Pattern Recognition 38(2005)1033–
1043
https://www.doczj.com/doc/ad18070950.html,/locate/patcog
Behavior classi?cation by eigendecomposition of periodic motions
Roman Goldenberg ?,Ron Kimmel,Ehud Rivlin,Michael Rudzsky
Computer Science Department,Technion—Israel Institute of Technology,Technion City,Haifa 32000,Israel
Received 23January 2004;received in revised form 25October 2004;accepted 8November 2004
Abstract
We show how periodic motions can be represented by a small number of eigenshapes that capture the whole dynamic mech-anism of the motion.Spectral decomposition of a silhouette of a moving object serves as a basis for behavior classi?cation by principle component analysis.The boundary contour of the walking dog,for example,is ?rst computed ef?ciently and accurately.After normalization,the implicit representation of a sequence of silhouette contours given by their corresponding binary images,is used for generating eigenshapes for the given motion.Singular value decomposition produces these eigen-shapes that are then used to analyze the sequence.We show examples of object as well as behavior classi?cation based on the eigendecomposition of the binary silhouette sequence.
?2005Pattern Recognition Society.Published by Elsevier Ltd.All rights reserved.
Keywords:Visual motion;Segmentation and tracking;Object recognition;Activity recognition;Non-rigid motion;Active contours;Periodicity analysis;Motion-based classi?cation;Eigenshapes
1.Introduction
Futurism is a movement in art,music,and literature that began in Italy at about 1909and marked especially by an effort to give formal expression to the dynamic energy and movement of mechanical processes.A typical example is the ‘Dynamism of a Dog on a Leash’by Giacomo Balla,who lived during the years 1871–1958in Italy,see Fig.1[1].In this painting one could see how the artist captures in one still image the periodic walking motion of a dog on a leash.Following a similar philosophy,we show how periodic mo-tions can be represented by a small number of eigenshapes that capture the whole dynamic mechanism of periodic mo-tions.Singular value decomposition of a silhouette of an ob-ject serves as a basis for behavior classi?cation by principle
?Corresponding author.Tel.:+97248294940;
fax:+97248293900.
E-mail addresses:romang@cs.technion.ac.il (R.Goldenberg),ron@cs.technion.ac.il (R.Kimmel),ehudr@cs.technion.ac.il (E.Rivlin),rudzsky@cs.technion.ac.il (M.Rudzsky).
0031-3203/$30.00?2005Pattern Recognition Society.Published by Elsevier Ltd.All rights reserved.doi:10.1016/j.patcog.2004.11.024
component analysis.Fig.2present a running horse video sequence and its eigenshape decomposition.One can see the similarity between the ?rst eigenshapes—Fig.2(c and d),and another futurism style painting “The Red Horseman”by Carlo Carra [1]—Fig.2(e).The boundary contour of the moving non-rigid object is computed ef?ciently and accu-rately by the fast geodesic active contours [2].After normal-ization,the implicit representation of a sequence of silhou-ette contours given by their corresponding binary images,is used for generating eigenshapes for the given motion.Sin-gular value decomposition produces the eigenshapes that are used to analyze the sequence.We show examples of object as well as behavior classi?cation based on the eigendecom-position of the sequence.
2.Related work
Motion-based recognition received a lot of attention in the ?eld of image analysis.The main reason is that direct usage of temporal data can improve our ability to solve a
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number of basic computer vision problems.Examples are image segmentation,tracking,object classi?cation,etc.In general,when analyzing a moving object,one can use two main sources of information:changes of the moving object position (and orientation)in space,and the object’s deformations.
Object position is an easy-to-get characteristic,applica-ble both for rigid and non-rigid bodies.It is provided by most of existing target detection and tracking systems.A number of techniques [3–6]were proposed for the detection of motion events and recognition of various types of mo-tions based on the analysis of the moving object trajectory and its derivatives.Detecting object orientation is a more challenging problem.It is usually solved by ?tting a model that may vary from a simple ellipsoid [6]to a complex 3-D vehicle model [7]or a speci?c aircraft-class model adapted for noisy radar images as in Ref.[8]
.
Fig.1.‘Dynamism of a Dog on a Leash’1912,by Giacomo Balla,Albright-Knox Art Gallery,
Buffalo.
Fig.2.(a)Running horse video sequence,(b)?rst 10eigenshapes,(c,d)?rst and second eigenshapes enlarged,(e)‘The Red Horseman’,1914,by Carlo Carra,Civico Museo d’Arte Contemporanea,Milan.
While object orientation characteristic is more applicable for rigid objects,it is object deformation that contains the most essential information about the nature of the non-rigid body motion.This is especially true for natural non-rigid ob-jects in locomotion that exhibit substantial changes in their apparent view.In this case the motion itself is caused by these deformations,e.g.walking,running,hopping,crawl-ing,?ying,etc.
There exists a large number of papers dealing with the classi?cation of moving non-rigid objects and their motions,based on their appearance.Lipton et al.describe a method for moving target classi?cation based on their static appear-ance [9]and using their skeletons [10].Polana and Nelson [11]used local motion statistics computed for image grid cells to classify various types of activities.An approach using the temporal templates and motion history images (MHI)for action representation and classi?cation was sug-gested by Davis and Bobick in Ref.[12].Cutler and Davis [13]describe a system for real-time moving object classi?-cation based on periodicity analysis.Describing the whole spectrum of papers published in this ?eld is beyond the scope of this paper,and we refer the reader to the surveys [14–16].
Related to our approach are the works of Yacoob and Black [17]and Murase and Sakai [18].In Ref.[17],human activities are recognized using a parameterized representa-tion of measurements collected during one motion period.The measurements are eight motion parameters tracked for ?ve body parts (arm,torso,thigh,calf and foot).
Murase and Sakai [18]describe a method for moving object recognition for gait analysis and lip reading.Their method is based on parametric eigenspace representation of the object silhouettes.Moving object segmentation is per-formed by using simple background subtraction.The eigen-basis is computed for one class of objects.Murase and Sakai used an optimization technique for ?nding the shift and scale parameters which is not trivial to implement.
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In this paper we concentrate on the analysis of defor-mations of moving non-rigid bodies.We thereby attempt to extract characteristics that allow us to distinguish be-tween different types of motions and different classes of objects.
Essentially we build our method based on approaches suggested in Refs.[18,17]by revisiting and improving some of the components.Unlike previous related methods,we ?rst introduce an advanced variational model for accurate segmentation and tracking.The proposed segmentation and tracking method allows extraction of moving object con-tours with high accuracy and,yet,is computationally ef?-cient enough to be implemented in a real time system.The algorithm analyzing high-precision object contours achieves better classi?cation results and can be easier adopted to var-ious object classes than the methods based on control points tracking[17]or background subtraction[18].
We then compute eigenbasis for several classes of objects (e.g.humans and animals).Moreover,at this?rst stage we select the right class by comparing the distance to the right feature space in each of the eigenbases.Next,we create a principle basis not only for static object view(single frames), but also for the whole period subsequences.For example,we create a principle basis for one human motion step and then project every period onto this basis.Unlike[18],we are not trying to build a separate basis for every motion type,but instead,build a common basis for the whole human motion class.Different types of human motions are then detected by looking at the projections of motion samples onto this basis.Apparently,this type of analysis can only be possible due to the high quality of segmentation and tracking results. We propose an explicit temporal alignment process for ?nding the shift and scale.Finally,we use a simple clas-si?cation approach to demonstrate the performances of our framework and test it on various classes of moving objects.
3.Our approach
Our basic assumption is that for any given class of moving objects,like humans,dogs,cats,and birds,the apparent object view in every phase of its motion can be encoded as a combination of several basic body views or con?gurations. Assuming that a living creature exhibits a pseudo-periodic motion,one motion period can be used as a comparable information unit.The basic views are then extracted from a large training set.An observed sequence of object views collected from one motion period is projected onto this basis. This forms a parameterized representation of the object’s motion that can be used for classi?cation.
Unlike[17]we do not assume an initial segmentation of the body into parts and do not explicitly measure the motion parameters.Instead,we work with the changing apparent view of deformable objects and use the parameterization induced by their form variability.
In what follows we describe the main steps of the process that include,
?Segmentation and tracking of the moving object that yield an accurate external object boundary in every frame.?Periodicity analysis,in which we estimate the frequency of the pseudo-periodic motion and split the video se-quence into single-period intervals.
?Frame sequence alignment that brings the single-period sequences to a standardized form by compensating for temporal shift,speed variations,different object sizes and imaging conditions.
?Parameterization by building an eigenshape basis from a training set of possible object views and projecting the apparent view of a moving body onto this basis.
3.1.Segmentation and tracking
Our approach is based on the analysis of deformations of the moving body.Therefore the accuracy of the segmentation and tracking algorithm in?nding the target outline is crucial for the quality of the?nal result.This rules out a number of available or easy-to-build tracking systems that provide only a center of mass or a bounding box around the target and calls for more precise and usually more sophisticated solutions.
Therefore,we decided to use the geodesic active contour approach[19]and speci?cally the‘fast geodesic active con-tour’method described in Ref.[2].The segmentation prob-lem is expressed as a geometric energy minimization.We search for a curve C that minimizes the functional
S[C]=
L(C)
g(C)d s,
where d s is the Euclidean arclength,L(C)is the total Eu-clidean length of the curve,and g is a positive edge indi-cator function in a3-D hybrid spatial-temporal space that depends on the pair of consecutive frames I t?1(x,y)and I t(x,y),where I t(x,y): ?R2×[0,T]→R+.It gets small values along the spatial-temporal edges,i.e.moving object boundaries,and higher values elsewhere.
In addition to the scheme described in Ref.[2],we also use the background information whenever a static back-ground assumption is valid and a background image B(x,y) is available.In the active contours framework this can be achieved either by modifying the g function to re?ect the edges in the difference image D(x,y)=|B(x,y)?I t(x,y)|, or by introducing additional area integration terms to the functional S(C):
S[C]=
L(C)
g(C)d s+ 1
C
|D(x,y)?c1|2d a + 2
\ C
|D(x,y)?c2|2d a,
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Fig.3.Non-rigid moving object segmentation and tracking.
where C is the area inside the contour C , 1and 2are ?xed parameters and c 1,c 2are given by c 1=average C [D(x,y)],c 2=average \ C [D(x,y)].
The latter approach is inspired by the ‘active contours with-out edges’model proposed by Chan and Vese [20].It forces the curve C to close on a region whose interior and exte-rior have approximately uniform D(x,y)values.A different approach to utilize the region information by coupling be-tween the motion estimation and the tracking problem was suggested by Paragios and Deriche in Ref.[21].
Fig.3shows some results of moving object segmentation and tracking using the proposed method.
Contours can be represented in various ways.Here,in or-der to have a uni?ed coordinate system and be able to apply a simple algebraic tool,we use the implicit representation of a simple closed curve as its binary image.That is,the contour is given by an image for which the exterior of the contour is black while its interior is white.3.2.Periodicity analysis
Here we assume that the majority of non-rigid moving objects are self-propelled alive creatures,whose motion is almost periodic.Thus,one motion period,like a step of a walking man or a rabbit hop,can be used as a natural unit of motion.Extracted motion characteristics can by normalized by the period size.
The problem of detection and characterization of periodic activities was addressed by several research groups and the prevailing technique for periodicity detection and measure-ments is the analysis of the changing 1-D intensity signals along spatio-temporal curves associated with a moving ob-ject or the curvature analysis of feature point trajectories [22–25].Here we address the problem using global charac-teristics of motion such as moving object contour deforma-tions and the trajectory of the center of mass.
By running frequency analysis e.g.Refs.[13,26]on such 1-D contour metrics as the contour area,velocity of the cen-ter of mass,principal axes orientation,etc.we can detect the basic period of the motion.Figs.4and 5present global motion characteristics derived from segmented moving ob-jects in two sequences.One can clearly observe the common dominant frequency in all three graphs.
The period can also be estimated in a straightforward manner by looking for the frame where the external object contour best matches the object contour in the current frame.Fig.6shows the deformations of a walking man contour during one motion period (step).Samples from two different steps are presented and each vertical pair of frames is phase synchronized.One can clearly see the similarity between the corresponding contours.An automated contour matching can be performed in a number of ways,e.g.by comparing contour signatures or by looking at the correlation between the object silhouettes in different frames as we chose to do in our work.Fig.7shows four graphs of inter-frame silhouette correlation values measured for four different starting frames taken within one motion period.It is clearly visible that all four graphs nearly coincide and the local maxima peaks are approximately evenly spaced.The period,therefore,was estimated as the average distance between the neighboring peaks.
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Fig.4.Global motion characteristics measured for walking man sequence.
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Fig.5.Global motion characteristics measured for walking cat sequence.
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Fig.6.Deformations of a walking man contour during one motion period (step).Two steps synchronized in phase are shown.One can see the similarity between contours in corresponding phases.
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i n t e r ?f r a m e c o r r e l a t i o n Fig.7.Inter-frame correlation between object silhouettes.Four graphs show the correlation measured for four initial frames.
3.3.Frame sequence alignment
One of the most desirable features of any classi?cation system is the invariance to a set of possible input transfor-mations.As the input in our case is not a static image,but a sequence of images,the system should be robust to both spatial and temporal variations.In the section below we de-scribe the methods for spatial and temporal alignment of input video sequence bringing it to a canonical,comparable form allowing later classi?cation.
3.3.1.Spatial alignment
We want to compare every object to a single pattern re-gardless of its size,viewing distance and other imaging con-ditions.This is achieved by cropping a square bounding box around the center of
mass of the tracked target silhouette and re-scaling it to a prede?ned size (see Fig.8).Note that this is not a scale invariant transformation unlike other reported normalization techniques e.g.Ref.[27].
Fig.8.Scale alignment.A minimal square bounding box around the center of the segmented object silhouette (a)is cropped and re-scaled to form a 50×50binary image (b).
One way to have orientation invariance is to keep a col-lection of motion samples for a wide range of possible motion directions and then look for the best match.This approach was used by Yacoob and Black in Ref.[17]to distinguish between different walking directions.Although here we experiment only with motions nearly parallel to the image plane,the system proved to be robust to small vari-ations in orientation.Since we do not want to keep models for both left-to-right and right-to-left motion directions,the right-to-left moving sequences are converted to left-to-right by horizontal mirror ?ip.
3.3.2.Temporal alignment
A good estimate of the motion period allows us to com-pensate for motion speed variations by re-sampling each period subsequence to a prede?ned duration.This can be done by interpolation between the binary silhouette im-ages themselves or between their parameterized represen-tation as explained below.Fig.9presents an original and re-sampled one-period subsequence after scaling from 11to 10frames.
Temporal shift is another issue that has to be addressed in order to align the phase of the observed one-cycle sample and the models stored in the training base.In Ref.[17]it was done by solving a minimization problem of ?nding the
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Fig.9.Temporal alignment.Top:original11frames of one period subsequence.Bottom:re-sampled10frames
sequence.
Fig.10.Temporal shift alignment:(a)average starting frame of all the training set sequences,(b)temporally shifted single-cycle test sequence,(c)correlation between the reference starting frame and the test sequence frames.
optimal parameters of temporal scaling and time shift trans-formations so that the observed sequence is best matched to the training samples.Polana and Nelson[11]handled this problem by matching the test one-period subsequence to reference template at all possible temporal translations. Murase and Sakai[18]used an optimization technique for ?nding the shift and scale parameters.Here we propose an explicit temporal alignment process.
Let us assume that in the training set all the sequences are accurately aligned.Then we?nd the temporal shift of a test sequence by looking for the starting frame that best matches the generalized(averaged)starting frame of the training samples,as they all look alike.Fig.10shows(a)the reference starting frame taken as an average over the tem-porally aligned training set,(b)a re-sampled single-period test sequence and,(c)the correlation between the reference starting frame and the test sequence frames.The maximal correlation is achieved at the seventh frame,therefore the test sequence is aligned by cyclically shifting it7frames to the left.
3.4.Parameterization
In order to reduce the dimensionality of the problem we ?rst project the object image in every frame onto a low-dimensional base.The base is chosen to represent all pos-sible appearances of objects that belong to a certain class, like humans,four-leg animals,etc.
Let n be number of frames in the training base of a certain class of objects and M be a training samples matrix,where each column corresponds to a spatially aligned image of a moving object written as a binary vector.In our experiments we use50×50normalized images,therefore,M is a2500×n matrix.The correlation matrix MM T is decomposed using Singular Value Decomposition as MM T=U V T,where U is an orthogonal matrix of principal directions and the is
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Fig.11.The principal basis for the‘dogs and cats’training set formed of20?rst principal component vectors.
a diagonal matrix of singular values.In practice,the decom-position is performed on M T M,which is computationally more ef?cient[28].The principal basis{U i,i=1,...,k}for the training set is then taken as k columns of U correspond-ing to the largest singular values in .Fig.11presents a principal basis for the training set formed of800sample im-ages collected from more than60sequences showing dogs and cats in motion.The basis is built by taking the k=20?rst principal component vectors.
We build such representative bases for every class of objects.Then,we can distinguish between various object classes by?nding the basis that best represents a given ob-ject image in the distance to the feature space(DTFS)sense. Fig.12shows the distances from more than1000various im-ages of people,dogs and cats to the feature space of people and to that of dogs and cats.In all cases,images of people were closer to the people feature space than to the animals’feature space and vise a versa.This allows us to distinguish between these two classes.A similar approach was used in Ref.[29]for the detection of pedestrians in traf?c scenes. If the object class is known(e.g.we know that the object is a dog),we can parameterize the moving object silhouette image I in every frame by projecting it onto the class basis. Let B be the basis matrix formed from the basis vectors {U i,i=1,...,k}.Then,the parameterized representation of the object image I is given by the vector p of length k as p=B T v I,where v I is the image I written as a vector. The idea of using a parameterized representation in motion-based recognition context is certainly not a new one.To name a few examples we mention again the works of Yacoob and Black[17],and of Murase and Sakai[18].
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Distance to the "people" feature space
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Fig.12.Distances to the‘people’and‘dogs and cats’feature spaces from more than1000various images of people,dogs and cats.
Cootes et al.[30]used similar technique for describing fea-ture point locations by a reduced parameter set.Baumberg and Hogg[31]used PCA to describe a set of admissible B-spline models for deformable object tracking.Chomat and Crowley[32]used PCA-based spatio-temporal?lter for human motion recognition.
Fig.13shows several normalized moving object images from the original sequence and their reconstruction from a parameterized representation by back-projection to the im-age space.The numbers below are the norms of differences between the original and the back-projected images.These norms can be used as the DTFS estimation.
Now,we can use these parameterized representations to distinguish between different types of motion.The refer-ence base for the activity recognition consists of temporally aligned one-period subsequences.The moving object sil-houette in every frame of these subsequences is represented by its projection to the principal basis.More formally,let {I f:f=1,...,T}be a one-period,temporally aligned set of normalized object images,and p f,f=1,...,T a pro-jection of the image I f onto the principal basis B of size k.Then,the vector P of length kT formed by concatenation of all the vectors p f,f=1,...,T,represent a one-period subsequence.By choosing a basis of size k=20and the nor-malized duration of one-period subsequence to be T=10 frames,every single-period subsequence is represented by a feature point in a200-dimensional feature space.
In the following experiment we processed a number of sequences of dogs and cats in various types of locomotion. From these sequences we extracted33samples of walking dogs,9samples of running dogs,9samples with gallop-ing dogs and14samples of walking cats.Let S200×m be
R.Goldenberg et al./Pattern Recognition 38(2005)1033–1043
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Fig.13.Image sequence parameterization.Top:11normalized target images of the original sequence.Bottom:the same images after the parameterization using the principal basis and back-projecting to the image basis.The numbers are the norms of the differences between the original and the back-projected images.
Fig.14.Feature points extracted from the sequences with walking,running and galloping dogs and walking cats and projected to the 3D space for visualization.
the matrix of projected single-period subsequences,where m is the number of samples and the SVD of the correla-tion matrix is given by SS T =U S S V S .In Fig.14we de-pict the resulting feature points projected for visualization to the 3-D space using the three ?rst principal directions {U S i :i =1,...,3},taken as the column vectors of U S cor-responding to the three largest eigen-values in S .One can easily observe four separable clusters corresponding to the four groups.
Another experiment was done over the ‘people’class of images.Fig.15presents feature points corresponding to several sequences showing people walking and running parallel to the image plane and running at oblique angle to the camera.Again,all three groups lie in separable clusters.
The classi?cation can be performed,for example,using the k -nearest-neighbor algorithm.We conducted the ‘leave one out’test for the dogs set above,classifying every sample by taking them out from the training set one at a time.The three-nearest-neighbors strategy resulted in 100%success
rate.
Fig.15.Feature points extracted from the sequences showing peo-ple walking and running parallel to the image plane and at 45?angle to the camera.Feature points are projected to the 3-D space for visualization.
Another option is to further reduce the dimensionality of the feature space by projecting the 200-dimensional feature vectors in S to some principal basis.This can be done using a basis of any size,exactly as we did in 3-D for visualiza-tion.Fig.16presents learning curves for both animals and people data sets.The curves show how the classi?cation er-ror rate achieved by one-nearest-neighbor classi?er changes as a function of principal basis size.The curves are obtained by averaging over a hundred iterations when the data set is randomly split into training and testing sets.The principal basis is built on the training set and the testing is performed using the ‘leave one out’strategy on the testing set.4.Concluding remarks
We presented a new framework for motion-based clas-si?cation of moving non-rigid objects.The technique is based on the analysis of changing appearance of moving ob-jects and is heavily relying on high accuracy results of seg-mentation and tracking by using the fast geodesic contour
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(a)(b)
Fig.16.Learning curves for (a)dogs and cats data set and (b)people data set.One-nearest-neighbor classi?cation error rate as a function of the number of eigenvectors in the projection basis.
approach.The periodicity analysis is then performed based on the global properties of the extracted moving object con-tours,followed by video sequence spatial and temporal nor-malization.Normalized one-period subsequences are param-eterized by projection onto a principal basis extracted from a training set of images for a given class of objects.A num-ber of experiments show the ability of the system to ana-lyze motions of humans and animals,to distinguish between these two classes based on object appearance,and to classify various type of activities within a class,such as walking,running,galloping.The ‘dogs and cats’experiment demon-strate the ability of the system to discriminate between these two very similar by appearance classes by analyzing their locomotion.
Acknowledgements
We thank Alfred M.Bruckstein for sharing with us his observation of the connection between the Futurism art movement and the periodic motion presentation using eigen-shapes.
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About the Author—ROMAN GOLDENBERG received the B.A.(Summa cum Laude)and Ph.D.degrees in Computer Science,from the Technion—Israel Institute of Technology,Haifa,in1995and2003,respectively.
From1994to1996,1999he was with IBM Research Lab in Haifa.Currently he is a research fellow at the Technion R&D Foundation and the Samuel Neeman Institute for Advanced Studies in Science and Technology.
His research interests include video analysis,tracking,motion-based recognition,PDE methods for image processing,and medical imaging. About the Author—RON KIMMEL received his B.Sc.(with honors)in computer engineering in1986,the M.S.degree in1993in electrical engineering,and the D.Sc.degree in1995from the Technion—Israel Institute of Technology.During the years1986–1991he served as an R&D of?cer in the Israeli Air Force.
During the years1995–1998he has been a postdoctoral fellow at Lawrence Berkeley National Laboratory,and the Mathematics Department, University of California,Berkeley.Since1998,he has been a faculty member of the Computer Science Department at the Technion,Israel. His research interests are in computational methods and their applications including topics in:Differential geometry,numerical analysis, non-linear image processing,geometric methods in computer vision,and numerical geometry methods in computer aided design,robotic navigation,and computer graphics.
Dr.Kimmel was awarded the Hershel Rich Technion innovation award,the Henry Taub Prize for excellence in research,Alon Fellowship, the HTI Post-doctoral Fellowship,and the Wolf,Gutwirth,Ollendorff,and Jury fellowships.He has been a consultant of HP research Lab in image processing and analysis during the years1998–2000,and to Net2Wireless/Jigami research group during2000–2001.
About the Author—EHUD RIVLIN received the B.Sc.and M.Sc.degrees in computer science and the M.B.A.degree from the Hebrew University in Jerusalem,and the Ph.D.from the University of Maryland.
Currently he is an Associate Professor in the Computer Science Department at the Technion,Israel Institute of Technology.His current research interests are in machine vision and robot navigation.
About the Author—MICHAEL RUDZSKY received his Ph.D.in physics and mathematics from the Institute of Space Research,Moscow in1980.He worked in the Scienti?c and Industrial Association for Space Research in Baku,Azerbajan till1990.
Since1991he was a research fellow in the Physics Department and since1995at the Computer Science Department of the Technion, Haifa,Israel.His current research interests include computer vision,pattern recognition and compression of images.
精选作文:人间真情(700字)作文 真情！是一个多么熟悉的词语呀！但是有多少人能够用真心真诚对待每个人每件事呢？说出来怕你不信，在我心中觉得待我最好不是妈妈，爸爸也不是爷爷奶奶及其其他家人，而是每天清晨默默无闻为我们付出城市美容师清洁工。清洁工阿姨每天忙碌的工作着，不论酷暑，严寒。之前每天清晨，我在放学的路上，一声声的扫地声，一阵阵的灰尘向我飘来，让我心生厌恶。那件事后，让我改变了。有一次周末，我们全家都在家，外面下着倾盆大雨，我们全家坐在沙发上津津有味的看着电视。咚咚咚一阵急促的脚步声过后，我家的门铃响了，我跑过去打开门，是小区里扫地阿姨，，看见她怀里还抱着厚厚几本书和本子，人也向抱着书的那侧倾斜，雨披把书也包的严严实实的，另一侧雨水把阿姨的衣服和裤子淋的湿湿的，雨水还在不停地滴落下来。小妹妹这些是你的书吗？我一看，这些不是前天收集出来复习资料和刚买的几本笔记本。阿姨还用不太标准的外乡口音的普通话气喘吁吁的说着：我在整理垃圾的时候，在一个黑色垃圾袋里看见这几本书，我依据本子上有门牌号给你送过来的，一种感激之情油然而生，虽然她的衣服被雨淋湿了，我的几本书在阿姨的怀里好好的躺着一点也没事。感谢你！垃圾每年的大年初一的早晨，天刚蒙蒙亮，城市美容师们把地上遗留下来的一大堆纸屑，一车车装进垃圾车里，这里刚清洁干净，又有人不停地创造，但是她们毫无怨言，还是默默无闻地挥动手里的扫把，她们的汗水浸湿她们的衣服。不论逢年过节还是节假日她们每天这样辛苦的付出着。我们每天走在干净的街道上时，我们要感谢城市美容师们用她们的真情让我们生活在这美好的环境中。六年级:胡? 篇一：人间真情作文400字 人间真情作文400字 人间处处有真情——互助的爱 “只要人人都献出一点爱，世界就会变成美好的人间。”这是一首大家熟悉的歌。起初我并不知道它的意思，但在一次难忘的经历中，让我真正懂得了这首歌的真正内涵。 我们家搬入新居以后，周围的邻居开始熟悉了。我偶然发现住在我家一楼的叔叔是位残疾人，出门很不方便。看到这一切，心里有些替他着急。心想：什么时候可以帮帮叔叔呀? 有一天，我放学回家，正好看见叔叔也下班回家，他的轮椅没在车旁边，因为腿有残疾，所以他不能下车去拿，只能坐在车上等着家人帮他把轮椅推过来。我见此情景，连忙主动上前问道：“叔叔，是需要轮椅吧?”叔叔说：“对，我是需要??”没等叔叔说完，我就一个箭步跑到轮椅前，把它推过来。叔叔看见轮椅推过来，感激地说：“小姑娘，真是太谢谢你了!”我说：“没关系，这是我应该做的。”然后，我把轮椅扶住，让叔叔从车上挪下来，坐到轮椅上。我又灵机一动，赶快把楼道门打开了，这样叔叔就可以顺利回家了。临进门前我跟叔叔说：“叔叔，以后帮你推车的事，就包在我身上了。”后来，我又慢慢地发现不仅仅我在关心叔叔，还有张奶奶、李姐姐等许许多多楼上楼下的热心人都在默默地帮助他。李姐姐经常帮叔叔家的孩子辅导功课，张奶奶平时做了好吃的，就会给叔叔端来。我们楼上楼下的邻居就像一家人一样。 人们常说：予人玫瑰，手有余香，帮助别人是快乐的。在社会及邻里这样的大家庭中，无论谁遇到困难，只要我们都能献出一点爱，那么这个大家庭就会成为美好的人间!从那刻起，我深深领悟到人间出处处要真情，世间才能多美好!
ABC Classification ABC分类法 Activity-Based Costing 业务量成本法/作业成本法 ACRS (Accelerated cost recovery system) 快速成本回收制度Action Message 行为/措施信息 AIS (Accounting information system) 会计信息系统 Allocation 已分配量 Anticipated Delay Report 拖期预报 A/P (Accounts Payable) 应付帐款 APICS (American Production & Inventory Control Society) 美国生产及库存控制协会 AQL (Acceptable quality Level) 可接受质量水平 A/R (Accounts Receivable) 应收帐款 Automatic Rescheduling 自动重排产 Available To Promise (APT) 可签约量 Backflush 倒冲法 Backlog 未完成订单/未结订单 Back Scheduling 倒序排产 BE analysis (Break-even analysis) 盈亏临界点分析，保本分析 Bill of Material (BOM) 物料清单 Business Plan 经营规划 B/V (Book value) 帐面价值 Capacity Requirements Planning (CRP) 能力需求计划
CBA (Cost-benefit analysis) 成本效益分析 CEO 首席执行官 CFO (Chief Financial Officer) 财务总裁 Closed Loop MRP 闭环物料需求计划 CPM (Critical path method) 关键路线法 CPP accounting (Constant purchasing power accounting) 不变购买力会计 Cumulative Lead Time 累计提前期 Cycle Counting 周期盘点 Demand 需求 Demand Management 需求管理 Demonstrated Capacity 实际能力 Dependent Demand 非独立需求 DFL (Degree of financial leverage) 财务杠杆系数 Direct-deduct Inventory Transaction Processing 直接增减库存法Dispatch List 派工单 DOL (Degree of operating leverage) 经营杠杆系数 ELS (Economic lot size) 经济批量 EOQ (Economic order quantity) 经济订货批量 FIFO (Fist-in,Fist-out) 先进先出法 Firm Planned Order 确认计划订单 FISH/LIFO (Fist-in,Still-here) 后进先出法
人间真情作文800字 篇一：人间真情作文800字 爱，是人类最美好的情感，爱，使人间充满温暖。一句话，一个动作，甚至一个眼神，都体现着真挚的爱，那一次，我被爱感动了。那天，正在梦乡的我被妈妈的一句话吵醒了怡婷，快起床了，等一下去帮我买东西。不要嘛！太阳公公都没‘醒’呢。妈妈拿我没办法，就去干活了。我在床上躺了会儿，就走到妈妈面前，说：要买什么东西呀？我打了个哈欠。妈妈拿出一元钱，说：去市场上买两斤豆腐，之后&&我拿起一块钱，走到门口，妈妈再三叮嘱：现在做买卖的都会用秤杆子坑人，别傻呼呼的，要看看秤，知道吗？哦，知道了。我不胜其烦地说。忽然，我听到了一阵叫声，卖豆腐咯，谁买豆腐！我随着声音跑了过去，看见了一个卖豆腐的叔叔，当我走到豆腐车旁时，正好他刚给别人称完豆腐，见我注视着他，便笑了笑，问道：小朋友，买几斤？瞧，果真像妈妈说的，做买卖的人，嘴都甜，能说会道，态度友好。哼，他企图用可亲可爱的样子欺骗我，没门！我决不会上当的。我心里暗暗告诫自己。买两斤，少一点也不要。我故意提高声音回答他。叔叔切了两块豆腐，放入袋子中，递给了我。他收了钱，又高声叫道：卖豆腐咯我捧着袋子，朝家走去，可谁知走了几步，被石头绊了一下，手一松，白花花的豆腐撒了一地，我急得快要哭了。心想：怎么办，等一下回家会被妈妈骂的。怎么办？这时，卖豆腐的叔叔走到我身边，用手轻轻地抚摸着我的头，安慰我说：别哭，叔叔在给你称一块。拿钱呢？&&不要钱了。说完，他切了两块豆腐，放进袋子里。我望着他那和蔼的面孔，不知说什么才好&& 我怀着激动的心情，捧着袋子向家中走去。远处，又传来阵阵叫喊声：卖豆腐咯啊！人间处处有真情，真情时时暖人心。有人曾说，世上并不缺少美，而是缺少发现美的眼睛。那次，我流泪了，流下感动的泪。清华实验六年级:杜雪怡 篇二：人间自有真情在(800字) 精选:人间自有真情在(800字)作文 记忆的窗帘徐徐拉开，只记得以前的悲凉：那时的我，不过刚出生八个月，就遭
Uncivilized behavior of Chinese tourists “You see, it’s common, most people didn’t care”said by one female tourist who is just throwing trash on the San Ya beach which has already been reduced to a dumping ground. When the beautiful island meets holiday crowd, under the hustling and bustling scene, we absolutely startled by the carving “Dao ci yi you”, dumbling beer bottles, spilling peels and half-burned branches. These facts are still far from enough.Uncivilized behavior makes headlines in the National Day holidays. At the memorable flag-raising ceremony at Tiananmen Square on October 1, over 120,000 participants left five tons of trash behind after showing their respect to the country. Sometimes, you could not find a place to sit –men take up all the benches in the park, lying full-length and blissfully snoring the afternoon away.Spitting, talking loudly and random littering are frequent scenes in tourist attractions. Countless visitors allow their children to use the city streets as a toilet, even though there is a toilet nearby. Newsstand and bookshop proprietors also suffer from bad-mannered people who tear the wrappers off magazines and read the contents without buying them. Bookstore visitors ignore the signs of asking them to rest only in designated areas instead of sitting on the stairwell. Similar stories are testing the tolerance of public opinion almost every day. We could be denied those undesirable manner. Nevertheless, it will be more persuasive to point out that China's tourism industry is not developing in a gradual way, but in a blowout manner. Growing wealth enables more Chinese people to seek leisure and fulfillment from traveling. Considering China's large population, some problems and challenges must emerge at the same time.We might be disappointed but not be desperate. Objectively speaking, before the national holiday, civilized traveling aroused surges of attention such as Wechat hot debate, media focus, screen exposure, which is beyond the past several years.Under the supervision of public opinion, the index of civilized golden week presents positive improvement. However,it’s still a serious short board that reflects on Chinese social moral deficiency. Not only should we take it seriously, but also wake up the consciousness of citizen responsibility and turn it in to action to make up. Forming harmonious social morality as if human’s maturation. We need time. I do believe the enlightened public moral education combined with perfected facilities will take several generations to nurture civilized behavior and display a positive image of Chinese tourists.
生物的界级分类（classification of kingdoms oforganisms） 根据生物的基本结构特点、特别是根据生物的演化概况所进行的各种“界”级分类系统。随着科学的发展，历史上曾先后出现过多种生物界级分类系统，主要有以下几种：（1）林奈的两界系统（1735），包括植物界和动物界；（2）海克尔的三界系统（1866年），包括原生生物界（Kingdom protista）、植物界和动物界；（3）李代尔（G.F.Leedale）的四界系统（1974），包括原核界（Kingdom Monera）（细菌、蓝藻）真菌界（Kingdom Fungi）、植物界和动物界；（4）魏泰克（R.H.Whittaker）的五界（1969），包括原核界、原生生物界、真菌界、植物界和动物界；（5）胡先骕的二总界系统（1965），包括始生和胞生两个总界，始生总界（Suprastatus Protobiota）只有病毒界，胞生总界（Suprastatus Cytobiota）包括细菌界（Status Bacteriobion－ta）、粘菌界（Status Myxobionta）、真菌界（Status Myco－bionta）、植物界（Status Phytobionta）和动物界（StatusZoobionta）；（6）陈世骧等的三总界六界分类系统（1979），即： Ⅰ.非细胞总界（Superkingdom Acytonia） 1.病毒界（包括类病毒） Ⅱ.原核总界（SuperkingdomProcaryota） 2.细菌界（Kingdom Mycomonera） 3.蓝藻界（Kingdom Phycomonera） Ⅲ.真核总界（Superkingdom Eucaryota） 4.植物界（Kingdom Plantae） 5.真菌界（Kingdom Fungi） 6.动物界（Kingdom Animalia） 生物的分界 面对着众多的生物，科学家为了便于研究起见，逐将生物分门别类。凡是形态功能相近的生物，即归为一类。显微镜未发明之前，传统上将生物划分为动物与植物王国，这是瑞典生物学家林奈(Carl von Linne)(图4.1)提出的二界分类法(two kingdom classification)。到了18世纪显微镜发明以后，科学家发现尚有许多低等的微生物兼具有动植物的特征，例如细菌、眼虫等等，
Listen to part of a lecture in a biology class. The class is discussing animal behavior. Okay Ok , the next kind of animal behavior I want to talk about might be familiar to you. You may have seen, for example, a bird that's in a the middle of a mating ritual. and suddenly it stops and prines preens . You know, takes a few moments to straight straighten its feathers. and then returns to the mating ritual. This kind of behavior, this doing something that seems completely out of place. It's is what we call a displacement activity. Displacement activities are activities that animals engaged animal's engaging in when they have conflicting drives. If we take our example for from a minute ago if the bird is afraid of its mate, it's conflicted. It wants to mate, but it's also afraid and wants to run away. So instead, it starts grooming itself. So the displacement activity the grooming, the straightening of its feathers seems to be an irrelevant behavior. So what do you think another example of the a displacement activity might be? How about an animal that , um, instead of fighting its enemy or running away, it attacks a plant or a bush. That's a really good suggestion, Carol Karl . But that's called redirecting. The animal is redirecting its behavior to another object. In this case, the plant or the bush. But that's not an irrelevant or inappropriate behavior. The behavior makes sense. it's appropriate under the circumstances, but what doesn't make sense is the object behaviors direct the behavior's directed towards. Okay Ok , who else? Carol? I think I've read in another class about an experiment were where an object that the animal was afraid of was put next to its food. next to the animal's food. And the animal, it was conflicted between confronting the object and eating the food, so instead
人间处处有真情 真情在人的手中，只要你伸出 手，它就会出现；真情在人的眼中，只要你张开眼，它就会流露；真情在人的口中，只要你一说话，它就会兑现；真情在人的心中，只要你敞开心；它就会留恋。生活中处处有真情，不信，你就亲自去体验。 一天早晨，妈妈不在家，只留下一张纸条让我早餐自己解决。我匆匆忙忙地刷牙洗脸后，背上书包就出门了。我走到一间早餐店，门前排着一支长长的队伍，我在队伍里等了十分钟左右，便轮到我了。我对阿姨说：“买两根油条。”我搜搜口袋，没找到钱，又把口袋翻了个底朝天，还是没有找到。于是我又翻了翻书包，仍然没找到。后面的人说：“没带钱还买东西，真大胆啊你。”说着就笑起来。我羞愧地低下了头，满脸通红，差点就哭了出来。这时，从后面走来一位叔叔，他对早餐店的阿姨说：我帮这位小朋友负钱。”我吃了一惊，抬头望了
望他：他高高的个子，一对乌黑的眉毛 下有双炯炯有神的眼睛。我激动的对他 说：“谢谢你，叔叔。”他笑着说：“不用 谢，下次细心一点就行了。”望着他远去 的背影，我想：一个与我素不相识的人， 怎么会热心的替我解围呢？ 从那以后，我一直乐于助人，他永 远激励着我。 人间处处有真情 “这是心的呼唤，这是爱的奉献……只 要人人都献出一点爱，世界将变成美好的 人间……”每当我听到这首亲切而温馨的 歌曲时，总控制不住热血腾沸。于是，一 件难忘的往事浮现在我的眼前—— 那是一个寒冷冬天的星期日。那年 我才十岁，上小学四年级。那一天，我到
同学家聚会后，下午四点钟左右，我怀着十分愉快的心情，骑着自行车往家赶。一路上，虽说我的心还沉浸在与同学聚会的喜悦之中，但是，凛冽的寒风吹在脸上就像刀割一样痛。突然，“叭”的一声，我的自行车卸链了。我不得不停下车，“该死的链条。”我心里诅咒道。我很不情愿地脱下手套，修理起自行车链条来。 此时，寒风越刮越猛，我的一双小手冷得像红萝卜。越是心急，链条越是不听使唤，我急得差点儿没哭出声来。 “叮铃铃……”突然，一阵清脆的自行车铃声在我身后响起，我心头不由得一热。“怎么呢，小妹妹？”这一声亲切的询问，让我心头一热。我抬起头一看，只见一位在漂亮的姐姐在我身旁停下车来。你见她，高高的个子，齐耳的短发，围着一条厚厚的围巾，两腮通红通红的。 “我的……自行车……链条坏了……”不知是激动，还是寒冷，我连说话都有些结结巴巴了。 “别着急，来，我帮你修！”说着，她
Public Behavior Thanks to years of huge effort, China is getting stronger and stronger. However, when the bright side is showing, something bad arises too ——terrible public behavior. When we are watching TV, it is normal to hear some bad behavior that happened. But the most common one in our daily life should be breaking traffic rules, or more accurately, running the red light. Large numbers of passengers, motorists, or even car drivers, ignoring the red light which means stop, continued on their way, abandoning their morality as well as lives. Tragedies that had happened seem to be unable to arouse their vigilance. As a result, more and more tragedies are going on. Others may value their lives indeed, but do not pay the same attention to the environment as they should, which makes spitting and littering a common but terrible phenomenon. When asked about the behavior, most of the public will surely admit that it is not right. And yet what force one to do the things he thinks is wrong? Just because of the need of convenience, or more deeply, the superficial understanding of environmental consciousness. And furthermore, the third bad behavior, as the least intolerable one, is the problem of queuing. Though there is an old saying: first come, first served, public seems not willing to obey it. Jumping the queue is widely seen everywhere. Worse still, sometimes there is even no queue but a noisy crowd, pushing and jostling as if they were enemies. It is sad that bad public behavior is far more than these. But I am strongly assured it will get better. All we need is time and education. Root the correct idea into public’s mind is particularly important. The Audience is not only the present generation, but the generation to come. 工信1409 叶欣
人间处处有真情作文300字 篇一：人间处处有真情 当我们正在嬉戏玩耍时，你们可曾想过，那样西南旱区的同胞们正在为喝水而发愁；当我们正在学习上课时，你们可曾想过，在那西南旱区的孩子们每天都要到遥远的山顶上打水在西南旱区，那些学生们一天只能喝半瓶水，一周只能洗一次碗筷，曾有人多次看到，那样吃完晚饭的学生们，在浑浊、只剩一点水的小池塘中捧水喝 有一次，一位记者来到旱区的一所学校里，看见每一个孩子的桌洞里都有半瓶水，记者很好奇，就问：你们桌洞里有这么多水，为什么不喝？其中有一个学生说：这些水我们不能喝，因为我们还要把它们带回家给爸爸妈妈喝。记者又问：那你们呢？那个学生说：渴了，我们就抿一抿，沾沾嘴唇，只要不是非常渴，我们就不喝。 这真是人间处处有真情啊！短短的几句话，让我们感到了孩子对父母的爱、浓浓的情，那么在我们身上，是否也有过这种爱？ 在人间，有许多真情的故事。 篇二：人间处处有真情 有一次，我和爸爸在外面玩，忽然看见一个人就要掉进水里了，旁边有一大群人都在笑话那个人是笨蛋。只有一个人走了过来，看见有个人抓着栏杆，快掉下去了，于是，他马上向那边跑去，想抓住那个人的手，因为来的太晚了，那个人掉进了水里，那个陌生人也跟着跳了下去，大概过了7、8分钟左右，一个人爬了上来，正是那个掉进水里的人，那个人跑过来说：快！快救人。人们打了110，可是，当110赶来下水去找的时候，已经没有人了。 于是，110又派了十多名警察，经过了几个小时的寻找，在海摊上找到了，可是，因为110发现的太晚了，在110过去的时候还有一丝气，别人都冲上去问，为什么要救那个人？ 陌生人吃力地说：因为人间处处有真情。说完，就死去了。 人们听了，都惭愧地低下了头，从此，只要人们看见哪里有危险，就会去帮助。 所以，我们一定要记住人间处处有真情这一句话，它虽然普遍，但这句话代表着人间处处有真、善、美。 篇三：人间处处有真情 人间到处都有情，都有爱，我经历过的和我见过的也有很多。但是，最令我感动的还算是去年冬天的一件事了。 那天，我走在上学的路上，只见雪后的地面坑坑洼洼的，行人,车辆稀稀疏疏，都在缓缓地移动。骑车的人更是小心翼翼，耸起双肩，夹紧两臂，眼睛注视着前方。 突然，在我面前的一位骑着车的中年人，重重地摔倒在地，挂在车保温瓶里的浓汤溅到了另一位年轻人的皮夹克上，皮夹克被弄上了油渍。过路的行人纷纷停下来看热闹，大家都认为一场舌战开始了。没想到，那位年轻人没顾得擦身上的油渍，却连忙弯腰把中年人扶了起来。中年人向年轻人道歉：真对不起，我急着给医院的病人送饭，今天路滑，一不小心同志，你的皮夹克人们又把目光投向了年轻人。只见他从口袋中拿出手绢，一边擦着皮夹克，一边笑着说：这么点小事，你别介意，你给病人送的汤一定很好喝，我的皮夹克真有口福，还尝了尝鲜呢！两个人都笑了，众人也笑了。 我望着渐渐散去的人群，心中有一种说不出的感觉。
Organizational Behavior Part1: Introduction to Organizational Behavior Chapter1:organizational behavior overview Organizational behavior (OB) is a field of study devoted to understanding, explaining, and ultimately improving the attitudes and behaviors of individuals and groups in organizations. OB source: 1.Industrial and organizational psychology 2. Social psychology 3. Sociology 4. Anthropology 5. Economics Integrative model of organizational behavior 1.Individual outcomes: Job performance;Organizational commitment 2.Individual mechanisms: Job satisfaction; stress; motivation; trust,justice and ethics; Learning and decision making Individual characteristics: Personality; cultural values; ability Group mechanisms: Teams; leaderships
根据材料，选取角度，自拟题目，写一篇不少于800字的文章；文体不限，诗歌除外。 当今社会发展快速变化，人们为工作废寝忘食，为生计奔走四方，但不能忘了人间真情，不要在遥远的距离中隔断了真情，不要在日常的忙碌中遗忘了真情，不要在日夜的拼搏中忽略了真情。 【试题来源】江苏省海安市2019届高三期中考试学业质量监测（正卷）语文试题 【答案解析】 人间处处有真情 真情似珍珠，闪烁着迷人的光彩；真情似火炬，传递着人间的温暖；真情似幸福的港湾，无论你走多远，始终温暖着你的心。我每天沐浴着真情，感悟着真情，由衷地感到：人间处处有真情。 真情来自长年累月的坚持。从我记事起，妈妈每天为全家做饭的身影就雕刻在我脑海中。早上，妈妈像个定时的小闹钟，五点半准时起床，厨房里响起了锅、碗、瓢、勺交响曲；中午，妈妈踩着四季之风，步履匆匆地从单位赶回家，交响曲又响彻厨房。一个人做一件事容易，难的是长年累月地坚持做一件事，而妈妈做到了，而且是心甘情愿、心情愉悦地做。每当我端起饭碗，就感受到妈妈浓浓的真情。是妈妈的真情让我茁壮成长，是妈妈的真情让我积极向上，是妈妈的真情在我幼小的心灵撒下一片阳光。真情，让我懂得了回报。 真情来自一杯清茶。我家附近有一面包店，只要你进门坐下，不论你买不买面包，店主都会微笑着端上一杯热气腾腾的柠檬茶，喝着滋润心田而又提神的茶，真情随着茶水在全身荡漾。你可以尽情地喝茶，有兴趣的话在书架上拿一本喜欢的书来消遣时光也是很不错的选择，店主绝不会给你脸色看，还热情地为你续茶，有时还攀谈几句。真情，让我懂得了向往。 真情来自同学的关爱。在学校举行的“跳蚤”书市活动中，我卖力地招揽生意，一会儿嗓子就喊哑了。“杜鹃花”（小杜的昵称）看见了，把老师的扩音器借来给我，说：“noodles（我的昵称），来，戴上！”一句普通的话，在我心底激起千层浪，惊涛拍岸般撞击着我的心房。真情，让我懂得关爱。 真情来自校讯通博客。小城说雨老师每周四雷打不动的作文指导，威如先生对博友的有求必应，十里荷香老师的香飘万“家”，雅茹、艺琳姐姐们在博友的每篇博文里留下的足迹……太多的名字让我感动，太多的名字让我感到温暖！真情，让我懂得博爱。 真情来自…… 真情无处不在，在每一句真诚的话语里，在每一个关切的眼神里，在每一件洗过的衣服里，在每一条殷殷评论里，在……我得到无数的真情，也要把真情回馈给每一个人，让生活更美好，让阳光更灿烂！勿忘真情，珍惜真情！ 【详解】本题考查学生的写作能力。这是一则材料作文，首先要分析概括材料的内容，把握
2020关于人间真情作文 关于人间真情作文篇一： 春天的一个黎明，一轮红日已从东方升起。阳光普照着大地，光辉洒满了大地，一天的早晨又开始了。 我一个人乘车上学，就是为了学会独立。因为乘这班车的人实在太多，我利用我的聪明才智才轻松上了车，哪像我后面的那位又矮又胖的阿姨，弄了半天才刚过门槛，她胖胖的脸上嵌着一双眯成线的眼睛，一双厚厚的嘴唇不停的翻动着，似乎在嘀咕着什么，脸上隐约显现了她的几分不满。 售票员来收钱了，她当时就站在我的身旁，我把身上所有的口袋翻了个几十遍也没见到一分钱的影子。天哪！我没带钱！旁边的售票员原有的三月里的桃花似的微笑已不见踪影，脸上却多了几片阴沉沉的乌云，她的`眼珠不停的上下移动的看着我，好像要到濒临火山爆发的临界点了。我的脸顿时火辣辣的，恨不得马上找个地缝钻下去。别的乘客都用异样的目光看着我，这是我的脸红的更强烈了。 正当我已跌倒在万丈深渊中时，一个沙哑的声音冲了出来：“我来帮她交！”我高兴地就像重获自由的小鸟一样，一时按捺不住心中的喜悦，激动地回头一看——原来是那位刚刚被我在暗地里讥讽过的阿姨。我顿时无语了。 我只是呆呆的望着她，她冲我笑了，笑得很灿烂，这是一种很温馨的笑，一种很甜蜜的笑。可我的脸却又一次红了…… 也许是出于胆小，也许是出于后怕发现我讥讽她而不给我付车钱，所以我只是道了一声“谢谢”，她到站了，向我挥手，示意她在跟我道别。 但如果还能见到她，我一定会说一声：“对不起！” 在这个世界上有许多温暖人心的真情一面，而这些真情却往往不存在回报！ 关于人间真情作文篇二： 有句话说得好：人间自有真情在！ 那是个炎热的暑假。中午我吃完饭后准备去上课，虽然外面很热，不过提早去坐在教室里吹空调休息一下也很舒服。当我出发走到红绿灯的那条马路时，是红灯，所以过马路的人们都在等待绿灯到来。这时一个骑着自行车的伯伯过来了，在炎炎的夏日下，他的身上没有任何一件可以遮挡太阳的物品。突然，他的身体晃了晃，接着就连人带车的倒了下来。很明显，他中暑了！ 在这种紧急的情况下，周围的路人都很着急。一个交通指挥员连忙跑过来问怎么回事，得知事情的情况后，指挥员连忙带着大家将那个伯伯送到附近的医院。到了医院后，大家问医生怎么样了，医生回答说是很严重的中暑，暂时不能出去，要先休息一会。
behavior的用法和辨析 今天和大家一起学习一下behavior的用法，快来一起学习吧，下面就和大家分享，来欣赏一下吧。 词汇精选：behavior的用法和辨析 一、详细释义： n. 行为;举止;态度[U] 例句： Their behavior towards me shows that they do not like me. 他们对我的举止说明他们不喜欢我。 例句： His strange behavior made the police suspicious. 他不寻常的举止引起了警察的怀疑。 (机器等的)运转状态，性能[U]
例句： The aircrafts behavior was satisfactory on its first test flight. 那架飞机在第一次试飞时运转情况令人满意。 (事物的)反应，变化;作用[U] 二、词义辨析： behavior,conduct,manner 这些名词均含有“行为，举止”之意。behavior普通用词，侧重指某人在特定场合对他人或在他人面前的行为、举止。conduct较正式用词，泛指某人的行动。侧重根据社会道德标准和责任感。manner主要用来指人的习惯行为。 三、相关短语： behavior therapy 行为疗法 behavior modification 行为修正 四、参考例句： Her behavior enraged him.
她的行为激怒了他。 Your behavior frenzied me. 你的行为使我愤怒。 Her odd behavior defies understanding. 她的古怪行为无法理解。 Her behavior alienated her friends. 她的行为使自己跟朋友们疏远了。His behavior borders upon madness. 他的行为几乎算得上疯狂。 My behavior is above reproach. 我的做法无可厚非。 Her behavior was beyond reproach. 她的行为无可指责。 She has a spontaneous behavior . 她的举止很自然。 His behavior was beyond bearing.
人间处处有真情作文500字小学作文4篇 ----WORD文档，下载后可编辑修改---- 下面是作者为各位老师家长学生收集整理的范本，欢迎借鉴参考阅读，您的努力学习和创新是为了更美好的未来，欢迎下载！ 人间处处有真情作文500字小学作文一 “只要人人都献出一点爱，世界就会变成美好的人间。”这是一首大家熟悉的歌。起初我并不知道它的意思，但在一次难忘的经历中，让我真正懂得了这首歌的真正内涵。 我们家搬入新居以后，周围的邻居开始熟悉了。我偶然发现住在我家一楼的叔叔是位残疾人，出门很不方便。看到这一切，心里有些替他着急。心想：什么时候可以帮帮叔叔呀? 有一天，我放学回家，正好看见叔叔也下班回家，他的轮椅没在车旁边，因为腿有残疾，所以他不能下车去拿，只能坐在车上等着家人帮他把轮椅推过来。我见此情景，连忙主动上前问道：“叔叔，是需要轮椅吧?”叔叔说：“对，我是需要......”没等叔叔说完，我就一个箭步跑到轮椅前，把它推过来。叔叔看见轮椅推过来，感激地说：“小姑娘，真是太谢谢你了!”我说：“没关系，这是我应该做的。”然后，我把轮椅扶住，让叔叔从车上挪下来，坐到轮椅上。我又灵机一动，赶快把楼道门打开了，这样叔叔就可以顺利回家了。临进门前我跟叔叔说：“叔叔，以后帮你推车的事，就包在我身上了。”后来，我又慢慢地发现不仅仅我在关心叔叔，还有张奶奶、李姐姐等许许多多楼上楼下的热心人都在默默地帮助他。李姐姐经常帮叔叔家的孩子辅导功课，张奶奶平时做了好吃的，就会给叔叔端来。我们楼上楼下的邻居就像一家人一样。 人们常说：予人玫瑰，手有余香，帮助别人是快乐的。在社会及邻里这样的大家庭中，无论谁遇到困难，只要我们都能献出一点爱，那么这个大家庭就会成为美好的人间!从那刻起，我深深领悟到人间出处处要真情，世间才能多美好! 人间处处有真情作文500字小学作文二 也许是看多了港台暴力片，也许是听多了英雄流血又流泪的传奇，我总是认为现在的社会，真情难觅，然而，一次偶然的经历，却使我发现，我错了! 那是一个阴沉的早晨，候车亭里只有我一人在低头等车。好一会儿，我抬起
人间处处有真情作文600字5篇 ----WORD文档，下载后可编辑修改---- 下面是小编收集整理的范本，欢迎您借鉴参考阅读和下载，侵删。您的努力学习是为了更美好的未来！ 人间处处有真情作文600字篇1：作者：无名小辈 我从二年级下学期就开始坐公交车，这几年来，其中有一件事我一直铭记在心。 那是我上三年级的时候，一天下午，我像往常一样上了15路公交车，上车一看，就还有一个空位了，便急忙坐下。 过了一会儿，一位白发苍苍、行走不便的老奶奶上了车，可是没有座位了，我望了望车上的人，有的低头看书，有的脸朝着窗外，有的在听音乐......没有一个人给老奶奶让座。 汽车发动了，老奶奶摇摇晃晃，差一点儿摔倒，可车上的人就像没看见似的。我难过极了，忙上前扶住老人，让老奶奶坐我的座位。 顿时，车上的人都向我投来诧异的目光，他们可能没料到我会给老人让座。 回到家，我跟妈妈说了这件事，没想到，妈妈把脸一沉，说：“你还是个小孩，让什么座!”，我刚想说什么，但一看妈妈那不容商量的眼神，我只好默认了。 事情还没完，过了几天，我又上了15路公交车，可车上没有一个空位了，而且非常挤。 我费了九牛二虎之力才挤到后面，这时，我又看到上次的老奶奶，
那个老奶奶也看见我，她朝我招招手，示意让我坐在她腿上。我起初不肯，但奶奶硬让我坐，我只好坐下了。 回到家，我跟妈妈讲述了一遍，并且“教训”了妈妈：“妈，妈，我们要尊老爱幼，勿以恶小而为之，勿以善小而不为。” 一句话，说得妈妈哑口无言。 是啊，当我们坐在公交车上，经常会上来一些老人、儿童、残疾人、孕妇和怀抱婴儿者，当车上没有座位时，却很少有人让座。让座----其实并没什么，只要人人献出一点爱，世界将会更美好。 人间处处有真情作文600字篇2：作者：陈实 一天中午，我一边哼着歌儿一边往回家的路上赶，正想开门，突然发现自己忘记带钥匙了。此时此刻邻居王爷爷正在开门，我焦急的望着他，心里想着：那要是我家的钥匙该多好呀!王爷爷似乎看穿了我的心思，便说：“你是不是没带钥匙呢?要不你到我家坐坐?”我犹豫了一下，还是说：“好吧!”王爷爷说：“不用换鞋了，直接进来吧。”于是，我小心翼翼地走进他的家门。 刚进大厅，一桌香喷喷的饭菜出现在我的眼前，我肚子里突然发出了“咕噜咕噜”的声音。王爷爷关切地说：“你是不是饿了?要不要跟我们一起吃饭?”接着，他拿来一把椅子放到我跟前，并让我坐下。我被他的热情感动了，就说：“爷爷，您别客气，我不坐了，我想用一下你家的电话，可以吗?”于是，他递给我一部老人手机说：“没事，你尽管打吧。”我接过电话，但是，我刚拔完数字“1”手机就没电了。王爷爷皱着眉头并递给我伍元钱说：“你到下面用那个