Keynote Speakers

Prof. Nikola Kasabov

IEEE Fellow and RSNZ Fellow, Auckland University of Technology, New Zealand

  

Professor Nikola Kasabov is Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He is the Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. Kasabov is the 2019 President of the Asia Pacific Neural Network Society(APNNS) and Past President of the International Neural Network Society (INNS). He is member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neuro-systems and Springer journal Evolving Systems. He is Associate Editor of several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 620 publications highly cited internationally. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University and CASIA China, Visiting Professor at ETH/University of Zurich and Robert Gordon University UK, Honorary Professor of Teesside University, UK.Prof. Kasabov has received a number of awards, among them:Doctor Honoris Causa from Obuda University, Budapest; INNS AdaLovelace Meritorious Service Award; NN Best Paper Award for2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Awardfor ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT Medal; Honorable Member of theBulgarian, the Greek and the Scottish Societies for ComputerScience. More information of Prof. Kasabov can be found from:
https://academics.aut.ac.nz/nkasabov.


Speech Title: 'Spiking Neural Networks and Brain-Inspired Computation for Advanced Audio-Visual Information Processing'


Abstract: The talk demonstrates that spiking neural networks (SNN), named as the third generation of artificial neural networks, can be used to build brain-inspired SNN systems (BI-SNN) that are capable of deep, incremental learning of temporal or spatio/spectro -temporal data and for various applications. Similarly, to how the brain learns, these BI-SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as they adopt self-organising learning principles of the brain. This is different from the traditional deep learning neural networks that usually have fixed structures and are difficult to adapt to new data.
The talk explains some basic notions and methods of SNN and BI-SNN, illustrated on an exemplar BI-SNN architecture NeuCube that is built according to a 3D brain spatial template (free software and open source along with a cloud-based version available from www.kedri.aut.ac.nz/neucube). NeuCube can learn both audio and visual information simultaneously, similar to how the brain does it. Through learning, a BI-SNN model creates associations between audio and visual information presented, that can be used for scene understanding.
BI-SNN systems result not only in better classification and prediction accuracy, when used on spatio-temporal audio-visual data, but they also allow to extract meaningful knowledge, thus opening a way of building open and transparent AI in the future.
Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer, 2019, https://www.springer.com/gp/book/9783662577134.

 


Prof. Shahram Latifi

IEEE Fellow, University of Nevada, USA
  

Shahram Latifi, an IEEE Fellow, received the Master of Science egree in Electrical Engineering from Fanni, Teheran University, Iran in 1980. He received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. Dr. Latifi is the director of the Center for Information and Communication Technology (CICT) at UNLV. He has designed and taught graduate courses on Bio-Surveillance, Image Processing, Computer Networks, Fault Tolerant Computing, and Data Compression in the past twenty years. He has given seminars on the aforementioned topics all over the world. He has authored over 200 technical articles in the areas of image processing, biosurveillance, biometrics, document analysis, computer networks, fault tolerant computing, parallel processing, and data compression. His research has been funded by NSF, NASA, DOE, Boeing, Lockheed and Cray Inc. Dr. Latifi was an Associate Editor of the IEEE Transactions on Computers (1999-2006) and Co-founder and General Chair of the IEEE Int'l Conf. on Information Technology. He is also a Registered Professional Engineer in the State of Nevada.

Speech Title: 'Facial Recognition- The most error-prone, yet enduring modern biometrics trait? '


Abstract: In recent years, there has been much progress in the area of Facial Recognition (FR) that address the shortcomings in conventional FR systems. Spoofing using a high resolution image, high false negative rates due to partial occlusion of the face (ex. mask), and high positive rates due to similarity of subjects are among such shortcomings. Aided by advancements in AI and image acquisition technology (i.e. high resolution 2D/3D) cameras, researchers have been able to push the quality of the facial recognition systems to an impressive new level. Despite the progress, there are still challenging issues lingering around ranging from technology matters (ex. real-time standoff detection) to policy concerns (ex. privacy and ethics). In this talk, I will address the progress in facial recognition and present the state of the art technologies developed by the world software giants such as Google, Facebook, Microsoft and Baidu in FR. Amid the growing concerns about misuse of FR by governments and other public entities, companies have started to move away from broad identification toward more restrictive forms of personal identification. At the end, I will focus on the trade-offs of restrictive FR and the need for including control, privacy and transparency in future systems.

Prof. Nannan Wang

Xidian University, China

  

Wang Nannan, Huashan Scholar distinguished professor and doctoral supervisor at Xidian University, is currently the director of Intelligent information processing center in State Key Laboratory of Integrated Services Networks. In recent years, he has been engaged in the research of computer vision and statistical machine learning. His research mainly involves cross-domain image reconstruction and credible identity authentication, including sketch-photo synthesis and recognition, image/video super-resolution reconstruction, image restoration, behavior analysis and recognition, person re-identification, etc. He has published over 150 papers in top international journals and conferences such as IEEE TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, ICML, etc. He has received Outstanding Youth Foundation from National Natural Science Foundation of China. He has been selected as Young Elite Scientists Sponsorship Program by China Association of Science and Technology (CAST). He has been awarded the first prize for Ministry of Education Natural Science Award, the first prize for Shaanxi Province Science and Technology Award, the second prize of China Society of Image and Graphics (CSIG) Natural Science Award. He is the recipient of the Chinese Association for Artificial Intelligence (CAAI) Outstanding Doctorate Dissertations Award and Shaanxi Province Outstanding Doctorate Dissertations Award.


Speech Title: ' Cross-domain Image Reconstruction


Abstract: As an important task for “Safe City” construction, city-level video surveillance has evolved from the first generation of "visible" and the second generation of "readable" to the third stage of "intelligible". Due to the wide spatial distribution of city level cameras and large differences in their types and parameters, it is a major challenge to realize the "intelligible" city level video surveillance system. This lecture mainly introduces the recent progress on cross-domain image reconstruction and credible identity authentication technology, including (1) Behavior analysis (abnormal behavior detection, behavior location and recognition): complete semantic information extraction through multi-scale boundary sensitive network for temporal action localization; the differentiation of reconstruction quality of normal and abnormal data through the detection of temporal-spatial fusion features; (2) Cross-modality person re-identification: improving the feature modality invariance by measuring and constraining the modality differences between cross-modality person high-dimensional features; (3) Video object clarity (underlying vision): Improving the representation ability of inter-frame temporal dependence by joint priori information and motion invariance; (4) Cross-domain image synthesis (heterogeneous image generation and image stylization): Transforming the images from different modalities into unified modality to achieve information completion. (5) Cross-domain image recognition (heterogeneous face image recognition): Improving the interpretability and accuracy of cross-domain image synthesis and recognition through representation disentanglement learning. This research can provide a systematic solution for the intelligent analysis of network video streaming; (6) Credible identity authentication: here “credible” mainly refers to reliability and security. The algorithm is supposed to not only defend against external attacks (adversarial learning), but also protect private information.


Prof. Guodong Ye

Guangdong Ocean University, China
  

Guodong Ye was born in China, He received the PhD degree in department of Electronic Engineering at City University of Hong Kong. From 2016 to 2018, He did the Post-doctoral research at Zhejiang University of China. At present, Dr. Ye is a Professor in Faculty of Mathematics and Computer Science at Guangdong Ocean University of China. His areas of interests are cryptography, application of chaotic system, compressive sensing, reversible information hiding, image encryption, and etc.

 

Speech Title: ' Compressive Sensing and Random Numbers Insertion based Image Encryption and Hiding Algorithm '


Abstract: Most current image encryption algorithms encrypt plain images directly into meaningless cipher images. Visually, a few of them are vulnerable to illegal attacks on a few sharing platforms or open channels when being transmitted. Therefore, this paper proposes a new meaningful image encryption algorithm based on compressive sensing and information hiding technology, which hides the existence of the plain image and reduces the possibility of being attacked. Firstly, the discrete wavelet transform (DWT) is employed to sparse the plain image. This is followed by confusion operation on pixel positions, where logistic-tent map is employed to produce a confusion sequence. And then the image is compressed and encrypted by compressive sensing to form an intermediate cipher image. Here, measurement matrix is generated using low-dimension complex tent-sine system. To further enhance recovery quality, we suggest that the inter-mediate cipher image be filled with random numbers according to the compression ratio and confusing them to obtain the secret image. Finally, two-dimensional (2D) DWT of the carrier image is performed, followed by singular value decomposition. The singular values of the secret image are embedded into the singular values of the carrier image with certain embedding strength to obtain the final visually meaningful encrypted image.