Abstract

We present a novel approach to optimally retarget videos for varied displays with differing aspect ratios by preserving salient scene content discovered via eye tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. Our approach is (a) content agnostic as the same methodology is employed to re-edit a wide-angle video recording or a close-up movie sequence captured with a static or moving camera, and (b) independent of video length and can in principle re-edit an entire movie in one shot. Our algorithm consists of two steps. The first step employs gaze transition cues to detect time stamps where new cuts are to be introduced in the original video via dynamic programming. A subsequent step optimizes the cropping window path (to create pan and zoom effects), while accounting for the original and new cuts. The cropping window path is designed to include maximum gaze information, and is composed of piecewise constant, linear and parabolic segments. It is obtained via L(1) regularized convex optimization which ensures a smooth viewing experience. We test our approach on a wide variety of videos and demonstrate significant improvement over the state-of-the-art, both in terms of computational complexity and qualitative aspects. A study performed with 16 users confirms that our approach results in a superior viewing experience as compared to gaze driven re-editing [JSSH15] and letterboxing methods, especially for wide-angle static camera recordings.

Abstract

Popular methods for news recommendation which are based on collaborative filtering and content-based filtering have multiple drawbacks. The former method does not account for the sequential nature of news reading and suffers from the problem of cold-start, while the latter, suffers from over-specialization. In order to address these issues for news recommendation we propose a Hybrid Recurrent Attention Machine (HRAM). HRAM consists of two components. The first component utilizes a neural network for matrix factorization. While in the second component, we first learn the distributed representation of each news article. We then use the historical data of the user in a sequential manner and feed it to an attention-based recurrent layer. Finally, we concatenate the outputs from both these components and use further hidden layers in order to make predictions. In this way, we harness the information present in the user reading history and boost it with the information available through collaborative filtering for providing better news recommendations. Extensive experiments over two real-world datasets show that the proposed model provides significant improvement over the state-of-the-art.

Abstract

Abstract. We propose a new long video dataset 1 (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.

Abstract

We present an online interactive tool1 that generates titles of blog titles and thus take the first step toward automating science journalism. Science journalism aims to transform jargon-laden scientific articles into a form that the common reader can comprehend while ensuring that the underlying meaning of the article is retained. In this work, we present a tool, which, given the title and abstract of a research paper will generate a blog title by mimicking a human science journalist. The tool makes use of a model trained on a corpus of 87, 328 pairs of research papers and their corresponding blogs, built from two science news aggregators. The architecture of the model is a two stage mechanism which generates blog titles. Evaluation using standard metrics indicate the viability of the proposed system.

Abstract

The internet has transformed the way we communicate ideas and share knowledge. Online discussion forums (ODFs) play an important role by providing a platform for the internet users to interact. There exist numerous ODFs where participants (ie the agents) can post the reviews of events, restaurants, movies, books, services, and so on. Such online forums are also commonly used as a web-based discussion platform to discuss academic topics, personal experiences, views, etc. We are motivated by forums such as Quora, Stack Exchange, Reddit, WikiAnswers, and Yahoo! Answers, which provide a platform for open-ended discussions, and by platforms such as Piazza and iClicker which supplement traditional classroom teaching. These forums are also used by massive online

Abstract

Civic crowdfunding (CC) is a practice with which interested players can raise funds for a civic project. With Blockchains gaining traction, CC can be implemented in a reliable, transparent and secure manner with smart contracts. The fundamental challenge in CC is free riding. PPR, the proposal by [Zubrickas, 2014] of refund bonus to the contributors in the case of the project not getting provisioned has interesting properties. As observed by [Chandra et al, 2016], PPR faces a challenge of race condition. To address this, their proposal, PPS considers the temporal aspects of a contribution. However, PPS is computationally complex and is difficult to explain to a layperson. In this work, we look for all important properties a refund bonus scheme must satisfy in order to discourage free riding while avoiding the race condition. We identify Contribution Monotonicity and Time Monotonicity as sufficient conditions for this. We propose three refund bonus schemes satisfying these two conditions leading to three novel mechanisms for CC-PPRG, PPRE, and PPRP. We show that PPRG is the most cost-effective mechanism when deployed as a smart contract. We then prove that under certain assumptions on valuations of the players, in PPRG, the project is funded at equilibrium

Abstract

Machine learning models are extensively being used in decision making, especially for prediction tasks. These models could be biased or unfair towards a specific sensitive group either of a specific race, gender or age. Researchers have put efforts into characterizing a particular definition of fairness and enforcing them into the models. In this work, mainly we are concerned with the following three definitions, Disparate Impact, Demographic Parity and Equalized Odds. Researchers have shown that Equalized Odds cannot be satisfied in calibrated classifiers unless the classifier is perfect. Hence the primary challenge is to ensure a degree of fairness while guaranteeing as much accuracy as possible. Fairness constraints are complex and need not be convex. Incorporating them into a machine learning algorithm is a significant challenge. Hence, many researchers have tried to come up with a surrogate loss which is convex in order to build fair classifiers. Besides, certain papers try to build fair representations by preprocessing the data, irrespective of the classifier used. Such methods, not only require a lot of unrealistic assumptions but also require human engineered analytical solutions to build a machine learning model. We instead propose an automated solution which is generalizable over any fairness constraint. We use a neural network which is trained on batches and directly enforces the fairness constraint as the loss function without modifying it further. We have also experimented with other complex performance measures such as H-mean loss, Q-mean-loss, F-measure; without the need for any surrogate loss functions. Our experiments …

Abstract

An important aspect of understanding narrative text is identification of actors, its mentions and coreferences among them. Coreference Resolution in Hindi is a relatively under-explored area. In this paper, we focus on the task of resolving coreferences of actor mentions in Hindi narrative text. We propose a linguistically grounded approach for the task using Markov Logic Networks (MLN). Our approach outperforms two strong baselines on a publicly available dataset and 4 other manually created datasets.

Abstract

This paper traces the nature of resistance in movements against land acquisition in Jagatsinghpur and Niyamgiri in the state of Odisha in India. At both sites, the movements were united in their opposition to the respective industrial projects, and to the state's notion of ‘development’ premised on resource extraction and large industry. At certain key junctures, the Niyamgiri resistance placed stress on the ‘sacred’ and the spiritual, while the resistance in Jagatsinghpur emphasised ‘traditional’ land- and water-based economic livelihoods. This paper uses these case studies to understand the role of overt and covert ‘performances’ of indigeneity in shaping environmental discourse as well as the state's response to resistance movements.

Abstract

A new method of obtaining exact solution for the rate matrix R in the Matrix-Analytic method in case of the phase state of dimension two is proposed. The method is based on symbolic solution of the determinental polynomial equation, and obtaining a linear matrix equation for the unknown rate matrix R by Cayley–Hamilton theorem. The method is applied to analyze the EnergyPerformance tradeoff of an Internet-of-Things device. A new randomized regime switching scheme is proposed, which, as it is shown by means of numerical experiment, provides significant decrease of energy consumption of the system under study.