论坛名称：2024概念认知学习应用前沿论坛

论坛地点：文津楼3425学术报告厅

论坛时间：2024年5月21日 8:30

活动负责人：郝飞

报告一题目：人机物智能

报告时间：2024年5月21日 8:50-9:25

报告人：杨天若教授

报告摘要：随着信息技术、计算机技术和通信技术的迅猛发展，人类社会逐渐成为一个人、机、物紧密耦合的三元混合空间，也称之为信息-物理-社会空间。如何在这个混合空间中为人类提供个性化、前瞻性的服务，人机物智能研究的终极目标之一。为此，本报告重点讲述了它的背景、现状以及我们一直从事的人机物智能的研究和应用。

报告人简介：杨天若, 学科首席教授，郑州大学学术副董事长，计算机与人工智能学院、软件学院经理。毕业于清华计算机系,获计算机和应用物理双学士，于加拿大维多利亚大学获计算机科学博士学位。加拿大工程院院士，加拿大工程研究院院士，欧洲科学院院士, IEEE/IET/AAIA会士，国家海外高层次人才入选者，世界顶尖1000名计算机和电子工程科学家,斯坦福全球前2%顶尖科学家,科睿唯安全球高被引学科学家，ACM杰出科学家。主要从事人-机-物智能研究。曾获得多项国际性奖励与荣誉:加拿大工程研究院John B. Stirling Medal奖章(2021), IEEE Sensor Council技术成就奖(2020), IEEE Canada C. C. Gotlieb计算机成就奖(2020)等。

报告二题目：Pattern Structures for Explainable AI

报告时间：2024年5月21日 9:25-10:00

报告人：Sergei O. Kuznetsov教授

报告摘要：In many areas of human activity transparency (interpretability, explainability) of decisions is crucial: medicine, engineering complex systems, finance, law. Pattern Structures (PS) is an extension of Formal Concept Analysis (FCA), which propose explainable approach for direct processing complex data without binarizing them. Pattern Structures offer a natural framework for mining complex and heterogeneous data. They are useful for formalizing and realizing methods of local pattern discovery such as biclustering, information fusion, and retrieval. In this talk we will discuss mathematical background of pattern structures, algorithmic aspects and applications in different applied domains.

报告人简介：Sergei O. Kuznetsov graduated from Moscow Institute for Physics and Technology and defended Doctor of Science thesis on Machine Learning Models Based onConcept Lattices in 2002 at the Computing Center of Russian Academy of Science. He is now full professor at the HSE University in Moscow, being the head of School for Data Analysis and Artificial Intelligence, International Laboratory for Intelligent Systems, and academic supervisor of the Data Science master program.

Sergei Kuznetsov教授，洪堡学者，1990年毕业于俄罗斯全俄信息科技研究所(VINITI)，获理论计算机科学博士学位，2002年于俄罗斯科学院Dorodnitsyn计算中心从事科学 研究工作，获理论计算机科学全博士学位。目前担任俄罗斯国立高等经济大学数据分析与人工智能学院经理，国际智能系统与结构分析实验室主任。主持俄罗斯科学基金3项，俄罗斯基础研究基金2项。主要研究方向为机器学习，形式概念分析，知识工程与发现，发表学术论文200余篇，谷歌学术引用7500余次。

报告三题目：Digital Twin Empowered Autonomous Network Management

报告时间：2024年5月21日 10:00-10:35

报告人：闵革勇教授

报告摘要：In the realm of smart networking, the synergy between digital twin and big data analytics emerges as a pivotal driving force and holds immense promise. Network digital twin provides a dynamic, high-fidelity virtual model of physical networks, while big data techniques offer powerful tools to efficiently process large volumes of network data. This talk will delve into the transformative potential of this synergy in the design, operation, and optimization of future networking systems. Our vision focuses on harnessing the collective power of digital twin and big data in network analytics and modelling, aiming to facilitate real-time system monitoring, proactive troubleshooting, automated optimization, and intelligent decision-making. This talk will further present cutting-edge methodologies in network big data modelling, real-time incremental data analytics, and cost-effective distributed computing platforms, to achieve accurate and timely anomaly detection and predictive analysis for network digital twin, towards more intelligent and responsive future Internet.

报告人简介：Professor Geyong Min is a Chair in High Performance Computing and Networking in the Department of Computer Science at the University of Exeter, UK. His research interests include Computer Networks, Cloud and Edge Computing, Mobile and Ubiquitous Computing, Systems Modelling and Performance Engineering. His recent research has been supported by European Horizon-2020, UK EPSRC, Royal Society, Royal Academy of Engineering, and industrial partners. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and IEEE Transactions on Wireless Communications, and at reputable international conferences, such as SIGCOMM-IMC, INFOCOM, and ICDCS. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing. He served as the General Chair or Program Chair of a number of international conferences in the area of Information and Communications Technologies.

报告四题目：Spot the Bot, or Coarse-grained Structure of a Natural Language

报告时间：2024年5月21日 10:50-11:25

报告人：Vasilii Gromov教授

报告摘要：This presentation concerns the problem of distinguishing human-written and bot-generated texts. In contrast to the classical problem formulation, we consider the problem to distinguish texts written by any person from those generated by any bot; this involves analysing the large-scale, coarse-grained structure of the language semantic space. To construct the training and test datasets, we propose to separate not the texts of bots, but bots themselves, so the test sample contains the texts of those bots (and people) that were not in the training sample. The large-scale simulation shows good classification results (a classification quality of over 96%), although varying for languages of different language families. We deliberately use the simplest classifiers to establish the comparative performance of the features used for classification (clustering characteristics, nonlinear dynamics characteristics, etc.).

报告人简介：Vasilii Gromov教授，2006年毕业于乌克兰第聂伯国立大学，获计算力学博士学位，2017年于乌克兰第聂伯国立大学从事科学研究工作，获数学模型和数字方法学全博士学位 。目前担任俄罗斯国立高等经济大学数据分析与人工智能学院副经理，发表学术论文200余篇。目前主要研究方向为自然语言处理