Professor Thuong Le-Tien
Department of Electrical Electronics Engineering, Hochiminh CIty University of Technology, Vietnam
Thuong Le-Tien (IEEEM'96) is a full Professor at the
HoChiMinh City University of Technology (HCMUT). He received the
Bachelor's and Master's Degrees in Electronics Engineering from the
HCMUT, and the Ph.D.Degree in Electronics-Telecommunications from the
University of Tasmania, UTAS, Australia.
Professor Le-Tien has been a Faculty Member with the Electrical Electronics Engineering Faculty, HCMUT since 1981 todate. He was a Visiting Scholar with the Ruhr University Bochum, Germany, from 1989 to 1992. He has been the Associate Researcher at the UTAS from 2000 until 2006, thenVisiting Professor for teaching, seminars, research collaboration at MannheimUniversity of Applied Science (since 2009), University Paris-13 (since 2001) todate. He served as the Deputy Department, Head for many years then Telecommunications Department Head from1998-2002. He was also appointed as theDirector of the Center for Overseas Studies at the HCMUT from 1998 until 2009. He has authored over 160 research articlesand many teaching textbooks for university students related to Electronics 1& 2, Antenna and Wave Propagation, Digital Signal Processing and Wavelets,and Communication Systems. His current researches interest including to Image & Biomedical Signal Processing, Digital Communication Systems and Electronics Circuits.
Professor Le-Tien has honored the title as a National Distinguished Lecturer and various Certificatesfor his Engineering Education Contribution from the Academic State Council , the chairman of National and the President of the National University of HoChiMinh City. He was also listed in theWho's Who In The World- Millennium 2000, Marquis, USA.
Abstract: Currently, a digital image can be visually changed with ease by using certain editing computer software. It is assumed that although digital authentication may leave no visual evidences indicating the tampering, they may have been altered the statistical properties of pixel values in the detected image. For authentication of images, the methods are often classified into two classes: the active authentication and the passive authentication. The classification is normally based on the fact whether the original image is available or not. In Active Authentication Techniques known as the watermarking techniques, the prior information about the image is indispensable to the process of authentication. To take the initiative in protecting a digital image for copyright authentication, a watermarking or a signature is embedded into the image but the visual quality of image is still maintained sufficiently. If the image contents are modified, the embedded data or the watermarks, which is considered as a secret message attached to the image, will be changed. The image authenticity is done by checking whether the true signature kept by the owner corresponds to the signature retrieved from the suspicious image. In contrast, in terms of digital forensics, the Passive Approach (or blind detection), the given image usually comes with the absence of any prior information. The goal is to find out whether the image has been tampered or not, whether it is a real scene taken from a particular camera (photography), or whether it is totally created by a computer software. The assumption that although high quality forgeries may leave no visual evidences indicating the tampering, they may have altered the statistical properties of the image at pixel scales, which are the only remained traces. These characteristics of statistics can be further analyzed and extracted to form distinguishing features with the ability of recognizing the modifications in the suspicious image. This note is a literature review on digital image authentication and focusing on both active and passive approaches, from the beginning works in 2003 to the recent ones in the first half of 2018. Most of the proposed methods are well classified into categories and analyzed with comments and citations. This also indicates the vital challenges and criteria of performance evaluation to the detection of forgery images, which are considered as the basis of comparison between different methods.
Professor Shahrul Azman Mohd Noah
Universiti Kebangsaan Malaysia (The National University of Malaysia), Malaysia
Prof. Shahrul Azman Mohd Noah is currently a
professor at the Centre for Artificial Intelligence Technology,
Universiti Kebangsaan Malaysia and currently heads the Knowledge
Technology research group. He received the BSc with honours in
Mathematics from the Universiti Kebangsaan Malaysia in 1992, MSc and PhD
degrees in Information Studies from the University of Sheffield, UK, in
1994 and 1998, respectively. He His current research work is focused on
semantic computing with special emphasis on information retrieval,
ontology and recommender systems. He has published various research
articles in these areas. Prof. Noah was previously a research fellow at
the Institute for Pure and Applied Mathematics (IPAM), UCLA. He served
as Co-editor for Multi-Conference on Artificial Intelligence Technology,
Information Retrieval and Knowledge Management Conference and Asia
Information Retrieval Societies Conference. Prof. Noah is currently a
member of the IEEE Computer Society and International Association for
Ontology and its Applications (IAOA). He also serves as the chair for
the Malaysian Information Retrieval and Knowledge Management Society. He
also serves as technical expert assessor for various research grants
such as the Multimedia Development Corporation (MDeC) IGS, MSC
Multimedia Super Corridor R&D (MGS) and MOSTI Technofund grant schemes.
Abstract: Recommender systems have great importance recently in academia, commercial activities and industry. They are widely used in various domains such as shopping (Amazon), music (Pandora), movies (Netflix), and travel (TripAdvisor). Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. It also has the effect of guiding users in a personalized way to interesting or useful items in a large space of possible options. The basic models of recommender systems work with two kinds of data: the user-item interactions, such as ratings or buying behaviour; and attributes about the users and items such as users’ profile and textual content of items. Methods that use the former are referred as collaborative filtering methods, whereas methods that use the latter are referred as content-based recommender methods. Another basic type of recommendation currently adopted by systems named knowledge-based recommender systems use explicitly specified user requirements whereby external knowledge bases and constrains are used to create the recommendation. Some recommender systems combine the strengths of various types of recommendation methods to create hybrid systems. Collaborative filtering techniques perform well when there is sufficient rating information. However, their effectiveness deteriorate when there is not enough rating information available, which is a well-known problem in recommender system called data sparsity. In this talk, I will first discuss some of the potential research areas in recommender systems. Later on, I will specifically focus on the use of contextual sentiments in improving the data sparseness issue, which subsequently improve the performance of recommender systems.
Professor Jiankun Hu
School of Engineering and Information Technology, UNSW Canberra (Australian Defence Force Academy), Australia
Dr. Jiankun Hu is a full professor at the School of Engineering and IT, University of New South Wales (UNSW) Canberra (also named UNSW at the Australian Defence Force Academy (UNSW@ADFA), Canberra, Australia. He is the invited expert of Australia Attorney-Generals Office. Prof. Hu has served at the Panel of Mathematics, Information and Computing Sciences (MIC), ARC ERA (The Excellence in Research for Australia) Evaluation Committee 2014. Prof. Hu's research interest is in the field of cyber security covering intrusion detection, sensor key management, and biometrics authentication. He has many publications in top venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Information Forensics & Security (TIFS), Pattern Recognition, and IEEE Transactions on Industrial Informatics. He is currently the Editor of the following international journals: (1) IEEE Transactions on Information Forensics and Security, (2) Journal of Security and Communication Networks, John Wiley; (3) Security and Privacy, Wiley. (4) IET Cyber-Physical Systems: theory & applications. (5) Area Editor for KSII Transactions on Internet and Information Systems.
Abstract: Energy big data analytics has attracted strong attention from industry, government and academic researchers. Many real-life success stories in gaining great financial and social benefits have been reported. Energy big data is normally referred to as data generated by the emerging smart grid (SG) technology which has been heralded as a technological paradigm shift that can effectively address the issue of limited fossil fuel reserve in earth and also can reduce carbon emissions. Different from other general big data analytics such as medical big data analytics and social network big data analytics, energy big data analytics involves unique research challenges on how to cope with realtime tight cyber-physical couplings, and security/safety of the SG system which has an enormous impact to the national critical infrastructure and social life. Unfortunately energy big data analytics from a cybersecurity perspective have been less explored. There is an urgent need for such research as modern cyber interconnected and computerized energy infrastructure has become the most targeted national critical infrastructure for cyberattacks. A recent high-profile cyberattack on the Ukraine power grid has compromised three power control centres, taking down 30 substations and leaving over 225,000 Ukrainians without power. Other high-profile attacks include penetrations over two US nuclear plants and the Struxnet attack over Iranian nuclear plant. Recently a new type of cyberattack called the false-data-injection attack has been discovered which can evade the widely deployed bad data detection method in the emerging smart grid (SG). In the talk, I'll provide state-of-art research developments in the energy big data analytics from cyber security aspect. Open research questions will be discussed.