Abstract

Given a coarse satellite image and a fine satellite image of a particular location taken at the same time, the high-resolution spatiotemporal image fusion technique involves understanding the spatial correlation between the pixels of both images and using it to generate a finer image for a given coarse (or test) image taken at a later time. This technique is extensively used for monitoring agricultural land cover, forest cover, etc. The two key issues in this technique are: (i) handling missing pixel data and (ii) improving the prediction accuracy of the fine image generated from the given test coarse image. This paper tackles these two issues by proposing an efficient method consisting of the following three basic steps: (i) imputation of missing pixels using neighborhood information, (ii) cross-scale matching to adjust both the Point Spread Functions Effect (PSF) and geo-registration errors between the course and high-resolution images, and (iii) error-based modulation, which uses pixel-based multiplicative factors and residuals to fix the error caused due to modulation of temporal changes. The experimental results on the real-world satellite imagery datasets demonstrate that the proposed model outperforms the state-of-art by accurately producing the high-resolution satellite images closer to the ground truth.

Abstract

Billboard advertisement is one of the dominant modes of traditional outdoor advertisements. A billboard operator manages the ad slots of a set of billboards. Normally, a user traversal is exposed to multiple billboards. Given a set of billboards, there is an opportunity to improve the revenue of the billboard operator by satisfying the advertising demands of an increased number of clients and ensuring that a user gets exposed to different ads on the billboards during the traversal. In this paper, we propose a framework to improve the revenue of the billboard operator by employing transactional modeling in conjunction with pattern mining. Our main contributions are three-fold. First, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue. Second, we propose an efficient user trajectory-based transactional framework using coverage pattern mining for improving the revenue of the billboard operator. Third, we conduct a performance study with a real dataset to demonstrate the effectiveness of our proposed framework.

Abstract

Melanin is a major contributor to the intensity and width of the retinal pigment epithelium (RPE) on optical coherence tomography (OCT). It is hypothesized, high melanin concentrations cause scattering making the RPE appear as a thick band and obscures adjacent layers. A split band appearance of the outermost hyperreflective band has been demonstrated in Albinism, meaning the RPE and Bruch’s Membrane appear as two separate bands. We performed a cross-sectional observational study to test the hypothesis that a reduction in melanin causes an increased ability to differentiate the hyperreflective bands seen on OCT images of the outer retina.

Abstract

When the Indian government declared the frst lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of fve buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To ofset the diference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown. Keywords: COVID-19, Residential energy consumption, Air conditioning energy consumption, Household electricity consumption, Case study, Data monitoring, India

Abstract

Binarization is an essential pre-processing step for many document image analysis tasks. Binarization of handwritten documents is more challenging than printed documents because of the non-uniform density of ink and the variable thickness of strokes. Instead of traditional scanners, people nowadays use the mobile camera to capture documents, including text written on white and glass boards. The quality of the camera-captured document images is often poor when compared with scanned document images. This impacts binarization accuracy. This paper presents a deep learning-based binarization framework called Deep Semantic Binarization (DSB) to binarize various document images. We pose document image binarization problem as a pixel-wise two-class classification task. Deep networks (including DSB) require many training images during training. However, the benchmark datasets with a limited number of training images are publicly available in the literature. We explore various training strategies, including transfer learning, to handle the data scarcity during training. Due to the unavailability of mobile-captured whiteboard and glass board images, we created two datasets, namely WBIDS-IIIT and GBIDS-IIIT, with associated ground truths. We validate DSBN on the public benchmark DIBCO dataset and WBIDS-IIIT and GBIDS-IIIT datasets. We empirically demonstrate that the DSB outperforms the state-of-the-art techniques for WBIDS-IIIT, GBIDS-IIIT and public datasets.

Abstract

Video frame prediction is an interesting computer vision problem of predicting the future frames of a video sequence from a given set of context frames. Video prediction models have found wide-scale perspective applications in autonomous navigation, representation learning, and healthcare. However, predicting future frames is challenging due to the high dimensional and stochastic nature of video data. This work proposes a novel cycle consistency loss to disentangle video representation into a low dimensional time-dependent pose and time-independent content latent factors in two different VAE based video prediction models. The key motivation behind cycle consistency loss is that future frame predictions are more plausible and realistic if they reconstruct the previous frames. The proposed cycle consistency loss is also generic because it can be applied to other VAE-based stochastic video prediction architectures with slight architectural modifications. We validate our disentanglement hypothesis and the quality of long-range predictions on standard synthetic and challenging real-world datasets such as Stochastic Moving MNIST and BAIR.

Abstract

witter played a dominant role during the 2014 general elections in India, ushering a right-wing party into power. Political leaders employed Twitter to augment their public image and push right-wing campaign agendas to millions of followers. A prominent and strategic use of Twitter was credited to Prime Ministerial candidate Narendra Modi, portrayed as a visionary leader supporting economic development, social empowerment and good governance. Within this narrative, women's empowerment debates underwent multiple transformations. Through this article, we aim to establish the nature of discussions lying at the intersections of feminist thinking and internet technology. We study the discursive trajectory of women's empowerment against the backdrop of a right-wing political (Hindutva) ideology playing out on Twitter. Utilising the qualitative methods of Thematic Analysis and Feminist Relational Discourse …

Abstract

Now you can pursue world class courses on Indian soil… we are grooming a new generation of global Indians.… Join now and be the best.(A ‘computer institute’brochure in small town India) India’s large under-twenty-five adult population has stoked huge demand for technical, IT job-ready education. India is home to the largest undertwenty-five demographic profile in the world, 604,394,787 people and 49.91 per cent of the total population (Census, 2011), requiring a

Abstract

Lives of Data is an intellectual response to the birth and evolution of data societies as instruments of knowledge and everyday infrastructures. The book, in a pioneering effort, charts the development and immersion of data infrastructures across socio-economic and institutional contexts in India through the articulation of social, political, economic, cultural worlds–and “data worlds”. Lives of data offers a unique contemporary snapshot of what is at once India’s data world and the digital every day. It does the above by connecting historical journeys of small and big data, their embodiments in model building and in their impacts on human socio-economic sustenance. Editor Sandeep Mertia focusses on “relationality” and “lineages, affinities, and relations of data in context-sensitive ways” to centre and ground questions and debates in the 14 essays that make up this volume. Beginning the collection of essays with a …

Abstract

The pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers exploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using social media has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out relevant discussions from a huge corpus of messages and posts. Social media usage varies with internet penetration and other socioeconomic factors, which might induce disparity in analyzing discussions across different geographies. However, print media is a ubiquitous information source, irrespective of geography. Further, topics discussed in news articles are already newsworthy, while on social media newsworthiness is a product of techno-social processes. Developing this fundamental difference, we study Twitter data during the second wave in India focused on six high-population cities with varied macroeconomic factors. Through a mixture of qualitative and quantitative methods, we further analyze two Indian newspapers during the same period and compare topics from both Twitter and the newspapers to evaluate situational awareness around the second phase of COVID on each of these platforms. We conclude that factors like internet penetration and GDP in a specific city influence the discourse surrounding situational updates on social media. Thus, augmenting …