;endP essimisticBound=BestV alue;endend;return(BestT ransaction);endAlgorithm FindOptimisticBound(TargetTransaction:T,Signature Table Entry:i)beginM opt=F indOptimisticMatch(T,i);D opt=F indOptimisticDist(T,i);return(f(M opt,D opt));endFigure3:The branch and bound algorithm for indexing market basketsAlgorithm FindOptimisticDist(TargetTransaction:T,Signature Table Entry:i){This algorithmfinds an optimistic(lower)bound on the hamming distance between the target transaction and the signature table entry i.A detailed description of this technique is provided in Section4.1of the paper}Algorithm FindOptimisticMatch(TargetTransaction:T,Signature Table Entry:i){This algorithmfinds an optimistic(upper)bound on the matches between the target transaction and the signature table entry i.A detailed description of this techniqueis provided in Section4.1of the paper}Figure4:Evaluating the optimistic bounds on the match and hamming distance Algorithm EvaluateObjective(Transaction:S,Transaction:T) beginx=Number of matches between S and T;y=Hamming Distance between S and T;return(f(x,y));endFigure5:Evaluating the objective function between two transactionstarget provides a pessimistic bound on the quality of the similarity function between the target transaction and any of the transactions in the database.3As the algorithm scans through the different supercoordinates, it continues to update(increase)the pessimistic bound, as better and better candidates are discovered.At the same time,those entries for which the optimistic bound is less than the current value of the pessimistic bound are pruned from contention.The performance of the algorithm is sensitive to the criterion used in order to sort the signature table en-tries.This is becausefinding closely matching trans-actions(and hence higher pessimistic bounds)earlier on can significantly improve pruning performance.The use of optimistic bounds in order to perform the sorting is only one possible implementation of the algorithm. Another interesting implementation would be to sort the entries in the signature table based on the simi-larity function between the respective supercoordinates of the signature table entries and the target.In other words,if B be the supercoordinate of the target,and B i be the supercoordinate of signature table entry i, then the sort order may be determined by using the similarity function between B and B i for each i.This provides a different order of search of the partitions, though the optimistic bounds are still used in order to evaluate the pruning criterion for a given signature ta-ble entry.This can improve the performance when the sort criterion is a better indication of the average case similarity between the target and the transactions in-dexed by a signature table entry.For the purposes of this paper,we always use the optimistic bounds in or-der to perform the sorting.It now remains to explain how the optimistic bounds may be found.4.1Finding optimistic boundsIn order tofind an optimistic bound on the similarity between the target transaction and a supercoordinate for the similarity function f(M,D),wefirst need to find optimistic bounds on the values of M and D. It follows from Lemma 2.1that if M opt and D opt 3The correctness of this assertion is quite obvious,since the value of the similarity function of the closest transaction to the target in a subset of the database is a lower bound on the similarity to the closest transaction in the database.。

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Multi-scale structural similarity for image quality assesment

Multi-scale structural similarity for image quality assesment

MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ASSESSMENT Zhou Wang1,Eero P.Simoncelli1and Alan C.Bovik2(Invited Paper)1Center for Neural Sci.and Courant Inst.of Math.Sci.,New York Univ.,New York,NY10003 2Dept.of Electrical and Computer Engineering,Univ.of Texas at Austin,Austin,TX78712 Email:zhouwang@,eero.simoncelli@,bovik@ABSTRACTThe structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene,and therefore a measure of structural similarity can provide a good approxima-tion to perceived image quality.This paper proposes a multi-scale structural similarity method,which supplies moreflexibility than previous single-scale methods in incorporating the variations of viewing conditions.We develop an image synthesis method to calibrate the parameters that define the relative importance of dif-ferent scales.Experimental comparisons demonstrate the effec-tiveness of the proposed method.1.INTRODUCTIONObjective image quality assessment research aims to design qual-ity measures that can automatically predict perceived image qual-ity.These quality measures play important roles in a broad range of applications such as image acquisition,compression,commu-nication,restoration,enhancement,analysis,display,printing and watermarking.The most widely used full-reference image quality and distortion assessment algorithms are peak signal-to-noise ra-tio(PSNR)and mean squared error(MSE),which do not correlate well with perceived quality(e.g.,[1]–[6]).Traditional perceptual image quality assessment methods are based on a bottom-up approach which attempts to simulate the functionality of the relevant early human visual system(HVS) components.These methods usually involve1)a preprocessing process that may include image alignment,point-wise nonlinear transform,low-passfiltering that simulates eye optics,and color space transformation,2)a channel decomposition process that trans-forms the image signals into different spatial frequency as well as orientation selective subbands,3)an error normalization process that weights the error signal in each subband by incorporating the variation of visual sensitivity in different subbands,and the vari-ation of visual error sensitivity caused by intra-or inter-channel neighboring transform coefficients,and4)an error pooling pro-cess that combines the error signals in different subbands into a single quality/distortion value.While these bottom-up approaches can conveniently make use of many known psychophysical fea-tures of the HVS,it is important to recognize their limitations.In particular,the HVS is a complex and highly non-linear system and the complexity of natural images is also very significant,but most models of early vision are based on linear or quasi-linear oper-ators that have been characterized using restricted and simplistic stimuli.Thus,these approaches must rely on a number of strong assumptions and generalizations[4],[5].Furthermore,as the num-ber of HVS features has increased,the resulting quality assessment systems have become too complicated to work with in real-world applications,especially for algorithm optimization purposes.Structural similarity provides an alternative and complemen-tary approach to the problem of image quality assessment[3]–[6].It is based on a top-down assumption that the HVS is highly adapted for extracting structural information from the scene,and therefore a measure of structural similarity should be a good ap-proximation of perceived image quality.It has been shown that a simple implementation of this methodology,namely the struc-tural similarity(SSIM)index[5],can outperform state-of-the-art perceptual image quality metrics.However,the SSIM index al-gorithm introduced in[5]is a single-scale approach.We consider this a drawback of the method because the right scale depends on viewing conditions(e.g.,display resolution and viewing distance). In this paper,we propose a multi-scale structural similarity method and introduce a novel image synthesis-based approach to calibrate the parameters that weight the relative importance between differ-ent scales.2.SINGLE-SCALE STRUCTURAL SIMILARITYLet x={x i|i=1,2,···,N}and y={y i|i=1,2,···,N}be two discrete non-negative signals that have been aligned with each other(e.g.,two image patches extracted from the same spatial lo-cation from two images being compared,respectively),and letµx,σ2x andσxy be the mean of x,the variance of x,and the covariance of x and y,respectively.Approximately,µx andσx can be viewed as estimates of the luminance and contrast of x,andσxy measures the the tendency of x and y to vary together,thus an indication of structural similarity.In[5],the luminance,contrast and structure comparison measures were given as follows:l(x,y)=2µxµy+C1µ2x+µ2y+C1,(1)c(x,y)=2σxσy+C2σ2x+σ2y+C2,(2)s(x,y)=σxy+C3σxσy+C3,(3) where C1,C2and C3are small constants given byC1=(K1L)2,C2=(K2L)2and C3=C2/2,(4)Fig.1.Multi-scale structural similarity measurement system.L:low-passfiltering;2↓:downsampling by2. respectively.L is the dynamic range of the pixel values(L=255for8bits/pixel gray scale images),and K1 1and K2 1aretwo scalar constants.The general form of the Structural SIMilarity(SSIM)index between signal x and y is defined as:SSIM(x,y)=[l(x,y)]α·[c(x,y)]β·[s(x,y)]γ,(5)whereα,βandγare parameters to define the relative importanceof the three components.Specifically,we setα=β=γ=1,andthe resulting SSIM index is given bySSIM(x,y)=(2µxµy+C1)(2σxy+C2)(µ2x+µ2y+C1)(σ2x+σ2y+C2),(6)which satisfies the following conditions:1.symmetry:SSIM(x,y)=SSIM(y,x);2.boundedness:SSIM(x,y)≤1;3.unique maximum:SSIM(x,y)=1if and only if x=y.The universal image quality index proposed in[3]corresponds to the case of C1=C2=0,therefore is a special case of(6).The drawback of such a parameter setting is that when the denominator of Eq.(6)is close to0,the resulting measurement becomes unsta-ble.This problem has been solved successfully in[5]by adding the two small constants C1and C2(calculated by setting K1=0.01 and K2=0.03,respectively,in Eq.(4)).We apply the SSIM indexing algorithm for image quality as-sessment using a sliding window approach.The window moves pixel-by-pixel across the whole image space.At each step,the SSIM index is calculated within the local window.If one of the image being compared is considered to have perfect quality,then the resulting SSIM index map can be viewed as the quality map of the other(distorted)image.Instead of using an8×8square window as in[3],a smooth windowing approach is used for local statistics to avoid“blocking artifacts”in the quality map[5].Fi-nally,a mean SSIM index of the quality map is used to evaluate the overall image quality.3.MULTI-SCALE STRUCTURAL SIMILARITY3.1.Multi-scale SSIM indexThe perceivability of image details depends the sampling density of the image signal,the distance from the image plane to the ob-server,and the perceptual capability of the observer’s visual sys-tem.In practice,the subjective evaluation of a given image varies when these factors vary.A single-scale method as described in the previous section may be appropriate only for specific settings.Multi-scale method is a convenient way to incorporate image de-tails at different resolutions.We propose a multi-scale SSIM method for image quality as-sessment whose system diagram is illustrated in Fig. 1.Taking the reference and distorted image signals as the input,the system iteratively applies a low-passfilter and downsamples thefiltered image by a factor of2.We index the original image as Scale1, and the highest scale as Scale M,which is obtained after M−1 iterations.At the j-th scale,the contrast comparison(2)and the structure comparison(3)are calculated and denoted as c j(x,y) and s j(x,y),respectively.The luminance comparison(1)is com-puted only at Scale M and is denoted as l M(x,y).The overall SSIM evaluation is obtained by combining the measurement at dif-ferent scales usingSSIM(x,y)=[l M(x,y)]αM·Mj=1[c j(x,y)]βj[s j(x,y)]γj.(7)Similar to(5),the exponentsαM,βj andγj are used to ad-just the relative importance of different components.This multi-scale SSIM index definition satisfies the three conditions given in the last section.It also includes the single-scale method as a spe-cial case.In particular,a single-scale implementation for Scale M applies the iterativefiltering and downsampling procedure up to Scale M and only the exponentsαM,βM andγM are given non-zero values.To simplify parameter selection,we letαj=βj=γj forall j’s.In addition,we normalize the cross-scale settings such thatMj=1γj=1.This makes different parameter settings(including all single-scale and multi-scale settings)comparable.The remain-ing job is to determine the relative values across different scales. Conceptually,this should be related to the contrast sensitivity func-tion(CSF)of the HVS[7],which states that the human visual sen-sitivity peaks at middle frequencies(around4cycles per degree of visual angle)and decreases along both high-and low-frequency directions.However,CSF cannot be directly used to derive the parameters in our system because it is typically measured at the visibility threshold level using simplified stimuli(sinusoids),but our purpose is to compare the quality of complex structured im-ages at visible distortion levels.3.2.Cross-scale calibrationWe use an image synthesis approach to calibrate the relative impor-tance of different scales.In previous work,the idea of synthesizing images for subjective testing has been employed by the“synthesis-by-analysis”methods of assessing statistical texture models,inwhich the model is used to generate a texture with statistics match-ing an original texture,and a human subject then judges the sim-ilarity of the two textures [8]–[11].A similar approach has also been qualitatively used in demonstrating quality metrics in [5],[12],though quantitative subjective tests were not conducted.These synthesis methods provide a powerful and efficient means of test-ing a model,and have the added benefit that the resulting images suggest improvements that might be made to the model[11].M )distortion level (MSE)12345Fig.2.Demonstration of image synthesis approach for cross-scale calibration.Images in the same row have the same MSE.Images in the same column have distortions only in one specific scale.Each subject was asked to select a set of images (one from each scale),having equal quality.As an example,one subject chose the marked images.For a given original 8bits/pixel gray scale test image,we syn-thesize a table of distorted images (as exemplified by Fig.2),where each entry in the table is an image that is associated witha specific distortion level (defined by MSE)and a specific scale.Each of the distorted image is created using an iterative procedure,where the initial image is generated by randomly adding white Gaussian noise to the original image and the iterative process em-ploys a constrained gradient descent algorithm to search for the worst images in terms of SSIM measure while constraining MSE to be fixed and restricting the distortions to occur only in the spec-ified scale.We use 5scales and 12distortion levels (range from 23to 214)in our experiment,resulting in a total of 60images,as demonstrated in Fig.2.Although the images at each row has the same MSE with respect to the original image,their visual quality is significantly different.Thus the distortions at different scales are of very different importance in terms of perceived image quality.We employ 10original 64×64images with different types of con-tent (human faces,natural scenes,plants,man-made objects,etc.)in our experiment to create 10sets of distorted images (a total of 600distorted images).We gathered data for 8subjects,including one of the authors.The other subjects have general knowledge of human vision but did not know the detailed purpose of the study.Each subject was shown the 10sets of test images,one set at a time.The viewing dis-tance was fixed to 32pixels per degree of visual angle.The subject was asked to compare the quality of the images across scales and detect one image from each of the five scales (shown as columns in Fig.2)that the subject believes having the same quality.For example,one subject chose the images marked in Fig.2to have equal quality.The positions of the selected images in each scale were recorded and averaged over all test images and all subjects.In general,the subjects agreed with each other on each image more than they agreed with themselves across different images.These test results were normalized (sum to one)and used to calculate the exponents in Eq.(7).The resulting parameters we obtained are β1=γ1=0.0448,β2=γ2=0.2856,β3=γ3=0.3001,β4=γ4=0.2363,and α5=β5=γ5=0.1333,respectively.4.TEST RESULTSWe test a number of image quality assessment algorithms using the LIVE database (available at [13]),which includes 344JPEG and JPEG2000compressed images (typically 768×512or similar size).The bit rate ranges from 0.028to 3.150bits/pixel,which allows the test images to cover a wide quality range,from in-distinguishable from the original image to highly distorted.The mean opinion score (MOS)of each image is obtained by averag-ing 13∼25subjective scores given by a group of human observers.Eight image quality assessment models are being compared,in-cluding PSNR,the Sarnoff model (JNDmetrix 8.0[14]),single-scale SSIM index with M equals 1to 5,and the proposed multi-scale SSIM index approach.The scatter plots of MOS versus model predictions are shown in Fig.3,where each point represents one test image,with its vertical and horizontal axes representing its MOS and the given objective quality score,respectively.To provide quantitative per-formance evaluation,we use the logistic function adopted in the video quality experts group (VQEG)Phase I FR-TV test [15]to provide a non-linear mapping between the objective and subjective scores.After the non-linear mapping,the linear correlation coef-ficient (CC),the mean absolute error (MAE),and the root mean squared error (RMS)between the subjective and objective scores are calculated as measures of prediction accuracy .The prediction consistency is quantified using the outlier ratio (OR),which is de-Table1.Performance comparison of image quality assessment models on LIVE JPEG/JPEG2000database[13].SS-SSIM: single-scale SSIM;MS-SSIM:multi-scale SSIM;CC:non-linear regression correlation coefficient;ROCC:Spearman rank-order correlation coefficient;MAE:mean absolute error;RMS:root mean squared error;OR:outlier ratio.Model CC ROCC MAE RMS OR(%)PSNR0.9050.901 6.538.4515.7Sarnoff0.9560.947 4.66 5.81 3.20 SS-SSIM(M=1)0.9490.945 4.96 6.25 6.98 SS-SSIM(M=2)0.9630.959 4.21 5.38 2.62 SS-SSIM(M=3)0.9580.956 4.53 5.67 2.91 SS-SSIM(M=4)0.9480.946 4.99 6.31 5.81 SS-SSIM(M=5)0.9380.936 5.55 6.887.85 MS-SSIM0.9690.966 3.86 4.91 1.16fined as the percentage of the number of predictions outside the range of±2times of the standard deviations.Finally,the predic-tion monotonicity is measured using the Spearman rank-order cor-relation coefficient(ROCC).Readers can refer to[15]for a more detailed descriptions of these measures.The evaluation results for all the models being compared are given in Table1.From both the scatter plots and the quantitative evaluation re-sults,we see that the performance of single-scale SSIM model varies with scales and the best performance is given by the case of M=2.It can also be observed that the single-scale model tends to supply higher scores with the increase of scales.This is not surprising because image coding techniques such as JPEG and JPEG2000usually compressfine-scale details to a much higher degree than coarse-scale structures,and thus the distorted image “looks”more similar to the original image if evaluated at larger scales.Finally,for every one of the objective evaluation criteria, multi-scale SSIM model outperforms all the other models,includ-ing the best single-scale SSIM model,suggesting a meaningful balance between scales.5.DISCUSSIONSWe propose a multi-scale structural similarity approach for image quality assessment,which provides moreflexibility than single-scale approach in incorporating the variations of image resolution and viewing conditions.Experiments show that with an appropri-ate parameter settings,the multi-scale method outperforms the best single-scale SSIM model as well as state-of-the-art image quality metrics.In the development of top-down image quality models(such as structural similarity based algorithms),one of the most challeng-ing problems is to calibrate the model parameters,which are rather “abstract”and cannot be directly derived from simple-stimulus subjective experiments as in the bottom-up models.In this pa-per,we used an image synthesis approach to calibrate the param-eters that define the relative importance between scales.The im-provement from single-scale to multi-scale methods observed in our tests suggests the usefulness of this novel approach.However, this approach is still rather crude.We are working on developing it into a more systematic approach that can potentially be employed in a much broader range of applications.6.REFERENCES[1] A.M.Eskicioglu and P.S.Fisher,“Image quality mea-sures and their performance,”IEEE munications, vol.43,pp.2959–2965,Dec.1995.[2]T.N.Pappas and R.J.Safranek,“Perceptual criteria for im-age quality evaluation,”in Handbook of Image and Video Proc.(A.Bovik,ed.),Academic Press,2000.[3]Z.Wang and A.C.Bovik,“A universal image quality in-dex,”IEEE Signal Processing Letters,vol.9,pp.81–84,Mar.2002.[4]Z.Wang,H.R.Sheikh,and A.C.Bovik,“Objective videoquality assessment,”in The Handbook of Video Databases: Design and Applications(B.Furht and O.Marques,eds.), pp.1041–1078,CRC Press,Sept.2003.[5]Z.Wang,A.C.Bovik,H.R.Sheikh,and E.P.Simon-celli,“Image quality assessment:From error measurement to structural similarity,”IEEE Trans.Image Processing,vol.13, Jan.2004.[6]Z.Wang,L.Lu,and A.C.Bovik,“Video quality assessmentbased on structural distortion measurement,”Signal Process-ing:Image Communication,special issue on objective video quality metrics,vol.19,Jan.2004.[7] B.A.Wandell,Foundations of Vision.Sinauer Associates,Inc.,1995.[8]O.D.Faugeras and W.K.Pratt,“Decorrelation methods oftexture feature extraction,”IEEE Pat.Anal.Mach.Intell., vol.2,no.4,pp.323–332,1980.[9] A.Gagalowicz,“A new method for texturefields synthesis:Some applications to the study of human vision,”IEEE Pat.Anal.Mach.Intell.,vol.3,no.5,pp.520–533,1981. [10] D.Heeger and J.Bergen,“Pyramid-based texture analy-sis/synthesis,”in Proc.ACM SIGGRAPH,pp.229–238,As-sociation for Computing Machinery,August1995.[11]J.Portilla and E.P.Simoncelli,“A parametric texture modelbased on joint statistics of complex wavelet coefficients,”Int’l J Computer Vision,vol.40,pp.49–71,Dec2000. [12]P.C.Teo and D.J.Heeger,“Perceptual image distortion,”inProc.SPIE,vol.2179,pp.127–141,1994.[13]H.R.Sheikh,Z.Wang, A. C.Bovik,and L.K.Cormack,“Image and video quality assessment re-search at LIVE,”/ research/quality/.[14]Sarnoff Corporation,“JNDmetrix Technology,”http:///products_services/video_vision/jndmetrix/.[15]VQEG,“Final report from the video quality experts groupon the validation of objective models of video quality assess-ment,”Mar.2000./.PSNRM O SSarnoffM O S(a)(b)Single−scale SSIM (M=1)M O SSingle−scale SSIM (M=2)M O S(c)(d)Single−scale SSIM (M=3)M O SSingle−scale SSIM (M=4)M O S(e)(f)Single−scale SSIM (M=5)M O SMulti−scale SSIMM O S(g)(h)Fig.3.Scatter plots of MOS versus model predictions.Each sample point represents one test image in the LIVE JPEG/JPEG2000image database [13].(a)PSNR;(b)Sarnoff model;(c)-(g)single-scale SSIM method for M =1,2,3,4and 5,respectively;(h)multi-scale SSIM method.。

基于问句类型的问句相似度计算_田卫东

基于问句类型的问句相似度计算_田卫东

lenSim（ A， B） = 1 － abs len（ A） － len（ B） len（ A） + len（ B） （ 3）
从结果可以看出， 前三对问句都有很好的相似度 ， 但都不 是相同的问题， 都会影响 FAQ 问答系统的性能； 第四对问题， 虽然有很低的相似度， 但却是相同的问题。原有的算法不能很 ， 好地计算这些问句 主要是下面几方面的原因 ： a） 没有考虑两个问句的类型是否一致 。 第一对问句就是 两个不同的问题， 一个是问人， 另一个是问数字。 第四对问句 是相同类型的问题， 都是关于地址方面的问题 ， 但是没有考虑 导致相似度很低。 类型问题， b） 没有考虑问句的句型。 第二对问句， 下面一句比上面 一句多一个否定词， 导致了结果的截然不同 。 c） 没有考虑词语之间的褒贬倾向 。第三对问句中 “好处 ” “坏处” 和 分别是褒义词和贬义词 ， 用基于知网的方法计算相 ［8 ］ 似度有很高的相似值 。 为此， 本文着重考虑问句的类型 。 为了解决此问题， 在计 算问句相似度的过程中 ， 引入了问句的类型作为问句相似度计 算的一个因素。问题分类的目的就是确定问题的类型 ， 主要是 为了增加约束条件， 方便信息检索和答案的提取 。 目前， 并没 有统一的问题分类体系 。为此， 很多问答系统都采用了较为复 杂的问题分类体系。 由于本文用到的是中文的问题分类 ， 因此将介绍中文问题 分类体系。 目前一些中文问题研究的问题分类体系都是 从 TＲEC 会议上的英文分类体系直接翻译过来 ， 采用的都是从粗 类到细类的层次分类体系 。虽然在某些类型上可能造成歧义 ， 总体来说可也得到了较高的分类精度 ， 还可以增加约束条件便
1－ 1 0
问句 清朝一共有哪几位皇帝？ 清朝总共有几位皇帝？ 哪个国家的货币最值钱？ 哪个国家的货币最不值钱？ 上网对人有什么好处？ 上网对人有什么坏处？ 白宫在哪？ 白宫的地址是多少？
“similarity in form” and “similarity in spirit”

“similarity in form” and “similarity in spirit”

2182018年31期总第419期ENGLISH ON CAMPUS“similarity in form” and “similarity in spirit”文/杨忆晰【Abstract】When it comes to the effect of translation, the theory of “similarity in form” and “similarity in spirit” is of necessity, especially similarity in spirit exerts influences on one aspect to another. The thesis focuses on both “similarity in form” and “similarity in spirit”, which is aimed at doing research on significance of twoelements in translation.【Key words】translation; similarity in form; similarity in spirit 【作者简介】杨忆晰，山西工商学院国际交流学院。

IntroductionTranslation is more than transformation, moreover, itserves as an indication of recreation. But how to recreate remains a question? When it comes to the effect of translation,the theory of “similarity in form” and “similarity in spirit”is of necessity, especially similarity in spirit exerts influences on one aspect to another.In Chinese translation theory, a lot of people put forwardthe theory about“similarity in form” and “similarity in spirit”.Ma Jianzhong(马建忠)was the first one talkingabout“spirit”(神似)，“Similarity in spirit”theory was firstly brought forward and expatiated by Mao Dun(茅盾)in 1921.After that Guo Momo(郭沫若)，Wen Yiduo(闻一多)advanced that the“spirit”and“tone”(“气势”)should be translated when discussing poetry translation.Chen Xiying (陈西滢)pointed out that the key of achieving“faithfulness”is the pursuit of“similarity in spirit”besides“similarity in form”.He also pointed out that the supreme artistic state of literature translation is “similarity in spirit”，which is the embodiment of the highest level of translated l i t e ra t u r e.Fu L e i (傅雷) p u t “f o r m ”a n d “s p i r i t ”，“similarity in form”and“similarity in spirit”as a pair of contradiction.He advocated“similarity in spirit” not “similarity in form”.Qian Zhongshu(钱钟书)pointed out“consummating”(化境)theory.While Xu Yuanchong(许渊冲)put forward“the theory of advantage and the ofresemblance”(“优势竞赛论”).DefinitionIn the 20th century, there are several heated argumentsabout “similarity in form” and“similarity in spirit” in translation circles.In terms of “form” and “spirit”，thereare two groups.One group holds that “similarity in form”should come first ; while the other group holds that it is hard to achieve “similarity in form”.So “similarity in spirit” shouldbe relied on.In the translation system, “similarity in spirit” occupies the leading position.Definitions vary in terms of “form” and “spirit” indiscussion.So it is necessary to have a definite definition.On the basis of Xu Jun’s (许钧)viewpoint，there are threelevels of “form”.Firstly, the language level, which refers to the pure “language” defined in linguistics，and the language’ s systematic structure composed by wordsand expressions, grammar, syntax and so on.Secondly, the parole level, meaning the subject’s creative construction.Thirdly, the style level.Different types of writing have different forms.In terms of popular statement in translation circles，the “spirit” includes not only “romantic charm(神韵)”, “emotional appeal(情调)”, “vigor(气势)”, and “style(风 格)”，also“meaning”，“mind”，and “content” which was said often.In translation, “spirit” can be explained to “similarity in spirit” and “vividness”.Then what’s the relationship between “form” and “spirit” ? According to Luo Xinzhang(罗新璋)，“form” and “spirit” is dialectically united.If there is no“form”, thenwhat does the “spirit” adhere to? Lin Yutang explained it more concretely.He pointed out that the most important thing in translated literatures is paying equal attention to style and content of the original text. We not only take notice of what it said but how it was said.Every writer has his own style,which is valuable for his works.So the article’s beauty depends on its style not content，and style is the central thing of art.So whenin the process of translation, we should pay attention to form and spirit all.To be brief, Lin’s translation standard call be summedup in one word—lifelikeness. He explained it as’word and sentence’s spirit and implied meaning”.If there is no conflict between”word’s spirit” and “sentence’ s spirit”，theexact expression of the main words should be ensured. But if there is conflict between them，what’ s more important is the expression of the whole sentence.Grasping the meaning of the sentence on the basis of words can convey the original text exactly, namely, lifelikeness，which is the ultimate purpose.Lin’s translation principle about “lifelikeness” is consistent with his character and the view of “personality”literature.He held that people should have a spirit of not being confined，express their own opinion and literature to display their personalities.Since the article’s essence is personality’s representation，if it is lost in translation,the charm will be lost.2018年31期总第419期ENGLISH ON CAMPUSSo in the process of translation，translators should attach importance to convey the romantic charm of the original text，thus to move the readers.In translated literatures，he translated literatures according to his own translation theory to achieve the aim of lifelikeness.Case StudyPersonally, I strongly hold Lin Yutang’s point of view, for we’ve stressed similarity in form for too many years during our English study without “lifelikeness” understanding and recreation. The best case is that I do find some stiff translation which fails to evoke readers’ interest and emotion. The translation of Sister Carrie is a good example. Sister Carrie, written by Theodore Dreiser serving as the representative of American naturalistic writers in 19th century, is a penetrating observer of desire in humanity. To catch a glimpse of the novel, I’m deeply overwhelmed by its exquisite psychological and environmental picture, which appropriately mirrors the “spirit” of Sister Carrie. In particular, depiction of complicating metropolis is a perfect mixture of a variety of emotions and desire. Therefore, in terms of translation, “spirit in similarity”occupies leading position, for what we want to convey to readers should be perfect picture of emotions and desire as well as much room for senses.First,let’s take a look at the excerpt from the novel Sister Carrie.The city has its cunning wiles, no less than the infinitely smaller and more human tempter. There are large forces which allure with all the soulfulness of expression possible in the most cultured human. The gleam of a thousand lights is often as effective as the persuasive light in a wooing and fascinating eye. Half the undoing of the unsophisticated and natural mind is accomplished by forces wholly superhuman.A blare of sound, a roar of life, a vast array of human hives, appeal to the astonished senses in equivocal terms. Without a counselor at hand to whisper cautious interpretations, what falsehoods may not these things breathe into the unguarded ear! Unrecognized for what they are, their beauty, like music, too often relaxes, then weakens, then perverts the simpler human perceptions.Here are two versions of translation of the excerpt.大城市具有自身种种诱人的花招，并不亚于那些教人学坏的男男女女，当然人比社会微小得多，也更富于人情味。

英语语言的修辞艺术智慧树知到答案章节测试2023年青岛滨海学院

英语语言的修辞艺术智慧树知到答案章节测试2023年青岛滨海学院

第一章测试1.Let us go forth to lead the land we love. （）is used in the above sentence.A:AssonanceB:ClimaxC:ConsonanceD:Alliteration答案:D2.How and why he had come to Princeton is a story of struggle, success, andsadness. （） is used in the above sentence.A:ConsonanceB:ClimaxC:AlliterationD:Assonance答案:C3.In alliteration, the same consonant sound is repeated at intervals in the initialposition of words. ( )A:对B:错答案:A4.Assonance is the “echoing” or “resemblance” of vowel sounds in the stressedsyllables of a sequence of words. （）A:错B:对答案:B5.Homeoteleuton means “similarity in beginning”. （）A:错B:对答案:A第二章测试1.General words are often used to express a/an ( ) and vague idea.A:FrenchB:abstractC:GreekD:specific答案:B2.Specific words are often used in providing ( ).A:conclusionB:detailsC:bodyD:introduction答案:B3.( ) is NOT a true statements.A:Colloquial words are seldom used in informal or familiar conversation.B:Formal words often appear in formal writings.C:Many formal words contain 3 or more than 3 syllables.D:Common words occur in all kinds of writings.答案:A4.General words are used to provide ( ) ideas.A:conclusionB:detailedC:mainD:introduction答案:C5.The classification of “professions” does not include ( ).A:journalismB:teachersC:scientistsD:doctors答案:A第三章测试1.He had no outside “extracurricular interests,” unless, of course, you thinkabout a monthly golf game that way. The above sentence is a ( ) sentence.A:looseB:balancedC:climaxD:periodic答案:A2.Extremism in the defense of liberty is no vice; moderation in the pursuit ofjustice is no virtue. In the above sentence, ( ) is used.A:anti-climaxB:alliterationC:climaxD:antithesis答案:D3.Patient ( ) he was, he was unwilling to wait three hours.A:asB:althoughC:yetD:but答案:A4.I raised my head, squared my shoulders, and set out in the direction of mydorm. In the above sentence, ( ) is used.A:anti-climaxB:parallelismC:antithesisD:climax答案:B5.We expect them to be enthusiastic. We expect them reliable. We may evenhave expectations that they be endless repositories of skill and knowledgefrom which we may partake at will. In the above sentence, ( ) is used.A:climaxB:antithesisC:parallelismD:anti-climax答案:A第四章测试1.Einstein likes to put a cloak on, as if he had just walked out of a fairy tale. ( )is used in the above sentence.A:EuphemismB:SimileC:SynecdocheD:Paradox答案:B2.I beg a thousand pardons. ( ) is used in the above sentence.A:SynecdocheB:ParadoxC:HyperboleD:Simile答案:C3.He is out visiting the necessary. ( ) is used in the above sentence.A:ParadoxB:SimileC:SynecdocheD:Euphemism答案:D4. A friend in need is a friend to be avoided. Paradox is used in the abovesentence.（）A:对B:错答案:A5.There are about 100 hands working in his factory. Synecdoche is used in theabove sentence. （）A:错B:对答案:B第五章测试1.The project, which seemed so promising, turned out to be a Pandora’s Box.（）is used in the above sentence.A:AllusionB:MetaphorC:PersonificationD:Simile答案:A2.The use of the car was an apple of discord between Joe and his wife. As anallusion, “an apple of discord” means ( ).A:the fightingB:the disputesC:the root of troubleD:happiness答案:C3.As an allusion, “Helen of Troy” often refers to the beautiful woman a s adisaster. （）A:错B:对答案:B4.Allusion might include literary quotations, idioms, proverbs, maxims,colloquialisms(俗语), etc.. （）A:对B:错答案:A5.Olive branch rhetorically means （）.A:triumphB:the root of troubleC:victoryD:a sign of peace答案:D第六章测试1.“What do you mean by ‘evolutionary mechanism’?” One of the students asked.In the above sentence, there is only one quotation. ( )A:对B:错答案:B2. A quotation could be words from a person’s speech, or from written works.（）A:错B:对答案:B3.The substitutes for the skewed word or words must also look and sound likethe original to convey instant or rapid association with the familiar saying.（）A:对B:错答案:A4.“An Appliance a day keeps the Windows away” is a skewed quotation.（）A:错B:对答案:B5. A skewed quotation could alter the original sayings’ structural pattern whenneeded. （）A:错B:对答案:A。