模拟国际会议主持人讲稿（共5篇）

第一篇：模拟国际会议主持人讲稿

Ladies and Gentlemen, I have the honor and the pleasure ,on behalf of the Organizing Committee ,to extend our cordial welcome to all the scientists and experts who have been invited to this internationalconferenceof “onebeltandoneroad“.Let me introduce myself.I am hujianjun, from Tsinghua University.It is a greate pleasure for me to be the chairman of the session today.When Chinese President Xi Jinping visited Central Asia and Southeast Asia in September and October of 2013, he raised the initiative of jointly building the Silk Road Economic Belt and the 21st-Century Maritime Silk Road, which have attracted close attention from all over the world.The Belt and Road Initiative is a way for win-win cooperation that promotes common development and prosperity and a road toward peace and friendship by enhancing mutual understanding and trust, and strengthening all-around exchanges.Now A series of results in different field have been obtained.The Congress will cover all aspects of One Belt and one Road，and the strategy , impact ,achievement of B&R will be included.I hope that the conference will improve our understanding of OBOR, and I also hope that the congress will provide the opportunity for personal exchange of scientific results ,facilitate the making of new acquaintances and to strengthen the personal friendships among participants from different parts of the world.Today’s speakers will share their thoughtson “onebeltandoneroad”.Then, we’ll have a Question and Answer session, which allows the everyone to ask some questions you may be interested.I am sure that you will find some topics to be presented both interesting and informative.下一环节

now,let’s go on the first stage of the conference ,which is about making an oral presentation about onebeltandoneroad.介绍演讲者

1.Now, I will be very excited to introduce the firstspeaker, Prof.XXX, who is a professor of political science at the Peking University.And his topic is “The One Belt And One Road: Actions and Future.”

2.Let me introduce the second speaker, who is very very rich, not in dollars, but in knowledge and experiences, She got her Ph.D in the economics ,Central SouthUniversity, followed by a series of teaching and research positions at OxfordUniversity.Please don’t hesitate to join me in welcoming our first speaker, Prof.XXX, whose topic is（“The Security Challenges to the “One Belt and One Road” Strategy and China's Choices “）.Welcome.3.Let’s welcome to professorXXX，whoisa Sociologist from Tsinghua University.He will give us a wonderful speech titled “Belt and road“--to promote China’s Marshall plan”.4.Today our 4th speaker is XXX, professor ofChicago University，The title of her presentation is “The“One Belt and One Road”:the Bridge between the Chinese Dream and the World Dream”.Let’s welcome to professor于彦芳.5.Our 5th speaker is XXX , as the president of Baidu company, he will give us an excellent report titled “The One Belt and One Road Grand Smart Banlance”.Let’s welcome to president yang with our warm applause.6.OurnextspeakerisprofessorXXX comefromColumbia University，whosetopictiteld“Examining the ＂One Belt and One Road＂Strategy in the Background of RMB Internationalization“，Let’s welcome to professorXXX.7.Today our 7th speaker is XXX, who is aExperienced professorofColumbia University, The title of her presentation is “Impact of the National Strategy of One Belt and One Road for World Investment Construction”.Let’s welcome to professorXXX.8.I will be very pleased and excitedtointroduceour8speaker，王

th欢，the director of National Energy Administration，andthetitleofhistopicis“＂Belt and Road＂Initiative Promote International Energy Cooperation“.9.OurlastspeakerisXXX，professorofFudanUniversity,will give us an excellent report titled”One Belt, One Road" Initiative: The Impacts on Economy of China’s Various Region

第二环节，讨论环节。

Ladies and gentleman, our distinguished speakers have finished their presentations, we now enter into the question and answer session.Isthereanyquestionyouwouldliketoaddresstoourspeakers.I believeourspeakerwillgiveyouanysatisfactoryanswers。Anyquestionplease? Any additional questions.? I hope the audience will participate in the discussion by rising their hands.Nobody, ok ,Iwould like ask professor 蒋 a question.””

提问：

1.what kind of opportunity the implementation of B&R can bring to college students? 2.what can we do and how to do in order to achieve self-value better when the country drive the B&R? 3.how does the construction of B&R make our country more influential?

控制进程：

Let’s keep on schedule and go ahead to the fourth paper.We’ll then go on with the last paper.Well, I am sure we could discuss longer, but unfortunately time is up.Thank you very much, Dr A.Our next speaker isDr C.Sorry, we don’t have any time for questions, so we have to proceed to the next paper.即将结束，时间限制

Okay, well, as time is limited.I am sorry to say that this session will have to stop here.Now ,let’s welcome the chairman ,Miss Fu to give us a Closing speech.Closing speech First of all, I would like to thank Mrs.Make and the organizing committee for having appointed me to serve as the chair of this conference.We are now very close to the end of this conference.I believe that our conference is a great success.More than 40 participants have come to Changsha to discuss not only general questions about one belt and one road but more concrete questions and problems and their possible solutions.It went smoothly as scheduled.In these two days the conference has covered so many important and complex problems in the flied of one belt and one road both theoretical and practical.All the presentations were very illuminating and informative.And the heated panel discussions were very stimulating and fruitful.It’s our hope that the result of the conference will carry the study of one belt and one road to a new stage.We all hope to maintain close contact and cooperation with each other in the field of future research work on one belt and one road.Now, with great joy and reluctant mind to part, we get together again to declare that the conference has drawn to a successful close.Thank you to every body who has contributed to the conference with reports and introductions。

As the chair of the conference, I would like to express my thanks again.Thank you for coming to the 2st international conference of one belt and one road.

第二篇：英语模拟国际会议讲稿

英语模拟国际会议

主持人：王×会议出席人：朱××会议中提问者两人：董×、赵××休会中途与嘉宾交谈两人：张×、唐×× 会议结束提问者：余×、龚× 主持人王×：Ladies and Gentleman: May I have your attention please? Our conference will begin in a few minutes.All the presenters are requested to be seated.Let me introduce myself,i am wang yuan from SCNU,it’s a privilege for me to chair this session.Once the ceremony has started,you are refrained from taking pictures, using flashbulbs or leaving your seats.3Q.Distinguished guests, distinguished delegates, ladies and gentlemen, and all the friends:At this special time of wonderful June, in this grand hall of the beautiful city, our respectable guests are here getting together.Academic Seminars of CAS are organized by the Bureau of Personnel and Education of CAS, and held by the CAS research institutes.Now, first of all, please allow me to give our hearty welcome to all of you present, and thank you, for your friendly coming.We feel so proud, and appreciated as well to be the host of the event.For this conference, we are following the agenda here.The meeting is supposed to last for five days，it is the first congress which covers the true sense of psychological education、moral education,basic education and higher education, application education fields.And it to be separated into two parts, to begin with, we’ll invite some representatives from our guests to give lectures about their latest researches and reports on the issue, and then we will have some symposiums.And finally I wish you an unforgettable and prefect experience here.Firstl,i’d like to introduce our first presenter,Professor Jan.She is the author of “cooperation and competition”.for the past six years,JAN has been honored many awards--a Pulitzer Prize winner, a national Medal of the economy and a National book award and so on.Now, please join me in welcoming our guest speaker today---JAN.,whose topic is “cooperation can improve our competitive”.发言人朱××：

Good morning!Mr.Chairman, your excellencies , fellow colleagues ,Ladies and Gentlemen!Firstly, i would like to thank zhuxiaoli for her gracious introduction.I am very glad to have this opportunity of sharing with you our view on cooperation.My topic of today is“ cooperation can improve our competitive ”.As we all know, competition is a common phenomenon in our society.It occurs in almost every field of our life, such as playing games, doing our study, hunting for jobs.As I stand in here with other Participants ,it’s also a fierce competition.Working hard at something and competing against others can inspire us to push ourselves further than we otherwise might.In other words, competition is required to prompt us to excel and to help us reach our fullest potential.Last of all, competition is seen as an open and fair race where success goes to the swiftest person regardless of his or her social backgrounds.We can say, in this sense, competition stimulates people's interest in work and helps society to go forword.However, as the wave of globalization has come and the development of society, we face more competitions from the outside world.Are we going to face the challenge all by ourselves alone? The answer is clear,Human beings are social beings and no one can exist alone in the society.If you want to play the game well, you have to play with others.You cannot play single-handed and win.You’ll always have to cooperate with your partners, who may make the social ladder for you to climb to the top.From cooperation, you build up trust and understanding, which does good to your future.And also it’s said that we get together to do something larger than one single person, that is to say ,cooperation can turn a small business into a big and strong one.You see,after the cooperation with IBM.Lenovo could challenge the Dell computer company as the world NO.2 PC maker, BenQ and SIMENS mobile, Sony and Ericssons, the two groups of companies are collaborating together to win more market.Everyday, there are over 10 thousand companies annexed because of the crucial competition,but there are collaborating together in order to acquire more competitive ability.From a whole nation’s aspect, all the nations should take the national interest as a common goal.Take China and India for example.India, along with Japan, is a main rival of China in Asia.For the history’s sake, India and China have already competed with each other for a long period of time.With the globalization’s steps getting faster, both China and India realize the importance of cooperation.Now they have already started collaborating in the field of IT and mineral exploitation, and the two countries have benefited a lot.As American previous president Bill Clinton ever said:“There are no forever friends nor rivals, but interest.”So, if situation changes, competition also could turn into cooperation.To sum up, competition and cooperation prevail throughout the world.We should, however, take advantage of the competition as a chance to promote the cooperation and finally be the winner in the competition.we should seek cooperation boardly to improve our competitive.That’s of my speech..Thank you very much, ladies and gentlemen.主持人王×：3Q, Dr.Jan.I think all the participants present here this morning will agree with me that your presentation is very informative and enlightening.Now, do anybody have some questions？

提问1号赵××：（麻烦想个问题，关于竞争与合作的）提问2号：董×：（麻烦想个问题，关于竞争与合作的）主持人王×：上半段时间到了，请大家休息10分钟，10分钟后会议继续。

课间：提问三号张×和唐×一起去喝水，在打水的地方碰到发言者朱×，然后开始对话。。

张×： 唐×： 朱×： 主持人王×：时间到了，大家安静就座。现在有请朱××给大家做一个总结。朱××：总结几句就可以 主持人：还有什么问题吗？ 提问者3号龚×：（想个问题）提问者4号余×：

（想个问题）

第三篇：英文国际会议讲稿

PPT(1)大家上午好！今天我汇报的主题是：基于改进型LBP算法的运动目标检测系统。运动目标检测技术能降低视频监控的人力成本，提高监控效率，同时也是运动目标提取、跟踪及识别算法的基础。图像信号具有数据量大，实时性要求高等特征。随着算法的复杂度和图像清晰度的提高，需要的处理速度也越来越高。幸运的是，图像处理的固有特性是并行的，尤其是低层和中间层算法。这一特性使这些算法，比较容易在FPGA等并行运算器件上实现，今天汇报的主题就是关于改进型LBP算法在硬件上的实现。

good morning everyone.My report is about a Motion Detection System Based on Improved LBP Operator.Automatic motion detection can reduce the human cost of video surveillance and improve efficiency [ɪ'fɪʃ(ə)nsɪ]，it is also the fundament of object extraction, tracking and recognition [rekəg'nɪʃ(ə)n].In this work, efforts ['efəts] were made to establish the background model which is resistance to the variation of illumination.And our video surveillance system was realized on a FPGA based platform.PPT(2)

目前，常用的运动目标检测算法有背景差分法、帧间差分法等。帧间差分法的基本原理是将相邻两帧图像的对应像素点的灰度值进行减法运算，若得到的差值的绝对值大于阈值，则将该点判定为运动点。但是帧间差分检测的结果往往是运动物体的轮廓，无法获得目标的完整形态。

Currently, Optic Flow, Background Subtraction and Inter-frame difference are regard as the three mainstream algorithms to detect moving object.Inter-frame difference based method need not model ['mɒdl] the background.It detects moving objects based on the frame difference between two continuous frames.The method is easy to be implemented and can realize real-time detection, but it cannot extract the full shape of the moving objects [6].PPT(3)

在摄像头固定的情况下，背景差分法较为简单，且易于实现。若背景已知，并能提供完整的特征数据，该方法能较准确地检测出运动目标。但在实际的应用中，准确的背景模型很难建立。如果背景模型如果没有很好地适应场景的变化，将大大影响目标检测结果的准确性。像这副图中，背景模型没有及时更新，导致了检测的错误。

The basic principle of background removal method is building a background model and providing a classification of the pixels into either foreground or background [3-5].In a complex and dynamic environment, it is difficult to build a robust [rə(ʊ)'bʌst] background model.PPT(4)

上述的帧间差分法和背景差分法都是基于灰度的。基于灰度的算法在光照条件改变的情况下，性能会大大地降低，甚至失去作用。

The algorithms we have discussed above are all based on grayscale.In practical applications especially outdoor environment, the grayscales of each pixel are unpredictably shifty because of the variations in the intensity and angle of illumination.PPT(5)为了解决光照改变带来的基于灰度的算法失效的问题，我们考虑用纹理特征来检测运动目标。而LBP算法是目前最常用的表征纹理特征的算法之一。首先在图像中提取相邻9个像素点的灰度值。然后对9个像素中除中心像素以外的其他8个像素做二值化处理。大于等于中心点像素的，标记为1，小于的则标记为0。最后将中心像素点周围的标记值按统一的顺序排列，得到LBP值，图中计算出的LBP值为10001111。当某区域内所有像素的灰度都同时增大或减小一定的数值时，该区域内的LBP值是不会改变的，这就是LBP对灰度的平移不变特性。它能够很好地解决灰度受光照影响的问题。

In order to solve the above problems, we proposed an improved LBP algorithm which is resistance to the variations of illumination.Local binary pattern(LBP)is widely used in machine vision applications such as face detection, face recognition and moving object detection [9-11].LBP represents a relatively simple yet powerful texture descriptor which can describe the relationship of a pixel with its immediate neighborhood.The fundamental of LBP operator is showed in Fig 1.The basic version of LBP produces 256 texture patterns based on a 9 pixels neighborhood.The neighboring pixel is set to 1 or 0 according to the grayscale value of the pixel is larger than the value of centric pixel or not.For example, in Fig1 7 is larger than 6, so the pixel in first row first column is set to 1.Arranging the 8 binary numbers in certain order, we get an 8 bits binary number, which is the LBP pattern we need.For example in Fig.1, the LBP is 10001111.LBP is tolerant ['tɒl(ə)r(ə)nt] against illumination changing.When the grayscales of pixels in a 9 pixels window are shifted due to illumination changing, the LBP value will keep unchanged.PPT(6)

图中的一些常见的纹理，都能用一些简单的LBP向量表示，对于每个像素快，只需要用一个8比特的LBP值来表示。

There are some textures , and they can be represent by some simple 8bit LBP patterns.PPT(7)

从这幅图也可以看出，虽然灰度发生了很大的变化，但是纹理特征并没有改变，LBP值也没有变化。

You can see, in these picture , although the grayscale change alot, but the LBP patterns keep it value.PPT(8)上述的算法是LBP算法的基本形式，但是这种基本算法不适合直接应用在视频监控系统中。主要有两个原因：第一，在常用的视频监控系统中，特别是在高清视频监控系统中，9个像素点覆盖的区域很小，在如此小的区域内，各个像素点的灰度值十分接近，甚至是相同的，纹理特征不明显，无法在LBP值上体现。第二，由于以像素为单位计算LBP值，像素噪声会造成LBP值的噪声。这两个原因导致计算出的LBP值存在较大的随机性，甚至在静止的图像中，相邻两帧对应位置的LBP值也可能存在差异，从而引起的误检测。

为了得到更好的检测性能，我们采用基于块均值的LBP算法。这种方法的基本原理是先计算出3×3个像素组成的的像素块的灰度均值，以灰度均值作为该像素块的灰度值。然后以3×3个像素块（即9×9个像素）为单位，计算LBP值。

The typical LBP cannot meet the need of practical application of video surveillance for two reasons: Firstly, a “window” which only contains 9 pixels is a small area in which the grayscales of pixels are similar or same to each other, and the texture feature in such a small area is too weak to be reflected by a LBP.Secondly, pixel noise will immediately cause the noise of LBP, which may lead to a large number of wrong detection.In order to obtain a better performance, we proposed an improved LBP based on the mean value of “block”.In our algorithm, one block contains 9 pixels.Compared with original LBP pattern calculated in a local 9 neighborhood between pixels, the improved LBP operator is defined by comparing the mean grayscale value of central block with those of its neighborhood blocks(see Fig.2).By replacing the grayscales of pixels with the mean value of blocks, the effect of the pixel noise is reduced.The texture feature in such a bigger area is more significant to be described by LBP pattern.PPT(9)

运用LBP描述背景，其本质上也是背景差分法的一种。背景差分法应用在复杂的视频监控场景中时，要解决建立健壮的背景模型的问题。驶入并停泊在监控画面中的汽车，被搬移出监控画面的箱子等，都会造成背景的改变。而正确的背景模型是正确检测出运动目标并提取完整目标轮廓的基础。如果系统能定时更新背景模型，将已经移动出监控画面的物体“剔除”出背景模型，将进入监控画面并且稳定停留在画面中的物体“添加”入背景模型，会减少很多由于背景改变而造成的误检测。

根据前一节的介绍，帧间差分法虽然无法提取完整的运动目标，但是它是一种不依赖背景模型就能进行运动目标检测的算法。因此，可以利用帧间差分法作为当前监控画面中是否有运动目标的依据。如果画面中没有运动目标，就定期对背景模型进行更新。如果画面中有运动目标，就推迟更新背景模型。这样就能避免把运动目标错误地“添加”到背景模型中。

In practical application, the background is changing randomly.For traditional background subtraction algorithm the incapability of updating background timely will cause wrong detection.In order to solve this problem, we propose an algorithm with dynamic self updating background model.As we know, Inter-frame difference method can detect moving object without a background model, but this method cannot extract the full shape.Background subtraction method can extract the full shape but needs a background model.The basic principle of our algorithm is running a frame difference moving object detection process concurrently [kən'kʌrəntli] with the background subtraction process.What’s time to update the background is according to the result of frame difference detection.PPT(10)

运动目标检测系统特别是嵌入式运动目标检测系统在实际应用中要解决实时性的问题。比如每秒60帧的1024×768的图像，对每个像素都运用求均值，求LBP等算法，那么它的运算量是十分巨大的，为此我们考虑在FPGA上用硬件的方式实现。

If LBP algorithm is implemented in a software way, it will be very slow.FPGA have features of concurrent computation, reconfiguration and large data throughput.It is suitable to be built an embedded surveillance system.The algorithm introduced above is implemented on a FPGA board.PPT(11)

这就是我们硬件实现的系统结构图。首先输入系统的RGB像素信号的滤波、灰度计算及LBP计算，得到各个像素块的LBP值。然后背景更新控制模块利用帧差模块的检测结果控制背景缓存的更新。区域判定模块根据背景差模块的输出结果，结合像素块的坐标信息，对前景像素块进行区域判定。

The structure of the system is showed in this figure.In this system, a VGA signal is input to the development board.and the LBP pattern is calculated , Frame difference module also compares the current frame and the previous frame to determine whether there is a moving object in the surveillance vision.If the surveillance vision is static for a certain amount of frame, the background model will be updated.PPT(12)图中是LBP计算模块。图中所示的窗口提取结构可以实现3×3像素块窗口的提取。像素信号按顺序输入该结构，窗口中的数据就会按顺序出现在Pixel1-Pixel9这9个寄存器中，从而在最短的延时内提取出相邻9个像素点的灰度值。行缓存的大小等于每一行图像包含的像素个数减1。将9个像素点的灰度值通过求均值模块，可以求出一个像素块的像素均值。

将像素块均值作为输入再次通过类似的结构，可以提取出3×3个相邻像素块的灰度值。这时行缓存的大小为每一行包含的像素块的个数减1。再用9个窗口的灰度值作为输入，用比较器阵列计算出最终的LBP值。

To achieve real time computation of the LBP, a circuit structure is put forward as showed in Fig.5.Two line buffers and nine resisters are connected in the way showed in the figure.Nine neighbor pixels are extracted with minimum ['mɪnɪməm] delay, and the mean value of this block is calculated by the mean value calculate module which contains some adders and shifters.The mean values of the blocks are inputted to a similar structure and extracted in a similar way, and the LBP is calculated by the consequence LBP calculate module.PPT(13)求均值模块采用如图3-12所示的四级流水方式实现。在算法的设计过程中，需要求出的是3×3像素块中9个像素的均值。但是在硬件实现时，为了更合理地利用硬件资源，只计算剔除中心像素后的8个像素的均值。这样做可以在不对计算结果造成太大影响的情况下减少加法器的使用。而且在求均值的最后一级流水，除8运算比除9运算更容易实现。因为8是2的整数幂，除8运算只需要将各个像素的和右移3位。而除9运算在FPGA中需要专用的DSP模块来完成。PPT(14)如图所示，块均值计算模块计算出的8个块均值被图3-11中的窗口提取模块提取出来，并作为比较器阵列的输入，比较器的输出结果用0和1表示。最终的比较结果按一定的顺序排列，重新拼接成一个8位的二进制数，即LBP值。LBP计算电路没有采用流水结构，在一个时钟周期内就能得到计算结果。

PPT(15)

这个是在系统测试中，实现对多个目标的检测。

In this system test ,we achieve a multi-object detection.PPT(16)

这个图是对动态背景更新的测试，在监控区域中划定一个目标区域，把一个静止的物体放置到目标区域中。在前3分钟内，系统会将其当做前景目标，矩形窗口会以闪烁的形式发出报警信号。3分钟过后，由于物体一直处于静止状态，系统检测到了10800个静止帧，于是更新背景模型。静止的物体被当做背景的一部分，此后窗口不再闪烁。经验证，该系统能够正确实现背景模型更新算法。

This is the test for the auto background update.We put a statics object in the surveillance area，at the beginning this is trusted as a moving object.after 3 minutes , the system receive ten thousand static frames ,and then update the background model.Then this object is regard as a part of the background.PPT(17)

此外为了验证系统对室外光照变化抑制能力，我们选取了大量有光照变化，并且有运动目标的视频对系统进行了测试。

In order to verify the resistance to the varation of illumination , a certification experiment is designed, and the ROC curves of the two algorithms based on LBP and grayscale are plotted and compared.A number of short video clips with shifty and fixed illumination, including positive samples with moving objects and negative samples without moving objects.PPT(18)

测试平台如图所示。用一台PC机作为测试信号的输出源，然后在PC机中播放视频，并将视频VGA信号发送给运动目标检测系统，模拟真实的监控环境。FPGA将输入信号和区域边框图形相叠加后在LCD上显示。

The picture of the certification experiment is showed in this picture.A PC acts as the source of the test signal which is input to the FPGA in the form of VGA.Passing through the FPGA board, video signal is displayed on a LCD screen.PPT(19)

并最终描绘了系统的ROC特性曲线。在没有光照强度变化的情况下，采用基于灰度的运动目标检测算法的性能略优于基于LBP值的运动目标检测算法，两种算法都能取得较好的检测效果。但是在图5-15中（测试集2），也就是在光照强度变化的情况下，画面整体灰度发生较大的改变，基于灰度的检测算法的性能大幅度下降，接近于失效。而采用LBP值的检测算法却能维持较好的性能。可见基于LBP的检测算法对抑制光照强度变化造成的误检测有较好的效果。

This two figure are the ROC curves of the experiments using our

algorithm and traditional grayscale-based algorithm.We can see in the Fig.1 which corresponds to the condition with fixed illumination, the performance of the grayscale-based algorithm is slightly better than these of LBP-based algorithm, they can both detect moving object effectively.But in Fig.2 which corresponds to the condition with shifty illumination, grayscale based algorithm deteriorates drastically and nearly lose efficacy ɪkəsɪ].But the improved LBP algorithm still keeps a good performance.PPT(20)

谢谢大家！

Thanks for your attention

第四篇：模拟国际会议演讲稿

1.Introduction Thank you very much.Mr.Jiao, for your kind introduction.Ladies and gentlemen, Good afternoon!My name is Lijia, came from Harbin Engineering University.I am honored to have been invited to speak at this conference.Before I start my speech, let me ask a question.Do you know what can affect the properties of foam concrete? Do you think how to reinforced the properties of foam concrete？Most of the investigations on foam concrete in the past have been confined（被限于）to neat cement paste, cement paste with partial replacement with admixtures and to cement–sand mixes.Today, my topic is about Influence of filler type on the properties of foam concrete.I want to share our interesting research result on reinforced concrete frame with you.The content of this presentation is divided into 4 parts: In section 1, I will introduce what is the foam concrete.In section 2, I will talk about Parameters investigated and mix compositions.In section 3, I will give Effect of water–solids ratio on design density.And finally, I will make a conclusion.2.Body Section 1: the foam concrete Now, I will introduce the foam concrete.Pre-formed(成型的)foam concrete is manufactured（加工）by adding foam, prepared by aerating（充气）a foaming agent solution, to cement paste or cement mortar（灰

浆）.The composition（合成物）, physical properties（性能）and uses of foam concrete were discussed in detail（详细的）by Valore, Short and Kinniburgh, Rudnai and Taylor.Although several investigations have been conducted on the properties of foam concrete, most of them deal with cement–sand mixes, neat cement paste with or without partial replacement（局部替换）using admixtures（掺合料）.Few studies report on the influence of filler type on the properties of foam concrete.By using fly ash（粉煤灰）as filler(fine aggregate细骨料)instead of sand, the high volume（体积）utilization（利用）of fly ash becomes possible可能, thus providing a means of eco-nomic（经济）and safe disposal（处理）of this waste product.Comparison（比较）of strength of air-cured foam concrete made with cement-sand and cement–fly ash for masonry（砌体结构）by Durack and Weiqing show that for products of comparable density（比较密度）, mixes with fly ash as fine aggregate in place of sand gave relatively higher strength.Section 2: Parameters(参数)investigated and mix compositions（组成成分）

So much for the foam concrete, now I will move on to Parameters(参数)investigated and mix compositions.As the experimental programme(实验程序)was aimed at studying the effect of the fillers on the properties like density（密度）, flow behaviour（流动特性）, water absorption（吸水率）and strength of foam concrete, the following mixes

were investigated by keeping the basic filler–cement ratio constant（恒定不变）at 1:1 by weight.The foam required for three densities（密度）of foam concrete viz.1000, 1250, 1500 kg/m3 were arrived at as per ASTMC 796-97.In the cement–sand–fly ash mixes 50% of the sand is replaced with fly ash and in the cement–fly ash mixes all the sand is replaced with fly ash.Section 3: Effect of water–solids ratio（水砂比率）on design density That bring me to Effect of water–solids ratio（水砂比率）on design density.I think this part is the most important in my presentation, I will explain in detail.As the foam is added to the wet foam concrete mix, the consistency（稠度）of the wet mix is very important to get the design density.Fig.2(a)and(b)show the variation of density ratio（密度变化率）(measured fresh density divided（分离）by design density)with water–solids ratio for mixes with different filler type for each of the design densities, viz., 1000 and 1500 kg/m3, respectively（分别地）.It is observed that at lower water–solids ratios, i.e., at lower consistency, the density ratio is higher than unity（个体）.The mix is too stiff（严格地）to mix properly thus causing the bubbles（气泡）to break during mixing resulting in increased density.At higher water–solids ratios there is also an increase in density ratio as higher water contents make the slurry（泥浆）too thin to hold the bubbles resulting in segregation（分离）of the foam from the mix along with segregation of the mix itself thus causing

an increase in measured density.Therefore, as shown in Fig.2(a)and(b), a density ratio of unity or nearly unity is achieved only at a particular consistency.This consistency requirement for the mix before adding foam to it can be expressed in terms of water–solids ratio.It is also observed that the water–solids ratio required to obtain a density ratio value of one, depends on the filler type.Section 4: Conclusion The conclusions drawn from this study and summarized below are applicable（合适的）to the characteristics of the materials（材料特性）used and the range of parameters（参数范围）investigated:(i)the consistency of pre-formed foam concrete mixtures(defined as the water–solids ratio for achieving the target（目标）density)mainly depends on the filler type, i.e., relatively higher for mixes with fly ash as filler compared to mixes with sand;(ii)the flow behaviour mainly depends on the foam volume and as the foam volume increases the flow decreases.For a given density, foam concrete with fly ash as filler showed relatively（相当的）higher flow values;(iii)for a given density, an increase in fly ash content of the mix results in increased strength.In comparison（比较）to cement–sand mixes, cement–fly ash mixes showed relatively higher water absorption（吸收）.That’s all.Thank you!Are there any questions?

The picture on this slide is

So much for......, now I will move on to......This slide shows the calculation result

As we know, the target user usually has taken a list of courses rather than a course, so we need to extent our probability calculation formulars.For example, suppose T={a,b}, Pr[x﹁T] the probability x occurs without either an a or b preceding it;Pr[x﹁T] the probability x occurs without either an a or b following it.This probability can be calculated exactly.So how to calculate it?

That bring me to Recommendation Algorithms.I think this part is the most important in my presentation, I will explain in detail.In conclusion, we proposed a novel precedence mining model, developed

To sum up, first, I introduced the motivation and the outline of work;second, I gave the definition of precedence mining model;third, I described some new recommendation algorithms using precedence information;forth, I showed our experimental results to compare the new algorithms with traditional ones.Finally, I made a conclusion of our work..That’s all.Thank you!Are there any questions?

第五篇：模拟国际会议演讲稿

Recsplorer:Recommendation Algorithms Based on Precedence Mining

1.Introduction Thank you very much, Dr.Li, for your kind introduction.Ladies and gentlemen, Good morning!I am honored to have been invited to speak at this conference.Before I start my speech, let me ask a question.Do you think recomemdations from others are useful for your internet shopping? Thank you.It is obvious that recommendations play an important role in our daily consumption decisions.Today, my topic is about Recommendation Algorithms Based on Precedence Mining.I want to share our interesting research result on recommendation algorithms with you.The content of this presentation is divided into 5 parts: in session 1, I will intruduce the tradictional recommendation and our new strategy;in session 2, I will give the formal definition of Precedence Mining;in session 3, I will talk about the novel recommendation algorithms;experimental result will be showed in session 4;and finally, I will make a conclusion.2.Body Session 1: Introduction The picture on this slide is an instance of recommemdation application on amazon.Recommender systems provide advice on products, movies,web pages, and many other topics, and have become popular in many sites, such as Amazon.Many systems use collaborative filtering methods.The main process of CF is organized as follow: first, identify users similar to target user;second, recommend items based on the similar users.Unfortunately, the order of consumed items is neglect.In our paper, we consider a new recommendation strategy based on precedence patterns.These patterns may encompass user preferences, encode some logical order of options and capture how interests evolve.Precedence mining model estimate the probability of user future consumption based on past behavior.And these probabilities are used to make recommendations.Through our experiment, precedence mining can significantly improve recommendation performance.Futhermore, it does not suffer from the sparsity of ratings problem and exploit patterns across all users, not just similar users.This slide demonstrates the differences between collaborative filtering and precedence mining.Suppose that the scenario is about course selection.Each quarter/semester a student chooses a course, and rates it from 1 to 5.Figure a)shows five transcripts, a transcript means a list of course.U is our target student who need recommendations.Figure b)illustrates how CF work.Assume similar users share at least two common courses and have similar rating, then u3 and u4 are similar to u, and their common course h will be a recommendation to u.Figure c)presents how precedence mining work.For this example, we consider patterns where one course follows another.Suppose patterns occour at least two transcrips are recognized as significant, then(a,d),(e,f)and(g,h)are found out.And d, h, and f are recommendation to u who has taken a, g and e.Now I will a probabilistic framework to solve the precedence mining problems.Our target user has selected course a , we want to compute the probability course x will follow, i.e., Pr[x|a].﹁howerve, what we really need to calculate is Pr[x|aX] rather than Pr[x|a].Because in our context, we are deciding if x is a good recommendation for the target user that has taken a.Thus we know that our target user’s transcript does not have x before a.For instance, the transcript no.5 will be omitted.In more common situation, our target user has taken a list of courses, T = {a,b,c,…} not

﹁just a.Thus, what really need is Pr[x|TX].The question is how to figure out this probability.I will answer it later.Session 2: Precedence Mining We consider a set D of distinct courses.We use lowercase letters(e.g., a, b, …)to refer to courses in D.A transcript T is a sequence of courses, e.g., a-> b-> c-> d.Then the definition of Top-k Recommendation Problem is as follows.Given a set transcripts over D for n users, the extra transcript T of a target user, and a desired number of recommendations k, our goal is to: 1.Assign a score score(x)(between 0 and 1)to every course x ∈ D that reflects how likely it is the target student will be interested in taking x.If x ∈ T , then score(x)= 0.2.Using the score function, select the top k courses to recommend to the target user.To compute scores, we propose to use the following statistics, where x, y ∈ D: f(x): the number of transcripts that contain x.g(x;y): the number of transcripts in which x precedes course y.This slide shows the calculation result of f(x)and g(x,y).For example, from the table, we know that f(a)is 10 and g(a,c)is 3.We propose a precedence mining model to solve the Top-k Recommendation Problem.Here are ﹁some notation: xy, which we have memtioned in session 1, refers to transcript where x occurs without a preceding y;x﹁y refers to transcript where x occurs without y following it.We use quantities f(x)and g(x,y)to compte probabilities that encode the precedence information.For instance, from formular 1 to 7.I would not tell the detail of all formulars.We just pay attention to

﹁formular 5, note that this quantity above is the same as: Pr[x﹁y |yx] which will be used to compute score(x).As we know, the target user usually has taken a list of courses rather than a course, so we need to

﹁extent our probability calculation formulars.For example, suppose T={a,b}, Pr[xT] the probability x occurs without either an a or b preceding it;Pr[x﹁T] the probability x occurs without either an a or b following it.This probability can be calculated exactly.So how to calculate it?

Session 3: Recommendation Algorithms Let’s review session 2.The main goal of the recommendation algorithms is to calculate the score(x), and then select the top k courses based on these scores.Traditional recommendation algorithms compute a recommendation score for a course x in D only based on its frequency of occurence.It does not take into account the courses taken by the target user.Our recommendation algorithms called SingleMC conquer the shortcoming of the traditional ones.It computes the score(x)using the formular 5.The detail is as follows: a student with a transcrip T of taken courses, for the course y ∈ T, if y and x appear together in transcripts satisfies the

﹁threshold θ, then compute the Pr[x﹁y |yx], reflecting the likelihood the student will take course x

﹁and ignoring the effect of the other courses in T;finally the maximum of Pr[x﹁y |yx] is choosen as the score(x).Here is the calculation formular of score(x)of SignleMC.For example, with the higer score, d will be recommended.Another new recommendation algorithm named Joint Probabilities algorithm, JointP for short, is proposed.Unlike SingleMC, JointP takes into account the complete set of courses in a transcript.In formular 12, we cannot compute its quantity exactly, Remember this problem we have mentioned.Our solution is to use approximations.This slide is about the first approximating formular.And this the second approximating formular.The system is courseRand, and data set for experiment contains 7,500 transcripts.This slide shows the new recommendation algoritms with black color and the traditional ones with blue color.The chart on this slide indicates our new recommendation algorithms beat the traditional ones in precision, because the former ones exploit patterns across all users, while the latter ones just use the similar users.The chart on this slide points out our new recommendation algorithms also beat the traditional ones in coverage for the same reason.Session 5: Conclusion and Summary In conclusion, we proposed a novel precedence mining model, developed a probabilistic framework for making recommendations and implemented a suite of recommendation algorithms that use the precedence information.Experimental result shows that our new algorithms perform better than the traditional ones, and our recommendation system can be easily generalized to other scenarios, such as purchases of books, DVDs and electronic equitment.To sum up, first, I introduced the motivation and the outline of work;second, I gave the definition of precedence mining model;third, I described some new recommendation algorithms using precedence information;forth, I showed our experimental results to compare the new algorithms with traditional ones.Finally, I made a conclusion of our work..That’s all.Thank you!Are there any questions?