Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning

Grouping Based Blind Interference Alignment for KK-user MISO Interference Channels

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for $K$-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.Comment: 5 pages, 3 figures, to appear in IEEE ISIT 201

Examining the Differences between Methodical and Amethodical ISD

This paper reports on a research program designed to investigate the differences in systems developer\u27s mental models as they develop information systems applying formal system development methods as compared to amethodicaldevelopment

Identifying Issues for the Bright ICT Initiative: A Worldwide Delphi Study of IS Journal Editors and Scholars

Information and communication technology (ICT) continues to change business as we know it. As ICT further integrates into our daily lives, it creates more opportunities to both help and hinder fundamental social problems throughout the world. In response to these growing and urgent societal needs, the Association for Information Systems approved the Bright ICT Initiative to extend IS research beyond a focus on business to take on the broader challenges of an ICT-enabled bright society. We conducted a Delphi study to provide guidance on where bright ICT-minded researchers might focus to produce their greatest impact. In this paper, we report on our findings. The Delphi panel comprised 182 globally distributed IS journal editors who participated in a three-round consensus-building process via the Internet. Our results provide a framework of eleven research priority areas and specific research topics for those engaged in future-oriented, socially conscious IS research