Image browsing and natural language paraphrases of semantic web annotations

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2007-5-“Image” metaphors and connotations in everyday language

Annual Review of Cognitive Linguistics 5 (2007), 173–192. issn ‒ / e-issn – © John Benjamins Publishing Company
174 Alice Deignan
(2) “But as the federal powers dug in their heels against change and violence increased…”
“Image” metaphors and connotations in everyday language
Alice Deignan
University of Leeds, UK
In this paper, I argue that the general notion of an image metaphor, which has been traditionally confined to so-called “one-shot metaphors”, as used in literary and poetic language, could be expanded to describe many expressions that are found in everyday language. Following Caballero (2003a), I argue that the division in cognitive linguistics of metaphors into “image” and “conceptual” is over-simplistic. I show that many of the most frequent metaphors in my data have characteristics which would qualify them for inclusion in both categories. I also argue that connotational meaning is an important characteristic of these expressions, unifying their literal and non-literal meanings. A detailed analysis of the Bank of English corpus concordance for heel shows the numerical importance of such metaphors. I refer to research into metaphor that takes an emergentist perspective, and which has led a number of other existing distinctions to be questioned. I argue that these expressions, termed “metaphoremes”, which are difficult to classify using existing distinctions, should be regarded as prototypical on the grounds of their frequency, rather than as anomalous. Keywords: metaphor, image, metonymy, emergentist, collocation, conceptual, corpus, concordance

基于无序图像的三维建模方法

Int J Comput VisDOI10.1007/s11263-007-0107-3Modeling the World from Internet Photo Collections Noah Snavely·Steven M.Seitz·Richard SzeliskiReceived:30January2007/Accepted:31October2007©Springer Science+Business Media,LLC2007Abstract There are billions of photographs on the Inter-net,comprising the largest and most diverse photo collec-tion ever assembled.How can computer vision researchers exploit this imagery?This paper explores this question from the standpoint of3D scene modeling and visualization.We present structure-from-motion and image-based rendering algorithms that operate on hundreds of images downloaded as a result of keyword-based image search queries like “Notre Dame”or“Trevi Fountain.”This approach,which we call Photo Tourism,has enabled reconstructions of nu-merous well-known world sites.This paper presents these algorithms and results as aﬁrst step towards3D modeling of the world’s well-photographed sites,cities,and landscapes from Internet imagery,and discusses key open problems and challenges for the research community.Keywords Structure from motion·3D scene analysis·Internet imagery·Photo browsers·3D navigation1IntroductionMost of the world’s signiﬁcant sites have been photographed under many different conditions,both from the ground and from the air.For example,a Google image search for“Notre Dame”returns over one million hits(as of September, 2007),showing the cathedral from almost every conceivable viewing position and angle,different times of day and night, N.Snavely( )·S.M.SeitzUniversity of Washington,Seattle,WA,USAe-mail:snavely@R.SzeliskiMicrosoft Research,Redmond,WA,USA and changes in season,weather,and decade.Furthermore, entire cities are now being captured at street level and from a birds-eye perspective(e.g.,Windows Live Local,1,2and Google Streetview3),and from satellite or aerial views(e.g., Google4).The availability of such rich imagery of large parts of the earth’s surface under many different viewing conditions presents enormous opportunities,both in computer vision research and for practical applications.From the standpoint of shape modeling research,Internet imagery presents the ultimate data set,which should enable modeling a signiﬁ-cant portion of the world’s surface geometry at high resolu-tion.As the largest,most diverse set of images ever assem-bled,Internet imagery provides deep insights into the space of natural images and a rich source of statistics and priors for modeling scene appearance.Furthermore,Internet imagery provides an ideal test bed for developing robust and gen-eral computer vision algorithms that can work effectively “in the wild.”In turn,algorithms that operate effectively on such imagery will enable a host of important applications, ranging from3D visualization,localization,communication (media sharing),and recognition,that go well beyond tradi-tional computer vision problems and can have broad impacts for the population at large.To date,this imagery is almost completely untapped and unexploited by computer vision researchers.A major rea-son is that the imagery is not in a form that is amenable to processing,at least by traditional methods:the images are 1Windows Live Local,.2Windows Live Local—Virtual Earth Technology Preview,http:// .3Google Maps,.4Google Maps,.Int J Comput Visunorganized,uncalibrated,with widely variable and uncon-trolled illumination,resolution,and image quality.Develop-ing computer vision techniques that can operate effectively with such imagery has been a major challenge for the re-search community.Within this scope,one key challenge is registration,i.e.,ﬁguring out correspondences between im-ages,and how they relate to one another in a common3D coordinate system(structure from motion).While a lot of progress has been made in these areas in the last two decades (Sect.2),many challenging open problems remain.In this paper we focus on the problem of geometrically registering Internet imagery and a number of applications that this enables.As such,weﬁrst review the state of the art and then present someﬁrst steps towards solving this problem along with a visualization front-end that we call Photo Tourism(Snavely et al.2006).We then present a set of open research problems for theﬁeld,including the cre-ation of more efﬁcient correspondence and reconstruction techniques for extremely large image data sets.This paper expands on the work originally presented in(Snavely et al. 2006)with many new reconstructions and visualizations of algorithm behavior across datasets,as well as a brief dis-cussion of Photosynth,a Technology Preview by Microsoft Live Labs,based largely on(Snavely et al.2006).We also present a more complete related work section and add a broad discussion of open research challenges for theﬁeld. Videos of our system,along with additional supplementary material,can be found on our Photo Tourism project Web site,.2Previous WorkThe last two decades have seen a dramatic increase in the capabilities of3D computer vision algorithms.These in-clude advances in feature correspondence,structure from motion,and image-based modeling.Concurrently,image-based rendering techniques have been developed in the com-puter graphics community,and image browsing techniques have been developed for multimedia applications.2.1Feature CorrespondenceTwenty years ago,the foundations of modern feature detec-tion and matching techniques were being laid.Lucas and Kanade(1981)had developed a patch tracker based on two-dimensional image statistics,while Moravec(1983)intro-duced the concept of“corner-like”feature points.Först-ner(1986)and then Harris and Stephens(1988)both pro-posedﬁnding keypoints using measures based on eigenval-ues of smoothed outer products of gradients,which are still widely used today.While these early techniques detected keypoints at a single scale,modern techniques use a quasi-continuous sampling of scale space to detect points invari-ant to changes in scale and orientation(Lowe2004;Mikola-jczyk and Schmid2004)and somewhat invariant to afﬁne transformations(Baumberg2000;Kadir and Brady2001; Schaffalitzky and Zisserman2002;Mikolajczyk et al.2005).Unfortunately,early techniques relied on matching patches around the detected keypoints,which limited their range of applicability to scenes seen from similar view-points,e.g.,for aerial photogrammetry applications(Hannah 1988).If features are being tracked from frame to frame,an afﬁne extension of the basic Lucas-Kanade tracker has been shown to perform well(Shi and Tomasi1994).However,for true wide baseline matching,i.e.,the automatic matching of images taken from widely different views(Baumberg2000; Schaffalitzky and Zisserman2002;Strecha et al.2003; Tuytelaars and Van Gool2004;Matas et al.2004),(weakly) afﬁne-invariant feature descriptors must be used.Mikolajczyk et al.(2005)review some recently devel-oped view-invariant local image descriptors and experimen-tally compare their performance.In our own Photo Tourism research,we have been using Lowe’s Scale Invariant Fea-ture Transform(SIFT)(Lowe2004),which is widely used by others and is known to perform well over a reasonable range of viewpoint variation.2.2Structure from MotionThe late1980s also saw the development of effective struc-ture from motion techniques,which aim to simultaneously reconstruct the unknown3D scene structure and camera positions and orientations from a set of feature correspon-dences.While Longuet-Higgins(1981)introduced a still widely used two-frame relative orientation technique in 1981,the development of multi-frame structure from mo-tion techniques,including factorization methods(Tomasi and Kanade1992)and global optimization techniques(Spet-sakis and Aloimonos1991;Szeliski and Kang1994;Olien-sis1999)occurred quite a bit later.More recently,related techniques from photogrammetry such as bundle adjustment(Triggs et al.1999)(with related sparse matrix techniques,Szeliski and Kang1994)have made their way into computer vision and are now regarded as the gold standard for performing optimal3D reconstruc-tion from correspondences(Hartley and Zisserman2004).For situations where the camera calibration parameters are unknown,self-calibration techniques,whichﬁrst esti-mate a projective reconstruction of the3D world and then perform a metric upgrade have proven to be successful (Pollefeys et al.1999;Pollefeys and Van Gool2002).In our own work(Sect.4.2),we have found that the simpler approach of simply estimating each camera’s focal length as part of the bundle adjustment process seems to produce good results.Int J Comput VisThe SfM approach used in this paper is similar to that of Brown and Lowe(2005),with several modiﬁcations to improve robustness over a variety of data sets.These in-clude initializing new cameras using pose estimation,to help avoid local minima;a different heuristic for selecting the initial two images for SfM;checking that reconstructed points are well-conditioned before adding them to the scene; and using focal length information from image EXIF tags. Schaffalitzky and Zisserman(2002)present another related technique for reconstructing unordered image sets,concen-trating on efﬁciently matching interest points between im-ages.Vergauwen and Van Gool have developed a similar approach(Vergauwen and Van Gool2006)and are hosting a web-based reconstruction service for use in cultural heritage applications5.Fitzgibbon and Zisserman(1998)and Nistér (2000)prefer a bottom-up approach,where small subsets of images are matched to each other and then merged in an agglomerative fashion into a complete3D reconstruction. While all of these approaches address the same SfM prob-lem that we do,they were tested on much simpler datasets with more limited variation in imaging conditions.Our pa-per marks theﬁrst successful demonstration of SfM tech-niques applied to the kinds of real-world image sets found on Google and Flickr.For instance,our typical image set has photos from hundreds of different cameras,zoom levels, resolutions,different times of day or seasons,illumination, weather,and differing amounts of occlusion.2.3Image-Based ModelingIn recent years,computer vision techniques such as structure from motion and model-based reconstruction have gained traction in the computer graphicsﬁeld under the name of image-based modeling.IBM is the process of creating three-dimensional models from a collection of input images(De-bevec et al.1996;Grzeszczuk2002;Pollefeys et al.2004).One particular application of IBM has been the cre-ation of large scale architectural models.Notable exam-ples include the semi-automatic Façade system(Debevec et al.1996),which was used to reconstruct compellingﬂy-throughs of the University of California Berkeley campus; automatic architecture reconstruction systems such as that of Dick et al.(2004);and the MIT City Scanning Project (Teller et al.2003),which captured thousands of calibrated images from an instrumented rig to construct a3D model of the MIT campus.There are also several ongoing academic and commercial projects focused on large-scale urban scene reconstruction.These efforts include the4D Cities project (Schindler et al.2007),which aims to create a spatial-temporal model of Atlanta from historical photographs;the 5Epoch3D Webservice,http://homes.esat.kuleuven.be/~visit3d/ webservice/html/.Stanford CityBlock Project(Román et al.2004),which uses video of city blocks to create multi-perspective strip images; and the UrbanScape project of Akbarzadeh et al.(2006). Our work differs from these previous approaches in that we only reconstruct a sparse3D model of the world,since our emphasis is more on creating smooth3D transitions be-tween photographs rather than interactively visualizing a3D world.2.4Image-Based RenderingTheﬁeld of image-based rendering(IBR)is devoted to the problem of synthesizing new views of a scene from a set of input photographs.A forerunner to thisﬁeld was the groundbreaking Aspen MovieMap project(Lippman1980), in which thousands of images of Aspen Colorado were cap-tured from a moving car,registered to a street map of the city,and stored on laserdisc.A user interface enabled in-teractively moving through the images as a function of the desired path of the user.Additional features included a navi-gation map of the city overlaid on the image display,and the ability to touch any building in the currentﬁeld of view and jump to a facade of that building.The system also allowed attaching metadata such as restaurant menus and historical images with individual buildings.Recently,several compa-nies,such as Google6and EveryScape7have begun creating similar“surrogate travel”applications that can be viewed in a web browser.Our work can be seen as a way to automati-cally create MovieMaps from unorganized collections of im-ages.(In contrast,the Aspen MovieMap involved a team of over a dozen people working over a few years.)A number of our visualization,navigation,and annotation capabilities are similar to those in the original MovieMap work,but in an improved and generalized form.More recent work in IBR has focused on techniques for new view synthesis,e.g.,(Chen and Williams1993; McMillan and Bishop1995;Gortler et al.1996;Levoy and Hanrahan1996;Seitz and Dyer1996;Aliaga et al.2003; Zitnick et al.2004;Buehler et al.2001).In terms of appli-cations,Aliaga et al.’s(2003)Sea of Images work is perhaps closest to ours in its use of a large collection of images taken throughout an architectural space;the same authors address the problem of computing consistent feature matches across multiple images for the purposes of IBR(Aliaga et al.2003). However,our images are casually acquired by different pho-tographers,rather than being taken on aﬁxed grid with a guided robot.In contrast to most prior work in IBR,our objective is not to synthesize a photo-realistic view of the world from all viewpoints per se,but to browse a speciﬁc collection of 6Google Maps,.7Everyscape,.Int J Comput Visphotographs in a3D spatial context that gives a sense of the geometry of the underlying scene.Our approach there-fore uses an approximate plane-based view interpolation method and a non-photorealistic rendering of background scene structures.As such,we side-step the more challenging problems of reconstructing full surface models(Debevec et al.1996;Teller et al.2003),lightﬁelds(Gortler et al.1996; Levoy and Hanrahan1996),or pixel-accurate view inter-polations(Chen and Williams1993;McMillan and Bishop 1995;Seitz and Dyer1996;Zitnick et al.2004).The bene-ﬁt of doing this is that we are able to operate robustly with input imagery that is beyond the scope of previous IBM and IBR techniques.2.5Image Browsing,Retrieval,and AnnotationThere are many techniques and commercial products for browsing sets of photos and much research on the subject of how people tend to organize photos,e.g.,(Rodden and Wood2003).Many of these techniques use metadata,such as keywords,photographer,or time,as a basis of photo or-ganization(Cooper et al.2003).There has recently been growing interest in using geo-location information to facilitate photo browsing.In particu-lar,the World-Wide Media Exchange(WWMX)(Toyama et al.2003)arranges images on an interactive2D map.Photo-Compas(Naaman et al.2004)clusters images based on time and location.Realityﬂythrough(McCurdy and Griswold 2005)uses interface ideas similar to ours for exploring video from camcorders instrumented with GPS and tilt sensors, and Kadobayashi and Tanaka(2005)present an interface for retrieving images using proximity to a virtual camera.In Photowalker(Tanaka et al.2002),a user can manually au-thor a walkthrough of a scene by specifying transitions be-tween pairs of images in a collection.In these systems,loca-tion is obtained from GPS or is manually speciﬁed.Because our approach does not require GPS or other instrumentation, it has the advantage of being applicable to existing image databases and photographs from the Internet.Furthermore, many of the navigation features of our approach exploit the computation of image feature correspondences and sparse 3D geometry,and therefore go beyond what has been possi-ble in these previous location-based systems.Many techniques also exist for the related task of retriev-ing images from a database.One particular system related to our work is Video Google(Sivic and Zisserman2003)(not to be confused with Google’s own video search),which al-lows a user to select a query object in one frame of video and efﬁcientlyﬁnd that object in other frames.Our object-based navigation mode uses a similar idea,but extended to the3D domain.A number of researchers have studied techniques for au-tomatic and semi-automatic image annotation,and annota-tion transfer in particular.The LOCALE system(Naaman et al.2003)uses proximity to transfer labels between geo-referenced photographs.An advantage of the annotation ca-pabilities of our system is that our feature correspondences enable transfer at muchﬁner granularity;we can transfer annotations of speciﬁc objects and regions between images, taking into account occlusions and the motions of these ob-jects under changes in viewpoint.This goal is similar to that of augmented reality(AR)approaches(e.g.,Feiner et al. 1997),which also seek to annotate images.While most AR methods register a3D computer-generated model to an im-age,we instead transfer2D image annotations to other im-ages.Generating annotation content is therefore much eas-ier.(We can,in fact,import existing annotations from pop-ular services like Flickr.)Annotation transfer has been also explored for video sequences(Irani and Anandan1998).Finally,Johansson and Cipolla(2002)have developed a system where a user can take a photograph,upload it to a server where it is compared to an image database,and re-ceive location information.Our system also supports this application in addition to many other capabilities(visual-ization,navigation,annotation,etc.).3OverviewOur objective is to geometrically register large photo col-lections from the Internet and other sources,and to use the resulting3D camera and scene information to facili-tate a number of applications in visualization,localization, image browsing,and other areas.This section provides an overview of our approach and summarizes the rest of the paper.The primary technical challenge is to robustly match and reconstruct3D information from hundreds or thousands of images that exhibit large variations in viewpoint,illumina-tion,weather conditions,resolution,etc.,and may contain signiﬁcant clutter and outliers.This kind of variation is what makes Internet imagery(i.e.,images returned by Internet image search queries from sites such as Flickr and Google) so challenging to work with.In tackling this problem,we take advantage of two recent breakthroughs in computer vision,namely feature-matching and structure from motion,as reviewed in Sect.2.The back-bone of our work is a robust SfM approach that reconstructs 3D camera positions and sparse point geometry for large datasets and has yielded reconstructions for dozens of fa-mous sites ranging from Notre Dame Cathedral to the Great Wall of China.Section4describes this approach in detail, as well as methods for aligning reconstructions to satellite and map data to obtain geo-referenced camera positions and geometry.One of the most exciting applications for these recon-structions is3D scene visualization.However,the sparseInt J Comput Vispoints produced by SfM methods are by themselves very limited and do not directly produce compelling scene ren-derings.Nevertheless,we demonstrate that this sparse SfM-derived geometry and camera information,along with mor-phing and non-photorealistic rendering techniques,is sufﬁ-cient to provide compelling view interpolations as described in5.Leveraging this capability,Section6describes a novel photo explorer interface for browsing large collections of photographs in which the user can virtually explore the3D space by moving from one image to another.Often,we are interested in learning more about the con-tent of an image,e.g.,“which statue is this?”or“when was this building constructed?”A great deal of annotated image content of this form already exists in guidebooks,maps,and Internet resources such as Wikipedia8and Flickr.However, the image you may be viewing at any particular time(e.g., from your cell phone camera)may not have such annota-tions.A key feature of our system is the ability to transfer annotations automatically between images,so that informa-tion about an object in one image is propagated to all other images that contain the same object(Sect.7).Section8presents extensive results on11scenes,with visualizations and an analysis of the matching and recon-struction results for these scenes.We also brieﬂy describe Photosynth,a related3D image browsing tool developed by Microsoft Live Labs that is based on techniques from this paper,but also adds a number of interesting new elements. Finally,we conclude with a set of research challenges for the community in Sect.9.4Reconstructing Cameras and Sparse GeometryThe visualization and browsing components of our system require accurate information about the relative location,ori-entation,and intrinsic parameters such as focal lengths for each photograph in a collection,as well as sparse3D scene geometry.A few features of our system require the absolute locations of the cameras,in a geo-referenced coordinate frame.Some of this information can be provided with GPS devices and electronic compasses,but the vast majority of existing photographs lack such information.Many digital cameras embed focal length and other information in the EXIF tags of imageﬁles.These values are useful for ini-tialization,but are sometimes inaccurate.In our system,we do not rely on the camera or any other piece of equipment to provide us with location,orientation, or geometry.Instead,we compute this information from the images themselves using computer vision techniques.We ﬁrst detect feature points in each image,then match feature points between pairs of images,andﬁnally run an iterative, 8Wikipedia,.robust SfM procedure to recover the camera parameters.Be-cause SfM only estimates the relative position of each cam-era,and we are also interested in absolute coordinates(e.g., latitude and longitude),we use an interactive technique to register the recovered cameras to an overhead map.Each of these steps is described in the following subsections.4.1Keypoint Detection and MatchingTheﬁrst step is toﬁnd feature points in each image.We use the SIFT keypoint detector(Lowe2004),because of its good invariance to image transformations.Other feature de-tectors could also potentially be used;several detectors are compared in the work of Mikolajczyk et al.(2005).In addi-tion to the keypoint locations themselves,SIFT provides a local descriptor for each keypoint.A typical image contains several thousand SIFT keypoints.Next,for each pair of images,we match keypoint descrip-tors between the pair,using the approximate nearest neigh-bors(ANN)kd-tree package of Arya et al.(1998).To match keypoints between two images I and J,we create a kd-tree from the feature descriptors in J,then,for each feature in I weﬁnd the nearest neighbor in J using the kd-tree.For efﬁciency,we use ANN’s priority search algorithm,limiting each query to visit a maximum of200bins in the tree.Rather than classifying false matches by thresholding the distance to the nearest neighbor,we use the ratio test described by Lowe(2004):for a feature descriptor in I,weﬁnd the two nearest neighbors in J,with distances d1and d2,then accept the match if d1d2<0.6.If more than one feature in I matches the same feature in J,we remove all of these matches,as some of them must be spurious.After matching features for an image pair(I,J),we robustly estimate a fundamental matrix for the pair us-ing RANSAC(Fischler and Bolles1981).During each RANSAC iteration,we compute a candidate fundamental matrix using the eight-point algorithm(Hartley and Zis-serman2004),normalizing the problem to improve robust-ness to noise(Hartley1997).We set the RANSAC outlier threshold to be0.6%of the maximum image dimension,i.e., 0.006max(image width,image height)(about six pixels for a1024×768image).The F-matrix returned by RANSAC is reﬁned by running the Levenberg-Marquardt algorithm(No-cedal and Wright1999)on the eight parameters of the F-matrix,minimizing errors for all the inliers to the F-matrix. Finally,we remove matches that are outliers to the recov-ered F-matrix using the above threshold.If the number of remaining matches is less than twenty,we remove all of the matches from consideration.Afterﬁnding a set of geometrically consistent matches between each image pair,we organize the matches into tracks,where a track is a connected set of matching key-points across multiple images.If a track contains more thanInt J Comput VisFig.1Photo connectivity graph.This graph contains a node for each image in a set of photos of the Trevi Fountain, with an edge between each pair of photos with matching features.The size of a node is proportional to its degree.There are two dominant clusters corresponding to day(a)and night time(d)photos.Similar views of the facade cluster together in the center,while nodes in the periphery,e.g.,(b) and(c),are more unusual(often close-up)viewsone keypoint in the same image,it is deemed inconsistent. We keep consistent tracks containing at least two keypoints for the next phase of the reconstruction procedure.Once correspondences are found,we can construct an im-age connectivity graph,in which each image is a node and an edge exists between any pair of images with matching features.A visualization of an example connectivity graph for the Trevi Fountain is Fig.1.This graph embedding was created with the neato tool in the Graphviz toolkit.9Neato represents the graph as a mass-spring system and solves for an embedding whose energy is a local minimum.The image connectivity graph of this photo set has sev-eral distinct features.The large,dense cluster in the cen-ter of the graph consists of photos that are all fairly wide-angle,frontal,well-lit shots of the fountain(e.g.,image(a)). Other images,including the“leaf”nodes(e.g.,images(b) and(c))and night time images(e.g.,image(d)),are more loosely connected to this core set.Other connectivity graphs are shown in Figs.9and10.4.2Structure from MotionNext,we recover a set of camera parameters(e.g.,rotation, translation,and focal length)for each image and a3D lo-cation for each track.The recovered parameters should be consistent,in that the reprojection error,i.e.,the sum of dis-tances between the projections of each track and its corre-sponding image features,is minimized.This minimization problem can formulated as a non-linear least squares prob-lem(see Appendix1)and solved using bundle adjustment. Algorithms for solving this non-linear problem,such as No-cedal and Wright(1999),are only guaranteed toﬁnd lo-cal minima,and large-scale SfM problems are particularly prone to getting stuck in bad local minima,so it is important 9Graphviz—graph visualization software,/.to provide good initial estimates of the parameters.Rather than estimating the parameters for all cameras and tracks at once,we take an incremental approach,adding in one cam-era at a time.We begin by estimating the parameters of a single pair of cameras.This initial pair should have a large number of matches,but also have a large baseline,so that the ini-tial two-frame reconstruction can be robustly estimated.We therefore choose the pair of images that has the largest num-ber of matches,subject to the condition that those matches cannot be well-modeled by a single homography,to avoid degenerate cases such as coincident cameras.In particular, weﬁnd a homography between each pair of matching im-ages using RANSAC with an outlier threshold of0.4%of max(image width,image height),and store the percentage of feature matches that are inliers to the estimated homogra-phy.We select the initial image pair as that with the lowest percentage of inliers to the recovered homography,but with at least100matches.The camera parameters for this pair are estimated using Nistér’s implementation of theﬁve point al-gorithm(Nistér2004),10then the tracks visible in the two images are triangulated.Finally,we do a two frame bundle adjustment starting from this initialization.Next,we add another camera to the optimization.We select the camera that observes the largest number of tracks whose3D locations have already been estimated, and initialize the new camera’s extrinsic parameters using the direct linear transform(DLT)technique(Hartley and Zisserman2004)inside a RANSAC procedure.For this RANSAC step,we use an outlier threshold of0.4%of max(image width,image height).In addition to providing an estimate of the camera rotation and translation,the DLT technique returns an upper-triangular matrix K which can 10We only choose the initial pair among pairs for which a focal length estimate is available for both cameras,and therefore a calibrated rela-tive pose algorithm can be used.。

通信行业英语中英对照手册(i)

通信行业英语中英对照手册(I)-OCU ISDN-Office Channel Unit ISDN的局内信道单元I/O Input / Output 输入/输出I2T Intelligent Interface Technology 智能接口技术IA Information Access 信息存取IA Intelligence Appliance 智能家电IA Internal Authentication 内部认证IA Internet Address 因特网地址IAB Internet Activities Board 因特网活动委员会IAB Internet Architecture Board 因特网体系委员会IAC Image Attenuation Coefficient 图像衰减系数IAC ISDN Access Control ISDN接入控制IAD Integrated Access Device 集成接入设备IAF Image Analysis Facility 图像分析设备IAM Initial Address Management 初始地址管理IAM Initial Address Messege 初始地址消息IAN Integrated Analog Network 综合模拟网IAN Irregularly Activated Network 不规则激活网络IANA Internet Assigned Number Authority 因特网分址机构IAP Internet Access Point 网际访问点IAP Internet Access Provider 因特网接入服务供应商IAR Intelligent Automatic Rerouting 智能型自动重选路由IAS Integrated Access Server 综合接入服务器IAS Interactive Application Server 交互应用服务器IB-DCA Interence-Based Dynamic Channel Allocation 基于干扰的信道动态分配IBC Information Bearer Channel 信息承载信道IBC Integrated Broadband Communication 综合宽带通信IBCN Integrated Broadband Communication Network 综合宽带通信网IBCN International Broadband Communication Network 国际宽带通信网络IBDN Integrated Building Distribution Network 楼宇综合布线网络IBGP Internal Border Gateway Protocol 内部边界网关协议IBI Intergrated Building Intelligent 综合大楼智能化IBMS Intelligent Building Management System 智能大厦管理系统IBN Integrated Broadband Network 综合宽带网IBS Intelligent Building System 智能大厦系统IBT Internet Browsing Terminal 因特网浏览终端IBWN Indoor Broadband Wireless Network 室内宽带无线网络IC Image Check 图像检验IC Image Compression 图像压缩IC Integrated Circuit 集成电路IC Interlock Code 互锁码ICB Incoming Call Barred 来话加锁ICBI Inter-Channel inter-Block Interference 信道间信息组间的干扰ICC Instantaneous Channel Characteristics 信道瞬态特性ICC Internet Call Center 因特网呼叫中心ICDN Integrated Communication Data Network 综合通信数据网络ICE In-Circuit Emulation 在线仿真ICE InterConnect Equipment 互连设备ICE Interface Configuration Environment 接口配置环境ICH Incoming CHannel 来话信道ICI Intelligent Communications Interface 智能通信接口ICI Inter-Carrier Interference 载波间干扰ICI Inter-Channel Interference 信道间干扰ICM Image Compression Manager 图像压缩管理器ICM Incoming Call Management 来话呼叫管理ICMP Inernal Control Message Protocol 内部控制信息协议ICMP Internet Control Message Protocol 因特网控制报文协议ICP Incoming Call Packet 呼入分组信息ICP Internal Connection Protocol 内部连接协议ICP Internet Content Provider 因特网内容服务供应商ICP Interworking Control Protocol 互通控制协议ICR Initial Cell Rate 初始信元率ICS ISDN Control Sublayer ISDN控制子层ICSA International Computer Security Associatiion 国际计算机安全协会ICT InComing Trunk 来话中继ICT Information and Communication Technology 信息和通信技术ICUG International Closed User Group 国际闭合用户群ICW Internet Call Waiting 因特网呼叫等待IDA Integrated Digital Access 综合数字接入IDA Interchange of Data between Administrations 机构间的数据交换IDA Internet Direct Access 因特网直接接入IDA Intrusion Detection Agent 入侵检测代理IDARA Improved Distributed Adaptive Routing Algorithm 改进的分布式自适应路由算法IDC Internet Data Center 因特网数据中心IDCC Integrated Data Communication Channel 综合数据通信信道IDCT Inverse Discrete Consine Transform 离散余弦逆变换IDD International Direct Dialing 国际直拨IDI Initial Domain Identifier 初始域标识符IDL Interactive Distance Learning 交互式远程学习IDL Interface Definition Language 接口定义语言IDL International Data Line 国际数据线路IDLC Integrated Digital Loop Carrier 综合数字环路载波IDMS Integrated Database Management System 综合数据库管理系统IDN Integrated Data Network 综合数据网络IDN Integrated Digital Network 综合数字网IDN Intelligent Data Network 智能数据网络IDN Interactive Data Network 交互式数据网络IDN International Directory Network 国际目录网络IDNET IDentification NETwork 认证网IDP Internet Directory Provider 因特网目录服务供应商IDPR Inter-Domain Policy Routing 域间策略路由选择IDR Intermediate Data Rate 中等数据速率IDRP InterDomain Routing Protocol 域间路由选择协议IDS Intrusion Detection System 入侵检测系统IDS Isochronous Data Services 等时数据业务IDSE International Data Switching Exchange 国际数据交换机(局)IDSE Internetworking Data Switching Exchange 互连网数据交换机(局)IDSL ISDN DSL ISDN数字用户线IDSP Intelligent Dynamic Service Provisioning 智能型动态业务提供IDSS Intelligent Decision Support System 智能决策支持系统IDT Integrated Digital Terminal 综合数字终端IDT Intelligent Data Terminal 智能数据终端IDT Interactive Data Terminal 交互数据终端IDTC International Digital Transmission Center 国际数字传输中心IDU InDoor Unit 室内单元IDU Interface Data Unit 接口数据单元IEC Integrated Ethernet Chip 集成以太网电路芯片IEC InterExchange Carrier 局间载波IEC International Electrotechnical Commission 国际电工委员会IEE Institute of Electrical Engineers 电气工程师学会(英国)IEEE Institute of Electrical and Electronics Engineers 电气和电子工程师学会(美国) IEN Internet Experiment Note 因特网实验备忘录IEP Internet Equipment Provider 因特网设备供应商IEPG Internet Engineering and Planning Group 因特网工程和规划组IES ISDN Earth Station 综合业务数字网络地球站IESG Internet Engineering Steering Group 因特网工程指导组IETF Internet Engineering Task Force 因特网工程任务组IEW Intelligent and Electronic Warfare 智能和电子战IF Intermediate Frequency 中频IFD InterFace Device 接口设备IFH Intelligent Frequency Hopping 智能跳频IFIP International Federation for Information Processing 国际信息处理联合会IFITL Integrated Fiber In The Loop 综合光纤环路IFPH Inter-network FreePHone 网间被叫付费电话IFS Interactive Financial Services 交互式金融服务IFS InterFace Specification 接口规范IFS International Freephone Service 国际免费电话(被叫付费电话)IFU Interworking Functional Unit 互通功能单元IG Interactive Graphics 交互式图形IG International Gateway 国际网关IGD Interaction Graphics Display 交互式图形显示IGL Interactive Graphics Library 交互式图形库IGMP Internet Group Management Protocol 因特网组管理协议IGP Interior Gateway Protocol 内部网关协议IGRP Interior Gateway Routing Protocol 内部网关路由协议IGS Information Group Separator 信息组分隔符IHDL Input Hardware Des criptive Language 输入硬件描述语言IHL Internet Header Length 因特网报头长度IHV Independent Hardware Vendor 独立硬件商II Image Information 图像信息IIA Interactive Instructional Authoring 交互式教学写作IIA Internet Image Appliance 网络影像家电IIAS Interactive Instructional Authoring System 交互式教学写作系统IIC Incoming International Center 入局国际中心IID Image Intensifier Device 图像增强设备IIIN Intelligent Integrated Information Network 智能综合信息网络IIP Interface Information Processor 接口信息处理器IIS Internet Information Server 因特网信息服务器IIS Internet Information Service 因特网信息服务IISP Interim Inter-switch Signaling Protocol 临时的交换机间的信令协议IITA Information Infrastructure Technology and Application 信息基础设施技术及应用IITF Information Infrastructure Task Force 信息基础设施任务组IKBS Intelligent Knowledge Based System 基于知识的智能系统IKE Internet Key Exchange 因特网密钥交换IL Insertion Loss 插入损耗ILC Intelligent Line Card 智能线路卡ILD Insertion Loss Deviation 插入损耗偏差ILEC Incumbent Local Exchange Carrier 在业的本地交换运营公司ILI Idle Line Indicating 空闲线路指示ILMI Integrated Local Management Interface 综合本地管理接口ILMI Interim Local Management Interface 临时本地管理接口ILSLA Injection Locked Semiconductor Laser Amplifier 注入锁定半导体激光放大器IM Image Mixing 图像混合IM Instant Messaging 即时传信IM Integrated Modem 集成式调制解调器IM Interface Module 接口模块IM Inverse Multiplexing 反向复用IMA Interactive Multimedia Association 交互式多媒体协会IMAP Interactive Mail Access Protocol 交互邮件访问协议IMAP Internet Messaging Access Protocol 因特网消息存取协议IMAP4 Internet Message Access Protocol 4 因特网信息存取协议第4版IMC Inter-Module Connector 模块间连接器IMC International Maintenance Center 国际维护中心IMCC Inter-Module Communication Controller 模块间通信控制器IMEI International Mobile Equipment Identity 国际移动设备标识IMF InterMediate Fiber 中间光纤IMIS Integrated Management Information System 综合管理信息系统IMNI Internal Multimedia Network Infrastructure 多媒体网络内部基础设施IMP Interface Message Processor 接口报文处理器IMP Interface Module Processor 接口模块处理器IMS Information Management System 信息管理系统IMS Interactive Multimedia Service 交互式多媒体服务IMSI International Mobile Subscriber Identifier 国际移动用户标识符IMT Intelligent Multimode Terminal 智能多模式终端IMTC Internatinal Multimedia Television Committee 国际多媒体电视委员会IMTS Improved Mobile Telephone Service 改进的移动电话业务IMTV Interactive Multimedia TeleVision 交互式多媒体电视IMUX Input MUltipleX 输入复用IN Integrated Netowrk 综合网络IN Intelligent Network 智能网IN Interconnected Network 互连网络IN Internal Node 内节点IN-SL IN Service Logic 智能网业务逻辑IN-SM Intelligent Network Switching Manager 智能网交换管理器IN-SSM Intelligent Network Switching State Manager 智能网交换状态管理器IN-SSM Intelligent Network Switching Status Model 智能网交换状态模型INA Information Network Architecture 信息网体系结构INA Integral Network Arrangement 整体网络布局INA Integrated Network Architecture 综合网络体系结构INAP Intelligent Network Application Part 智能网应用部分INAP Intelligent Network Application Protocol 智能网应用协议INC Integrated Network Connection 综合网络连接INCC International Network Controlling Center 国际网络控制中心INCM Intelligent Network Conceptual Model 智能网概念模型INCS-1 Intelligent Network Capability Set-1 智能网能力组1INDB Intelligent Network DataBase 智能网数据库INDBMS Intelligent Network DataBase Management System 智能网数据库管理系统INE Intelligent Network Element 智能网元素INFM Intelligent Nework Functional Model 智能网功能模型INFO Integrated Network using Fiber Optics 采用光纤的综合网INI Intelligence Network Interface 智能网络接口INIC ISDN Network Identification Code ISDN网标识码INM Integrated Network Management 综合网络管理INM Integrated Network Monitoring 综合网监视INMARSAT INternational MARritime SAT ellite organization 国际海事卫星组织INMC International Network Management Center 国际网络管理中心INMOS IN service Management and Operation System 智能网业务管理及运行系统INMS Integrated Network Management System 综合网络管理系统INMS Intelligent Network Management System 智能网络管理系统INN Intermediate Network Node 中间网络节点INNO IN Network Operator 智能网运营商INP Intelligent Network Processor 智能网络处理器INS Information Network System 信息网络系统INS Intelligent Network Service 智能网络服务INSAT INternational SATellite 国际卫星INSES IN Services Emulation System 智能网业务仿真系统INSOS IN Service Operation System 智能网业务*作系统INSP Intelligent Network Service Provider 智能网服务供应商INSS Intelligent Network Service Subscriber 智能网业务用户INSTS IN Surveillance and Testing System 智能网监视和测试系统INT Interactive News Television 交互式电视新闻INTB IN TestBed 智能网试验台INTCO INT ernational COde of signal 国际信号码INTELSAT INternational TELecommunication SATellite 国际通信卫星(组织) INTIP INT egrated Information Processing 综合信息处理INTS Integrated National Telecommunication System 国家综合电信系统INTS INTernational Switch 国际交换INTS Inter-Network Time Slot 网络内部时隙INTSE INTelligent System Environment 智能系统环境IO Integrated Optics 集成光学IOAS Intelligence Office Automatic System 智能办公室自动化系统IOBB Input Output BroadBand 宽带输入输出IOC Input.Output Channel 输入/输出信道IOC Input / Output Controller 输入/输出控制器IOC Integrated Optical Circuit 集成光路IOC INTELSAT Operations Center 国际卫星组织*作中心IOC InterOffice Channel 局间信道IOCA Image Object Content Architecture 图像对象内容体系结构IOD Information On Demand 信息点播IODC International Operator Direct Calling 国际运营商直接呼叫IOLA Input / Output Link Adapter 输入/输出链路适配器IOLC Input / Output Link Control 输入/输出链路控制IOM Image-Oriented Memory 面向图像的存储器IOM Input / Output Multiplexer 输入/输出多路转换器IOM Integrated-Optic Modulator 集成光学调制器ION Integrated On-demand Network 综合按需服务网络IONI ISDN Optical Network Interface ISDN光网络接口IOP Input / Output Processor 输入输出处理器IOPDS Integrated-Optic Position / Displacement Sensor 集成光学位置/位移传感器IOS Integrated Office System 集成办公室系统IOS Intelligent Office System 智能办公室系统IOS Interactive Operating System 交互式*作系统IOS Internet Operating System 因特网*作系统IOS Internetwork Operating System 网间*作系统IOSB Input / Output Status Block 输入/输出状态块IOSC Input / Output Switching Channel 输入/输出交换通道IOSN Intelligent Optical Shuttle Node 智能光信息往返节点IOT Intra Office Trunk 局内中继IOTB Input / Output Transfer Block 输入/输出传送块IP Image Processing 图像处理IP Information Processing 信息处理IP Intelligent Peripheral 智能外设IP Internet Protocol 因特网协议IP Internetwork Protocol 网际协议IP Internetworking Protocol 组网协议IP Interworking Protocol 互通协议IPA Image Processing Algorithm 图像处理算法IPA Inerworking by Port Access 端口接入的互通IPBX International PBX 国际PBXIPC Integrated Peripheral Channel 集成外围通道IPC Intelligent Peripheral Controller 智能外设控制器IPC Inter-Personal Communications 人际通信IPC Inter-Process Communication 进程间通信IPC Inter-Processor Communication 处理器间通信IPCDN IP over Cable Data Network 电缆数据网传送IPIPCE International Path Core Element 国际通路核心单元IPCP IP Control Protocol IP控制协议IPCSM Input Port Controller SubModule 输入端口控制器的子模块IPDC IP Device Control IP设备控制IPE In-band Parameter Exchange 带内参数交换IPEI International Portable Equipment Identity 国际便携式设备标识IPF Image Processing Facility 图像处理设备IPG Interactive Program Guide 交互式节目指南IPG Inter-Packet Gap 分组信息间隙IPI Initial Protocol Identifier 初始协议标识符IPI Intelligent Peripheral Interface 智能外围接口IPL Initial Program Load 初始程序装入IPLB IP Load Balancing IP负载平衡IPLC International Public Leased Circuit 国际公用出租线路IPLI Internet Private Line Interface 因特网专用线接口IPLTC International Private Leased Telecommunication Circuit 国际专用租线通信电路IPM Inter-Personal Messeging 人际传信IPM-EOS Inter-Personal Message Element Of Service 人际报文业务单元IPM-UA Inter-Personal Messeging User Agent 人际传信用户代理IPME Inter-Personal Messaging Environment 人际传信环境IPMS Inter-Personal Messaging Service 人际传信业务IPMS Inter-Personal Messaging System 人际传信系统IPMS-MS Inter-Personal Messaging System Message Store 人际传信系统信息存储IPMS-UA Inter-Personal Messaging System User Agent 人际传信系统用户代理IPN Instant Private Network 瞬时专用网络IPN Inter-Personal Notification 人际通知IPng Internet Protocol next generation 下一代因特网协议IPOA IP Over ATM ATM网络承载IPIPP Internet Payment Provider 因特网支付业务提供商IPP Internet Platform Provider 因特网平台供应商IPPR Image Processing and Pattern Recognition 图像处理和模式识别IPR Intellectual Property Rights 知识产权IPS Image Processing System 图像处理系统IPS Information Processing System 信息处理系统IPS Information Protection System 信息保护系统IPS Intelligent Protection Switching 智能保护交换IPsec IP security protocol IP安全协议IPSF IP Service Function IP业务功能IPSS International Packet Switched Service 国际分组交换业务IPT Information Processing Technique 信息处理技术IPT Information Providing Terminal 信息提供终端IPUI International Portable User Identity 国际便携式用户标识IPv6 IP version 6 第六版IPIPX Internet Packet eXchange 因特网分组交换IPX Internetwork Packet eXchange 网际包(分组)交换IPX Interprocess Packet eXchange 进程间分组交换IQ Information Query 信息查询IQL Interactive Query Language 交互式查询语言IR Incoming Route 入路由IR Information Retrieval 信息检索IR InfraRed 红外IR Intelligent Robot 智能机器人IR Internal Router 内部路由器IRC Internet Relay Chat 因特网中继交谈IrDA Infra-red Data Association 红外数据协会IRFU Integrated Radio Frequency Unit 综合无线电频率单位IRI InfraRed Image 红外图像IRIM InfreRed Interface Module 远端接口模块IRIS Integrated platform for Regional Information System 地区信息系统用综合平台IRL Inter-Repeater Link 中继器间链路IRLAP InfraRed Link Access Protocol 红外链接存取协议IrLAP IrDA Link Access Protocol IrDA链路接入协议IRM Integrated Reference Model 综合参考模型IrMC Infrared Mobile Communication 红外移动通信IRN Information Resource Network 信息资源网络IRN Intermediate Routing Node 中间路由选择节点IRP Internal Reference Point 内部参考点IRP International Routing Plan 国际路由规划IRQ Information Repeat reQuest 信息重传请求IRSG Internet Research Steering Group 因特网研究指导组IRSU ISDN Remote Subscriber Unit ISDN远端用户单元IRTF Internet Research T ask Force 因特网研究任务工作组IS Imaging System 成像系统IS Information Science 信息科学IS Information System 信息系统IS Integrated Service 综合业务IS Intelligence System 智能系统IS Interactive Service 交互式业务IS Interactive Signal 交互信号IS Interface Specification 接口规范IS Interim Standard 临时标准IS-IS Intermediate System-to-Intermediate System 中间系统到中间系统ISA Industry Standard Architecture 工业标准体系结构ISA Information System Architecture 信息系统结构ISA Interim Standard Architecture 临时标准体系ISAN Integrated Service Analog Network 综合业务模拟网ISAP Interactive Speech Application Platform 交互语言应用平台ISAPI Internet Server Application Programming Interface 因特网服务器应用编程接口ISB Intelligent Signaling Bus 智能信令总线ISB Interface Schduling Block 接口调度块ISC International Switching Center 国际交换中心ISC Internet Software Consortium 因特网软件联盟ISC InterStellar Communications 星际通信ISCC International Service Coordination Center 国际业务协调中心ISCCI International Standard Commerical Code for Indexing 国际标准商用索引代码ISCII International Standard Code for Information Interchange 国际标准信息交换代码ISCP ISDN Signaling Control Part ISDN信令控制部分ISDCN Integrated Service Digital Center Network 综合业务数字中心网ISDN Integrated Service Digital Network 综合业务数字网ISDN-BA ISDN Basic rate Access ISDN基本速率接入ISDN-BRI ISDN Basid Rate Interface ISDN基本速率接口ISDN-PRA ISDN Primary Rate Access ISDN一次群速率接入ISDN-PRM ISDN Protocol Reference Model ISDN协议参考模型ISDN-SN ISDN Subscriber NumberISDN-UP ISDN User Part ISDN用户部分ISDS Integrated Switched Data Service 综合交换数据业务ISDT Integrated Service Digital Terminal 综合业务数字终端ISDX Integrated Service Digital eXchange 综合业务数字交换ISE Integrated Service Exchange 综合业务交换局ISE Integrated Switch Element 综合交换单元ISE Intelligent Synthesis Environment 智能综合环境ISEC Internet Service and Electronic Commerce 因特网服务和电子商务ISH Information Super Highway 信息高速公路ISIDE Interactive Satellite Integrated Data Exchange 交互式卫星综合数据交换ISL Inter-Satellite Link 卫星之间的链路ISLAN Integrated Services Local Area Network 综合业务局域网ISM Intelligent Synchronous Multiplexer 智能同步复用器ISM Interactive Storage Media 交互式存储媒体ISM Interface Subscriber Module 用户接口模块ISM Internet Server Manager 因特网服务器管理器ISM Internet Service Manager 因特网服务器管理程序ISMA Idle Signal Multiple Access 空闲信号多址ISMAN Integrated Services Metropolitan Area Network 综合业务城域网ISMC International Switching Maintenance Center 国际交换维护中心ISMS Image Store Management System 图像存储管理系统ISN Information System Network 信息系统网络ISN Integrated Services Network 综合业务网ISN Integrated Synchronous Network 综合同步网ISN International Signaling Network 国际信令网ISN Internet Shopping Network 因特网购物网络ISN Internet Support Node 因特网支持节点ISO International Standardization Organization 国际标准化组织ISOC Internet SOCiety 因特网学会ISODE ISO Development Environment ISO开发环境ISP Interactive Session Protocol 交互式会晤协议ISP Intermediate Service Part 中间业务部分ISP International Signaling Point 国际信令点ISP International Standardized Profile 国际标准化规格ISP Internet Service Provider 因特网服务供应商ISP Interoperable Systems Project 可互*作系统计划ISPBX Integrated Services PBX 综合业务PBXISPC International Signaling Point Code 国际信令点码ISR Initial Submission Rate 初始提供速率ISR International Simple Resell 国际简单转卖ISR Interrupt Service Routine 中断服务程序ISSLL Integrated Services over Specific Link Layer 专用链路层上的综合业务ISSS Interactive Subscriber Service Subsystem 交互式服务子系统ISTC International Satellite Transmission Center 国际卫星传输中心ISTC International Switching and Testing Center 国际交换和测试中心ISTV Integrated Service T eleVision 综合业务广播电视ISU Idle Signal Unit 空闲信号单元ISU Isochronous Slot Utilization 等时隙利用ISUP ISDN User Part ISDN用户部分ISV Independent Software Vendor 独立软件销售商ISVR Inter Smart Video Recorder 灵巧型视频录像机IT Information Technology 信息技术IT Information Theory 信息论IT International Transit 国际转接ITA International Telegraph Alphabet 国际电报字母表ITA2 International Telegraph Alphabet No.2 国际电报字母表第二版ITC Information Transfer Channel 信息传递信道ITC Intelligent Terminal Controller 智能终端控制器ITC International Telecommunication Center 国际电信中心ITC International Telephone Center 国际电话中心ITC International Television Center 国际电视中心ITC International Transit Center 国际转接中心ITC International Transmission Center 国际传输中心ITC InterToll Communication 长途局间通信ITCC International Telecommunication Control Center 国际电信控制中心ITD Interaural Time Difference 声源到达听者两耳的时间差ITDM Intelligent Time-Division Multiplexer 智能时分多路复用ITE Information Technology Equipment 信息技术设备ITE International Telephone Exchange 国际电话交换台ITF Information Transport Function 信息传送功能ITM ISDN Trunk Module 综合业务数字网中继模块ITMC International Transmission Maintenance Center 国际传输维护中心ITN Integrated Teleprocessing Network 综合远程处理网络ITN Intelligent Telecommunication Node 智能电信节点ITPC International Television Program Center 国际电视节目中心ITR Instantaneous Transmission Rate 瞬时传输速率ITR Internet Talk Radio 因特网无线对话ITS Independent Television Service 独立电视服务ITS Information Transfer System 信息转换系统ITS Information Transmission System 信息传输系统ITS Insertion Test Signal 插入测试信号ITS Intelligent Transport System 智能交通系统ITS International Telecommunication Service 国际电信业务ITSC International Telephone Service Center 国际电话业务中心ITSO International Telecommunications Satellite Organization 国际电信卫星组织ITSP Internet Telephony Service Provider IP电话业务提供商ITT InterToll Trunk 长途电话中继线ITTP Intelligent Terminal Transfer Protocol 智能终端转换协议ITTS Intelligent Target Tracking System 智能目标跟踪系统ITU International Telecommunication Union 国际电信联盟ITU-R ITU-Radio communications sector 国际电信联盟无线电通信组ITU-T ITU-Telecommunication standardization sector 国际电信联盟电信标准化组ITV Interactive TeleVision 交互式电视IU Interface Unit 接口单元IUI Intelligent User Interface 智能用户接口IUI Inter-User Interference 用户间干扰IUO Intelligent Underlay Overlay 智能双层网IUR Internet Usage Record 因特网使用记录IV Interactive Video 交互式视频IV Interface Vector 接口向量IVA Iitial Video Address 初始视频地址IVANS Insurance value Added Network Services 保险业增值网络服务IVAP Internal Videotex Application Provider 内部可视图文应用供应商IVBC International Videoconference Booking Center 国际电视会议登记中心IVC Independent Virtual Channel 独立虚拟信道IVC International Videoconference Center 国际会议电视中心IVD Interactive Video Disk 交互式视频盘IVD Interpolated Voice Data 内插语音数据IVDS Interactive Video Database Services 交互式视频数据库业务IVDT Integrated Voice Data Terminal 综合话音数据终端IVE International Videotex Equipment 国际可视图文设备IVG Interactive Video Game 交互式视频游戏IVHS Intelligent Vehicle and Highway System 智能车辆和公路系统IVIS Interactive Video Information System 交互视频信息系统IVMS Integrated Voice-Messaging System 综合语音信息系统IVN Interactive Video Network 交互式视频网络IVOD Interactive Video On Demand 交互式视频点播IVOT Inter-network teleVOTing 网间电子投票业务IVPN International Virtual Private Network 国际虚拟专用网IVR Integrated Voice Response 综合语音响应IVR Interactive Voice Response 交互式语音应答IVS Intelligent Video Smoother 智能视频平滑器IVS Interactive Video Service 交互式视频业务IVS Interactive Videodisc System 交互式视盘系统IW Information War 信息战IWAN Integrated services Wireless-Access Network 综合业务无线接入网络IWC Indoor Wireless Channel 室内无线信道IWC Interferometric all-optical Wavelength Converter 干涉全光波长变换器IWC Interferometric Wavelength Converter 干涉波长变换器IWCS Integrated Wideband Communication System 综合宽带通信系统IWF InterWorking Facility 互通设备IWF InterWorking Function 互通功能IWK Issuer Working Key 发行卡的工作密钥IWS Intelligent Work Station 智能工作站IWS Intelligent Workstation Support 智能工作站支持IWU InterWorking Unit 互通单元IXC Inter-eXchange Carrier 交互运营商IXP Internet eXchange Point 因特网交换点。

【英语教学法课件】Unit1Languageandlanguagelearning

3. Richards, J. & Rogers, T.S. Approaches and Methods in Language Teaching. (《语言教学(jiāo xué)路子与方法》1986)
4. Howatt, A.P.R. A History of English Language Teaching第十.五页(，共《78页。英语语言教学(jiāo
Audiolingualism
第二十五页，共78页。
听说法 (shuōf ǎ)
Audio-Lingual Method
❖ ‘Listen and repeat’ drilling activities are the most important classroom activities.
❖ Mistakes are immediately corrected and correct utterances are immediately praised.
❖ Functional view– communicative categories, communicative ability (to be able to communicate)
❖ Interactional view– to communicate appropriately (communicative strategies, cultural awareness, etc.)
❖ Language is a rule-based system and with a knowledge of the finite rules (language competence), infinite sentences can be produced

认知语言学流——入门必读起源,发展史,学术观点,研究方法

认知语言学认知语言学作为一种新的语言研究范式，产生于20世纪70年代，成熟于80年代，其标志是1989年在德国第一届国际认知语言学大会的召开和《认知语言学》的创刊。

其奠基性人物有G. Lakoff, R. Langacker, C. Fillmore, L. Talmy, M. Johnson, M. Turner, W.Chafe。

后来J.R. Taylor, D. Geeraerts, G. Fauconnier, E.Sweetser, A.Goldberg等人对认知语言学的发展做出了开创性贡献。

认知语言学是现代语言学中一个相当广泛的理论运动（movement）的总称。

它包含许多不同的途径、方法、研究重点。

这些不同的途径、方法和重点由共同的理论假设统一起来。

其中最重要的假设是语言是人类认知不可分离的一部分，任何对语言现象的真知灼见的分析都必须包含在人类认知能力之中。

认知语言学不能取代其他语言学理论或流派，相反，它与其他流派是互补的。

第一节认知语言学的产生、研究目标与语言观1.1 认知语言学产生的理论动因1.1.1 认知语言学产生的学术背景认知语言学属于认知科学的重要组成部分，产生于第二代认知科学（其主要理论观点将在1.3中讨论）。

第一代认知科学在认识论与方法论上具有以下特征。

第一，符号计算。

它认为理智(reason)与体验分离(disembodied)，是直义或客观的(literal), 就像形式逻辑一样是符号系统的运算。

因此，心智就是一个抽象的计算程序，心智的硬件（大脑和身体）对心智没有影响。

第二，意义就是心理表征。

可以作两种理解。

首先，意义根据符号之间的内在关系定义，意义就是符号计算的结果；表征即概念。

其次符号是外在现实的内在表征，即意义对应于客观现实；那么，表征就是形式系统之外某物的符号表征。

概而言之，思想可以用形式符号系统表征，而符号本身是没有意义的，思想是这些符号根据规则计算的结果。

ai advantage英语作文

ai advantage英语作文AI AdvantageIn recent years, the development of artificial intelligence (AI) has revolutionized the way we live and work. AI has rapidly integrated into various industries, providing numerous benefits and advantages. In this essay, we will explore the advantages of AI and how it is shaping our future.One of the main advantages of AI is its ability to improve efficiency and productivity. AI-powered systems can automate repetitive tasks, such as data entry, analysis, and customer service, allowing employees to focus on more strategic and creative work. This not only saves time but also increases accuracy and reduces human error. For example, AI algorithms can process large amounts of data in seconds, enabling companies to make faster and more informed decisions.Another advantage of AI is its ability to personalize experiences for users. AI-powered algorithms can analyze user data and behavior to provide personalized recommendations, content, and services. This customization enhances the user experience, increases engagement, and fosters customer loyalty. For example, AI-driven recommendation engines one-commerce websites can suggest products based on a user's browsing history and preferences, leading to higher conversion rates.AI also has the advantage of enhancing safety and security. AI-powered systems can detect patterns and anomalies in data to identify potential risks and threats. For example, AI algorithms can analyze video footage to recognize suspicious behavior and alert security personnel. Moreover, AI can be used to improve cybersecurity by detecting and mitigating cyber attacks inreal-time. By leveraging AI technology, organizations can better protect their assets and data from external threats.Furthermore, AI can improve healthcare outcomes and patient care. AI-powered systems can analyze medical data, such as images, genetic information, and patient records, to diagnose diseases, predict outcomes, and recommend treatment plans. This enables healthcare providers to deliver more accurate and personalized care to patients. For example, AI algorithms can interpret medical images, such as X-rays and MRIs, with higher accuracy than human radiologists, leading to faster and more reliable diagnoses.In addition, AI can drive innovation and create new opportunities for businesses. AI-powered technologies, such asmachine learning, natural language processing, and computer vision, can enable companies to develop new products, services, and business models. For example, AI chatbots can provide 24/7 customer support, AI-powered predictive analytics can optimize supply chain management, and AI-driven recommendation engines can personalize marketing campaigns. By embracing AI, organizations can gain a competitive edge and stay ahead of the curve.In conclusion, AI offers numerous advantages across various industries, including improved efficiency, personalized experiences, enhanced safety and security, better healthcare outcomes, and increased innovation. As AI continues to advance and evolve, its potential to transform our society and economy is limitless. By harnessing the power of AI, we can unlock new possibilities, drive growth, and create a better future for all.。

英语作文-人工智能助力电商平台推荐系统

英语作文-人工智能助力电商平台推荐系统Artificial Intelligence (AI) has significantly transformed various industries, and one of the areas where its impact is profound is in enhancing e-commerce platforms through recommendation systems. These systems play a crucial role in helping online platforms recommend products and services tailored to individual preferences, thereby improving user experience and boosting sales.E-commerce platforms rely heavily on recommendation systems to personalize the shopping experience for each user. Traditionally, these systems used basic algorithms like collaborative filtering or content-based filtering to suggest products based on purchase history or product attributes. While effective to some extent, these approaches have limitations in accurately predicting user preferences, especially in the context of rapidly changing consumer behavior and preferences.Enter artificial intelligence and machine learning. AI has revolutionized recommendation systems by enabling platforms to analyze vast amounts of data beyond just purchase history and product attributes. Machine learning algorithms can now process data on user behavior, including browsing patterns, search history, time spent on each product page, mouse movements, and even demographic information. This holistic approach provides a more comprehensive understanding of user preferences and intentions.One of the key advancements AI brings to e-commerce recommendation systems is its ability to perform real-time analysis. Unlike static rule-based systems, AI can continuously learn and adapt to new data, ensuring that recommendations remain relevant and up-to-date. This dynamic adaptation is crucial in fast-paced industries where trends can change rapidly, such as fashion and electronics.Moreover, AI-powered recommendation systems can identify subtle patterns in user behavior that human analysts may overlook. For example, AI algorithms can detect correlations between seemingly unrelated products that co-occur in user purchases or browsing sessions. This capability allows platforms to offer cross-selling opportunities,where complementary products are recommended together, thereby increasing the average order value.Another significant advantage of AI is its ability to personalize recommendations at scale. Whether a platform has thousands or millions of users, AI can create unique profiles for each individual based on their interactions with the platform. This personalization goes beyond just suggesting products; it extends to the presentation and timing of recommendations. For instance, AI can determine the optimal placement of recommendations on a webpage or tailor recommendations based on the user's current browsing session.Furthermore, AI enhances the accuracy of recommendations by integrating multiple sources of data. Beyond user interactions, AI can incorporate external factors such as seasonality, trends in social media, and even weather patterns (for certain types of products like clothing or outdoor gear). By considering these contextual factors, recommendation systems can offer more relevant suggestions that align with the user's current needs and preferences.Ethical considerations also play a crucial role in the development and deployment of AI in recommendation systems. As AI algorithms influence consumer choices, there is a responsibility to ensure transparency and fairness. Platforms must be transparent about how recommendations are generated and give users control over their preferences and data privacy. Moreover, AI systems should be designed to avoid reinforcing biases or promoting harmful content, ensuring that recommendations are beneficial and respectful to all users.Looking ahead, the future of AI in e-commerce recommendation systems holds promise for further advancements. As AI technology continues to evolve, we can expect more sophisticated algorithms that enhance personalization, improve prediction accuracy, and adapt to emerging consumer behaviors in real-time. Additionally, advancements in natural language processing and computer vision will likely expand the scope of AI applications in understanding and recommending products based on textual descriptions or image recognition.In conclusion, artificial intelligence has revolutionized e-commerce recommendation systems by enabling platforms to offer personalized and relevant suggestions to users at scale. Through advanced machine learning techniques, AI enhances accuracy, adapts in real-time, and considers a wide range of data sources to optimize user experience and drive business growth. As AI technology advances, the future of e-commerce recommendation systems looks promising, with continued improvements in personalization and user engagement.。

计算机视觉技术在自然语言处理中的使用方法

计算机视觉技术在自然语言处理中的使用方法自然语言处理（Natural Language Processing, NLP）是人工智能领域中重要的研究方向之一，旨在让计算机能够理解、分析和生成自然语言。

而计算机视觉技术（Computer Vision）则专注于使计算机能够从图像和视频中理解和获取信息。

这两个领域的结合为解决自然语言处理问题提供了更全面的方法。

计算机视觉技术在自然语言处理中的应用可以大致分为以下几个方面：1. 图像标注与描述生成图像标注是指给定一张图像，生成相应的文字描述。

通过结合计算机视觉技术的图像理解能力和自然语言处理的语义分析能力，可以让计算机生成更加准确和详细的图像描述。

这对于图像搜索、图像检索和辅助视觉障碍人士等应用有重要意义。

2. 视觉问答系统视觉问答系统是指根据给定的图像和提出的问题，通过理解图像内容并生成合适的自然语言回答来解答用户的问题。

计算机通过将图像转化为特征向量，然后与问题进行匹配，得到问题的答案。

这种系统结合了计算机视觉技术和自然语言处理技术，为用户提供了一种直观、便捷的交互方式。

3. 文字图像转换文字图像转换是指将具有文字内容的图像转换成为可供计算机进行自然语言处理的文本数据。

通过使用计算机视觉技术对图像中的文字进行识别和提取，可以使得文本数据能够进行更深层次的自然语言处理，如文本分类、情感分析等。

4. 视觉场景理解视觉场景理解是指使计算机能够从图像中识别和理解不同的视觉场景，如目标检测、物体识别和图像分割等任务。

这些任务的结果可以进一步用于自然语言处理任务，如自动图像字幕生成和智能图像搜索等。

5. 文本和图像的关联分析在某些应用中，文本和图像之间存在着紧密的关联。

例如，商品评论通常伴随着商品图片，新闻文章可能包含相关的图像等。

计算机视觉技术可以帮助自然语言处理系统更好地理解文本和图片之间的关系，提高文本理解的准确性。

综上所述，计算机视觉技术在自然语言处理中发挥着重要的作用。

随着科技的发展人工智能英语作文

随着科技的发展人工智能英语作文全文共3篇示例，供读者参考篇1The Development of Artificial Intelligence with Technological AdvancementsAs a student in the 21st century, it's impossible to ignore the rapid pace of technological advancements, particularly in the field of artificial intelligence (AI). The concept of AI, once confined to the realms of science fiction, has now become an integral part of our daily lives. From virtual assistants like Siri and Alexa to self-driving cars and intelligent robotics, AI has infiltrated nearly every aspect of our existence. As we stand on the precipice of a technological revolution, it's crucial to understand the implications and potential of this transformative technology.At its core, AI is the simulation of human intelligence processes by machines, particularly computer systems. These systems are designed to mimic human cognitive functions, such as learning, problem-solving, and decision-making. The field of AI encompasses a broad range of technologies, includingmachine learning, natural language processing, computer vision, and robotics, among others.One of the most significant drivers of AI development has been the exponential growth of computational power and the availability of vast amounts of data. Modern computers possess the ability to process and analyze enormous datasets, enabling them to identify patterns and make predictions with unprecedented accuracy. This has led to breakthroughs in areas such as image and speech recognition, language translation, and predictive analytics.Machine learning, a subset of AI, has been particularly instrumental in this progress. It involves training algorithms on vast amounts of data, allowing them to learn and improve over time without being explicitly programmed. This approach has revolutionized fields like recommendation systems, fraud detection, and predictive maintenance, among others.The impact of AI on our lives is already evident in countless ways. Virtual assistants like Siri and Alexa have become household names, helping us with tasks ranging from setting reminders to controlling smart home devices. Online shopping platforms leverage AI algorithms to provide personalized recommendations based on our browsing and purchase history.Social media networks employ AI to detect and remove harmful content, while email services use it to filter out spam and phishing attempts.Moreover, AI has made significant strides in the field of healthcare, aiding in disease diagnosis, drug discovery, and personalized treatment plans. In the realm of transportation, self-driving cars and autonomous vehicles are poised to revolutionize the way we commute, promising increased safety and efficiency on our roads.However, as with any transformative technology, the development of AI is not without its challenges and ethical considerations. One of the primary concerns is the potential for job displacement, as AI systems become increasingly capable of performing tasks traditionally carried out by humans. This has sparked debates around the need for reskilling and workforce adaptation to ensure a smooth transition into an AI-driven economy.Privacy and data security are also critical issues, as AI systems rely heavily on vast amounts of personal data for training and decision-making. There is a delicate balance to be struck between harnessing the power of data and protecting individual privacy rights.Furthermore, the development of AI raises questions about bias and fairness. AI algorithms can inadvertently perpetuate and amplify societal biases present in the data they are trained on, leading to discriminatory outcomes. Ensuring transparency, accountability, and ethical governance in the development and deployment of AI systems is paramount.Despite these challenges, the potential benefits of AI are too significant to ignore. As students, we stand at the forefront of this technological revolution, poised to shape and be shaped by the advancements in AI. Embracing this technology and developing the necessary skills to navigate this rapidly evolving landscape is crucial for our future success.Educational institutions play a pivotal role in preparing the next generation for an AI-driven world. Curricula should emphasize not only technical skills in areas like data science, machine learning, and programming but also critical thinking, ethical reasoning, and interdisciplinary collaboration. By fostering a well-rounded understanding of AI and its implications, we can cultivate a workforce capable of harnessing the power of this technology responsibly and ethically.Moreover, as students, we have a unique opportunity to shape the trajectory of AI development. By actively engaging inresearch, innovation, and entrepreneurship, we can contribute to the creation of AI solutions that address pressing societal challenges, from climate change to healthcare and beyond.In conclusion, the development of artificial intelligence is an unstoppable force, propelled by rapid technological advancements. As students, we stand at the forefront of this revolution, poised to witness and contribute to the transformative impact of AI on our lives. While the challenges are significant, the potential benefits are too vast to ignore. By embracing this technology with a critical and ethical mindset, we can harness the power of AI to create a better, more prosperous, and more equitable future for all.篇2With the Development of Technology, Artificial IntelligenceAs a student living in the 21st century, I can't help but be in awe of the rapid advancements in technology, particularly in the field of artificial intelligence (AI). It's a topic that has captivated my imagination and sparked countless debates among my peers and teachers. The notion of machines possessing human-like intelligence, once confined to the realms of science fiction, is now a reality that is shaping the world around us.From personal assistants like Siri and Alexa to self-driving cars and intelligent robots, AI has already woven its way into our daily lives. Its impact is undeniable, and its potential is vast. As students, we are witnessing firsthand how AI is transforming the educational landscape, offering new tools and resources that were unimaginable just a few years ago.One of the most exciting applications of AI in education is personalized learning. With the ability to analyze vast amounts of data and adapt to individual learning styles, AI-powered systems can tailor educational content and teaching methods to each student's unique needs and strengths. This approach has the potential to revolutionize the traditional one-size-fits-all model of education, ensuring that no student is left behind or held back.AI-assisted tutoring and adaptive learning platforms are already becoming commonplace in many classrooms. These systems can provide real-time feedback, identify areas where students are struggling, and adjust the pace and content accordingly. This level of personalization was previously unattainable, and it promises to bridge the gap between students who learn at different rates or have diverse learning preferences.Moreover, AI is opening up new avenues for research and discovery. By analyzing vast amounts of data and identifying patterns that may be invisible to the human eye, AI algorithms are aiding scientists in fields as diverse as medicine, astronomy, and environmental studies. As students, we are excited by the prospect of contributing to these groundbreaking endeavors, leveraging the power of AI to unlock new knowledge and solve complex problems.However, as with any transformative technology, the rise of AI also raises important ethical and societal concerns. One of the most pressing issues is the potential impact on employment. As AI systems become more advanced and capable of performing tasks traditionally reserved for humans, there is a legitimate fear of job displacement across various industries. This threat has sparked debates about the need for robust workforce retraining programs and the exploration of new economic models that can accommodate the changing landscape of work.Another crucial concern is the potential for AI to perpetuate or amplify existing biases and inequalities. AI systems are trained on vast datasets, and if those datasets reflect societal biases or lack diversity, the resulting models may reinforce harmful stereotypes or discriminate against certain groups. As students,we must grapple with these ethical dilemmas and advocate for the responsible development and deployment of AI technologies that prioritize fairness, transparency, and accountability.Despite these challenges, I remain optimistic about the transformative potential of AI. As a student, I am excited to be part of a generation that will shape the future of this technology and harness its power to address some of the most pressing challenges facing humanity.One area where AI could make a profound impact is in addressing global issues such as climate change, food insecurity, and disease prevention. By analyzing vast amounts of data and identifying patterns and trends, AI systems can aid in developing more effective strategies for mitigating environmental degradation, optimizing agricultural practices, and detecting early warning signs of disease outbreaks.Furthermore, AI has the potential to revolutionize fields like education, healthcare, and scientific research. Imagine a world where personalized learning is the norm, where medical diagnoses are more accurate and tailored to individual patients, and where breakthroughs in areas like renewable energy and sustainable development are accelerated by the power ofAI-driven analysis and problem-solving.However, realizing this potential will require a concerted effort from researchers, policymakers, and society as a whole. As students, we must engage in thoughtful discussions about the ethical implications of AI and advocate for responsible development and deployment practices. We must also ensure that AI technologies are accessible and beneficial to all, regardless of socioeconomic status or background.One way to achieve this is by promoting education and training in AI-related fields, fostering a diverse and inclusive workforce that can shape the development of these technologies. Additionally, we must encourage interdisciplinary collaboration, bringing together experts from various fields to tackle complex challenges and ensure that AI systems are designed with a holistic understanding of their potential impacts.As I look to the future, I am filled with a sense of wonder and excitement about the possibilities that AI holds. However, I also recognize the immense responsibility that comes with shaping this transformative technology. As students, we have the unique opportunity to be at the forefront of this revolution, to learn and grow alongside these advancements, and to contribute our perspectives and ideas to the ethical and responsible development of AI.It is a future filled with both challenges and opportunities, but one thing is certain – the age of artificial intelligence is upon us, and it will fundamentally change the way we live, learn, and understand the world around us. As students, it is our duty to embrace this revolution, to ask difficult questions, and to ensure that AI becomes a force for good, empowering humanity to reach new heights of knowledge, innovation, and progress.篇3The Rise of AI: How Technology is Reshaping Our WorldAs a student living in the digital age, I can't help but be in awe of the rapid advancements in artificial intelligence (AI) technology. It's a topic that both excites and unsettles me, as I ponder the implications it will have on our future. On one hand, AI holds the promise of revolutionizing various industries, streamlining processes, and solving complex problems that have long eluded us. On the other hand, the prospect of intelligent machines surpassing human capabilities raises ethical concerns and existential questions about our role in an increasingly automated world.Let's start with the remarkable progress AI has made in recent years. From voice assistants like Siri and Alexa toself-driving cars and sophisticated language models, AI has become an integral part of our daily lives, often without us even realizing it. The ability of machines to process vast amounts of data, learn patterns, and make decisions at lightning speed is nothing short of astonishing. In fields like healthcare, AI is being used to analyze medical images, identify potential diseases, and even assist in drug development, potentially saving countless lives.Moreover, AI has revolutionized the way we interact with technology. Natural language processing (NLP) has made it possible for us to communicate with machines using our native tongues, breaking down language barriers and making technology more accessible to people from diverse backgrounds. AI-powered translation tools have also facilitated cross-cultural communication and understanding, bringing the world closer together.However, as awe-inspiring as these advancements are, they also raise important questions about the ethics and societal impact of AI. One of the most significant concerns is the potential for job displacement as machines become capable of performing tasks traditionally done by humans. While some argue that AI will create new job opportunities in fields such asprogramming and data analysis, there is a legitimate fear that many professions, particularly those involving repetitive or routine tasks, could be automated, leading to widespread unemployment and economic disruption.Another pressing issue is the potential for AI to perpetuate or even amplify existing biases and discrimination. Since AI systems are trained on data created by humans, they can inherit and reinforce societal biases present in that data, leading to unfair and discriminatory outcomes. For instance, if anAI-powered hiring system is trained on data that reflects existing gender or racial biases in the workforce, it may continue to discriminate against certain groups, further exacerbating inequalities.Furthermore, the increasing reliance on AI raises concerns about privacy and data security. As AI systems become more sophisticated, they require access to vast amounts of personal data to function effectively. This data could potentially be misused, compromised, or even weaponized by malicious actors, posing a significant threat to individual privacy and security.Despite these concerns, I believe that the potential benefits of AI outweigh the risks, provided we approach its development with caution, ethical considerations, and robust regulatoryframeworks. One area where AI could have a profound impact is in addressing global challenges such as climate change, food insecurity, and disease outbreaks. By harnessing the power of machine learning and data analysis, AI could help us develop more sustainable and efficient solutions, predict and mitigate environmental risks, and accelerate scientific research and discovery.Moreover, AI has the potential to revolutionize education and make learning more personalized and accessible.AI-powered tutoring systems could adapt to individual learning styles and pace, providing tailored instruction and feedback to students. Virtual reality and augmented reality technologies, powered by AI, could create immersive and interactive learning experiences, making complex concepts more engaging and easier to understand.As a student, I am particularly excited about the potential of AI to enhance creativity and artistic expression. AI-powered tools could assist writers, musicians, and artists in generating ideas, exploring new styles, and pushing the boundaries of their creativity. While some may argue that AI could eventually replace human artists, I believe that true creativity and emotional expression will always remain a uniquely human endeavor, andAI will simply be a tool to augment and enhance our creative abilities.However, for AI to truly benefit humanity, we must address the ethical and societal concerns head-on. This requires a multifaceted approach involving policymakers, technologists, ethicists, and the general public. Robust regulations and guidelines must be put in place to ensure the responsible development and deployment of AI systems, with a focus on transparency, accountability, and the protection of individual rights and privacy.Additionally, we must prioritize education and awareness about AI, both in formal educational settings and in broader public discourse. It is crucial that people understand the strengths and limitations of AI, as well as its potential impacts on society. Only through informed and open discussions can we navigate the complexities of this technology and shape its development in a way that benefits all of humanity.Ultimately, the rise of AI is both exhilarating and daunting, but it is a reality we cannot ignore. As a student, I feel a sense of responsibility to engage with this technology, to understand its implications, and to be part of the conversations that will shape its future. While the path ahead is uncertain, I am confident thatwith the right approach, AI can be a powerful tool for solving some of the world's most pressing challenges and improving the human condition. It is up to us, the next generation, to harness the potential of AI while mitigating its risks, ensuring that it serves the greater good of society.。

给客户推销相机的英语作文

As a high school student with a keen interest in photography, Ive always been passionate about capturing the world around me. This passion led me to learn about different types of cameras and their capabilities. Recently, I had the opportunity to introduce a new camera model to a potential customer, and Id like to share my experience.It was a sunny Saturday afternoon when I walked into the local electronics store for a parttime job. The store was bustling with people looking for the latest gadgets. I spotted a middleaged man browsing through the camera section, seemingly overwhelmed by the variety of options. Sensing his hesitation, I approached him with a friendly smile.Excuse me, sir, I began, I noticed youre looking at cameras. Id be happy to help you find the perfect one for your needs.He looked relieved and nodded. Im trying to find a camera for my upcoming trip to the mountains. I want something that can capture the beauty of the scenery, but Im not sure which one to choose.I listened carefully to his requirements and decided to introduce him to our latest model, the Eagle Eye 360. This camera is designed for adventurers and nature lovers like him, offering highquality images and a wide range of features.The Eagle Eye 360 is equipped with a 24megapixel sensor, ensuring that every detail of the mountain landscape is captured with clarity, I explained. It also has a 3inch LCD screen, allowing you to easily frame your shots andreview your photos on the spot.The mans eyes lit up with interest, so I continued, One of the standout features of this camera is its 360degree panoramic mode. With just a simple sweep of the lens, you can capture the entire mountain vista in a single, stunning image. Imagine being able to share the breathtaking views with your friends and family as if they were standing right beside you.He seemed impressed but still had some concerns. That sounds great, but what about the battery life? Ill be out in the wilderness for days, and I dont want to worry about recharging.I smiled, confident in the cameras capabilities. The Eagle Eye 360 has an impressive battery life of up to 500 shots on a single charge. Plus, its compatible with standard AA batteries, so you can easily replace them if needed. You wont have to worry about running out of power during your trip.As we continued our conversation, I showed him the cameras other features, such as its builtin GPS for geotagging your photos, its weatherresistant body for protection against the elements, and its userfriendly interface for easy operation even for beginners.The man was clearly intrigued and asked to test the camera. I handed it to him, and he took a few test shots, marveling at the image quality and ease of use. After a few more questions and a comparison with other models, he finally decided to purchase the Eagle Eye 360.As he walked out of the store with his new camera, I felt a sense of accomplishment. Not only had I helped him find the perfect tool for his mountain adventure, but I had also gained valuable experience in sales and customer service.This experience taught me the importance of understanding the customers needs, showcasing the products features effectively, and addressing any concerns they might have. Its not just about selling a product its about creating a connection and ensuring that the customer is satisfied with their purchase.In conclusion, introducing a camera to a customer involves more than just listing its specifications. Its about understanding their needs, showcasing the cameras capabilities, and providing a solution that meets their requirements. As a high school student, this experience has not only enhanced my knowledge of cameras but also given me insights into the world of sales and customer relations.。

人工智能技术让生活更美好英语作文

人工智能技术让生活更美好英语作文全文共3篇示例，供读者参考篇1How AI Technology Makes Life BetterAs a student in the 21st century, I can't help but be in awe of the rapid advancements in artificial intelligence (AI) technology over the past few decades. AI seems to have infiltrated almost every aspect of our lives, from the smart assistants on our phones to the recommendation algorithms that power our favorite streaming platforms. While there are certainly valid concerns about the implications of this powerful technology, I believe that AI has the potential to improve our lives in countless ways.One of the most exciting applications of AI is in the field of healthcare. With the ability to process vast amounts of data and identify patterns that may be imperceptible to human analysts, AI systems are being used to aid in disease diagnosis, drug discovery, and personalized treatment planning. Imagine a future where AI could analyze your genetic makeup, medical history, and lifestyle factors to develop a tailored healthcare planthat maximizes your chances of staying healthy and catching any potential issues early on. This could be a game-changer in terms of improving patient outcomes and reducing healthcare costs.Another area where AI is making a significant impact is in education. AI-powered tutoring systems and adaptive learning platforms can analyze a student's strengths, weaknesses, and learning style, and then tailor the educational content and delivery method accordingly. This personalized approach could help students learn more effectively and efficiently, while also providing valuable insights to teachers on how to better support their students' individual needs. Additionally, AI-powered language translation tools could break down barriers in international communication and knowledge-sharing, fostering a more interconnected and inclusive global community of learners.As someone who loves to travel, I'm also intrigued by the potential of AI in the transportation industry. Self-driving cars and autonomous delivery drones could revolutionize the way we move goods and people, potentially reducing traffic congestion, increasing efficiency, and improving safety on our roads and in the skies. AI-powered route optimization algorithms could alsohelp logistics companies plan more efficient delivery routes, cutting down on fuel consumption and carbon emissions.Beyond these more practical applications, AI is also making its mark in the realm of creativity and entertainment. AI-powered music composition tools can analyze existing songs and genres to generate entirely new musical works, potentially opening up new avenues for artistic expression and collaboration between humans and machines. Similarly, AI-generated art and literature could push the boundaries of what's possible in these creative fields, sparking new ideas and perspectives that might not have emerged otherwise.Of course, with any powerful technology, there are valid concerns and ethical considerations that must be addressed. The potential for AI systems to perpetuate biases present in their training data, or to be used for nefarious purposes like surveillance or manipulation, is a real and pressing issue. There are also legitimate concerns about the potential displacement of human workers by AI-powered automation, and the need to ensure that this technology is developed and deployed in a responsible and equitable manner.However, I believe that these challenges can be overcome through responsible governance, transparent developmentpractices, and a commitment to ethical AI principles. By working to mitigate the risks and potential downsides of AI, while simultaneously fostering its positive applications, we can harness the power of this transformative technology to create a better world for all.As a student, I'm excited to see how AI will continue to shape and influence the world around us in the years to come. From personalized education and healthcare to more efficient transportation and logistics, to new frontiers in creativity and entertainment, the possibilities seem endless. While we must remain vigilant and proactive in addressing the potential risks and ethical concerns, I believe that AI technology has the potential to make our lives easier, more convenient, and ultimately, better.In conclusion, the rapid advancements in AI technology are undoubtedly shaping the world we live in, and will continue to do so in profound ways. As students and members of this increasingly technology-driven society, it's our responsibility to stay informed and engaged in the conversations around AI, to ensure that this powerful tool is harnessed for the greater good of humanity. By embracing the positive applications of AI while remaining mindful of its potential pitfalls, we can work towards afuture where this technology truly makes our lives better in every sense of the word.篇2How AI Technology Makes Life BetterArtificial Intelligence, or AI for short, is one of the most exciting and rapidly advancing fields of technology today. As a student, I can't help but be amazed at the countless ways AI is positively impacting our lives and making the world a better place. From smart home assistants that make everyday tasks more convenient to groundbreaking medical research powered by machine learning, AI is transforming how we live, work, and approach problem-solving.One area where AI shines is in enhancing our modern conveniences and quality of life. Take smart home devices like Amazon's Alexa or Google Home for example. With just a simple voice command, we can set reminders, control our home's lighting and temperature, play our favorite music, and even order household essentials with ease. AI makes these virtual assistants incredibly intuitive, understanding our natural language and responding accordingly. No more fumbling withtiny buttons or memorizing complex commands – AI allows technology to adapt to us instead of the other way around.But AI's benefits extend far beyond hands-free grocery shopping. Recommendation engines powered by AI algorithms are becoming smarter at curating personalized content for us based on our preferences and behaviors. From Netflix's intelligent suggestions for shows and movies to Spotify'sspot-on music recommendations, AI ensures we discover new content tailored to our unique tastes. In our digital world overflowing with choices, this intelligent curation is a gamechanger that saves us countless hours of aimless browsing.AI is also rapidly advancing fields like healthcare and scientific research in ways that will positively impact millions of lives. Machine learning algorithms can analyze vast amounts of medical data, spot patterns invisible to the human eye, and aid in early disease detection and prevention. Google's AI system has already proven capable of detecting breast cancer from mammograms with greater accuracy than human radiologists. As AI continues developing, it holds the potential to accelerate medical breakthroughs, leading to more effective treatments and ultimately saving lives.The possibilities of AI in education are equally exciting. Intelligent tutoring systems can provide adaptive, personalized learning experiences tailored to each student's unique strengths, weaknesses, and pace of learning. No more one-size-fits-all approach – AI can revolutionize how we acquire knowledge. Additionally, AI writing assistants can help students improve their grammar, structure, and overall communication skills by providing intelligent feedback in real-time. As an English student, I can attest to how invaluable such tools are in honing our writing abilities.Looking beyond individual quality-of-life improvements, AI is also tackling some of humanity's grandest challenges. Researchers are leveraging machine learning to develop more sustainable practices in energy, agriculture, and manufacturing. AI systems can optimize energy usage in smart grids, enabling a transition towards renewable sources. Predictive analytics can help farmers increase crop yields while minimizing resource consumption. The potential applications are endless, and AI could very well be our greatest ally in combating climate change and achieving a more eco-friendly future.Of course, with such a transformative technology comes valid concerns over its ethical implications and potential misuse.AI systems can perpetuate human biases if their training data is skewed or lacks diversity. There are also fears about AI displacing human workers or being weaponized for malicious purposes. As AI continues advancing, we must remain vigilant about developing robust governance frameworks that prioritize ethics, transparency, and accountability.Despite these complexities, I firmly believe the positive impacts of AI will far outweigh the risks as long as we approach its development thoughtfully and responsibly. After all, every revolutionary technology carries uncertainties – it's up to us as a society to steer AI in an direction that promotes the greater good of humanity.As a student, I'm endlessly inspired by AI's potential to reshape our world for the better. From empowering our personal lives with seamless conveniences to driving sustainability and life-changing scientific breakthroughs, AI is an incredibly powerful tool awaiting our creative application. It's our responsibility to continue nurturing this technology while upholding ethical principles, so that future generations can experience an AI-augmented world of unprecedented progress and prosperity.In the coming decades, I have no doubt AI will become an indispensable part of our daily lives and society, just as the internet and mobile technology are today. As both creators and consumers of AI, we have a unique opportunity to shape this unfolding revolution in a way that improves lives, solves global issues, and propels humanity towards an incredibly bright future. For me, that's the most exciting prospect – playing a role in harnessing AI's infinite possibilities to create a better world for all.篇3How AI Technology Makes Life BetterAs a student living in the 21st century, I can't help but be in awe of the rapid advancements in artificial intelligence (AI) technology. From digital assistants that can understand and respond to natural language to self-driving cars and intelligent robots, AI seems to be revolutionizing nearly every aspect of our lives. While some may view this technological progress with trepidation, I firmly believe that AI has the potential to vastly improve our quality of life and solve many of the world's most pressing challenges.One of the most tangible ways AI is already enhancing our lives is through the realm of personal assistance. Digital assistants like Siri, Alexa, and Google Assistant have become ubiquitous, allowing us to accomplish tasks and retrieve information with simple voice commands. No longer do we have to fumble with our phones or laptops to set a reminder, check the weather, or find the nearest restaurant. These AI-powered assistants have made our lives more convenient and efficient, freeing up valuable time that can be better spent on more meaningful pursuits.Beyond personal assistance, AI is revolutionizing the field of healthcare. Machine learning algorithms can now analyze vast amounts of medical data, identify patterns, and aid in early disease detection and diagnosis. AI-powered systems can even assist in treatment planning and drug discovery, potentially leading to more effective and personalized therapies. As a result, healthcare professionals can make more informed decisions, improving patient outcomes and potentially saving countless lives.Another area where AI is making a significant impact is in education. Intelligent tutoring systems can adapt to individual learning styles and provide personalized instruction, ensuringthat no student is left behind. AI-powered language learning apps can provide real-time feedback and customized lessons, making the process of acquiring a new language more efficient and engaging. Additionally, AI-powered writing assistants can help students improve their writing skills by providing feedback on grammar, structure, and coherence.Furthermore, AI has the potential to tackle some of the world's most pressing environmental challenges. Machine learning algorithms can be used to analyze climate data, model future scenarios, and identify effective strategies for mitigating the impacts of climate change. AI-powered systems can also optimize energy consumption in buildings and cities, reducing our carbon footprint and promoting sustainability.In the realm of transportation, self-driving cars and intelligent traffic management systems have the potential to significantly reduce road accidents, alleviate traffic congestion, and improve overall mobility. AI-powered vehicles can navigate more efficiently, respond to changing conditions in real-time, and potentially eliminate human error, which is a leading cause of accidents.While the benefits of AI are undeniable, it is crucial to address the ethical considerations and potential risks associatedwith this technology. As AI systems become more advanced and autonomous, concerns arise regarding privacy, bias, and the potential for misuse or unintended consequences. It is imperative that we develop robust ethical frameworks and guidelines to ensure that AI is developed and deployed in a responsible and equitable manner.One of the primary ethical concerns surrounding AI is the issue of privacy and data protection. AI systems rely heavily on vast amounts of data, including personal information, to learn and make decisions. There is a risk that this data could be misused or fall into the wrong hands, compromising individual privacy and security. We must establish clear policies and regulations to govern the collection, storage, and use of personal data, ensuring that individuals have control over their information and that their privacy is protected.Another ethical consideration is the potential for AI systems to perpetuate or amplify existing biases and discrimination. AI algorithms can inherit the biases present in the data they are trained on, leading to unfair or discriminatory decisions. For example, if an AI system is trained on data that reflects historical biases against certain groups, it may continue to make biased decisions, exacerbating existing inequalities. It is crucial that weactively work to identify and mitigate these biases, ensuring that AI systems are fair, transparent, and accountable.Additionally, there are concerns about the impact of AI on employment and the workforce. As AI systems become more capable and autonomous, there is a risk that certain jobs and tasks may become automated, potentially leading to job losses and economic disruptions. While AI may create new job opportunities in fields such as data analysis, software development, and AI system management, it is essential that we prepare for these workforce transitions and provide adequate support and retraining programs for affected workers.Despite these challenges, I remain optimistic about the potential of AI to create a better world. By addressing ethical concerns head-on, developing robust governance frameworks, and fostering a culture of responsible innovation, we can harness the power of AI to solve complex problems, promote sustainability, and improve the overall quality of life for people around the globe.As a student, I am excited to be part of this technological revolution and to witness firsthand the ways in which AI is transforming our world. I am committed to learning about AI, understanding its implications, and using this knowledge tocontribute to the development of ethical and beneficial AI solutions.In conclusion, AI technology has the potential to make our lives better in countless ways, from enhancing personal productivity and convenience to revolutionizing healthcare, education, and environmental protection. However, we must also confront the ethical challenges posed by this powerful technology and work towards developing responsible and equitable AI systems. By embracing AI while upholding our values of privacy, fairness, and human dignity, we can create a future where technology and humanity coexist harmoniously, working together to build a better world for all.。

Natural Language Processing

Natural Language Processing1INTRODUCTIONNatural Language Processing (NLP) is the computerized approach to analyzing text that is based on both a set of theories and a set of technologies. And, being a very active area of research and development, there is not a single agreed-upon definition that would satisfy everyone, but there are some aspects, which would be part of any knowledgeable person’s definition. The definition I offer is:Definition: Natural Language Processing is a theoretically motivated range ofcomputational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications.Several elements of this definition can be further detailed. Firstly the imprecise notion of ‘range of computational techniques’ is necessary because there are multiple methods or techniques from which to choose to accomplish a particular type of language analysis.‘Naturally occurring texts’ can be of any language, mode, genre, etc. The texts can be oral or written. The only requirement is that they be in a language used by humans to communicate to one another. Also, the text being analyzed should not be specifically constructed for the purpose of the analysis, but rather that the text be gathered from actual usage.The notion of ‘levels of linguistic analysis’ (to be further explained in Section 2) refers to the fact that there are multiple types of language processing known to be at work when humans produce or comprehend language. It is thought that humans normally utilize all of these levels since each level conveys different types of meaning. But various NLP systems utilize different levels, or combinations of levels of linguistic analysis, and this is seen in the differences amongst various NLP applications. This also leads to much confusion on the part of non-specialists as to what NLP really is, because a system that uses any subset of these levels of analysis can be said to be an NLP-based system. The difference between them, therefore, may actually be whether the system uses ‘weak’ NLP or ‘strong’ NLP.‘Human-like language processing’ reveals that NLP is considered a discipline within Artificial Intelligence (AI). And while the full lineage of NLP does depend on a number of other disciplines, since NLP strives for human-like performance, it is appropriate to consider it an AI discipline.‘For a range of tasks or applications’ points out that NLP is not usually considered a goal in and of itself, except perhaps for AI researchers. For others, NLP is the means for 1 Liddy, E. D. In Encyclopedia of Library and Information Science, 2nd Ed. Marcel Decker, Inc.accomplishing a particular task. Therefore, you have Information Retrieval (IR) systems that utilize NLP, as well as Machine Translation (MT), Question-Answering, etc.GoalThe goal of NLP as stated above is “to accomplish human-like language processing”. The choice of the word ‘processing’ is very deliberate, and should not be replaced with ‘understanding’. For although the field of NLP was originally referred to as Natural Language Understanding (NLU) in the early days of AI, it is well agreed today that while the goal of NLP is true NLU, that goal has not yet been accomplished. A full NLU System would be able to:1. Paraphrase an input text2. Translate the text into another language3. Answer questions about the contents of the text4. Draw inferences from the textWhile NLP has made serious inroads into accomplishing goals 1 to 3, the fact that NLP systems cannot, of themselves, draw inferences from text, NLU still remains the goal of NLP.There are more practical goals for NLP, many related to the particular application for which it is being utilized. For example, an NLP-based IR system has the goal of providing more precise, complete information in response to a user’s real information need. The goal of the NLP system here is to represent the true meaning and intent of the user’s query, which can be expressed as naturally in everyday language as if they were speaking to a reference librarian. Also, the contents of the documents that are being searched will be represented at all their levels of meaning so that a true match between need and response can be found, no matter how either are expressed in their surface form. OriginsAs most modern disciplines, the lineage of NLP is indeed mixed, and still today has strong emphases by different groups whose backgrounds are more influenced by one or another of the disciplines. Key among the contributors to the discipline and practice of NLP are: Linguistics - focuses on formal, structural models of language and the discovery of language universals - in fact the field of NLP was originally referred to as Computational Linguistics; Computer Science - is concerned with developing internal representations of data and efficient processing of these structures, and; Cognitive Psychology - looks at language usage as a window into human cognitive processes, and has the goal of modeling the use of language in a psychologically plausible way. DivisionsWhile the entire field is referred to as Natural Language Processing, there are in fact two distinct focuses – language processing and language generation. The first of these refersto the analysis of language for the purpose of producing a meaningful representation, while the latter refers to the production of language from a representation. The task of Natural Language Processing is equivalent to the role of reader/listener, while the task of Natural Language Generation is that of the writer/speaker. While much of the theory and technology are shared by these two divisions, Natural Language Generation also requires a planning capability. That is, the generation system requires a plan or model of the goal of the interaction in order to decide what the system should generate at each point in an interaction. We will focus on the task of natural language analysis, as this is most relevant to Library and Information Science.Another distinction is traditionally made between language understanding and speech understanding. Speech understanding starts with, and speech generation ends with, oral language and therefore rely on the additional fields of acoustics and phonology. Speech understanding focuses on how the ‘sounds’ of language as picked up by the system in the form of acoustical waves are transcribed into recognizable morphemes and words. Once in this form, the same levels of processing which are utilized on written text are utilized. All of these levels, including the phonology level, will be covered in Section 2; however, the emphasis throughout will be on language in the written form.BRIEF HISTORY OF NATURAL LANGUAGE PROCESSINGResearch in natural language processing has been going on for several decades dating back to the late 1940s. Machine translation (MT) was the first computer-based application related to natural language. While Weaver and Booth (1); (2) started one of the earliest MT projects in 1946 on computer translation based on expertise in breaking enemy codes during World War II, it was generally agreed that it was Weaver’s memorandum of 1949 that brought the idea of MT to general notice and inspired many projects (3). He suggested using ideas from cryptography and information theory for language translation. Research began at various research institutions in the United States within a few years.Early work in MT took the simplistic view that the only differences between languages resided in their vocabularies and the permitted word orders. Systems developed from this perspective simply used dictionary-lookup for appropriate words for translation and reordered the words after translation to fit the word-order rules of the target language, without taking into account the lexical ambiguity inherent in natural language. This produced poor results. The apparent failure made researchers realize that the task was a lot harder than anticipated, and they needed a more adequate theory of language. However, it was not until 1957 when Chomsky (4) published Syntactic Structures introducing the idea of generative grammar, did the field gain better insight into whether or how mainstream linguistics could help MT.During this period, other NLP application areas began to emerge, such as speech recognition. The language processing community and the speech community then was split into two camps with the language processing community dominated by thetheoretical perspective of generative grammar and hostile to statistical methods, and the speech community dominated by statistical information theory (5) and hostile to theoretical linguistics (6).Due to the developments of the syntactic theory of language and parsing algorithms, there was over-enthusiasm in the 1950s that people believed that fully automatic high quality translation systems (2) would be able to produce results indistinguishable from those of human translators, and such systems should be in operation within a few years. It was not only unrealistic given the then-available linguistic knowledge and computer systems, but also impossible in principle (3).The inadequacies of then-existing systems, and perhaps accompanied by the over-enthusiasm, led to the ALPAC (Automatic Language Processing Advisory Committee of the National Academy of Science - National Research Council) report of 1966. (7) The report concluded that MT was not immediately achievable and recommended it not be funded. This had the effect of halting MT and most work in other applications of NLP at least within the United States.Although there was a substantial decrease in NLP work during the years after the ALPAC report, there were some significant developments, both in theoretical issues and in construction of prototype systems. Theoretical work in the late 1960’s and early 1970’s focused on the issue of how to represent meaning and developing computationally tractable solutions that the then-existing theories of grammar were not able to produce. In 1965, Chomsky (8) introduced the transformational model of linguistic competence. However, the transformational generative grammars were too syntactically oriented to allow for semantic concerns. They also did not lend themselves easily to computational implementation. As a reaction to Chomsky’s theories and the work of other transformational generativists, case grammar of Fillmore, (9), semantic networks of Quillian, (10), and conceptual dependency theory of Schank, (11) were developed to explain syntactic anomalies, and provide semantic representations. Augmented transition networks of Woods, (12) extended the power of phrase-structure grammar by incorporating mechanisms from programming languages such as LISP. Other representation formalisms included Wilks’ preference semantics (13), and Kay’s functional grammar (14).Alongside theoretical development, many prototype systems were developed to demonstrate the effectiveness of particular principles. Weizenbaum’s ELIZA (15) was built to replicate the conversation between a psychologist and a patient, simply by permuting or echoing the user input. Winograd’s SHRDLU (16) simulated a robot that manipulated blocks on a tabletop. Despite its limitations, it showed that natural language understanding was indeed possible for the computer (17). PARRY (18) attempted to embody a theory of paranoia in a system. Instead of single keywords, it used groups of keywords, and used synonyms if keywords were not found. LUNAR was developed by Woods (19) as an interface system to a database that consisted of information about lunar rock samples using augmented transition network and procedural semantics (20).In the late 1970’s, attention shifted to semantic issues, discourse phenomena, and communicative goals and plans (21). Grosz (22) analyzed task-oriented dialogues and proposed a theory to partition the discourse into units based on her findings about the relation between the structure of a task and the structure of the task-oriented dialogue. Mann and Thompson (23) developed Rhetorical Structure Theory, attributing hierarchical structure to discourse. Other researchers have also made significant contributions, including Hobbs and Rosenschein (24), Polanyi and Scha (25), and Reichman (26).This period also saw considerable work on natural language generation. McKeown’s discourse planner TEXT (27) and McDonald’s response generator MUMMBLE (28) used rhetorical predicates to produce declarative descriptions in the form of short texts, usually paragraphs. TEXT’s ability to generate coherent responses online was considered a major achievement.In the early 1980s, motivated by the availability of critical computational resources, the growing awareness within each community of the limitations of isolated solutions to NLP problems (21), and a general push toward applications that worked with language in a broad, real-world context (6), researchers started re-examining non-symbolic approaches that had lost popularity in early days. By the end of 1980s, symbolic approaches had been used to address many significant problems in NLP and statistical approaches were shown to be complementary in many respects to symbolic approaches (21).In the last ten years of the millennium, the field was growing rapidly. This can be attributed to: a) increased availability of large amounts of electronic text; b) availability of computers with increased speed and memory; and c) the advent of the Internet. Statistical approaches succeeded in dealing with many generic problems in computational linguistics such as part-of-speech identification, word sense disambiguation, etc., and have become standard throughout NLP (29). NLP researchers are now developing next generation NLP systems that deal reasonably well with general text and account for a good portion of the variability and ambiguity of language.LEVELS OF NATURAL LANGUAGE PROCESSINGThe most explanatory method for presenting what actually happens within a Natural Language Processing system is by means of the ‘levels of language’ approach. This is also referred to as the synchronic model of language and is distinguished from the earlier sequential model, which hypothesizes that the levels of human language processing follow one another in a strictly sequential manner. Psycholinguistic research suggests that language processing is much more dynamic, as the levels can interact in a variety of orders. Introspection reveals that we frequently use information we gain from what is typically thought of as a higher level of processing to assist in a lower level of analysis. For example, the pragmatic knowledge that the document you are reading is about biology will be used when a particular word that has several possible senses (or meanings) is encountered, and the word will be interpreted as having the biology sense.Of necessity, the following description of levels will be presented sequentially. The key point here is that meaning is conveyed by each and every level of language and that since humans have been shown to use all levels of language to gain understanding, the more capable an NLP system is, the more levels of language it will utilize.(Figure 1: Synchronized Model of Language Processing)PhonologyThis level deals with the interpretation of speech sounds within and across words. There are, in fact, three types of rules used in phonological analysis: 1) phonetic rules – for sounds within words; 2) phonemic rules – for variations of pronunciation when words are spoken together, and; 3) prosodic rules – for fluctuation in stress and intonation across a sentence. In an NLP system that accepts spoken input, the sound waves are analyzed and encoded into a digitized signal for interpretation by various rules or by comparison to the particular language model being utilized.MorphologyThis level deals with the componential nature of words, which are composed of morphemes – the smallest units of meaning. For example, the word preregistration can be morphologically analyzed into three separate morphemes: the prefix pre, the root registra, and the suffix tion. Since the meaning of each morpheme remains the same across words, humans can break down an unknown word into its constituent morphemes in order to understand its meaning. Similarly, an NLP system can recognize the meaning conveyed by each morpheme in order to gain and represent meaning. For example, adding the suffix –ed to a verb, conveys that the action of the verb took place in the past. This is a key piece of meaning, and in fact, is frequently only evidenced in a text by the use of the -ed morpheme.LexicalAt this level, humans, as well as NLP systems, interpret the meaning of individual words. Several types of processing contribute to word-level understanding – the first of these being assignment of a single part-of-speech tag to each word. In this processing, words that can function as more than one part-of-speech are assigned the most probable part-of-speech tag based on the context in which they occur.Additionally at the lexical level, those words that have only one possible sense or meaning can be replaced by a semantic representation of that meaning. The nature of the representation varies according to the semantic theory utilized in the NLP system. The following representation of the meaning of the word launch is in the form of logical predicates. As can be observed, a single lexical unit is decomposed into its more basic properties. Given that there is a set of semantic primitives used across all words, these simplified lexical representations make it possible to unify meaning across words and to produce complex interpretations, much the same as humans do.launch (a large boat used for carrying people on rivers, lakes harbors, etc.)((CLASS BOAT) (PROPERTIES (LARGE)(PURPOSE (PREDICATION (CLASS CARRY) (OBJECT PEOPLE))))The lexical level may require a lexicon, and the particular approach taken by an NLP system will determine whether a lexicon will be utilized, as well as the nature and extent of information that is encoded in the lexicon. Lexicons may be quite simple, with only the words and their part(s)-of-speech, or may be increasingly complex and contain information on the semantic class of the word, what arguments it takes, and the semantic limitations on these arguments, definitions of the sense(s) in the semantic representation utilized in the particular system, and even the semantic field in which each sense of a polysemous word is used.SyntacticThis level focuses on analyzing the words in a sentence so as to uncover the grammatical structure of the sentence. This requires both a grammar and a parser. The output of this level of processing is a (possibly delinearized) representation of the sentence that reveals the structural dependency relationships between the words. There are various grammars that can be utilized, and which will, in turn, impact the choice of a parser. Not all NLP applications require a full parse of sentences, therefore the remaining challenges in parsing of prepositional phrase attachment and conjunction scoping no longer stymie those applications for which phrasal and clausal dependencies are sufficient. Syntax conveys meaning in most languages because order and dependency contribute to meaning. For example the two sentences: ‘The dog chased the cat.’ and ‘The cat chased the dog.’ differ only in terms of syntax, yet convey quite different meanings.SemanticThis is the level at which most people think meaning is determined, however, as we can see in the above defining of the levels, it is all the levels that contribute to meaning. Semantic processing determines the possible meanings of a sentence by focusing on the interactions among word-level meanings in the sentence. This level of processing can include the semantic disambiguation of words with multiple senses; in an analogous way to how syntactic disambiguation of words that can function as multiple parts-of-speech is accomplished at the syntactic level. Semantic disambiguation permits one and only one sense of polysemous words to be selected and included in the semantic representation of the sentence. For example, amongst other meanings, ‘file’ as a noun can mean either a folder for storing papers, or a tool to shape one’s fingernails, or a line of individuals in a queue. If information from the rest of the sentence were required for the disambiguation, the semantic, not the lexical level, would do the disambiguation. A wide range of methods can be implemented to accomplish the disambiguation, some which require information as to the frequency with which each sense occurs in a particular corpus ofinterest, or in general usage, some which require consideration of the local context, and others which utilize pragmatic knowledge of the domain of the document.DiscourseWhile syntax and semantics work with sentence-length units, the discourse level of NLP works with units of text longer than a sentence. That is, it does not interpret multi-sentence texts as just concatenated sentences, each of which can be interpreted singly. Rather, discourse focuses on the properties of the text as a whole that convey meaning by making connections between component sentences. Several types of discourse processing can occur at this level, two of the most common being anaphora resolution and discourse/text structure recognition. Anaphora resolution is the replacing of words such as pronouns, which are semantically vacant, with the appropriate entity to which they refer (30). Discourse/text structure recognition determines the functions of sentences in the text, which, in turn, adds to the meaningful representation of the text. For example, newspaper articles can be deconstructed into discourse components such as: Lead, Main Story, Previous Events, Evaluation, Attributed Quotes, and Expectation (31). PragmaticThis level is concerned with the purposeful use of language in situations and utilizes context over and above the contents of the text for understanding The goal is to explain how extra meaning is read into texts without actually being encoded in them. This requires much world knowledge, including the understanding of intentions, plans, and goals. Some NLP applications may utilize knowledge bases and inferencing modules. For example, the following two sentences require resolution of the anaphoric term ‘they’, but this resolution requires pragmatic or world knowledge.The city councilors refused the demonstrators a permit because they fearedviolence.The city councilors refused the demonstrators a permit because they advocatedrevolution.Summary of LevelsCurrent NLP systems tend to implement modules to accomplish mainly the lower levels of processing. This is for several reasons. First, the application may not require interpretation at the higher levels. Secondly, the lower levels have been more thoroughly researched and implemented. Thirdly, the lower levels deal with smaller units of analysis, e.g. morphemes, words, and sentences, which are rule-governed, versus the higher levels of language processing which deal with texts and world knowledge, and which are onlyregularity-governed. As will be seen in the following section on Approaches, the statistical approaches have, to date, been validated on the lower levels of analysis, while the symbolic approaches have dealt with all levels, although there are still few working systems which incorporate the higher levels.APPROACHES TO NATURAL LANGUAGE PROCESSINGNatural language processing approaches fall roughly into four categories: symbolic, statistical, connectionist, and hybrid. Symbolic and statistical approaches have coexisted since the early days of this field. Connectionist NLP work first appeared in the 1960’s. For a long time, symbolic approaches dominated the field. In the 1980’s, statistical approaches regained popularity as a result of the availability of critical computational resources and the need to deal with broad, real-world contexts. Connectionist approaches also recovered from earlier criticism by demonstrating the utility of neural networks in NLP. This section examines each of these approaches in terms of their foundations, typical techniques, differences in processing and system aspects, and their robustness, flexibility, and suitability for various tasks.Symbolic ApproachSymbolic approaches perform deep analysis of linguistic phenomena and are based on explicit representation of facts about language through well-understood knowledge representation schemes and associated algorithms (21). In fact, the description of the levels of language analysis in the preceding section is given from a symbolic perspective. The primary source of evidence in symbolic systems comes from human-developed rules and lexicons.A good example of symbolic approaches is seen in logic or rule-based systems. In logic-based systems, the symbolic structure is usually in the form of logic propositions. Manipulations of such structures are defined by inference procedures that are generally truth preserving. Rule-based systems usually consist of a set of rules, an inference engine, and a workspace or working memory. Knowledge is represented as facts or rules in the rule-base. The inference engine repeatedly selects a rule whose condition is satisfied and executes the rule.Another example of symbolic approaches is semantic networks. First proposed by Quillian (10) to model associative memory in psychology, semantic networks represent knowledge through a set of nodes that represent objects or concepts and the labeled links that represent relations between nodes. The pattern of connectivity reflects semantic organization, that is; highly associated concepts are directly linked whereas moderately or weakly related concepts are linked through intervening concepts. Semantic networks are widely used to represent structured knowledge and have the most connectionist flavor of the symbolic models (32).Symbolic approaches have been used for a few decades in a variety of research areas and applications such as information extraction, text categorization, ambiguity resolution, and lexical acquisition. Typical techniques include: explanation-based learning, rule-based learning, inductive logic programming, decision trees, conceptual clustering, and K nearest neighbor algorithms (6; 33).Statistical ApproachStatistical approaches employ various mathematical techniques and often use large text corpora to develop approximate generalized models of linguistic phenomena based on actual examples of these phenomena provided by the text corpora without adding significant linguistic or world knowledge. In contrast to symbolic approaches, statistical approaches use observable data as the primary source of evidence.A frequently used statistical model is the Hidden Markov Model (HMM) inherited from the speech community. HMM is a finite state automaton that has a set of states with probabilities attached to transitions between states (34). Although outputs are visible, states themselves are not directly observable, thus “hidden” from external observations. Each state produces one of the observable outputs with a certain probability.Statistical approaches have typically been used in tasks such as speech recognition, lexical acquisition, parsing, part-of-speech tagging, collocations, statistical machine translation, statistical grammar learning, and so on.Connectionist ApproachSimilar to the statistical approaches, connectionist approaches also develop generalized models from examples of linguistic phenomena. What separates connectionism from other statistical methods is that connectionist models combine statistical learning with various theories of representation - thus the connectionist representations allow transformation, inference, and manipulation of logic formulae (33). In addition, in connectionist systems, linguistic models are harder to observe due to the fact that connectionist architectures are less constrained than statistical ones (35); (21). Generally speaking, a connectionist model is a network of interconnected simple processing units with knowledge stored in the weights of the connections between units (32). Local interactions among units can result in dynamic global behavior, which, in turn, leads to computation.Some connectionist models are called localist models, assuming that each unit represents a particular concept. For example, one unit might represent the concept “mammal” while another unit might represent the concept “whale”. Relations between concepts are encoded by the weights of connections between those concepts. Knowledge in such models is spread across the network, and the connectivity between units reflects their structural relationship. Localist models are quite similar to semantic networks, but the links between units are not usually labeled as they are in semantic nets. They perform。

与人工智能相关的英语单词

与人工智能相关的英语单词1. Artificial Intelligence (AI): 人工智能，它是计算机科学的分支，旨在模拟人类的智能和思维。

AI包括机器学习、自然语言处理、计算机视觉等技术，这些技术可以用于创建智能机器人、智能助理、自动驾驶汽车等。

举例：Google's AI language model can answer complex questions and translate text between languages.2. Machine Learning (ML): 机器学习是AI的一个分支，它是指通过计算机程序从数据中学习并做出决策。

ML使用算法来分析大量数据并自动识别模式和趋势，从而进行预测和分类。

举例：Amazon's ML algorithm can predict which products a customer might be interested in based on their previous purchases and browsing history.3. Natural Language Processing (NLP): 自然语言处理是AI的一个分支，它是指将人类语言转化为计算机可理解的形式。

NLP包括文本分析、情感分析、语音识别等技术，这些技术可以用于创建聊天机器人、语音助手、自动翻译等。

举例：Apple's Siri can understand and respond to human speech through NLP technology.4. Computer Vision (CV): 计算机视觉是AI的一个分支，它是指将图像转化为计算机可理解的形式。

CV包括图像识别、目标检测、人脸识别等技术，这些技术可以用于创建智能监控系统、自动驾驶汽车、智能家居等。

举例：Facebook's CV technology can identify and tag friends in photos posted on the social network.5. Deep Learning (DL): 深度学习是机器学习的一个分支，它是指使用深度神经网络进行学习。

人工智能机器人陪伴购物英语作文

人工智能机器人陪伴购物英语作文AI-Powered Shopping Companions: Revolutionizing the Retail Experience.Artificial intelligence (AI) is rapidly transformingthe retail industry, and one of its most promising applications is the creation of AI-powered shopping companions. These virtual assistants provide customers with personalized recommendations, help them navigate stores,and even complete purchases.Personalized Shopping Recommendations.One of the key benefits of AI shopping companions is their ability to provide highly personalized shopping recommendations. By analyzing customer data, such as purchase history, browsing behavior, and demographics,these companions can create a detailed profile of each user. This information is then used to recommend products thatare tailored to their individual needs and preferences.For example, a fashion shopping companion might recommend a specific dress to a customer based on their previous purchases, saved items, and body measurements. A home goods companion could suggest a particular furniture item that complements the customer's existing decor. By offering such targeted recommendations, AI companions can help customers discover products that they might not have otherwise considered.In-Store Navigation.AI shopping companions can also serve as virtual tour guides, helping customers navigate physical retail stores. Through augmented reality (AR) technology, these companions can overlay digital content on the user's view of the store. This allows customers to easily find specific products,view product information, and even try on virtual items without having to physically handle them.For instance, a grocery shopping companion coulddisplay a map of the store, highlighting the aisle wherethe desired item is located. A clothing store companion could allow customers to virtually try on different outfits using their smartphone camera. By providing such seamlessin-store navigation, AI companions can enhance the shopping experience and make it more efficient.Automated Purchases.In addition to providing recommendations and aiding navigation, AI shopping companions can also automate the purchase process. By integrating with mobile payment systems, these companions allow customers to complete transactions without having to wait in line or interact with a cashier. This is particularly convenient for online shoppers who want to make quick and convenient purchases.AI shopping companions use machine learning algorithms to verify customer identities, process payments, and handle any necessary paperwork. They can also track orders and provide updates on delivery status. By automating the checkout process, these companions streamline the shopping experience and make it more convenient for customers.Enhanced Customer Engagement.Beyond their practical functionality, AI shopping companions also provide retailers with a valuable opportunity to enhance customer engagement. By interacting with these virtual assistants, customers can ask questions, receive personalized advice, and even provide feedback. This fosters a more interactive and engaging shopping experience, which can lead to increased brand loyalty and repeat purchases.For instance, a beauty product shopping companion could offer tips on how to use a particular product or recommend complementary items. A travel booking companion couldassist customers in finding the best deals and creating custom itineraries. By providing such personalized and interactive support, AI companions foster strong customer relationships and build brand affinity.Challenges and Future Prospects.While AI shopping companions offer numerous benefits, there are also some challenges and considerations to address. One concern is the potential for bias in product recommendations. AI algorithms rely on data to make decisions, and if the underlying data is biased, the recommendations may also be biased. This could lead to unfair or discriminatory treatment of certain customer groups.Another challenge is the need for robust security measures. AI shopping companions handle sensitive customer data, such as payment information and purchase history. Ensuring the security of this data is paramount to maintaining customer trust and preventing fraud.Despite these challenges, the future of AI shopping companions looks bright. As AI technology continues to advance, these companions will become increasingly sophisticated and personalized. They may incorporate new capabilities such as image recognition, natural language processing, and predictive analytics to provide even more tailored and seamless shopping experiences.Conclusion.AI-powered shopping companions are transforming the retail industry by providing personalized recommendations, assisting with in-store navigation, automating purchases, and enhancing customer engagement. While there are still some challenges to overcome, the potential benefits of these virtual assistants are undeniable. As AI technology matures, we can expect to see even more innovative and groundbreaking applications of AI in the retail sector.。

L Translingual Information Management by Natural Language Processing

http://www.cs.titech.ac.jp/
Department of Computer Science Tokyo Institute of Technology
c The author(s) of this report reserves all the rights.
Preface
iv processing and Kevin Knight gave me his sincere and valuable comments on my research work. Finally, I also would like to express a deep debt of gratitude to my wife, Tomoko Okumura for her constant encouragement and support.
Translingual information management that can choose appropriate information and obtain useful knowledge from the ood of global information is being increasingly demanded. Among the related technologies, natural language processing is one of the most promising for meeting information needs because natural language processing can deal with documents that have essential roles in information. This research is aimed at developing a practical system for managing translingual information. The system is based on technologies for natural language processing. Translingual information management has a fundamental cycle of information needs and information search. In order to enhance the search quality and to accelerate the cycle, this research focuses on three core technologies: coordinate structure analysis, cross-language information retrieval, and information navigation. First, we propose a model for analyzing coordinate structure. This model provides top-down scope information on the correct syntactic structure by taking advantage of the symmetric patterns of parallelism. The analysis of coordinate structure creates a bottleneck when dealing with documents because most long sentences contain a coordinate structure. The model results in a high-quality search because it enables accurate analysis. Second, we propose the GDMAX method, which is a method for translating query terms for use in cross-language information retrieval (CLIR). CLIR is a key component in translingual information management. This method produces high-quality search results by choosing appropriate translation terms for CLIR. Finally, in order to make translingual information management ecient, we propose a method for information navigation that classi es documents and enables a user to navigate by using 5W1H (who, when, where, what, why, how, and predicate) information.

自然语言处理与认知语言理解相关的书籍-概述说明以及解释

自然语言处理与认知语言理解相关的书籍-概述说明以及解释1.引言1.1 概述概述部分的内容可以包括自然语言处理（Natural Language Processing，简称NLP）和认知语言理解（Cognitive Language Understanding）的基本介绍和背景。

自然语言处理是一门研究如何使计算机能够理解和处理人类语言的领域。

它结合了计算机科学、人工智能和语言学等多个学科的知识和方法，旨在将自然语言转换为计算机能够理解和处理的形式，实现计算机与人类之间的自然语言交互。

认知语言理解则侧重于研究人类对语言的理解和思维过程。

它探索语言如何与人的认知、思维和心理过程紧密相关，如何通过语言表达和传达信息，以及如何从语言中获取和推断有关世界的知识。

自然语言处理和认知语言理解在很多领域中都具有重要的应用价值。

它们可以被应用于机器翻译、信息检索、语音识别、文本分类、情感分析等任务中，使得计算机能够更好地理解和解释人类语言的含义和上下文。

本文旨在介绍与自然语言处理和认知语言理解相关的书籍，以帮助读者深入了解这两个领域的研究和应用。

接下来的章节将分别介绍自然语言处理相关的书籍和认知语言理解相关的书籍，并对它们的内容和贡献进行详细阐述。

通过阅读这些书籍，读者可以更好地理解和应用自然语言处理和认知语言理解的技术和方法。

1.2文章结构2. 正文2.1 自然语言处理相关书籍:在自然语言处理领域，有许多经典的著作可以供我们学习和参考。

以下是几本值得推荐的书籍：2.1.1 书籍1:《自然语言处理综论》这本书是自然语言处理领域的经典教材，由斯坦福大学的Daniel Jurafsky 和James H. Martin 合著。

它全面介绍了自然语言处理的基础知识和常见技术，包括词法分析、句法分析、语义分析等。

该书深入浅出，适合初学者和有一定基础的读者阅读。

2.1.2 书籍2:《Speech and Language Processing》这是一本由Dan Jurafsky 和James H. Martin 合著的畅销教材，被公认为是自然语言处理领域的必读之作。

Is Image Steganography Natural

Index Terms — Steganography, Information Hiding, Image Models, Natural Images. EDICS — 2-MODL Modeling, 5-AUTH Authentication and Watermarking.
1
Introduction
* Internal Accession Date Only 1 Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica, Montevideo, Uruguay. Work done while author was with HP Laboratories Palo Alto and Electrical and Computer Engineering Dept., University of Minnesota, Minneapolis, MN 55455 2 Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN Approved for External Publication © Copyright Hewlett-Packard Company 2004
This work is partially supported by the Oﬃce of Naval Research grants N000140310399 and N000140310176, by the Presidential Early Career Award for Scientists and Engineers (PECASE), and a National Science Foundation CAREER Award. † Instituto de Computaci´ on, Facultad de Ingenier´ ıa, Universidad de la Rep´ ublica, Montevideo, Uruguay. Work done while the author was with Information Theory Research, Hewlett-Packard Laboratories, Palo Alto, CA 94304, and Electrical and Computer Engineering Department, University of Minnesota, Minneapolis, MN 55455. ‡ Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN 55455. § Information Theory Research, Hewlett-Packard Laboratories, Palo Alto, CA 94304.

有关动物海报英语作文十图

有关动物海报英语作文十图Animal Poster English Composition: Ten ImagesIntroduction:Animals are an essential part of our world and play a significant role in maintaining the balance of nature. Through the following ten images, we will explore some of the diverse and beautiful creatures that inhabit our planet.Image 1: LionThe majestic lion symbolizes strength and power. As the king of the jungle, lions are known for their regal appearance and fierce hunting abilities. Their distinct mane and powerful roar make them both feared and respected by other animals.Image 2: ElephantElephants are one of the largest land mammals and are known for their intelligence and social nature. These gentle giants play a crucial role in maintaining the ecosystems they inhabit and are revered for their close-knit family groups and impressive memory.Image 3: DolphinDolphins are highly intelligent and playful marine mammals that captivate people with their acrobatic displays and fascinating communication abilities. Known for their friendly nature and curiosity towards humans, dolphins are often regarded as symbols of joy and freedom.Image 4: PandaPandas are iconic animals known for their distinctive black and white fur and adorable appearance. These rare and endangered bears are symbols of conservation and environmental awareness, with efforts being made to protect their natural habitats and ensure their survival.Image 5: TigerTigers are powerful and stealthy predators that command respect in the wild. Known for their striking stripes and mesmerizing gaze, these big cats are symbols of courage and strength, yet are also highly endangered due to habitat loss and poaching.Image 6: PenguinPenguins are fascinating birds that have adapted to life in extreme cold environments. With their unique waddling walk and graceful swimming abilities, penguins are beloved for theirplayful antics and strong sense of community within their colonies.Image 7: ButterflyButterflies are delicate and colorful insects that undergo a remarkable transformation from caterpillar to flying beauty. Symbolizing growth, change, and the beauty of nature, butterflies are admired for their intricate patterns and graceful flight.Image 8: GiraffeGiraffes are the tallest land animals, with long necks and striking coat patterns that make them unmistakable. These gentle giants are known for their calm demeanor and unique browsing habits, using their long necks to reach high branches for food.Image 9: OrangutanOrangutans are intelligent and charismatic primates that share a close genetic connection with humans. Known for their expressive faces and arboreal lifestyle, orangutans are symbols of the importance of protecting our closest animal relatives and their habitats.Image 10: HummingbirdHummingbirds are tiny yet mighty birds known for their incredible speed and agility in flight. With their iridescent feathers and ability to hover in mid-air, hummingbirds are symbols of beauty and grace in the avian world.Conclusion:The ten images of animals showcased in this poster serve as a reminder of the diversity and wonder of the natural world. From the powerful lion to the delicate butterfly, each creature plays a vital role in the interconnected web of life on Earth. By appreciating and protecting these animals, we can ensure a brighter future for all species and the planet as a whole.。

英文作文ai数字

英文作文ai数字Title: The Impact of Artificial Intelligence on Digital Numerals。

Artificial Intelligence (AI) has revolutionized numerous aspects of our lives, and one area where its influence is particularly pronounced is in the realm of digital numerals. From predictive analytics to automated decision-making processes, AI has significantly transformed how numbers are utilized, analyzed, and interpreted in various fields. In this essay, we will explore the profound impact of AI on digital numerals and its implications for society.One of the primary ways AI has transformed digital numerals is through predictive modeling and data analysis. AI algorithms can analyze vast amounts of numerical data at speeds far beyond human capabilities. This ability enables organizations to extract valuable insights from complex datasets, leading to more informed decision-makingprocesses. For instance, in finance, AI-powered algorithms can analyze market trends and predict future stock prices with a high degree of accuracy, aiding investors in making strategic investment decisions.Moreover, AI has revolutionized the field of digital marketing by leveraging numerical data to personalize advertisements and optimize marketing strategies. Through machine learning algorithms, AI can analyze consumer behavior patterns and preferences based on numerical data such as browsing history, purchase behavior, and demographic information. This enables marketers to tailor their advertising campaigns to specific target audiences, thereby increasing the effectiveness of their marketing efforts.In addition to predictive analytics, AI has also enhanced the accuracy and efficiency of numerical computations. Complex mathematical calculations that once required significant time and computational resources can now be performed rapidly and accurately by AI-powered systems. This has implications across various industries,including engineering, scientific research, and manufacturing, where precise numerical computations are essential for designing products, conducting simulations, and optimizing processes.Furthermore, AI has played a pivotal role in advancing the field of artificial neural networks (ANNs), which are computational models inspired by the structure and function of the human brain. ANNs are capable of learning from numerical data and making predictions or decisions based on their learned knowledge. This capability has led to significant advancements in areas such as image recognition, natural language processing, and autonomous vehicles, where numerical data forms the basis for decision-making and problem-solving.However, the widespread integration of AI into digital numerals also raises ethical and societal concerns. One major concern is the potential for algorithmic bias, where AI systems may inadvertently perpetuate or exacerbate existing inequalities in society. For example, biased algorithms used in credit scoring or job recruitmentprocesses can result in unfair outcomes, disproportionately affecting certain demographic groups.Moreover, the increasing reliance on AI for numerical analysis and decision-making raises questions about accountability and transparency. As AI systems become more sophisticated, they may operate as "black boxes," making it challenging to understand how they arrive at their decisions. This lack of transparency can undermine trust in AI systems and hinder their acceptance and adoption in society.In conclusion, AI has had a profound impact on digital numerals, revolutionizing how numerical data is analyzed, interpreted, and utilized across various domains. From predictive analytics to numerical computations andartificial neural networks, AI has enabled unprecedented advancements in the understanding and manipulation of numerical data. However, ethical and societal concerns such as algorithmic bias and transparency remain significant challenges that must be addressed to ensure the responsibleand equitable deployment of AI technologies in the realm of digital numerals.。

手持式电子阅读器正文-实践教学

手持式电子阅读器摘要本作品是以JingWei板及自制电路为硬件平台，以Windows CE为软件平台开发的一种嵌入式掌上设备。

作品具有通信录编辑与浏览、文本记事、中英文文档阅读、手写输入与识别、闹钟、图片浏览、音频文件录放、语音记事、环境温、湿度参数实时监测、人体脉搏测试、屏幕硬拷贝输出、视频捕捉等功能。

本作品硬件由JingWei板、外围根本扩展电路和A V扩展选件三局部组成。

其中，硬件接口逻辑通过修改Jingwei板上的CPLD内部逻辑实现。

在硬件设计上，注重选择低功耗、高集成度的器件。

系统应用软件使用EVC++开发，采用模块化设计方法，使每个模块相互独立，具有可移植性，大大减少了程序的代码量。

在软件设计中，注重人机界面的人性化，设计了统一风格的人机交互界面。

本作品可以作为集电子文档管理与阅读、语音图像实时采集、环境参数监测、个人护理等功能于一体的多功能手持式个人数字助理。

关键词文档/图片阅读音频录放信号测量屏幕硬拷贝视频捕捉AbstractA Hand E-reader based on JingWei board、Extended board and Windows CE is here. It has the following functions: Calling card editing and reading, WORDPAD, Document reading in Chinese or English, Handwriting inputting and recognition, Image browsing, Alarm clock, Audio playing and recording，Video capturing, Temperature and Humidity measuring，Pulse measuring and Printing.This production’s hardware is composed of JingWei Board, extended circuits and extended A V module. Configuring CPLD of JingWei board achieved hardware’s interface Logic. Low power and high integration chips were chosen. The Friendly menus in a unitive style were designed. Programs were designed by EVC++ in blocking and absolute which reduced a lot of codes.This production can be used as a PDA that has the functions mentioned above.Key words: Document/Image reading Audio playing and recording signal test Screen-printing Video capturing第一章系统设计方案一、研制背景嵌入式系统在各行各业中有着广泛的应用。

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Image Browsing and Natural Language Paraphrases ofSemantic Web AnnotationsChristian Halaschek-Wiener, Jennifer Golbeck, Bijan Parsia,Vladimir Kolovski, and Jim HendlerMaryland Information and Network Dynamics Lab, 8400 Baltimore Ave.College Park, MD, 20740 USA{halasche,golbeck,kolovski,hendler}@bparsia@Abstract. Recently, there has been interest in marking up digital images withannotations describing the content of the images using Web based ontologiesencoded in the W3C’s Web Ontology Language, OWL. The annotations aresubsequently exploited to improve the user experience of large collections ofimages, whether by enhance search or by a structured browsing experience. Inthe latter case, the complexity and unfamiliarity of logic-based ontology lan-guages may do more to impede, than aid, the user. To alleviate this problem, wepropose using automatic generation of natural language (NL) paraphrases ofOWL statements to assist browsing image content. In this paper, we provide anoverview of our NL generation approach and an empirical evaluation of the useof our paraphrases for image browsing.1 IntroductionRecently there has been interest in using Semantic Web ontologies encoded in the Web Ontology Language [3], OWL, to formally represent the semantic content of digital images.[1]. Given semantically rich image metadata, collections can be more accurately searched and browsed, with new knowledge derived from existing annota-tions. Additionally, by exploiting standard Web mechanisms, one is able to link exist-ing image collections to arbitrary knowledge repositories and vice versa.The canonical OWL interchange syntax is based on the XML serialization of RDF (RDF/XML). Neither RDF nor XML were designed readability in mind (much less casual end user readability). There are several alternative surface syntaxes designed with more readability in mind [14, 15] and, due to the correspondence with first order logic, there are a number of traditional logic notations available. However, these all require a fairly deep understanding of OWL, logic, or both which, aside from being comparatively rare, seems unnecessary for the purpose of navigating through image collections. Recently, there has been work in providing natural language translations of OWL concept definitions, providing users with a format that is easier to both readand understand [2, 6, 8, 9]. In [2], we provided an algorithm for paraphrasing OWL concepts with a controlled natural language.In this paper, we propose the use of automatically generated NL paraphrase of OWL classes and individuals to aid in browsing image. We feel that the paraphrases will make allow users to more effectively use and enjoying using the semantic annota-tions when browsing image collections, and to better understand the content of the browsed images.Most semantic markup of images includes a large number of instance assertions, corresponding to features of concrete particular depicted in the images. Therefore, we extend the algorithm in [2] to provide natural language paraphrases of OWL asser-tions about individuals, as well as of class definitions. We have implemented this algorithm as a plug-in to an ontology-based, image annotation and browsing tool, PhotoStuff [1]. Lastly, we provide an empirical evaluation of the effectiveness of these NL paraphrases for the task of browsing and interacting with image collections.2 Background[2] presents an approach for automatically generating natural language paraphrases for OWL concept definitions that attempts to preserve the underlying meaning of the logic based definition. The approach is applicable to any ontology where the classes and properties are named using certain standard naming conventions. The algorithm is fairly straightforward considering the good quality results achieve. The most sophisti-cated NL processing tool the algorithm uses is a part-of-speech (POS) tagger, which is used to improve the fluency of the generated NL description.The first step in the approach is to generate a parse tree corresponding to the rela-tions between the OWL class and other entities. Creating the tree provides additional flexibility to alter it (post-process) in any way deemed necessary (a sample parse tree is provided in [2]).In [2], it has been noted that property names from several major ontologies reveal that, while properties could theoretically be named with arbitrary words, their names are generally parsed into one of a small number of simple phrase structures. These structures can algorithmically be restructured, by using a POS tagger, providing a (more) natural language style format. Below, Table 1 lists these phrase structure cate-gories, along with their reformatted natural language translations.Table 1. Common class and property phrase structure, along with natural language translationsAfter generating the parse tree, there are several steps in generating the NL output, including a pre-processing step where the tree is modified to eliminate nodes contain-ing owl:Thing, etc. Further details are available in [2]. In general, the approach gener-ates full English sentences whenever possible. However, we have found that render-ing complex concepts entirely in NL sometimes results in very lengthy, difficult to understand sentences. In some cases, using a bulleted, nested list format for such complex sets of conditions was much clearer. For example, part of the definition of Beaujolais from the wine ontology1 is given below in Table 2.Table 2. Bulletized natural language rendering of OWL class BeaujolaisA Beaujolais is a Wine that:- is made from at most 1 grape, which is Gamay Grape- has Delicate flavor- has Dry sugar- has Red color- has Light body1 Wine OWL Ontology: /2001/sw/WebOnt/guide-src/wine.rdf3 OWL Individual NL ParaphrasesWe can categorize images based on the types of things they depict. In the SemSpace2 portal, a nested list rendering of the class hierarchy is the initial interface to the col-lection. Thus, NL paraphrases of concepts might help users determine which catego-ries are likely to contain images of interests. However, images, especially photo-graphs, generally depict concrete objects that are naturally represented in OWL as individuals with various types and properties. Thus, if paraphrases are to help the user understand the contents of particular images, we must extend the algorithm in [2] to handle NL paraphrases of OWL individuals as well.3.1 Approach OverviewThe approach adopted here provides an NL rendering of OWL individuals based on the direct relations of that individual (e.g., type assertions, labels, defined relations, etc.). In order to generate an NL paraphrase for an OWL individual, first a NL parse tree is generated for that individual [2]. In contrast to the approach for OWL concepts [2], this tree will be at most one level deep, as only the direct relations are used. As the tree is created, only rdf:type assertions corresponding to the (possibly inferred) most specific classes of the individual are added as edges. (In certain ontologies with shallow, informative class graphs, it might be preferable to add all the types of the individuals, or to control the depth in a different way.) The relation edges are labeled by two distinct mechanisms: An rdf:type edge in the tree is given an “is a”label. For all additional relations, the POS tagger is run over the “local part” of the property’s URI (as in [2]) to provide more legible labels for the relations. Finally, the labels for the objects of the relations are added to the object nodes. Figure 1 depicts a subset of the NL parse tree and the original RDF/XML for a sample individual, Storey Mus-grave.Fig. 1. Subset of RDF/XML and of NL Parse Tree for OWL Individual Storey Musgrave2 /After this post-processing the NL rendering is generated. The current approach uses the following template for producing OWL individual NL renderings: First the label edges of the NL tree for the given individual are retrieved. This label is used to begin the NL sentence. Following this, a comma separated rendering of all rdf:type edges is generated. Then a bullet list of all the additional relations is produced. The template is shown below in Table 3.Table 3. RDF/XML serialization of individual Storey Musgrave vs. the automatically genera-ted natural language rendering. Italicized words are invariant of the individual being renderedIndividual is a rdf:type that- relation 1- relation 2- …- relation nFor example, the NL rendering for the OWL individual Storey Musgrave is pre-sented below in Table 4 (note that the original RDF/XML for Storey Musgrave is provided in Table 3).Table 4. Automatically generated natural language rendering of Storey Musgrave4 NL Captions for Browsing Image CollectionsIn this section we provide details of using automatically generated NL paraphrases for browsing image collections in our image annotation tool, PhotoStuff [1]. The main motivation for using NL paraphrases for browsing image collections and their annota-tions is that it will provide a more enjoyable user experience. Essentially, the user will be provided with the details about image contents in a human readable and under-standable format, which allows them to focus on the images and what they depict rather than the cryptic details of the representation language. One additional benefit of publishing the natural language rendering of the images is that it can allow exiting search engine technology to index such content.4.1Implementation DetailsPhotoStuff is a platform independent, open source, image annotation tool that allows users to annotate images and their sub-regions using concepts from any number of ontologies specified in OWL. PhotoStuff can also load pre-existing annotations, which can then be browsed or used in subsequent annotations.An experimental implementation of our NL generation algorithm has been pro-vided as a plug-in for PhotoStuff. Within PhotoStuff, the NL captions are used in two main ways. First, when an image is selected and loaded into the image canvas, NL renderings for the instances depicted within regions are provided as pop-ups when the regions are moused over (see Figure 2). This provides the user with a quick, human readable display of the individual depicted within that region.Fig. 2. Region NL Caption Pop-UpAdditionally, the NL for individuals depicted within regions can be viewed by right-clicking the regions, and selecting “View NL”. This puts the natural language paraphrases in a NL info pane, as shown below in Figure 3. The figure illustrates that in the NL info pane, the generated natural language includes hyperlinks for all addi-tional individuals and OWL class that occur in the rendering of the current individual or class. This allows the user to browse the image collection based on the existing annotations in a Web-like manner. Further, the hyperlinks can be right clicked, pro-viding the option to filter the thumbnail strip to only show images that depict that instance or instances of that class.Fig. 3. NL Info Pane for Browsing Image Annotations in NL Format5 EvaluationTo evaluate the benefits of the NL format, we conducted a pilot user study. Our hy-pothesis was that users would prefer the NL format for viewing data when the task was to gain an understanding of the meaning of a concept.Our pilot study included seven subjects. Subjects ranged in age from 20 to 37 and all were students working toward bachelors, masters, or Ph.D. degrees. We tested the subjects’ preferences for method of viewing classes separately from viewing in-stances. For both classes and instances, we choose three examples: one very simple, one of medium complexity, and one very complicated example.When viewing classes, we used the Wine ontology mentioned earlier. Our simple class is AlsatianWine, which only had one restriction. Anjou was the medium com-plexity class, with four simple restrictions and an intersection combined with a restric-tion. The most complex class was Beaujolais, with six simple restrictions, including cardinality, and an intersection combined with a restriction.Each class was presented to the subjects in SWOOP [13]. For the study, four addi-tional formats for viewing each class was provided in SWOOP (see Figure 4).Figure 4. Different view renderings used for evaluation of class descriptions. Clock-wise from upper left: Concise format, Turtle, Abstract Syntax, and RDF/XML.Users were asked to view the concept with the goal of understanding it’s meaning, rather than the modeling features. They were allowed to take as much time as they needed and, when finished, they were asked to rank the formats according to their preference, with 1 being best and 5 being worst. If the subjects felt two formats were equally good, they were allowed to give them the same ranking.For all three classes, the order of the rankings was the same. From best to worst, the formats were ranked, and shown below in Table 5.Table 5. Rankings from best to worst of view format of class descriptions1. Natural Language2. Concise Format3. Abstract Syntax4. Turtle5. RDF/XMLAdditionally, the average ranking and standard deviations for the various formats is presented below in Table 6.Table 6. Average rankings and standard deviations for the five formats used to dis-play class informationAverage Rank (Standard Deviation)Class ConciseFormat AbstractSyntaxNaturalLanguageRDF/XML TurtleAlsatianWine 2.00(1.41) 3.57(1.13) 1.57(0.79) 4.86(0.38) 3.86(0.69) Anjou 1.86(0.69) 2.71(0.49) 1.14(0.38) 5.00(0.00) 4.00(0.00) Beaujolias 2.14(0.69) 2.71(0.49) 1.00(0.00) 5.00(0.00) 3.86(0.38)Using a Wilcoxon matched-pairs signed-ranks test, we found that the NL format significantly outperformed the Concise Format (ranked second on average) for both Anjou and Beaujolais with p<0.05. There was not a significant benefit over the Con-cise format for the simplest class, AlsatianWine, but NL did significantly outperform the Abstract Syntax, which was the average third ranked format. This allows us to conclude that in the pilot study the NL format offers significant benefits to users when they are trying to understand the meaning of classes, particularly complex classes.To evaluate the NL format with instances, subjects were given three instances from the collection of astronaut data3. The first instance, Bryan O'Conner, had only one property: the depiction that tied him to an image. The second instance, John Young, had a depiction and one additional property. The last instance, Storey Musgrave, was tied to three regions and four properties, including both Datatype and Object proper-ties. Subjects viewed the data about each instance in a tabular format and in the NL format with the goal of understanding the information about each instance. Subjects took the time they needed without limits and then were asked to choose which format they preferred.For each of the three instances, the NL format was preferred 6:1 over the instance form. When subjects commented on their preference, it focused largely on the fact that the NL format was more concise, displaying only relevant information.We conclude this section by stating that the results of this pilot study show that us-ers feel that the NL format provides them with an advantage when trying to under-stand both classes and instances.6 Discussion and Future DirectionsWhile our initial evaluations support the usefulness of the approach presented here, we note there are a few limitations, which we leave as future work. First, if individu-als participate in a large number of relations, then the generated bullet list, or con-3 SemSpace portal instance data. Available at /rdf/dumpcatenated sentence, can be quite long and cumbersome to read through. One potential solution to ease this problem is to use a ranking metric when displaying the relations in the NL generated format. [4, 5] propose relationship-ranking metrics that could potentially be used to filter irrelevant relations, thus presenting the user with a smaller NL paraphrase.An additional solution to this problem we would like to investigate is the to utilize text summarization techniques for the output from the NL generation algorithm. [10, 11, 12] present a variety of techniques for summarizing such textual data. Solutions such as these could possibly be applied to the NL output, thus presenting the user with a succinct version of the output from the NL generation engine.In this work, we use a predefined template view of the NL rendering of instances. Under some circumstances, particular users may wish to define their own templates. Thus we would like to support using custom templates for particular ontologies or class descriptions.In our pilot study, we focused on user understanding of OWL data and found, gen-erally, that users preferred the NL paraphrases for that purpose. While we found this format to be preferred for understanding image annotations, the extent of which these paraphrases work to improve the overall image browsing experience remains to be seen. Therefore we plan to perform further evaluations of the approach.7 Related WorkIn this section we present related work in the area of automatically generating natural language translations of Semantic Web ontological concepts and instances. [6] pro-vides an instructional use of NL paraphrases for understanding OWL Concepts. The authors mention a plug-in to Protégé, the Class Description Display4plug-in that provides simpler NL descriptions that resemble OWL Abstract Syntax. This work has been refined into the so-called Manchester syntax.5 In [7], a subset of English is intro-duced called Attempto Controlled English (ACE). ACE is translated into first-order logic and thus can be used as a formal notation. Therefore, ACE is a formal language with the semantics of first order logic. In comparison, our approach converts OWL classes and individuals, which are based on a decidable subset of FOL called Descrip-tion Logics, into a NL description. [8] describes an XML-based NL generation for RDF and DAML+OIL. In this work, a pipeline of XSLT transformations implements the sequence of processing stages in the orthodox pipeline architecture for NL genera-tion. The generator uses predefined XSLT text plan templates for specific ontologies, following a domain-specific approach of shallow generation. However, it is unclear whether this approach works efficiently for more complex OWL ontologies. [9] pre-sents an approach for automatic generation of reports from OWL ontologies, using natural language generation tools. Our work here differs in that we do not rely on a lexicon for NL generation.4 Class Description Display plug-in. Available at /downloads/cdc/5 /resources/reference/manchester_syntax/7 ConclusionsIn this paper we have provided an extension of our previous work [2] to automatically generate natural language paraphrases for OWL individuals. Given this, we have proposed using this approach for browsing Semantic Web annotations of image col-lections. This provides the user with a more pleasant experience of the annotations.We have additionally presented an experimental implementation of the approach in an ontology based, image annotation tool, PhotoStuff. Finally, we have provided an empirical evaluation of the usage of the NL paraphrases for browsing image collec-tions, finding that it was in fact useful for the task.This work was supported in part by grants from Fujitsu, Lockheed Martin, NTT Corp., Kevric Corp., SAIC, the National Science Foundation, the National Geospa-tial-Intelligence Agency, DARPA, US Army Research Laboratory, and NIST. We would also like to thank Aditya Kalyanpur and Mike Grove for all of their contribu-tions to this work.References1. Halaschek-Wiener, C., Schain, A., Golbeck, J., Grove, M., Parsia, B., Hendler, J.: A FlexibleApproach for Managing Digital Images on the Semantic Web, 5th International Workshop on Knowledge Markup and Semantic Annotation, Galway, Ireland, November 7, 2005.2. Hewlett, D., Kalyanpur, A., Kolovski, V., Halaschek-Wiener, C.: Effective Natural Lan-guage Paraphrasing of Ontologies on the Semantic Web. End User Semantic Web Interac-tion Workshop, International Semantic Web Conference (ISWC), November 2005, Galway, Ireland.3. Dean, M., Schreiber, G.: OWL Web Ontology Language Reference. W3C Recommendation.February 2004. /TR/2004/REC-owl-semantics-20040210/4. Aleman-Meza, B., Halaschek-Wiener, ., Arpinar, B., Ramakrishnan, C., Sheth. 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