Proceedings of 2nd International Conference on Computer Applications & Information Security (ICCAIS)
The 2nd International Conference on Computer Applications & Information Security (ICCAIS’ 2019) is inviting authors to submit original contributions in the event research area. ICCAIS’ 2019 is a selective single-track conference, covering all aspects of Networking and Information Security, Computer Applications, Electrical Engineering & Computer Science, Network Management, Network Function Virtualization, Software Defined Networks, Network Applications and Convergence of IT and Telecom Networks. The core track is accompanied by a series of workshops and poster sessions.
Papers accepted and presented at ICCAIS’ 2019 will be published open access on the conference Web site and will be submitted for possible inclusion in IEEE Xplore Digital Library. Authors of selected papers accepted for publication in ICCAIS’ 2019 proceedings will be invited to submit an extended version of their papers to the conference related journals.
Co-authorship networks represent a graph, in which vertices are authors, and edges represent research papers written in co-authorship. Every paper could generate several edges in such a graph, if a number of coauthors is greater than two. Co-authorship networks play important role in understanding the structure of research collaborations usually resulted in joint research papers. Moreover, when analyzing university ranking and research staff publishing activity, coauthorship network may help identifying both, efficient research communities and also people, who lack proper collaborators while having poor research results. Our paper is devoted to the visualization and interpretation of the former sets using as an example co-authorship network of National Research University Higher School of Economics (HSE), Moscow, Russia, while we also discuss the possible solutions for recommending collaborators for the latter set of researchers with low academic profile. Our paper is a case study for our university, which can be extended to larger co-authorship networks using research indexing services.
The progress of deep learning models in image and video processing leads to new artificial intelligence applications in Fashion industry. We consider the application of Generative Adversarial Networks and Neural Style Transfer for Digital Fashion presented as Virtual fashion for trying new clothes. Our model generate humans in clothes with respect to different fashion preferences, color layouts and fashion style. We propose that the virtual fashion industry will be highly impacted by accuracy of generating personalized human model taking into account different aspects of product and human preferences. We compare our model with state-of-art VITON model and show that using new perceptual loss in deep neural network architecture lead to better qualitative results in generating humans in clothes.
We present a model for freight train time prediction based on station network analysis and specific feature engineering. We discuss the first pipeline to improve the freight flight duration prediction in Russia. While every freight company use only reference book made by RZD (Russian Railways) based on railroad distances with accuracy measured in days, we argue that one could predict the flight duration with error less than twenty hours while decreasing error to twelve hours for certain type of freight trains.