Clustering Approach for Detecting Multiple Types of Adversarial Examples.

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture.

Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack.

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it.

Prediction of outcomes in young adults with aneurysmal subarachnoid hemorrhage.

Subarachnoid hemorrhage (SAH) is rare in young adults and little is known about aneurysms in this subgroup. The effect of clinical and prognostic factors on the outcome based on the Glasgow Outcome Scale (GOS) scores and the predictors of unfavorable outcomes were analyzed in young adults with aneurysmal SAH. A retrospective review of the clinical parameters, including age, sex, hypertension, smoking status, hyperlipidemia, location of the cerebral aneurysm, size of the aneurysm, multiplicity, perioperative complication such as hydrocephalus, vasospasm, and hematoma, and Hunt and Hess and Fisher grading on presentation, was conducted in 108 young adults (mean age 34.