Answer set programming and its Applications

ASP is an emerging declarative programming paradigm. It has been used in several practical applications. This talk will present the basic idea of ASP and demonstrates its use in several applications such as distributed constraints optimization problems, reasoning about truthfulness of agents’ statements, smart home scheduling etc.

Prof. Tran Cao Son – New Mexico State University (USA)

Tran Cao Son received his doctoral degree from the University of Texas at El Paso in 2000. He is currently a Computer Science Professor at the New Mexico State University in Las Cruces. Before joining NMSU, he was a post-doc at the Knowledge System Laboratory at Stanford University for almost a year. His main research interests are in knowledge representation and reasoning, especially logic programming and answer set programming and its applications in planning, negotiation, and multi-agent systems.

Developing Intelligent Systems based on Internet of Things: Some preliminary results

Recently, there has been a great interest to develop intelligent systems based on Internet of Things (IoT), which connects physical objects like sensors nodes to collect real time data accessible through the Internet. Nowadays, in simple terminology, IoT includes almost things such as cell phones, building maintenance services, jet engine of an airplane. It also aids clinicians in diagnosis of heart monitor implant or farmers in a biochip transponder in farm animals. The IoT-connected devices transfer data over a network and are the component members of IoT. In this talk, we would like to summarize our preliminary recent achievements in developing intelligent systems based on IoT typically smart city, electricity generation system, and air pollution minimization: A smart city utilizes the information and communication technology to make efficient consumption of limited resources like space, mobility, energy, etc. This research focuses on developing an effective system for impairments monitoring, traffic monitoring, and smart city innovation with digitalized software for fast and effective implementations; Electrical energy generation from multiple sensors for household appliances and industrial areas is conducted. Electricity from the renewable energy sources such as stress generated by the body weight, heat generated by human body, and movements of the body can be measured by different sensors and transferred to the control system for storing; Air pollution minimization is performed using IoT. Various sensors have been used such as temperature sensor, humidity sensor, smoke sensor and many others to collect data from dust and environment. This model allows finding vehicles which releases more carbon dioxide to reduce the pollution.

Assoc. Prof. Le Hoang Son – VNU University of Science (Vietnam)

An application of Deep learning in smart city traffic management

The report is on an effort to build smart traffic network management based on video surveillance and traffic light centralized in Vietnamese cities. A presentation of smart transportation management infrastructure with integration of network IP cameras, video analytic application running on high performance computing systems, automatic traffic data recognition, automatic calculation of traffic light control strategy for maximal flows in main city corridors. For extracting traffic flow data to input in optimization modelling system, an attempt to deploy deep learning/ tensorflow on specific hardware and software infrastructure: results and challenges.

Assoc. Prof. Pham Hong Quang – Vietnam Academy of Science and Technology (Vietnam)

Pham Hong Quang graduated from the Faculty of Applied Mathematics in Russian (1983), got Ph.D degree (1987) in Game Theory. From 2010 he is Director of Center for Informatics and Computing – Vietnam Academy of Science and Technology. His research interests are optimization control, high performance computing, signal processing and embedded computing… He has deep experiences in designing and realization of ICT infrastructure for Intelligent Transportation System, Smart Traffic Management for urban and highway network.

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

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/