篇一：國際會議作報告英語發言稿

thank you, prof. …. my name is …..

i’m from ….. i am very pleased to be here to join this forum. the topic of my presentation is properties of rapid construction materials for soil pavement of field airfield.

as is shown in the picture, the main parts of my research are about soil pavement.

my presentation will include these four parts:

first, some background information about this research; second, the main work we have done; third, some conclusions we have got and the last: innovation and presentation of our published *****s.

why i choose this item i think it can be illustrated from the following four parts. first, the existing quantity of airfields is still not sufficient and the airfields have many short***ings especially in war time. second, the ***plementary facilities, such as highway runways are far less than airfields, however, have more weakness.

third, a certain amount of field airfield is quite necessary considering some emergencies such as rescue and disaster relief. forth, the field airfield can fill the void of airfield and they can be ***bined to be airfield ***work.

the meaning and aim of this research contains three parts. fast, convenient and validity, fast means the field airfield must be constructed

as fast as possible, convenient means the construction should need the minimum equipment, labor and materials considering the actual construction condition, validity means the constructed airfield is able to support the operation of given aircraft in specifically time.

just like many other territories, the situation of the research is that the army takes advanced line. the army declares that they can reach to anywhere on the earth in 96 hours, the most important method for force projection is though aircraft, thus rapid construction of pavement is the key problem for rapid force transportation.

the main work we have done can be summarized as four parts, materials choosing, scheme ******, mechanical properties research and water-stable properties research.

we choose two kinds of soils, which are got from xi’an, shanxi province and jiuquan, gansu province separately. the sand from ba river was considerate to investigate the influence of sand to the properties of stabilized soil. the chosen three kinds of powders are cement, lime and new-type stabilizer developed by chang’an university.

the principles in considering the function of 4 kings of fibers are referring different length, type and mixing them.

on account of the time, i will make a brief description about the experiment scheme. in summary, three parts were proposed to distinguish the affecting factors in ****** experiment scheme. they are powder control, fiber control and other factors.

taking powder control for example, the dosage of cement is respectively 6%, 8% and 10% when the soil is stabilized only by cement, while the dosage of cement decrease to 3%, 5% and 7% when the lime is addicted to stabilized soil. the following two factors are stabilizer and sand.

six kinds of experiments were performed to investigate the influence of above factors to the mechanical properties of stabilized soil. the aim of ***paction test is to find the maximum dry density and optimum moisture content. the aim of ***pression strength test is to determine the optimum dosage of cement, lime, powder stabilizer and fiber, meanwhile evaluating the performance of stabilized soil.

the aim of splitting tension strength test is similar to ***pression strength test, the left picture is sample stabilized by cement, while the right picture is the sample stabilized by fiber and cement. the direct sheer is another important parameter in geotechnical engineering. it influences the foundation bearing capacity and many other properties especially for soil base and base course.

the left picture shows the course of ******

sample and the right picture shows the test process.

the cbr test and rebound modulus test are referenced from highway test specification to evaluating the ***prehensive capacities of each structure level of the pavement. for both the two tests, the left picture shows the course of ****** sample and the right picture shows the test process. what should be noted is that the number of sample is at least 6, the last result is the average value of these date got from test after eliminating the bad results.

four kinds of experiments were performed to investigate the influence of above factors to the water-stable properties of stabilized soil. the scouring test is not the stated experiment in current specification. it is performed by us through looking up large quantity of interrelated literature, and two different ways to carry out.

the left picture shows the method of vibration table and the right picture shows the method of fatigue test instrument. pe***rant test refers to the experiment in relating concrete specification. the left picture shows the process of saturation, the right picture shows the test process.

cantabria test and other tests are all original experiments; they are used in stabilized soil for first time, here i will not develop my narrative.

as regards the innovation, i think it throughout the whole research, including materials choosing, scheme ******, mechanical and water-stable experiments. i think it can be drawled from the following keywords, such as soil choosing, sand, powders, fibers, and so on. three main parts can be summarized.

first, selecting two kinds of soils, three kinds of powders, several ***binations; second, several kinds of fibers, different length and admixture; third, ***prehensive experiments, test method and test instrument.

第二部分：英語國際學術會議開幕詞

international conference on space technology

distinguished guests, distinguished delegates, ladies and gentlemen, and all the friends:

at this special time of wonderful september, in this grand hall of the beautiful city, our respectable guests are here getting together. jointly sponsored by the international astronautical federation (iaf), international academy of astronautics (iaa) and the international institute of space law (iisl), undertaken by china national convention center at beijing, the sixty-fourth session of the international astronautical congress will be open. now, first of all, please allow me to give our hearty wel***e to all of you present, and thank you, for your friendly ***ing.

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 space science and exploration, space applications and operations, space infrastructure, space and society multidimensional 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 finally i wish you an unf***ettable and prefect experience here.

thank you!

篇三：英文國際會議講稿

***(1)

大家上午好！今天，我的課題是基于改進的lbp算法的運動目標檢測系統。

運動目標檢測技術可以降低**監控的人力成本，提高監控效率。它也是運動目標提取、跟蹤和識別算法的基礎。圖像信號具有數據量大、實時性要求高等特點。隨著算法復雜度和圖像清晰度的提高，處理速度越來越高。

幸運的是，圖像處理的固有特性是并行的，特別是低層和中層算法。這一特性使這些算法，比較容易在fpga等并行運算器件上實現，今天匯報的主題就是關于改進型lbp算法在硬件上的實現。

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 ['f()ns]，it is also the fundament of object extraction, tracking and recognition

[rekg'n()n]. in this work, efforts ['efts] 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.

***(2)

目前常用的運動目標檢測算法有背景差分、幀差分等。幀間差分法的基本原理是將相鄰兩幀圖像的對應像素點的灰度值進行減法運算，若得到的差值的絕對值大于閾值，則將該點判定為運動點。但幀差分檢測的結果往往是運動目標的輪廓，無法得到目標的完整形狀。

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 ['mdl] 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].

***(3)

當攝像機固定時，背景差分法簡單易行。在背景已知且能提供完整特征數據的情況下，該方法能準確地檢測出運動目標。然而，在實際應用中，很難建立準確的背景模型。

如果背景模型不適應場景的變化，將極大地影響目標檢測結果的準確性。在這副圖中，背景模型沒有及時更新，導致檢測錯誤。

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 ***plex and dynamic environment, it is difficult to build a robust [r()'bst] background model.

***(4)

上述幀差分法和背景差分法都是基于灰度的。當光照條件改變時，基于灰度的算法的性能將大大降低甚至喪失其功能。

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.

***(5)

為了解決基于灰度的算法因光照變化而失效的問題，我們考慮利用紋理特征檢測運動目標。lbp是最常用的紋理特征表示算法之一。首先，從圖像中提取9個像鄰像素的灰度值。

然后，9個像素中除中心像素外的8個像素被二值化。大于

如果等于中心像素，則標記為1；如果小于，則標記為0。最后，以統一的順序排列圍繞中心像素的標記值以獲得lbp值。圖中計算的lbp值為10001111。當某區域內所有像素的灰度都同時增大或減小一定的數值時，該區域內的lbp值是不會改變的，這就是lbp對灰度的平移不變特性。

它可以解決灰度受光線影響的問題。

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 ****** 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 , the lbp is 10001111. lbp is tolerant ['tl()r()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.

***(6)

圖中的一些常見的紋理，都能用一些簡單的lbp向量表示，對于每個像素快，只需要用一個8比特的lbp值來表示。

there are some textures , and they can be represent by some ****** 8bit lbp patterns. ***(7)

從這幅圖像還可以看出，雖然灰度變化很大，但紋理特征沒有改變，lbp值也沒有改變。

you can see, in these picture , although the grayscale change alot, but the lbp patterns keep it value.

***(8)

上述算法是lbp算法的基本形式，但這種基本算法不適合直接應用于**監控系統。主要有兩個原因：第一，在常用的**監控系統中，特別是在高清**監控系統中，9個像素點覆蓋的區域很小，在如此小的區域內，各個像素點的灰度值十分接近，甚至是相同的，紋理特征不明顯，無法在lbp值上體現。

第二，由于以像素為單位計算lbp值，像素噪聲會造成lbp值的噪聲。這兩個原因導致計算出的lbp值存在較大的隨機性，甚至在靜止的圖像中，相鄰兩幀對應位置的lbp值也可能存在差異，從而引起的誤檢測。

為了獲得更好的檢測性能，我們采用了基于塊均值的lbp算法。該方法的基本原理是計算由3×3像素組成的像素塊的灰度均值，并將灰度均值作為像素塊的灰度值。然后在3×3像素塊（9×9像素）中計算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 **all area in which the grayscales of pixels are similar or same to each other, and the texture feature in such a **all 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. ***pared with original lbp pattern calculated in a local 9 neighborhood between pixels, the improved lbp operator is defined by ***paring the mean grayscale value of central block

with those of its neighborhood blocks (see ).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.

***(9)

用lbp描述背景實質上是一種背景差分方法。當背景差分法應用于復雜的**監控場景時，需要解決建立魯棒背景模型的問題。駛入并停泊在監控畫面中的汽車，被搬移出監控畫面的箱子等，都會造成背景的改變。

正確的背景模型是正確檢測運動目標和提取目標完整輪廓的基礎。如果系統能定時更新背景模型，將已經移動出監控畫面的物體“剔除”出背景模型，將進入監控畫面并且穩定停留在畫面中的物體“添加”入背景模型，會減少很多由于背景改變而造成的誤檢測。

根據前一節的介紹，幀間差分法雖然無法提取完整的運動目標，但是它是一種不依賴背景模型就能進行運動目標檢測的算法。因此，幀差法可以作為當前監控畫面中是否存在運動目標的依據。如果畫面中沒有運動目標，就定期對背景模型進行更新。

如果屏幕中有移動對象，則稍后會更新背景模型。這樣就能避免把運動目標錯誤地“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 [kn'krntli] with the background subtraction process. what’s time to update the background is according to the result of frame difference detection.

***(10)

運動目標檢測系統，特別是嵌入式運動目標檢測系統，在實際應用中需要解決實時性問題。比如每秒60幀的1024×768的圖像，對每個像素都運用求均值，求lbp等算法，那么它的運算量是十分巨大的，為此我們考慮在fpga上用硬件的方式實現。

if lbp algorithm is implemented in a software way, it will be very slow. fpga have features of concurrent ***putation, 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.

***(11)

這是我們硬件實現的系統結構圖。首先，將rgb像素信號的濾波、灰度計算和lbp計算輸入系統，得到各像素塊的lbp值。然后后臺更新控制模塊利用幀差分模塊的檢測結果來控制后臺緩沖區的更新。

區域確定模塊根據背景差分模塊的輸出結果合像素塊的坐標信息，確定前景像素塊的區域。

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 ***pares 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.

***(12)

圖中是lbp計算模塊。圖中所示的窗口提取結構可以實現3×3像素塊窗口的提取。當像素信號按順序輸入結構時，窗口中的數據將按順序顯示在9個寄存器pixel1-pixel9中，

因此，在最短的延遲時間內提取9個像鄰像素的灰度值。行緩存的大小等于每行的像素數減去1。將9個像素點的灰度值通過求均值模塊，可以求出一個像素塊的像素均值。

利用像素塊的均值作為輸入，再利用相似結構，提取3×3相鄰像素塊的灰度值。此時，行緩存的大小是每行包含的像素塊數減去1。然后以9個窗口的灰度值作為輸入，通過比較器陣列計算出最終的lbp值。

to achieve real time ***putation of the lbp, a circuit structure is put forward as showed in two line buffers and nine resisters are connected in the way showed in the figure. nine neighbor pixels are extracted with minimum ['mnmm] 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.

***(13)

均值模塊以四級流水線模式實現，如圖3-12所示。在算法的設計過程中，需要求出3×3像素塊中9個像素的平均值。但在硬件實現中，為了更合理地利用硬件資源，僅計算去除中心像素后8像素的平均值。

這樣可以在不影響計算結果的情況下減少加法器的使用。而且在求均值的最后一級流水，除8運算比除9運算更容易實現。因為8是2的整數冪，所以8的除法只需要將每個像素的和右移3位。

9級運算需要在fpga中使用特殊的dsp模塊。

***(14)

如圖所示，塊均值計算模塊計算出的8個塊均值被圖3-11中的窗口提取模塊提取出來，并作為比較器陣列的輸入，比較器的輸出結果用0和1表示。最終比較結果按一定順序排列，并重新組合成8位二進制數，即lbp值。lbp計算電路不采用流水線結構，計算結果可以在一個時鐘周期內得到。

***(15)

這是為了在系統測試中實現對多個目標的檢測。

in this system test ,we achieve a multi-object detection.

***(16)

此圖像是對動態背景更新的測試。 在監視區域中定一目標區域，并在目標區域中放置靜態對象。在前三分鐘內，系統將其作為前臺目標，矩形窗口將以閃爍的形式發出報警信號。3分鐘后，由于物體靜止，系統檢測到10800個靜止幀，因此背景模型被更新。

靜止對象用做背景的一部分，之后窗口不再閃爍。經驗證，系統能夠正確實現后臺模型更新算法。

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.

***(17)

此外為了驗證系統對室外光照變化抑制能力，我們選取了大量有光照變化，并且有運動目標的**對系統進行了測試。

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 ***pared. a number of short video clips with shifty and fixed illumination, including positive

samples with moving objects and negative samples without moving objects .

***(18) 測試平臺如圖所示。用一臺pc機作為測試信號的輸出源，然后在pc機中****，并將**vga信號發送給運動目標檢測系統，模擬真實的監控環境。fpga將輸入信號和區域邊框圖形相疊加后在lcd上顯示。

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.

***(19)

最后給出了系統的roc特性曲線。在沒有光照強度變化的情況下，采用基于灰度的運動目標檢測算法的性能略優于基于lbp值的運動目標檢測算法，兩種算法都能取得較好的檢測效果。但是在圖5-15中（測試集2），也就是在光照強度變化的情況下，畫面整體灰度發生較大的改變，基于灰度的檢測算法的性能大幅度下降，接近于失效。

而采用lbp值的檢測算法卻能維持較好的性能。可見，基于lbp的檢測算法對于抑制由光強變化引起的誤檢具有良好的效果。

this two figure are the roc curves of the experiments using ouralgorithm and traditional grayscale-based algorithm . we can see in the 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 which corresponds to the condition with shifty illumination, grayscale based algorithm deteriorates drastically and nearly lose efficacy ['efks].

but the improved lbp algorithm still keeps a good performance.

***(20)

謝謝大家！

thanks for your attention

英語國際學術會議開幕詞

international conference on remote sensing technology

distinguished guests, distinguished delegates, ladies and gentlemen, and all the friends:

at this special time of wonderful march, in this grand hall of the beautiful campus, our respectable guests are here getting together . jointly sponsored by china remote sensing association, undertaken by remote sensing institution of nuist at nanjing, the first international conference on remote sensing technology , will be open. now, first of all, please allow me to give our hearty wel***e to all of you present, and thank you , for your friendly ***ing.

we feel so proud, and appreciated as well to be the host of the event.

it is a great honor for us to have all you here to attend this conference, of which the theme is the academic exchange about the advanced technologies on rs. here i’d be delighted to introduce our conventioneers in brief. apart from our faculty and students, most of the delegates and guests are prestigious experts and scientists, who are related in these fields from all over the world.

with many significant achievements, they are the most dynamic leaders in the movements of the science and technology. as the host, i would like to take this opportunity to give you a general introduction about our school. nanjing university of information science & technology (nuist), founded in 1960 and renamed from nanjing institute of meteorology in xx, was designated in 1978 as one of the key institutions of higher learning in china.

the university consists of 24 departments or colleges, 12 scientific research institutions and one international training center. the university, covering an area of 140 hectares with a floor space of 4xx0 square meters, boasts 42 basic and special laboratories such as key laboratory of meteorological disasters and sino-american remote sensing laboratory. with a total collection of over 1,170,000 books, the library was listed as one of the most ***pleted literature libraries in china in terms of atmospheric sciences.

for this conference, we are following the agenda here. the meeting is supposed to last for three days，and 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.

during the conference we are pleased to be your guide to this city. if anything needed, don’t hesitate to contact us. we believe by our collaboration we are sure to make this gathering a consummation.

and finally i wish you an unf***ettable and prefect experience here.

thanks!

篇五：模擬國際會議演講稿

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recsplorer:re***mendation 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 re***emdations from others are useful for your inter*** shopping thank you.

it is obvious that re***mendations play an important role in our daily consumption decisions.

today, my topic is about re***mendation algorithms based on precedence mining. i want to share our interesting research result on re***mendation algorithms with you. the content of this presentation is divided into 5 parts:

in session 1, i will intruduce the tradictional re***mendation 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 re***mendation 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 re***memdation application on amazon.

re***mender systems provide advice on products, movies,web pages, and many other topics, and have be***e popular in many sites, such as amazon. many systems use collaborative filtering methods. the main process of cf is ***anized as follow:

first, identify users similar to target user; second, re***mend items based on the similar users. unfortunately, the order of consumed items is neglect. in our *****, we consider a new re***mendation strategy based on precedence patterns.

these patterns may en***pass 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 re***mendations. through our experiment, precedence mining can significantly improve re***mendation 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 re***mendations. figure b) illustrates how cf work.

assume similar users share at least two ***mon courses and have similar rating, then u3 and u4 are similar to u, and their ***mon course h will be a re***mendation 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 re***mendation 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 ***pute the probability course x will follow, , 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 re***mendation 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 ***mon situation, our target user has taken a list of courses, t = 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 (, a, b, … ) to refer to courses in d. a transcript t is a sequence of courses, , a -> b -> c -> d.

then the definition of top-k re***mendation 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 re***mendations 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 re***mend to the target user.

to ***pute 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 re***mendation 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 ***pte 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

***pute 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=, 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: re***mendation algorithms

let’s review session 2. the main goal of the re***mendation algorithms is to calculate the score(x), and then select the top k courses based on these scores. traditional re***mendation algorithms ***pute a re***mendation 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 re***mendation algorithms called singlemc conquer the short***ing of the traditional ones. it ***putes 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 ***pute 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 re***mended.

another new re***mendation algorithm named joint probabilities algorithm, jointp for short, is proposed. unlike singlemc, jointp takes into account the ***plete set of courses in a transcript. in formular 12, we cannot ***pute 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 re***mendation algoritms with black color and the traditional ones with blue color.

the chart on this slide indicates our new re***mendation 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 re***mendation 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 ****** re***mendations and implemented a suite of re***mendation algorithms that use the precedence information. experimental result shows that our new algorithms perform better than the traditional ones, and our re***mendation system can be easily generalized to other scenarios, such as purchases of books, ***s 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 re***mendation algorithms using precedence information; forth, i showed our experimental results to ***pare the new algorithms with traditional ones. finally, i made a conclusion of our work..

that’s all. thank you! are there any questions

第六部分：英語國際學術會議開幕詞

distinguished guests, distinguished delegates, ladies and gentlemen, and all the friends:

at this special time of wonderful august, with a pleasant subtropical climate in xiamen, our respectable guests are here getting together , undertaken by xmu , the xx 10th international conference on natural ***putation and the xx 11th international conference on fuzzy systems and knowledge discovery , will be open. now, first of all, please allow me to give our hearty wel***e to all of you present, and thank you , for your friendly ***ing. we feel so proud, and appreciated as well to be the host of the event.

it is a great honor for us to have all you here to attend this conference, of which the theme is the academic exchange about the advanced technologies on ***puter science. here i’d be delighted to introduce our conventioneers in brief. apart from our faculty and students, most of the delegates and guests are prestigious experts and scientists, who are related in these fields from all over the world.

with many significant achievements, they are the most dynamic leaders in the movements of the science and technology.

i**c-fskd is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to ***puters, circuits, systems, control, ***munications, and more. this is an exciting and emerging interdisciplinary area in which a wide range of theory and methodologies are being investigated and developed to tackle ***plex and challenging problems. as the host, i would like to take this opportunity to give you a general introduction about our school..

xiamen university(xmu), founded in 1921 ,is the first university in china founded by overseas chinese. before 1949, it was named university of amoy. the school motto:

"pursue excellence, strive for perfection (自強不息, 止于至善)". now this university ranked the 13th in china, which is in the front rank in china and maintain the top 20 ranking in china. this university is one of the ***prehensive universities directly affiliated with the education ministry, is located in the city of xiamen in fujian province.

in 1995 it was included in the list of the “211 project” for the state key construction; in xx it became one of china’s higher-level universities designated for the state key construction of the “985 project”.

for this conference, we are following the agenda here. the meeting is supposed to last for three days，and 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.

during the conference we are pleased to be your guide to this city. if anything needed, don’t hesitate to contact us. we believe by our collaboration we are sure to make this gathering a consummation.

and finally i wish you an unf***ettable and prefect experience here.

thanks!

? 國際會議邀請函

Description of the Antibiotics ShaoZengHua from Class Biology engineer 091 Abstract: Antibiotics is a kind of secondary metabolite that was produced by Microorganism or advanced flora and fauna is antiviral or have other activity.It can disturb other cell’s growth.Antibiotics plays an important role in the people medical treatment development.But using antibiotics for a long period of time lead to the bacteria come into being drug resistant.So we need to know the harm of misusing the antibiotics besides we should use the antibiotics reasonably.Key words: antibiotics, mechanism, the phenomenon of misusage, using with reason.Main body: The typical example of antibiotics is penicillin.The penicillin discovery and the depuration are one of the greatest discoveries in the human history.There have been about ten thousand antibiotics since penicillin apply to clinic use.There are about 200 kind of antibiotics applies in clinical.The antibiotic widespread application saved the innumerable lives;the antibiotic is still at this point doctor treats in the course of infection the essential drugs.Antibiotics not only could destroys bacteria but also could decreased and destroyed the molds, mycoplasma and other that can make person fall ill.Antibiotics have four mechanisms against the hurtfully Microbe.1.Impeding the bacteria composite the cell wall, result in the bacteria blasting to death.2.Interaction with the bacterium cell membrane, the enhancement bacterium cell membrane's permeability, open on the membrane the ion channel, let the bacterium interior the useful material leak off the mycelium or electrolyte balanced being out of balance dies.3.Use with the bacterium ribosome or its response substrate, to suppress the protein mutually synthesis it is mean that the cell survival must the structure protein and the enzyme cannot synthesize.4.Impeding the DNA’s copy and transcription of the bacterium.Even though there are so many advantages of antibiotics there come out a phenomenon that as soon as we fall ill we will take many antibiotics.Than the bacteria have the drug tolerance.The super bacteria’s appear just was owing to the antibiotics’ misusing.The other way, the antibiotics also have the adverse reaction.If we always misusing the antibiotics maybe have an exactly opposite effect.So we must use the antibiotics reasonable.1.If we can use lower level antibiotics than we do not use the higher level 2.We should strictly according to what the doctor had said.Taking the antibiotics in accordance with what the doctor had said.3.Do not taking the antibiotics voluntary.4.Gives off heat reason unclear not to be suitable uses the antibiotic except the condition to be seriously injured, and suspected highly for the bacterium infected person outside, gives off heat reason unclear not to be suitable uses the antibiotic In a word, antibiotic is a double-edged sword.If we use it with reason there will be fewer pains.But misusing the antibiotic and if it continue in this way, we will come back to the situation that we do not have any antibiotics to use in 40 years ago.So in my opinion using antibiotics reasonable is extremely.1.能用窄譜抗生素就不用廣譜的， 能用低級的就不用高級的。2.3.服用抗生素要嚴格按照醫囑、按規定間隔定時服藥。如每日4 次， 即每6 小時服1 次；每日3 次， 即每8 小時服1 次， 而不是指在每日三頓飯時服用。4.按處方規定用藥， 切忌隨意使用。隨意反復使用抗生素， 不但不能殺滅體內致病菌，而且容易使殘留的致病菌產生耐藥性。

5、因細菌性感染導致發熱的， 在經抗生素治療體溫正常后， 要及時停用抗生素。

6、最好不要因預防而使用抗生素， 特別是廣譜抗生素。

7、病毒性感染一般不使用抗生素， 如果發熱原因不明， 且無可疑細菌感染征 象者， 不宜使用抗生素。

阻礙細菌細胞壁的合成，導致細菌在低滲透壓環境下膨脹破裂死亡，以這種方式作用的抗生素主要是β-內酰胺類抗生素。哺乳動物的細胞沒有細胞壁，不受這類藥物的影響。

與細菌細胞膜相互作用，增強細菌細胞膜的通透性、打開膜上的離子通道，讓細菌內部的有用物質漏出菌體或電解質平衡失調而死。以這種方式作用的抗生素有多粘菌素和短桿菌肽等。

與細菌核糖體或其反應底物（如tRNA、mRNA）相互所用，抑制蛋白質的合成——這意味著細胞存活所必需的結構蛋白和酶不能被合成。以這種方式作用的抗生素包括四環素類抗生素、大環內酯類抗生素、氨基糖苷類抗生素、氯霉素等。

阻礙細菌DNA的復制和轉錄，阻礙DNA復制將導致細菌細胞分裂繁殖受阻，阻礙DNA轉錄成mRNA則導致后續的mRNA翻譯合成蛋白的過程受阻。以這種方式作用的主要是人工合成的抗菌劑喹諾酮類（如氧氟沙星）。

抗生素不僅能殺滅細菌而且對霉菌、支原體、衣原體等其它致病微生物也有良好的抑制和殺滅作用。青霉素的發現和提純是人類歷史上最偉大的發現之一。自1941年青霉素應用于臨床后，人們相繼發現了上萬種抗生素，有200余種抗生素應用于臨床。抗生素的廣泛應用已挽救了無數生命，時至今日抗生素仍然是醫生治療感染過程中不可缺少的藥品。

抗生素是一類由微生物（包括細菌、真菌、放線菌屬）或高等動植物在生活過程中所產生的具有抗病原體或其它活性的一類次級代謝產物，能干擾其他生活細胞發育功能的化學物質。在人類醫療發展中起了重要作用。但是長期使用抗生素是的細菌長生了耐藥性。所以要了解濫用抗生素的危害并且合理使用抗生素。

抗生素（antibiotics）是。現臨床常用的抗生素有微生物培養液液中提取物以及用化學方法合成或半合成的化合物。目前已知天然抗生素不下萬種。

病菌耐藥性增強。一些抗生素的有效率， 已經從20 年前的90%跌到現在的20%以下。除產生

耐藥性外， 濫用抗生素產生的過敏和毒性反應， 還將誘發體內菌群失調和繼發感染， 對人的聽力、肝、腎產生危害。

1、能用窄譜抗生素就不用廣譜的， 能用低級的就不用高級的。

2、用口服劑能解決的就不要打針， 肌肉注射能解決的就不要靜脈滴注。

3、服用抗生素要嚴格按照醫囑、按規定間隔定時服藥。如每日4 次， 即每6 小時服1 次；每日3 次， 即每8 小時服1 次， 而不是指在每日三頓飯時服用。4.按處方規定用藥， 切忌隨意使用。隨意反復使用抗生素， 不但不能殺滅體內致病菌，而且容易使殘留的致病菌產生耐藥性。

5、因細菌性感染導致發熱的， 在經抗生素治療體溫正常后， 要及時停用抗生素。

6、最好不要因預防而使用抗生素， 特別是廣譜抗生素。

7、病毒性感染一般不使用抗生素， 如果發熱原因不明， 且無可疑細菌感染征 象者， 不宜使用抗生素。我國濫用抗生素藥物現象嚴重

據新華社信息，世界衛生組織的一份調查報告顯示，我國住院患者抗生素藥物使用率高達80 % ，其中使用廣譜抗生素和聯合使用兩種以上抗生素的占58 % ，遠遠高于30 %的國際水平。在我國，濫用抗生素現象還具有明顯的地區特征，在一些經濟發達地區存在抗生素應用檔次過高、療程過長或不必要用藥等過度用藥現象；而在經濟不發達地區，則出現劑量不足、療程過短或使用假冒偽劣藥品等用藥不足的現象，使致病微生物沒能徹底殺來而產生耐藥性。這兩種情況均導致耐藥生物的大量出現。

抗生素等抗菌劑的抑菌或殺菌作用，主要是針對“細菌有而人（或其它高等動植物）沒有”的機制進行殺傷，有4大類作用機理：

使用原則

臨床應用抗生素時必須考慮以下幾個基本原則：

（一）嚴格掌握適應證凡屬可用可不用的盡量不用；而且除考慮抗生素的抗菌作用的針對性外，還必須掌握藥物的不良反應和體內過程與療效的關系。

（二）發熱原因不明者不宜采用抗生素除病情危重且高度懷疑為細菌感染者外，發熱原因不明者不宜用抗生素，因抗生素用后常使致病微生物不易檢出，且使臨床表現不典型，影響臨床確診，延誤治療。

（三）病毒性或估計為病毒性感染的疾病不用抗生素。抗生素對各種病毒性感染并無療效，對麻疹、腮腺炎、傷風、流感等患者給予抗生素治療是無害無益的。咽峽炎、上呼吸道感染者90%以上由病毒所引起，因此除能肯定為細菌感染者外，一般不采用抗生素。

（四）皮膚、粘膜局部盡量避免反應應用抗生素因用后易發生過敏反應且易導致耐藥菌的產生。因此，除主要供局部用的抗生素如新霉素、桿菌肽外，其它抗生素特別是青霉素G的局部應用盡量避免。在眼粘膜及皮膚燒傷時應用抗生素要選擇告辭適合的時期和合適的劑量。

（五）嚴格控制預防用抗生素的范圍在下列情況下可采用預防治療：

1．風濕熱病人，定期采用青霉素G，以消滅咽部溶血鏈球菌，防止風濕熱復發。

2．風濕性或先天性心臟病進行手術前后用青霉素G或其它適當的抗生素，以防止亞急性細菌性心內膜炎的發生。

3．感染灶切除時，依治病菌的敏感性而選用適當的抗生素。

4．戰傷或復合外傷后，采用青霉素G或四環素族以防止氣性壞疽。

5．結腸手術前采用卡那霉素，新霉素等作腸道準備。

6．嚴重燒傷后，在植皮前應用青霉素G消滅創面的溶血性鏈球菌感染?；虬磩撁婕毦退幟艚Y果采用適當的抗生素防止敗血癥的發生。

7．慢性支氣管炎及支氣擴張癥患者，可在冬季預防性應用抗生素（限于門診）。

8．顱腦術前1天應用抗生素；可預防感染。

（六）強調綜合治療的重要性在應用抗生素治療感染性疾病的過程中，應充分認識到人體防御機制的重要性，不能過分依賴抗生素的功效而忽視了人體內在的因素，當人體免疫球蛋白的質量和數量不足、細胞免疫功能低下，或吞噬細胞性能與質量不足時，抗生素治療則難以秦效。因此，在應用抗生素的同進應盡最大努力使病人全身狀況得到改善；采取各種綜合措施，以提高機體低抗能力，如降低病人過高的體溫；注意飲食和休息；糾正水、電解質和堿平衡失調；改善微循環；補充血容量；以及處理原發性疾病和局部病灶等。

通俗地講，抗生素就是用于治療各種細菌感染或抑制致病微生物感染的藥物?？股乜梢允悄承┪⑸锷L繁殖過程中產生的一種物質，用于治病的抗生素除由此直接提取外；還有完全用人工合成或部分人工合成的。

重復使用一種抗生素可能會使致病菌產生抗藥性。之所以現在提出杜絕濫用抗生素此乃是原因之一?？茖W地使用抗生素是有的放矢。通常建議做細菌培養并作藥敏試驗，根據藥敏試驗的結果選用極度敏感藥物，這樣就避免了盲目性，而且也能收到良好的治療效果。

? 國際會議邀請函

尊敬的 您好：

第十八屆工業工程與工程管理國際學術會議將于20xx年9月3日至5日在吉林長春舉行，大會主題：“集工業工程專家之智慧，促進產業創新與發展，助推全球經濟騰飛”。

作為工業工程與工程管理領域的重要國際性學術盛會，它將回顧、總結、推廣過去一年來這一領域里所取得的創新成果，其使命即為這一領域的專家、學者和企業界人士提供一個理論研究和成果交流的學術平臺，本屆會議將特邀國內外數位在此領域學術卓越的學者專家做精彩的主題報告。

本次大會由中國機械工程學會工業工程分會和IEEE北京分會聯合主辦、吉林大學和中國一汽集團公司教育培訓中心承辦。在本次會議上發表的文章將會被IEEEXpore收錄，同時被EI檢索。

由中國機械工程學會工業工程分會主辦的“20xx第二屆工業工程企業應用與實踐高峰論壇”將與國際會議同期召開，屆時將有數位管理經驗豐富的國內外知名企業高管講述工業工程應用案例，并深入到中國一汽轎車生產一線，由專家、現場技術人員，實地講解工業工程在汽車行業的應用與創新。

您在研究領域的成就，有目共睹，您在學術界的威望，人皆仰慕，我們誠摯地邀請您，撥冗光臨此次盛會，您的到來將使大會更加精彩。

一、會議日程：

1. 20xx年9月2日，嘉賓報到。

主題報告。

3. 20xx年9月4日，各分會場小組專題會議報告。

高峰論壇報告交流。

4. 20xx年9月5日，參觀考察

二、會務相關事項：

1.報到地點：長春國際會展中心(距長春龍嘉國際機場22公里)。

2. 大會網址： xx

3. 聯系人：xx

電話：xx

手機：xx

Emai: xx

附件：

附件1：大會日程

附件2：參會回執

中國機械工程學會工業工程分會 第18屆工業工程與工程管理國際學術會議組委會

二〇xx年六月二十五日

附件一：大會日程

IEEE第18屆工業工程與工程管理國際學術會議日程

附件二：

IEEE第18屆工業工程與工程管理國際學術會議參會回執

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