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).

Aesthetic guideline driven photography by robots

Abstract: Robots depend on captured images for perceiving the environment. A robot can replace a human in capturing quality photographs for publishing. In this paper, we employ an iterative photo capture by robots (by repositioning itself) to capture good quality photographs. Our image quality assessment approach is based on few high level features of the image combined with some of the aesthetic guidelines of professional photography. Our system can also be used in web image search applications to rank images. We test our quality assessment approach on a large and diversified dataset and our system is able to achieve a classification accuracy of 79%. We assess the aesthetic error in the captured image and estimate the change required in orientation of the robot to retake an aesthetically better photograph. Our experiments are conducted on NAO robot with no stereo vision. The results demonstrate that our system can be used to capture professional photographs which are in accord with the human professional photography.

Evaluating content-centric vs. user-centric ad affect recognition

Abstract: Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.

Selective shielding: a crosstalk-free bus encoding technique

Abstract: With CMOS process technology scaling to deep submicron level, propagation delay across long on-chip buses is becoming one of the main performance limiting factors in high-performance designs. Propagation delay is very significant when adjacent wires are transitioning in opposite direction (i.e., crosstalk transitions) as compared to transitioning in the same direction. As crosstalk transitions have significant impact on propagation delay, several bus encoding techniques have been proposed in literature to eliminate such transitions. In this work, we propose a technique, namely, selective shielding, to eliminate crosstalk transitions. Compared to the conventional shielding technique, our technique significantly reduces the number of extra wires. We give a lower bound on the number of wires required to encode n-bit data using the selective shielding technique. We show that our technique achieves better energy savings and requires less area as compared to the other techniques.

Bringing Semantics in Word Image Retrieval

Abstract: Performance of the recognition free approaches for document retrieval, heavily depends on the exact or approximate matching of images (in some feature space) to retrieve documents containing the same word. However, the harder problem in information retrieval is to effectively bring semantics into the retrieval pipeline. This is further challenging when the matching is based on visual features. In this work, we investigate this problem, and suggest a solution by directly transferring the semantics from the textual domain. Our retrieval framework uses (i) the language resources like Word Net and (ii) an annotated corpus of document images, to retrieve semantically relevant words from a large word image database. We demonstrate the method on two languages - English and Hindi, and quantitatively evaluate the performance on annotated word image databases of more than a Million images.

A note on informal economy and ICT

Abstract: This note offers a perspective on the informal markets as an assortment of human players and networks contributing to a socio‐business culture of economic survival. The note also offers a few thoughts on specific segments of the informal workforce in India based on ethnographic evidence and a sociological assessment. The author begins with an appraisal of the socio‐economic phenomenon of informality including an anthropological framing of the “informal.” An important focus is the IT‐based businesses and the variety of players ranging from middle‐range businesses to floating or freelancing actors. The note ends with a spotlight on the internet fuelled gig economy where ICTs move beyond being a product or a service and take over as the business platform ushering a new era of informal or gig economy.