【直播预告 | IEEE Fellow齐聚】机器人与软件工程前沿国际会议（FRSE 2023）

谢涛教授， 北 京大学

IEEE Fellow

欧洲科学院外籍院士

北京大学计算机学院软件科学与工程系主任

ACM Fellow、 AAAS Fellow、CCF会士

Title: Software Dependability in the Age of Large Language Models

• A bstract

As an example of exploiting the synergy between AI and software engineering, the field of intelligent software engineering (denoting intelligent [software engineering] or AI for software engineering) has emerged with various advances in recent years. This field focuses on instilling intelligence in approaches developed to address various software engineering tasks to accomplish high effectiveness and efficiency, including but not limited to accomplishing the goal of high software dependability. At the same time, software systems incorporating AI components also face some unique challenges related to software dependability. This talk will discuss recent research and future directions in the field of intelligent software engineering for accomplishing the goal of high software dependability in the age of large language models.

金芝教授 ， 北京大学

IEEE Fellow

国家杰出青年基金获得者

中国计算机学会常务理事

中国科学院数学与系统科学研究院研究员

Title:   Environment Modeling based Requirements Engineering

• Abstract

Requirements engineering is to observe the real world, locate system problems, and decide the system capabilities. There are different perspectives on the problem observation. Cyber physical systems directly interact with their operative environment. It is more than important to identify the features and issues of the environment and to deal with the concerns initiated by the environment. The environmental model based approach is taking the environment as the first-class citizen when conducting the requirement engineering. Through modeling the environment, and analyzing the environment static and dynamic characteristics, the behaviors that the systems should have and the constraints that need to be met can be derived systematically. This lecture presents what the environment is, how to model the environment, and how to determine the system capabilities from the perspective of the environment. It also touches a little about some of the emergent concerns about the environment safety issues.

王英旭教授， 加拿大卡尔加里大学

IEEE Fellow

PhD,FBCS, FI2CICC, FAAIA, FWIF and P.Eng

Title: Breakthroughs in Cognitive Robots and AI Programming Underpinned by Discoveries in Intelligence Science and Software Science

• A bstract

Breakthroughs of basic research in Intelligence Science (IS) have triggered the emergence of Cognitive Robots (CR)  and AI Programming (AIP) technologies towards autonomous software engineering. The latest advances enable the synergy of classical pre-programmed AI and pre-trained AI to an unprecedented platform for machine intelligence generation mimicking human brains and brain-inspired cognitive systems.  It is discovered in IS and CC in general, and in CR and SE in particular, that the target entities had already out of the denotational power of the classic domains of real (R) and binary (B) numbers. This leads to the latest discoveries on that the basic unit of human knowledge as a hyperstructure is a binary relation (bir). Therefore, a new framework of Intelligence Mathematics (IM) has been created for rigorously manipulating the cognitive entities in the brain and cognitive robots spanning from formal concepts, semantics, knowledge, causalities, inferences, and consciousness by contemporary mathematical means for advancing both AI  and SE.
This keynote lecture presents fundamental theories and ground-breaking technologies for designing and implementing cognitive robots based on AIP and autonomous intelligence generation methodologies beyond classic reflexive neural networks and imperative programming platforms. The latest advances have paved a new way for cognitive computing and autonomous software generation, which put the house before the cart for the software and AI industries. The keynote will demonstrate disruptive technologies encompassing autonomous systems, cognitive robots, real-time autonomous learning and trustworthy systems for symbiotic human-machine applications.

Prof. Philippe Fournier-Viger

深圳大学特聘教授

Title: Advances and C hallenges for the A utomatic D iscovery of I nteresting P atterns in D ata

• Abstract

Intelligent systems and tools can play an important role in various domains such as for factory automation, robotics, and software engineering. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of data such interaction logs, images, and videos. Managing the data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems such as robots and other applications.

The talk will first briefly review early study on designing algorithms for identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns.  Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.

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