Saliency Prediction for Mobile User Interfaces

We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons, text, etc. in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.Comment: Paper accepted at WACV 201

Effects of Gibberellic acid (GA3) on shelf life and physiochemical properties of mango (Mangifera indica L. var Bombay green)

The present study investigated the effect of GA3 on the physicochemical properties and shelf life of mango (Mangifera indica L. var. Bombay green) from 31st May 2022 to 8th June 2022 at the Central Laboratory of GPCAR collage Gothgaun, Morang, Nepal. The study's goal was to find the right gibberellic acid concentration to use in mangoes that were collected at a mature stage to delay fruit ripening, preserve quality, and lengthen shelf life. The experiment was laid out in a Completely Randomized Design (CRD) with five treatments and four replications. Mature freshly harvested mango fruits of uniform size were dipped into an aqueous solution of gibberellic acid at 0 ppm (T1), 100 ppm (T2), 200 ppm (T3), 300 ppm (T4) and 400 ppm (T5) for 10 minutes. Data on physicochemical parameters (mango pulp pH, total soluble solids, titratable acidity, physiological weight loss, and shelf life) were statistically analyzed through biochemical analyses. Further, fruits treated with 400 ppm of GA3; resulted in the lowest physiological loss in weight (22.08%), the minimum pulp pH (5.02), and the minimum titratable acidity (0.14%) on the 8th day after storage. The highest total soluble solid (19.85°B) was recorded with GA3 @400ppm, while the lowest soluble solids (16.90°B) were recorded with control ppm on the 8th day after storage. Fruits treated with GA3 at 400 ppm had the longest shelf life (7.17 days), while fruits treated with GA at 300 ppm had the shortest shelf life (7.19 days). Therefore, the best results were obtained when gibberellic acid was applied at 400 ppm, which extended the shelf life and physiochemical traits of mango fruits

A Deep Reinforcement Learning Approach to Rare Event Estimation

An important step in the design of autonomous systems is to evaluate the probability that a failure will occur. In safety-critical domains, the failure probability is extremely small so that the evaluation of a policy through Monte Carlo sampling is inefficient. Adaptive importance sampling approaches have been developed for rare event estimation but do not scale well to sequential systems with long horizons. In this work, we develop two adaptive importance sampling algorithms that can efficiently estimate the probability of rare events for sequential decision making systems. The basis for these algorithms is the minimization of the Kullback-Leibler divergence between a state-dependent proposal distribution and a target distribution over trajectories, but the resulting algorithms resemble policy gradient and value-based reinforcement learning. We apply multiple importance sampling to reduce the variance of our estimate and to address the issue of multi-modality in the optimal proposal distribution. We demonstrate our approach on a control task with both continuous and discrete actions spaces and show accuracy improvements over several baselines

Quest of Data Colonialism and Cyber Sovereignty: India’s Strategic Position in Cyberspace

The dawn of the neocolonial project has seen the emergence of a new space: data. Data is a raw material that can be stitched, processed and marketed in the same way as the East India Company (EIC) used to do with India’s cotton. EIC, which started as one of the world’s first joint-stock companies, turned into a wild beast, building a corporate lobby with the help of lawyers and MP shareholders to amend legislation in its favor. The EIC became a particularly atrocious and innovative colonial project that directly or indirectly controlled continents, thanks to an army larger than the army of any nation-state at the time. The Drain Theory of Dadabhai Naroji have opened India’s eyes to how the EIC was taking raw material from the country and converting it into a finished product that was marketed in India again in the same way as raw data is being processed outside India and then marketed here today. In today’s digital era, big corporations need not own big armies, as companies are protected by nation-states and bailed out when required. Today, one does not need to travel overseas to explore and conquer Gold, God and Glory; instead, they are a click away. The neocolonial project runs on digital platforms, while the popular narrative of bridging the digital divide and giving internet access to millions of people resembles the idea of the “white savior” liberating the “noble savage” through modern Western education. Facebook’s grand plan of providing free internet to all can be best understood as a neocolonial strategy to mine the data of billions by equating it with water and land. Similarly, the Cambridge Analytica scandal provides an example of how neocolonial forces can influence the fundamental democratic process of electing a government. Therefore, nations endorsing democratic values should be especially wary of the trap of neocolonialist forces, as such nations are particularly vulnerable to their project. This paper critically study the cyber security infrastructure and policies in India and analyze the India’s approach towards cyber sovereignty and data colonialism and thereafter examine the India’s strategic position in cyberspace and suggest policy recommendations

Urban localization using robust filtering at multiple linearization points

Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments