Speakers
ISCTIS 2023 Keynote Speakers
Prof. Noor Zaman Jhanjhi, Taylor’s University, Malaysia
Dr. Noor Zaman Jhanjhi is currently working as Associate Professor | Programme Director - of Postgraduate Research Programmes [Computing] | Director of the Center for Smart society 5.0 [CSS5] and Cluster Head for the Cybersecurity cluster at the School of Computer Science Faculty of Innovation and Technology, Taylor’s University, Malaysia.
Prof. Yong Li, ChongQing University, China
Yong Li, Ph.D., is a full professor and Ph.D. advisor and a Member of IEEE. His main research interests include information theory and coding, computer vision, quantum key distribution, and medical big data. He is currently presiding over several national and provincial-level projects. He has published more than 20 papers in international SCI journals (15 JCR Q1 papers), including IEEE Transactions on Information Theory, IEEE Transactions on Image Processing, IEEE Transactions on Communications, IEEE Communications Letters, IEEE Wireless Communications Letters. He has also published many other papers in top-level conferences, such as IEEE Globecom and IEEE ICC. He was awarded 6 national invention patents, 2 US patents, and 1 software copyright, and he has applied for 3 international PCT patents and 5 national invention patents.
ISCTIS 2022 Keynote Speakers
Prof. Jingbo Xia, Xiamen University Tan Kah Kee College, China
Speech Title:SGCN: Spatially Gradient Convolution Network for Certificate Document Image Manipulation Localization
Abstract: Current tampering detection methods pay more attention to natural content images. The research on tampering algorithms for certificate document images is relatively limited, but certificate document images are the most commonly tampered images, and they cause great harm to society. In this work, we propose a network ASGC-Net for certificate document-like image manipulation detection based on the spatial attention mechanism. To achieve a network that can better localize text tampering cues, we also propose a novel spatially constrained convolution that can effectively suppress image content and adaptively learn manipulation detection features by capturing the difference features between the neighborhood and the center of the convolution space. To increase the network's ability to capture tampering cues at multiple scales of images, we add multilayer cross-scale connections inspired by FPN networks. Experimental results show that the algorithm can locate the tampered regions of certificate document images more accurately than general-purpose manipulation detection algorithms.
Prof. Liyang Xie, Northeastern University, China
Speech Title: On virtual big data, with an application to parameter estimation problem
Abstract: Big data is significant for a variety of science and technology problems. Nevertheless, big data is hard to obtain except some special areas. Different from practical activities, Monte Carlo sampling can easily generate a great number of sample data (observations) from a population of random variable. This presentation addresses the application of big data in establishing criterion for parameter estimation. Based on a sample of a Weibull distribution with known parameters, the estimates of the parameters can be obtained by an appropriate parameter estimation method, and the errors of the estimates can be evaluated easily by comparing with the true values. As different criteria underlain the parameter estimation method subject to different errors, the criteria can be evaluated based on the parameter estimation results from a large number of samples. In the situation of small size of samples, it is demonstrated that such established criterion yields more accurate, more robust parameter estimates than theoretical criterion does, since the theoretical criterion suffers from sample uncertainty effect more.
A.Prof. Bo Jiao, Xiamen University Tan Kah Kee College, China
Speech Title: Normalized Laplacian Spectrum and Internet topology
Abstract: Compressing network scale is of great significance for reducing the cost of network simulation and network visualization. This report introduces our recent research on the normalized Laplacian spectrum and Internet topology. Specifically, we found that the spectrum is scale-independent and indicates many structural characteristics of the topology. In addition, we designed a graph sampling algorithm using the spectral features, which can reduce the scale of the Internet topology by more than 96% while maintaining important graph properties.