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: Computer science & Authentication. 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).

RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation

Abstract: The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velocity that needs to be adapted for each segmentation task. The Convolutional Neural Networks (CNN) address this issue by learning robust features in a supervised end-to-end manner. However, CNNs employ millions of network parameters, which require a large amount of data during training to prevent over-fitting and increases the memory requirement and computation time during testing. Moreover, since CNNs pose segmentation as a region-based pixel labeling, they cannot explicitly model the high-level dependencies between the points on the object boundary to preserve its overall shape, smoothness or the regional homogeneity within and outside the boundary. We present a Recurrent Neural Network based solution called the RACE-net to address the above issues. RACE-net models a generalized LDM evolving under a constant and mean curvature velocity. At each time-step, the curve evolution velocities are approximated using a feed-forward architecture inspired by the multiscale image pyramid. RACE-net allows the curve evolution velocities to be learned in an end-to-end manner while minimizing the number of network parameters, computation time, and memory requirements. The RACE-net was validated on three different segmentation tasks: optic disc and cup in color fundus images, cell nuclei in histopathological images, and the left atrium in cardiac MRI volumes. Assessment on public datasets was seen to yield high Dice values between 0.87 and 0.97, which illustrates its utility as a generic, off-the-shelf architecture for biomedical segmentation.

Blockchain-Enabled Certificate-Based Authentication for Vehicle Accident Detection and Notification in Intelligent Transportation Systems

Abstract: As the communications among the vehicles, the Road-Side Units $(\textit {RSU})$ and the Edge Servers $(\textit {ES})$ take place via wireless communication and the Internet, an adversary may take the opportunity to tamper with the data communicated among various entities in an Internet of Vehicles (IoV) environment. Therefore, it demands secure communication among the involved entities in an IoV-based Intelligent Transportation System (ITS) deployment. In this work, we design a new blockchain-enabled certificate-based authentication scheme for vehicle accident detection and notification in ITS, called BCAS-VADN. In BCAS-VADN, through the authentication process, each vehicle can securely notify accident related transactions to its nearby Cluster Head $(\textit {CH})$ , if an accident is detected on roads either by its own or neighbor vehicle(s). The $\textit {CH}$ then securely sends the transactions received from the vehicles to its $\textit {RSU}$ and subsequently, these transactions are also received securely by the $\textit {ES}\text{s}$ . The $\textit {ES}$ is responsible for preparing partial block containing transactions and Merkle tree root, and a digital signature on those information, and then forwarding to its associated Cloud Server $(\textit {CS})$ in the Blockchain Center $(\textit {BC})$ for complete block creation, verification and addition of the block using the designed consensus process. Due to blockchain technology usage, it is shown that BCAS-VADN is not only secure against various potential attacks, but also maintains transparency, immutability and decentralization of the information. Furthermore, a comprehensive comparative analysis reveals that BCAS-VADN achieves better security and more functionality attributes, and has low communication and computational overheads as compared to other competitive authentication schemes in IoV. In addition, the practical demonstration using the blockchain technology has been also provided.

Addressing the R-D(*) anomalies with an S-1 leptoquark from SO(10) grand unification

Abstract: Motivated by the R-D(*) anomalies, we investigate an SO(10) grand unification scenario where a charge -1/3 scalar leptoquark (S-1) remains as the only new physics candidate at the TeV scale. This l ...

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation

Abstract: Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central region which is the optic disc and the optic cup region within the disc are examined to determine one of the important cues for glaucoma diagnosis called the optic cup-to-disc ratio (CDR). CDR calculation requires accurate segmentation of optic disc and cup. Another important cue for glaucoma progression is the variation of depth in ONH region. In this paper, we first propose a deep learning framework to estimate depth from a single fundus image. For the case of monocular retinal depth estimation, we are also plagued by the labeled data insufficiency. To overcome this problem we adopt the technique of pretraining the deep network where, instead of using a denoising autoencoder, we propose a new pretraining scheme called pseudo-depth reconstruction, which serves as a proxy task for retinal depth estimation. Empirically, we show pseudo-depth reconstruction to be a better proxy task than denoising. Our results outperform the existing techniques for depth estimation on the INSPIRE dataset. To extend the use of depth map for optic disc and cup segmentation, we propose a novel fully convolutional guided network, where, along with the color fundus image the network uses the depth map as a guide. We propose a convolutional block called multimodal feature extraction block to extract and fuse the features of the color image and the guide image. We extensively evaluate the proposed segmentation scheme on three datasets- ORIGA, RIMONEr3, and DRISHTI-GS. The performance of the method is comparable and in many cases, outperforms the most recent state of the art.

"Answer ka type kya he?": Learning to Classify Questions in Code-Mixed Language

Abstract: Code-Mixing (CM) is defined as the embedding of linguistic units such as phrases, words, and morphemes of one language into an utterance of another language. CM is a natural phenomenon observed in many multilingual societies. It helps in speeding-up communication and allows wider variety of expression due to which it has become a popular mode of communication in social media forums like Facebook and Twitter. However, current Question Answering (QA) research and systems only support expressing a question in a single language which is an unrealistic and hard proposition especially for certain domains like health and technology. In this paper, we take the first step towards the development of a full-fledged QA system in CM language which is building a Question Classification (QC) system. The QC system analyzes the user question and infers the expected Answer Type (AType). The AType helps in locating and verifying the answer as it imposes certain type-specific constraints. In this paper, we present our initial efforts towards building a full-fledged QA system for CM language. We learn a basic Support Vector Machine (SVM) based QC system for English-Hindi CM questions. Due to the inherent complexities involved in processing CM language and also the unavailability of language processing resources such POS taggers, Chunkers, Parsers, we design our current system using only word-level resources such as language identification, transliteration and lexical translation. To reduce data sparsity and leverage resources available in a resource-rich language, in stead of extracting features directly from the original CM words, we translate them commonly into English and then perform featurization. We created an evaluation dataset for this task and our system achieves an accuracy of 63% and 45% in coarse-grained and fine-grained categories of the question taxanomy. The idea of translating features into English indeed helps in improving accuracy over the unigram baseline.