2023年欧亚计算机科学与信息技术国际会议（英国牛津大学召开）

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2022-11-28 19:18:04

2023-08-25

【EI检索】2023年第二届欧亚计算机科学与信息技术国际会议（FCSIT 2023）

重要信息

会议网址：www.ecfcsit.org

会议时间：2023年9月15-17日

召开地点：英国牛津大学

出版社：IEEE CPS

截稿时间：2023年9月10日

录用通知：投稿后2周内

收录检索：EI,Scopus

主办单位：华南理工大学、北京工业大学

英国牛津大学召开

会议简介

★2023第二届欧亚计算机科学与信息技术国际会议（FCSIT 2023）---Ei Compendex＆Scopus-Call for papers

｜2023年9月15-17日,英国牛津｜网址: www.ecfcsit.org

★FCSIT 2023将围绕“计算机科学与信息技术”研究领域而展开，来自世界各地的主要研究人员和行业专家将通过论文和口头报告介绍研究成果。本次会议将于2023年7月21-23日在英国牛津大学召开，在会议期间您将有机会聆听到前沿的学术报告，见证该领域的成果与进步。

会议日程预览：

会议时间	内容
2023年9月15日	注册+接待
2023年9月16日	开幕式+KN演讲+分会场报告
2023年9月17日	分会场报告+实验室参观

关于出版和索引

所有接受的论文将在线出版IEEE CPS，将被Ei Compendex，SCOPUS，Google Scholar，Cambridge Scientific Abstracts（CSA），Inspec， ISTP等检索，优秀论文将在国际期刊上发表。

Speakers 2023

Keynote Speaker Ⅰ

Prof. Xinguo Yu

Central China Normal University, China

Paper Title: Progress and Prospects of Solving Math Problems

Abstract: Automatically solving math problems is a long active research problem since 1960s and it has become a hot research problem in the recent years. The algorithms for solving math problems involved in computer vision and pattern recognition because solving is a process of reading and identifying patterns. What are the progress in developing algorithms for solving math problems? To answer this question, we build a state transform theory to explain and analyze representative algorithms. Then we outline the main progresses in this research area according to this theory. The exiting algorithms belong to two schools of model-based and sequence-to-sequence. Through critically analyze the merits and demerits of two schools, we talk the prospects of solving math problems.

Biography: Xinguo Yu is the dean of CCNU Wollongong Joint Institute and a professor of National Engineering Research Center for E-Learning at Central China Normal University, Wuhan, China. He is a senior member of both IEEE and ACM, and an adjunct professor of University of Wollongong, Australia. He is a vice director of Smart Educational Technology Branch Society under Automation Society in China, and the chair of Hubei Society of Artificial Intelligence in Research and Education. He received B.Sc. degree in Mathematics from Wuhan University of Technology, M. Eng degree from Huazhong University of Science and Technology, another M. Eng. degree from Nanyang Technological University, Singapore and Ph.D. degree in Computer Science from National University of Singapore. His current research mainly focuses on intelligent educational technology, educational robotics, multimedia analysis, computer vision, and machine learning. He has published over 170 research papers. He is Associate Editor and Guest Editors for several international journals. He was general chairs or program chairs or Keynote speakers for more than 30 international conferences. He is the main founder of annual International Conference on Artificial Intelligence in Education and Research.

Keynote Speaker Ⅱ

Prof. Petia Radeva

IAPR Fellow

Universitat de Barcelona, Spain

Paper Title: A new Self-supervised framework for Food Fine-grained recognition

Abstract: Deep Learning (DL) has made remarkable progress in tasks such as face and lip recognition or cancer detection in medical images, achieving super-human performance. However, when it comes to classifying a large number of classes, such as in fine-grained recognition, there is still much room for improvement, especially for groups of classes that are easily confused. Additionally, DL relies on greedy methods that require thousands of annotated images, which can be a time-consuming and tedious process. To address these issues, self-supervised learning offers an efficient way to leverage a large amount of non-annotated images and make DL models more robust and accurate. In this talk, we will present our work on self-supervised learning and fine-grained recognition, highlighting how this approach can help solve complex computer vision problems like food image recognition. Food classes have high variability, significant similarity between classes, and a vast number of unannotated images. By using self-supervised learning and fine-grained recognition, we demonstrate how these challenges can be overcome.

Biography: Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), Head of the Consolidated Research Group “Computer Vision and Machine Learning” at the University of Barcelona (CVMLUB) at UB (www.ub.edu/cvmlub) and Senior researcher in Computer Vision Center (www.cvc.uab.es). She was PI of UB in 7 European, 3 international and more than 25 national projects devoted to applying Computer Vision and Machine learning for real problems like food intake monitoring (e.g. for patients with kidney transplants and for older people). Petia Radeva is a REA-FET-OPEN vice-chair since 2015 on, and international mentor in the Wild Cards EIT program since 2017. She is an Associate editor in Chief of Pattern Recognition journal (Q1, IP=7.196) and International Journal of Visual Communication and Image Representation (Q2, IP=3.13). She is a Research Manager of the State Agency of Research (Agencia Estatal de Investigación, AEI) of the Ministry of Science and Innovation of Spain. Petia Radeva belongs to the top 2% of the World ranking of scientists with the major impact in the field of TIC according to the citations indicators of the popular ranking of Stanford. Also, she was selected in the first 6% of the ranking of Spanish and foreign most cited female researchers from any field according to the Ranking of CSIC: https://lnkd.in/djx2Yz5p. Moreover, she was awarded IAPR Fellow since 2015, ICREA Academia assigned to the 30 best scientists in Catalonia for her scientific merits since 2014, received several international awards (“Aurora Pons Porrata” of CIARP, Prize “Antonio Caparrós” for the best technology transfer of UB, etc). She supervised 23 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings, her Google scholar h-index is 50 with more than 10000 cites.

征稿主题/会议征稿

算法与生物信息学、地理信息系统、人工智能、全球导航卫星系统、自动化软件工程、网格与可扩展计算、生物信息学和科学计算、高性能计算、生物医学工程、人机交互、编译器和解释、信息检索、计算智能、信息系统、计算机动画、智能信息与数据库系统、计算机体系结构与VLSI、计算机基础教育、IT政策和业务管理......

更多征稿主题请访问： http://www.ecfcsit.org/cfp.html

投稿方式

1.会议邮箱：info@ecfcsit.org

2.CMT在线投稿：https://cmt3.research.microsoft.com/FCSIT2022

请作者按照官网模板格式进行排版。排版好的论文全文（word+pdf版）发送至CMT在线系统或者会议邮箱。

投稿注意事项：

1.大会官方语言为英语，必须为全英文稿件；

2.保证文章原创性，未在国内外公开刊物或其它学术会议上发表过。

3.文章篇幅一般在5-12页之间，不少于5页，超过6页将收取超页费；

4.请按照官网上模板的格式编排。审稿周期约为3-7个工作日；

5.稿件不允许有剽窃行为，涉嫌抄袭的文章将不会送审。

6.提交摘要：即只参会做报告，不出版文章；