Abstract

An effective news recommendation system should harness the his- torical information of the user based on her interactions as well as the content of the articles. In this paper we propose a novel deep learning model for news recommendation which utilizes the content of the news articles as well as the sequence in which the articles were read by the user. To model both of these information, which are essentially of different types, we propose a simple yet effective architecture which utilizes a 3-dimensional Convolutional Neural Network which takes the word embeddings of the articles present in the user history as its input. Using such a method en- dows the model with the capability to automatically learn spatial (features of a particular article) as well as temporal features (fea- tures across articles read by a user) which signify the interest of the user. At test time, we use this in combination with a 2-dimensional Convolutional Neural Network for recommending articles to users. On a real-world dataset our method outperformed strong baselines which also model the news recommendation problem using neural networks.

Abstract

Science journalism is the art of conveying a detailed scientific research paper in a form that nonscientists can understand and appreciate while ensuring that its underlying information is conveyed accurately. It aims to transform jargon-laden scientific articles into a form that a common reader can comprehend while ensuring that the meaning of the article is retained. It plays a crucial role in making scientific content suitable for consumption by the public at large. Recent advances in Deep Learning research and it’s applications in natural language processing have made way to impressive results in Natural Language Generation. We leverage these advances to explore the possibility of their use in journalism, science journalism in particular, as comprehension of scientific content is much harder challenge than most of the other forms of content, like shorthand, which journalists use while writing articles. In this work, we introduce the problem of automated science journalism and present ways to automate some parts of the workflow by automatically generating the ‘title’ of a blog version of a scientific paper. We have built a corpus of 87, 328 pairs of research papers and their corresponding blogs from two science news aggregators and have used it to build ScienceBlogger - a pipeline-based architecture consisting of a two-stage mechanism to generate the blog titles. To demonstrate the models, we built an interactive tool, where a user can give abstract and title of a research paper, which would be processed by our APIs to produce a blog title, along with some relevant information about the model used for the generation. Evaluation using standard metrics indicate viability of the proposed system.

Abstract

The Virtual Labs project (vlabs) is a country wide effort to provide access to internet and browser based virtual laboratories to science and engineering college students in India. There are currently over 800 experiments packaged in 70 vlabs in more than ten science and engineering disciplines. More than five hundred workshops on these virtual labs have been conducted and the experiments have been used over 2 million times. Vlabs are an important Government initiative in higher learning in engineering in India. This paper examines virtual labs from a usability perspective, focusing on vlabs. Usability of user-interface elements (technical usability) as well as how they aid learning (pedagogical usability) has been considered. We adapt Nielsen's technical usability heuristics and Nokelainen's pedagogical usability criteria to virtual labs and evaluate the usability of the top three most used vlabs. We construct checklists for each of these criteria and evaluate the vlabs against this checklist. Our analysis reveals a lack of usability focus in lab design. Lab design and implementation guidelines seem to be deficient in covering key usability criteria. We make suggestions on how to quickly improve the usability of these labs.

Abstract

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

Abstract

We propose a novel motion segmentation formulation over spatio-temporal depth images obtained from stereo sequences that segments multiple motion models in the scene in an unsupervised manner . The motion segmentation is obtained at frame rates that compete with the speed of the stereo depth computation. This is possible due to a decoupling framework that first delineates spatial clusters and subsequently assigns motion labels to each of these cluster with analysis of a novel motion graph model. A principled computation of the weights of the motion graph that signifies the relative shear and stretch between possible clusters lends itself to a high fidelity segmentation of the motion models in the scene. Keywords: Motion

Abstract

Static power consumption is one of the most critical issues in CMOS digital circuits, and FinFET technology is being recognized as a valid solution for the problem. In this chapter, we utilize a logic-level leakage current estimation technique relying on an internal node voltage-based model. The model is implemented in the form of VHDL packages. By utilizing the capability of the model, the behavior of major leakage component has been analyzed separately for FinFET technology scaling over single- and multi-stage digital standard cells.

Abstract

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.

Abstract

Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblurthen-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter δ, we are able to achieve generic free-form refocusing over a single image.

Abstract

t. In addition to maximizing the revenue, retailers also aim at diversifying product offerings for facilitating sustainable revenue generation in the long run. Thus, it becomes a necessity for retailers to place appropriate itemsets in a limited k number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizing the revenue, but they do not consider itemset diversification i.e., there could be duplicate (repetitive) items in the selected top-utility itemsets. Furthermore, given utility and support thresholds, the number of candidate itemsets of all sizes generated by existing utility mining approaches typically explodes. This leads to issues of memory and itemset retrieval times. In this paper, we present a framework and schemes for efficiently retrieving the top-utility itemsets of any given itemset size based on both revenue as well as the degree of diversification. Here, higher degree of diversification implies less duplicate items in the selected top-utility itemsets. The proposed schemes are based on efficiently determining and indexing the topλ high-utility and diversified itemsets. Experiments with a real dataset show the overall effectiveness and scalability of the proposed schemes in terms of execution time, revenue and degree of diversification w.r.t. a recent existing scheme.

Abstract

The present work is a multi-temporal satellite based spatial dynamic study of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The study discusses the importance and the status of mangroves on both global and regional scales. Maximum likelihood estimate method of supervised classification technique has been used to classify the land use-land cover changes in the Pichavaram Reserve Forest, Killai Reserve Forest and Pichavaram Extension. The status of the classes has been monitored using Landsat ETM+ of 2000, 2006, 2011, and OLI of 2016 satellite imageries. The total area of the Pichavaram mangrove showed a net increase of approximately 11.41% of the total study area within a span of 15 years (2000–2016)