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).

Reliable and Fault-Tolerant IoT-Edge Architecture

Abstract: Edge computing has emerged as an effective solution for delay sensitive IoT applications. In the edge-cloud hierarchy, reliability and fault tolerance are important issues. This paper proposes a novel agent-based reliable and fault-tolerant hierarchical IoT-cloud architecture which can survive the failures of the edge servers. In the proposed architecture, the cloud is distributed over four levels (cloud-fog-mist-dew) based on the processing power and distance from the end IoT device. It makes the whole system reliable by replicating the data on the edge of the network. The proposed system is fault tolerant as it redirects the application on an alternate server. In case of a server's failure, redirection is done at the best possible level in the hierarchy, based on delay-tolerance of the IoT application. The application keeps on working even if the system fails on any level, on cloud, fog or mist. The solution is proposed using mobile agents (MAs) on servers to share systems' state and other important information with other agents in the hierarchy, to be used for application redirection in case of server's failure. The proposed system is simulated using Matlab and results prove its efficiency.

Development of reference building models for India

Abstract: The commercial sector accounts for 8.6% of total electricity consumption of India and increasing with 5% rate annually due to rapid urbanization. The building's major energy consuming end-uses are air-conditioning including heating, cooling, lighting, and equipment. The development and implementation of energy efficiency codes and measures will provide sustainable future. The development process requires analyses of current building construction and operation practices. The current building construction practices can be analyzed by developing reference buildings representative of national building stock. In this research, the development methodology of reference building, developed reference office buildings for India and its application have been presented. The reference buildings are developed for low-rise and high-rise office buildings with 8-h and 24-h operation based on data collection of 230 buildings constructed in last ten years. This methodology can be applied to other buildings types for development of reference buildings with the appropriate data. From the analyses of reference buildings, it has found that the primary energy conservation measures would be envelope thermal properties for 8-h operational buildings while internal loads (occupancy, lighting, and equipment) in 24-h operational building. The energy performance index (EPI) of reference office buildings are better than ECBC 2017. Besides, some of the minimum requirements have not met. The lighting and glass specifications of reference buildings are better than ECBC level due market and government policies while HVAC system specially chiller COP and its controls are needed to be improved.

Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages

Abstract: A large percentage of the world’s population speaks a language of the Indian subcontinent, comprising languages from both Indo-Aryan (eg Hindi, Punjabi, Gujarati, etc) and Dravidian (eg Tamil, Telugu, Malayalam, etc) families A universal characteristic of Indian languages is their complex morphology, which, when combined with the general lack of sufficient quantities of high-quality parallel data, can make developing machine translation (MT) systems for these languages difficult Neural Machine Translation (NMT) is a rapidly advancing MT paradigm and has shown promising results for many language pairs, especially in large training data scenarios Since the condition of large parallel corpora is not met for Indian-English language pairs, we present our efforts towards building efficient NMT systems between Indian languages (specifically Indo-Aryan languages) and English via efficiently exploiting parallel data from the related languages We propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related language pairs We also propose a Multilingual Transfer Learning technique to leverage parallel data from multiple related languages to assist translation for low resource language pair of interest Our experiments demonstrate an overall average improvement of 5 BLEU points over the standard Transformer-based NMT baselines