International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures

Abstract: Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.

Overtaking Maneuvers in Simulated Highway Driving using Deep Reinforcement Learning

Abstract: Most methods that attempt to tackle the problem of Autonomous Driving and overtaking usually try to either directly minimize an objective function or iteratively in a Reinforcement Learning like framework to generate motor actions given a set of inputs. We follow a similar trend but train the agent in a way similar to a curriculum learning approach where the agent is first given an easier problem to solve, followed by a harder problem. We use Deep Deterministic Policy Gradients to learn overtaking maneuvers for a car, in presence of multiple other cars, in a simulated highway scenario. The novelty of our approach lies in the training strategy used where we teach the agent to drive in a manner similar to the way humans learn to drive and the fact that our reward function uses only the raw sensor data at the current time step. This method, which resembles a curriculum learning approach is able to learn smooth maneuvers, largely collision free, wherein the agent overtakes all other cars, independent of the track and number of cars in the scene.

SecSVA: Secure Storage, Verification, and Auditing of Big Data in the Cloud Environment

Abstract: With the widespread popularity of Internet-enabled devices, there is an exponential increase in the information sharing among different geographically located smart devices. These smart devices may be heterogeneous in nature and may use different communication protocols for information sharing among themselves. Moreover, the data shared may also change with respect to various Vs (volume, velocity, variety, and value) to categorize it as big data. However, as these devices communicate with each other using an open channel, the Internet, there is a higher chance of information leakage during communication. Most of the existing solutions reported in the literature ignore these facts. Keeping focus on these points, in this article, we propose secure storage, verification, and auditing (SecSVA) of big data in cloud environment. SecSVA includes the following modules: an attribute-based secure data deduplication framework for data storage on the cloud, Kerberos-based identity verification and authentication, and Merkle hash-tree-based trusted third-party auditing on cloud. From the analysis, it is clear that SecSVA can provide secure third party auditing with integrity preservation across multiple domains in the cloud environment.

EEG-NIRS Based Assessment of Neurovascular Coupling During Anodal Transcranial Direct Current Stimulation - a Stroke Case Series

Abstract: A method for electroencephalography (EEG) - near-infrared spectroscopy (NIRS) based assessment of neurovascular coupling (NVC) during anodal transcranial direct current stimulation (tDCS). Anodal tDCS modulates cortical neural activity leading to a hemodynamic response, which was used to identify impaired NVC functionality. In this study, the hemodynamic response was estimated with NIRS. NIRS recorded changes in oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) concentrations during anodal tDCS-induced activation of the cortical region located under the electrode and in-between the light sources and detectors. Anodal tDCS-induced alterations in the underlying neuronal current generators were also captured with EEG. Then, a method for the assessment of NVC underlying the site of anodal tDCS was proposed that leverages the Hilbert-Huang Transform. The case series including four chronic (>6 months) ischemic stroke survivors (3 males, 1 female from age 31 to 76) showed non-stationary effects of anodal tDCS on EEG that correlated with the HbO2 response. Here, the initial dip in HbO2 at the beginning of anodal tDCS corresponded with an increase in the log-transformed mean-power of EEG within 0.5Hz-11.25Hz frequency band. The cross-correlation coefficient changed signs but was comparable across subjects during and after anodal tDCS. The log-transformed mean-power of EEG lagged HbO2 response during tDCS but then led post-tDCS. This case series demonstrated changes in the degree of neurovascular coupling to a 0.526 A/m2 square-pulse (0---30 s) of anodal tDCS. The initial dip in HbO2 needs to be carefully investigated in a larger cohort, for example in patients with small vessel disease.

On Ladder Logic Bombs in Industrial Control Systems

Abstract: In industrial control systems, devices such as Programmable Logic Controllers (PLCs) are commonly used to directly interact with sensors and actuators, and perform local automatic control. PLCs run software on two different layers: (a) firmware (i.e. the OS) and (b) control logic (processing sensor readings to determine control actions).