逻辑严谨、观点突出的演讲稿，更能引起听众的共鸣。为帮大家了解演讲稿的写作格式与技巧，以下是小编精心整理的《模拟国际学术会议演讲》的相关内容，希望能给你带来帮助!
第一篇：模拟国际学术会议演讲
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?
第二篇：模拟国际学术会议相关事宜
首届“科大杯”创新科技发展模拟国际研讨会学术活动计划书
The 1st NUDT Graduate Students Symposium
on Scientific & Technological Innovation
院训练部：
为了进一步提高我校研究生的综合素质，培养新时期面向国际、走向未来的新型的综合军事人才，同时也为了提升我校研究生的人文素养，加强语言在科学技术发展以及社会发展过程中的中介力量;本着“科技强军”的战略宗旨，以“突出学术特色、注重交流能力、提升科技品位”为主题，以满足我校培养一流综合性人才的目标需求，外语系指导研究生英语活动中心(PEAC)特拟定2011年春季学期开展首届“科大杯”创新科技发展模拟国际研讨会学术活动。
活动以本学期所开设三门研究生英语选修课(包括 国际会议交流英语、科技论文撰写与写作、科技英语翻译)为课程依托，以课程内30课时所授知识为智力支持，以课程外10学时为时间和基础，以研究生活动中为组织平台，以所有选课学员以及意愿参与的未选课学员为对象，拟开展首届“科大杯”创新科技发展模拟国际研讨会学术活动。
活动计划如下：
(一)会议前准备阶段
1. 任课老师根据学员课堂表现情况，从选课学员中按学员院别每个学院推荐一至两名学员进入“模拟国际会议会务组(Academic Committee)”。负责会议前期收集和评审各院学员论文稿件工作。
2. 按任课老师上课要求，各选课学员(或未选课但愿意参与的学员)在课程课内学时结束前，即“4月26号”前，提交论文及摘要一份，并随之注明oral或poster(说明会议中希望做presentation或展板)。各任课老师收集后交至Academic Committee。未选课学员则交至Secretariat。由Secretariat转为提交给Academic Committee。(未选课学员的论文征集工作从announcement或call for paper宣传发出之时至4月26号时截止，具体联系人员和联系方式以及地址都会在announcement中做明确说明。)
3. PEAC“模拟国际会议会务秘书处(Secretariat)”在全校研究生中做好此次模拟 1
国际会议活动宣传工作。并由PEAC在军网或校报以及宣传栏发出announcement，和做好“call for papers”的工作，分发conference brochure。 PEAC的宣传工作会在3月19日之前做好，宣传时间一直截至4月26日为止。
4. 论文要求内容上在本专业研究领域具有一定创新和突破，格式完全按照“论文撰写课程”上所要求的标准格式。(选修翻译课的学员可酌情提交所翻译的其他学者的论文，但是在提交时要注明。如果论文被选做presentation或poster，该学员则模拟为代表发言。)
(二)会议审稿阶段
1. 5月10日。所有国际会议交流英语选课学员以及每院所选出的Academic Committee会务组学员集中于国际会议交流英语课教室(101-307)，进行审稿讨论及定度工作。选修国际交流课的学员按院别分别在1班和2班的教室加入Academic Committee会务组的讨论工作。其他两门课程老师自行安排未被选入Academic Committee的学员。
2. Academic Committee从各自所在院的上交稿件中选出4-8篇优秀论文在会议上做oral presentation。(具体各院篇数按分会场时间来决定。保证presentation每篇20分钟时间)另外，从全校论文选出3篇优秀论文做大会的主题发言。选出1至2篇做展板。
3.所选学员论文名单由Secretariat或各任课老师负责通知到各学员，并让其做好presentation或poster的准备。
(三)模拟国际会议召开阶段
1. PEAC负责会议的组织：负责签到(每位与会学员都必须在签到台签到。);负责记录学员与会参与的情况。并在会议结束后将学员签到表交给各任课老师以及国际交流与培训中心教研室主任各一份。
2. 5月17号上午9：00，地点101教学楼(具体待定)。举行首届“科大杯”创新科技发展模拟国际研讨会。上午9：00—9：30，大会暨全体会议(general assembly & plenary session)，邀请研究生院、人文与社科学院以及外语系领导做welcome speech。9：30—10：30，keynote speech。10：40-12：00做两个学院的分会场
parallel sessions。(两个学院待定，因此分会场时间较短，原则上选取提交论文较少或人数基数较少的学院。)主会场和分会场共三个场地。具体场地安排见PEAC Secretariat做出的Conference Brochure中的Conference Program。(这里是由主到分)
3. 5月24号上午9:00—11：00，地点101教学楼(具体待定)，详细规定参见Conference Program。举行其余学院的分会场parallel sessions。(评委分会场评奖时间包含在内。)
4. 主会场及分会场各位主持人由国际会议交流英语课任课老师在课上指定或由学员毛遂自荐。
5. 分会场按模拟国际会议流程开展：presentation、Q& A、poster sessions。poster sessions和walk in and talk安排在presentation session休息15-20分钟中进行。PEAC会议秘书处准备相应的饮水和点心供与会人员享用。
6. 所有分会场尽量邀请该院专家教授与会做评委。由PEAC提前跟各个学院联系，并向专家教授正式发出邀请函。该学期所有研究生授课教员也应与会做评委。
7. 由各分会场所有评委在各分会场所做陈述或展出的论文中选出三篇优秀论文，评出
一、
二、三等奖。
8. 11：10—12：00。颁奖大会。六个分会场学员等人员集中于主会场，详细规定参见Conference Program(这里是由分到主)
(四)模拟国际会议学术活动后续工作阶段
1. 各门课程任课老师根据学生与会情况量分。对于表现突出的学员，如选入Academic Committee审稿的学员、选为chairperson的学员以及获奖学员应该给与加分奖励。
2. 对于不参与实践学时的学员，成绩记录为“缺考”。任课教员在登录成绩时必须与国际交流与培训中心教研室主任核对模拟会议缺课学员名单后再进行。
3. 实践学时分数占据该门功课总分的30%。
4. 模拟国际会议学术活动结束后，由PEAC会议秘书处讲获奖学员论文装订成册，送给获奖学员留作纪念。
第三篇：模拟国际会议PPT
一、基本内容
标题页、目录页、章节内容、声明、参考文献、致谢
其中，章节内容通常包括主题介绍、实验或者计算过程、结果、结论或总结
二、PPT制作步骤
1) 确定章节内容，对各部分内容进行逻辑性分析和重要性排序
2) PPT初步成型
3) PPT详细设计
4) 检查完善
三、设计原则
目的明确、思路清晰、逻辑性强
文字、表格、图表合理搭配，并善于使用结构图
简洁大方、有较好的视觉效果
四、设计内容
版式设计
模板设计
配色设计
动画设计
切换设计
效果设计
说明：
1) PPT是辅助说明的工具，使表达内容达到易于接受、赏心悦目的效果。
2) PPT制作熟能生巧，注意搜集好的设计和素材，制作时信手拈来。
3) PPT的使用效果与演讲者的表达技巧密切相关，演讲者应该以饱满的热情，尽力将自己
熟知的内容分享给观众。
第四篇：英文国际学术会议开幕词演讲稿
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 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. 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 2004, 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 420000 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 completed 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 unforgettable and prefect experience here.
Thanks!
第五篇：英文国际学术会议开幕词演讲稿
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 2014 10th International Conference on Natural Computation and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery , will be open. 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.
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 Computer 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.
ICNC-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 computers, circuits, systems, control, communications, 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 complex 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 (自强不息, 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 comprehensive 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 2000 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 unforgettable and prefect experience here.
Thanks!