AI in Electro-Optical/Infrared camera surveillance systems

AI in Electro-Optical/Infrared camera surveillance systems

Nowadays, Electro-Optical and Infrared (EO/IR) technology plays a critical role in many military, defense, security and industry applications; as it provides the day-night and long-range visualization capability, improves the user’s ability to automatically identify targets, performs threat assessment, raises situational awareness, as well as supports weapons engagement through automatic surveillance and fire control solutions through line-of-sight. In this talk, we will introduce several modern EO/IR camera systems that are currently being developed in Viettel R&D Institute. Furthermore, we will propose a number of artificial intelligence applications that equip our EO/IR camera surveillance systems with the ability to automatically perform detection, localization, recognition, identification and tracking of all ground/air/maritime target types in real time. Our solutions are based on some most advanced machine-learning and deep-learning models trained on large-scale data. Several techniques of online learning and multi-sensor fusion will also be proposed to provide our system with high-performance accuracies and low false alarm rates, even in bad-seeing conditions or complex backgrounds.

Dr. Dao Duc Minh – Viettel R&D (Vietnam)

Dr. Minh Dao received the B.Sc. degree in Electrical Engineering from Hanoi University of Technology, Vietnam in 2007, the double Master degree in Information and Communication Technologies from Polytechnic University of Turin, Italy, and Karlsruhe Institute of Technology, Germany in 2009, and the Ph.D.in Electrical and Computer Engineering from The Johns Hopkins University, Baltimore MD, USA, in 2015. From 2015 to 2017, he worked as a Research Scientist in the U.S. Army Research Laboratory (ARL) in Maryland, USA. In June of 2017, he joined Viettel R&D Institute, where he currently leads the Image Processing R&D team. His research interests are broadly in the areas of signal/image/video processing, statistical machine learning, computer vision and artificial intelligence.

Structured prediction for the summarisation and alignment of videos

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

20 years of Vietnamese Spoken Language Processing: Research & Achievements

In this talk, I will represent some of various research carried out over the last 20 years in the area of spoken language processing and discusses the major themes and advance made in the last 20 years of research at the Artificial Intelligence Laboratory (AILab), the University of Science (VNUHCM), in order to show the outlook of technology, progress and applications that have been achieved in this field.

Assoc. Prof. Vu Hai Quan – VNUHCM University of Science (Vietnam)

Vu Hai Quan received the Ph.D. degree from University of Trento (Italy) in Information and Telecommunications. He did postdoctoral training at University of Leuven (Belgium) from 2005 until 2006. He served as Vice President Vietnam National University – HoChiMinh city. His research interrest is about AI, particularly including: Acoustic Modeling, Language Modeling for LVCSR; Corpus Developments (audio & text) Audio, Music Retrieval; Speech Translation. He has published various articles on international journals and participated in many academic research projects. He won two-time Vietnamese Talent Awards for the TTS and the Automatic Speech Recognition systems.

Evolutionary computation and its roles in AI

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.

Assoc. Prof. Bui Thu Lam – Le Quy Don University (Vietnam)

Dr. Lam Thu BUI received the Ph.D. degree in computer science from the University of New South Wales (UNSW), Australia, in 2007. He did postdoctoral training at UNSW from 2007 until 2009.
He has been involved with academics including teaching and research since 1998. Currently, he is an Associate Professor and Dean of IT Faculty, Le Quy Don Technical University, Hanoi, Vietnam. He is doing research in the field of evolutionary computation, specialized with evolutionary multiobjective optimization. He is the co-editor of the book Multiobjective Optimization in Computational Intelligence: Theory and Practice (IGI Global Information Science Reference Series); and the General Chair of the Ninth International Conference on Simulated Evolution and Learning – SEAL2012.
Dr. Bui is EiC of Journal of Science and Technology: Section on Information and Communication Technology (LQDTU-JICT), a member of the Editorial Board, International Journal of Computational Intelligence and Applications (IJCIA), and was the Vice-Chair of the Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society. He has been a member of the program committees of several conferences and workshops in the field of evolutionary computing, such as the IEEE Congress on Evolutionary Computation and the Genetic and Evolutionary Computation Conference.

AI in smart city infrastructure management

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

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.

From ML Algorithms to ML Systems

From ML Algorithms to ML Systems

I will present 10 lessons that we’ve learned from building battle-tested machine learning systems at Microsoft.

Dr. Kenneth Tran – Microsoft Research (USA)

Kenneth Tran is a Principal Research Engineer in the Deep Learning Group, Microsoft Research. His research expertise and experience includes Deep Learning, Reinforcement Learning, Optimization, and Distributed Computing. At Microsoft, he led the research and development for strategic AI projects such as Deep Reinforcement Learning for real-world control problems, Project FarmBeats: AI & IoT for Agriculture, and Computer Vision API for Cognitive Services. In addition, Kenneth is also the chief mentor of Microsoft AI School’s advanced projects class. Kenneth received his Ph.D. in Computational & Applied Mathematics from The University of Texas at Austin.

AI Strategy and Implementation on Got It’s Knowledge as a Service Platform

AI Strategy and Implementation on Got It’s Knowledge as a Service Platform

Got It’s mission is to connect and economically empower people everywhere. We’re enabling knowledge to be traded directly between one human being and another. Got It’s Knowledge as a Service (KaaS) is delivered on-demand via a “knowledge-time” unit: a 10- or 20-minute chat session in which a user with a knowledge problem is connected immediately with a domain expert at a set price. And like many other services, KaaS guarantees a solution to your knowledge-based problem. While there are some specific expert-based services for specific topics, Got It’s KaaS is the first platform for multiple topics. In this talk we will present how Got It leverages AI to understand a user’s knowledge-based problem, match a problem with a suitable expert in seconds, audit chat sessions, and update expert’s ranking. We had amazing results, our AI powered KaaS platform already served over three million sessions from over twenty five thousand experts from seventy nine countries with great user satisfaction.

Dr. Hung Tran – Got It (USA)

Dr. Hung Tran is the founder of Got It, Inc., a tech startup developing the world’s first Knowledge as a Service (KaaS) platform to instantly connect a knowledge seeker with a vetted expert for an interactive and personalized explanation. Got It is led by an experienced executive team including former executives from tech giants like Google, Lyft, Rakuten, etc., and is headquartered in Silicon Valley with an engineering office in Hanoi, Vietnam. The company has raised $15M in funding from well-known investors in Silicon Valley like Capricorn Investment Group who is also an early investor of SpaceX, Tesla, and PlanetLabs.
Dr. Hung Tran received a VEF Fellowship in 2007 and obtained his Ph.D. in Computer Science focusing on data mining and big data analytics from the University of Iowa. Prior to the Fellowship, he served as the leader of Vietnam OpenCourseWare Project working with MIT and Rice University to build a national scale open courseware program from inception to launch serving millions of college students in Vietnam. Dr. Tran received his Bachelor of Engineering in Information Technology from Hanoi University of Science and Technology. He is also a recipient of numerous national and international technology and entrepreneurship awards.

 

AI2’s startup incubator: progress and directions

AI2’s startup incubator: progress and directions

he Allen Institute for AI (AI2) is a Seattle-based research institute founded by Microsoft co-founder and philanthropist Paul Allen. Led by renown AI researcher Oren Etzioni, AI2 has about 100 research scientists, software engineers, and other staffs that are dedicated to long-term research to advance AI, particularly in natural language processing, commonsense knowledge representation and reasoning, computer vision, and machine learning. Within AI2, the startup incubator seeks to commercialize AI by investing in and advising early-stage AI-focused startups, as well as incubating and spinning out technologies and ideas from within the institute. In this talk I will give an overview of the incubator’s progress and the road ahead.

Dr. Ha Vu – Allen Institute for AI (USA)

Vu Ha (Hà Anh Vũ) is a technologist with 20 years of experience building products at some of the largest companies in the world. Most notably, Vu led applied research and engineering teams at Microsoft’s AdCenter Labs and Bing. He co-founded SemanticScholar.org and led its development through its first public launch. Vu is currently a technical director at the Allen Institute for AI’s startup incubator, advising startups on real-world AI and mentoring technical folks who wish to start an AI company, as part of the incubator’s CTO residency program.

From Human Machine Interaction to Human Machine Intelligence

From Human Machine Interaction to Human Machine Intelligence

The levels of cooperation between HUMANS and MACHINES are in forms of Interaction, Integration, and Intelligence: Interaction can be described as stimulus-response which implies the Machines are just wait and do what Humans order; Integration implies partnership between the human and computer in which information is exchanged for physically WORKING together; Intelligence implies partnership between the human and computer in which information is exchanged for THINKING together. There is a continuum from Interaction to Integration then Intelligence. Doing research in HMI extends the level of human and machine cooperation from Interaction to Integration then Intelligence. I devote this talk in the discussion about the transformation from Interaction to Integration then Intelligence.

Assoc. Prof. Le Thanh Ha – Vietnam National University (Vietnam)

Le Thanh Ha received B.S. and M.S. degrees in Information Technology from the College of Technology, Vietnam National University, Hanoi. In 2005, he received a Korean Government Scholarship for Ph.D program at the Department of Electronics Engineering at Korea University and got Ph.D degree in 2010. After graduation, he joined the Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi as an Associate Professor. His research interests are image/video analysis and processing, satellite image processing and computer vision. He has deep experiences in teaching Digital Image Processing, Computer Vision, Multimedia Communication courses for both undergraduate and postgraduate programs. He has also been principle and main investigator of many fundamental research and technology development projects funded by both domestic and international organizations. He also makes contributions in serving many domestic and international ICT academic conferences including KSE, NICS, ATC, SoICT, ICEIC, … In addition, he is a member of the Institute of Electrical and Electronics Engineers (IEEE), The Institude of Electronics, Information and Communication Engineers (IEICE) and The Vietnamese Association for Pattern Recognition (VAPR).