Month: April 2018

Structured prediction for the summarisation and alignment of videos

In this talk, I present two approaches for the automated summarisation and the automated alignment of videos based on structured prediction. The first approach aims to summarise an action video by a selection of its frames while simultaneously recognising its main action. The second approach aims to find the best alignment of two given videos and can be used as a generalised distance for video classification. Both approaches are similar in spirit and leverage structured prediction and the structural SVM framework for inference and for training. Potentially, they could be coupled with deep learning layers to capture the best of both worlds.

Prof. Massimo Piccardi – University of Technology Sydney (Australia)

Massimo Piccardi (M.Eng., 1991, Ph.D. Bologna, 1995) is a professor at University of Technology Sydney (UTS) where he serves as a discipline leader for the School of Electrical and Data Engineering and as a program leader for the Global Big Data Technology Centre, a University-supported task force in communications and big data analytics. His main research areas are computer vision, multimedia, machine learning, and, more recently, natural language processing. Prof. Piccardi has published over a hundred and fifty papers to date, is a Senior Member of the IEEE and serves as an Associate Editor for journal “Computer Vision and Image Understanding.

20 years of Vietnamese Spoken Language Processing: Research & Achievements

Evolutionary computation and its roles in AI

Evolutionary computation is a nature-inspired computing paradigm. It has been a popular research area in AI in the last couple of decades. In this talk, I will cover the recent development in evolutionary algorithms, especially using directional information for guiding evolutionary search. In particular, I will introduce the concept of direction of improvement in both aspects convergence and spreading. Some newly proposed works from our research group related to this will also discussed during the talk.

AI in smart city infrastructure management

Today, smart city technologies are driving new solutions to tackle emerging challenges in urbanization such as traffic congestion, air and noise pollution, safety and crime, emergency response, climate change, economic growth, and delivery of city services. These technologies are relying heavily on a highly complex smart city network infrastructure, including multiple layers, multiple communication technologies, multiple vendor equipment, and multiple traffic patterns. Such an infrastructure enables real-time situational awareness in the urban system by its ability to gather and integrate data at scale, securely and privately, from environmental, critical infrastructure, health and personal sensors. A significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing smart city networks. Recent advances in network softwarization and programmability through Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), the proliferation of new sources of data, and the availability of low-cost and seemingly infinite storage and compute resources from the cloud are paving the way for the adoption of machine learning (ML) and artificial intelligence (AI) to realize cognitive network management in support of autonomic networking in smart city. In this talk, we will review challenges and issues when applying ML and AI in smart city network management, with a focus on infrastructure orchestration. We will also present a use-case of a smart city model built at the heart of Montreal, Canada, in collaboration with global players, including Ericsson, Ciena, Videotron, and Telus.

Prof. Nguyen Kim Khoa – University of Québec (Canada)

Kim Khoa Nguyen is Associate Professor in the Department of Electrical Engineering at the University of Quebec’s Ecole de technologie supérieure (ETS), Montreal, Canada. He has a PhD from Concordia University in Electrical and Computer Engineering. He served as CTO of Inocybe Technologies, a leading company in software-defined networking (SDN) solutions. He was the architect of the Canarie’s GreenStar Network and also involved in establishing CSA/IEEE standards for green ICT. He has led R&D in large-scale projects with Ericsson, Ciena, Telus, and InterDigital. He published extensively, and holds several industrial patents. His expertise includes smart city, cloud computing, IoT, big data, data center, network optimization, high speed networks, and green ICT.

Cognitive Banking

Ngày này, ngành ngân hàng đang phải đối mặt với sự tái sinh khi thời đại kỹ thuật số biến đổi thành thời đại nhận thức. Thành công phụ thuộc vào sự chuyển đổi triệu để cho phép tích hợp của phân tích nâng cao, trí tuệ nhân tạo, máy học, người máy, blockchains và hơn nữa. 64% số người được khảo sát trong năm 2016, chiẹu quả của tổ chức họ vẫn đang không thay đổi hoặc sụt giảm trong vòng 3 năm qua. Khai thác số lượng dữ liệu khổng lồ đang ngủ, dữ liệu do ngân hàng sở hữu, phần lớn là không có cấu trúc là nền tảng để tiếp cận đến từng cá nhân khách hàng, chuyển đổi các hoạt động vận hành, hưởng lợi từ đổi mới của fintech. Vậy làm thế nào bạn có thể tận dụng những công nghệ này để xây dựng một ngân hàng nhận thức?

Mr. Nguyen Manh Khang – IBM (Vietnam)

Khang is Big Data Architect in IBM’s Analytics Group. He has been trusted advisor for customers across industries such as banking, telecommunication, public sector and retails for digital transformation journey. He has been visible on the market as the leader and strategic advisor with the role of IBM Software Architect before promoted to ASEAN.
With more than 10 years in IT industry. Khang experienced a lot of positions at MNC (Multinational Corporation) and local companies such as Software Engineering, Project Manager, Solution Architect, Presale. Khang has strong background and experience in solution architecture and industry knowledge.