Month: April 2018

Applied machine learning for decision making in real-life problems

Applied machine learning for decision making in real-life problems

Machine learning has been a big shot in the last few years. In this talk I just want to highlight two examples (spam and fraud detections) where we can use machine learning to solve the decision making in real-life problems.
The first one is how to use machine learning to detect spam traffics from huge data set (e.g., 1M records/second). The data size, however, is not only the main challenge but also the ambitious spam definition as well as quality of training data (if you can collect). The second one is focus on a challenge where your training data is not only low quality but also extremely unbalanced.

Dr. Le Sy Quang – Google (UK)

Quang Sy Le got Ph.D degree from Japan Advanced Institute of Science and Technology (JAIST, Japan). He has more than ten years working in different academia projects including e.g., Molecular Evolution, 1000 Genome Projects. He is now moving to Google (England) to work on machine learning projects for large data sets.

How to Make Chatbots Smarter: Computational Semantics Beyond Events and Roles

How to Make Chatbots Smarter: Computational Semantics Beyond Events and Roles

Current chatbot systems have mostly used a swallow semantic representation of text, which have focused on extracting propositional meaning, capturing “who does what to whom, how, when and where”. These chatbots tend to disregard significant meaning encoded in human language. For this reason, it is difficult for them to understand utterances such as “What is the tallest mountain in Vietnam?” or “What is the largest prime less than 2018?”. In this talk, I show that a deep understanding and reasoning of natural language is required in order for a chatbot to better understand an input utterance and produce an appropriate action. This goal can be achieved by semantic parsing, an area within the field of natural language processing. Chatbots can be made more intelligent only by computer scientists with a deep knowledge of computational semantics.

Dr. Le Hong Phuong – Vietnam National University (Vietnam)

Le Hong Phuong received his PhD in computer science at Université de Lorraine, France in 2010; master of information technology at Institut de la Francophonie pour l’Informatique (IFI) in 2005; bachelor of science in applied mathematics-informatics at Hanoi University of Science in 2002. He was a researcher and assistant professor at Ecole des Mines de Nancy and INRIA Lorraine, France from 2010 to 2011. He is currently the head of the Data Science Laboratory at Hanoi University of Science, Vietnam National University. He has been also an associate researcher at FPT Research & Development, FPT Corporation since 2013; a scientific counselor for several AI companies. He has been working in the fields of natural language processing and applied mathematics for nearly 20 years. He has published over 40 scientific papers and has been in programme committees of many national and international scientific conferences. He is the author of some softwares toolkits which are widely used in the Vietnamese text processing community. His website is at http://mim.hus.vnu.edu.vn/phuonglh/

Virtual and Augmented Reality: Applications and Issues in a Smart City context

Virtual and Augmented Reality: Applications and Issues in a Smart City context

Virtual and Augmented Reality has the potential revolutionise how we play, work and learn. In this talk we will consider the fundamentals of Virtual and Augmented Reality technologies including how these technologies can be applied to education, entertainment and data visualisation applications associated with the Smart Cities paradigm. There is a great potential to make use of Virtual and Augmented Reality technologies to enable Smart City applications, however we must solve a number of problems first. Solving these problems will require both an understanding of the data to be visualised and the physiology and psychology of the human user. We cannot afford to ignore either and so in this talk I will touch on the human aspects of Virtual and Augmented applications to Smart Cities and show how an understanding of human perception is crucial to building effective solutions to these problems.

Assoc. Prof. Perry Stuart – University of Technology Sydney (Australia)

Stuart Perry received the B.S. degree (first-class honors) in electrical engineering and the Ph.D. degree from the University of Sydney, Sydney, Australia, in 1995 and 1999, respectively. He has previously worked for the Commonwealth Science and Industrial Research Organisation, Australia (CSIRO), Australia, Defence Science and Technology Organization (DSTO), Australia and Canon Information Systems Research Australia (CiSRA). He is currently an Associate Professor at University of Technology Sydney in FEIT’s Perceptual Imaging Laboratory (PILab) conducting research into colour and perceptual quality in 3D environments. His research interests include virtual and augmented reality, image processing, and perceptual quality.

Deep Learning for Natural Language Processing and Beyond

Deep Learning for Natural Language Processing and Beyond

The Tutorial begins with the basic of feed-forward neural network and relevant fundamental knowledge for deep learning. We then introduce more specialized neural network models, including Convolutional Neural Network, Recurrent Neural Network, and attention-based models. In the second part, we will present how these models and techniques can be applied to some interesting problems of natural language processing including sentiment classification, textual entailment recognition, natural language generation, and question answering. The last part of the tutorial will show how we can adapt deep learning and natural language processing techniques for program analysis.

Assoc. Prof. Nguyen Le Minh – Japan Advanced Institute for Science and Technology (Japan)

Minh Le Nguyen is currently an Associate Professor of School of Information Science, JAIST. He leads the lab on Machine Learning and Natural language Understanding at JAIST. He received his B.Sc. degree in information technology from Hanoi University of Science, and M.Sc. degree in information technology from Vietnam National University, Hanoi in 1998 and 2001, respectively. He received his Ph.D. degree in Information Science from School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) in 2004. He was an assistant professor at School of information science, JAIST from 2008-2013. His research interests include machine learning, natural language understanding, question answering, text summarization, machine translation, big data mining, and Deep Learning.