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

Word problem solving has always been a challenging task as it involves reasoning across sentences, identification of operations and their order of application on relevant operands. Most of the earlier systems attempted to solve word problems with tailored features for handling each category of problems. In this paper, we present a new approach to solve simple arithmetic problems. Through this work we introduce a novel method where we first learn a dense representation of the problem description conditioned on the question in hand. We leverage this representation to generate the operands and operators in the appropriate order. Our approach improves upon the state-of-the-art system by 3% in one benchmark dataset while ensuring comparable accuracies in other datasets.

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 looks at the responses of organised labour to the “development” project exemplified by the steel plant, the big dam and the power plant. More precisely, it analyses the presence (or not) of an ecological critique in trade union/labour responses to industrial projects. The paper explores theoretical connections between class-based politics and ecological questions. It delineates some of the specific processes and conditions which can facilitate the entry of ecological issues into the imaginations of a trade union, using the experiences of the Chhattisgarh Mukti Morcha in central India in the process. It argues that “class consciousness” is hardly a deterrent for an engagement with ecology, if one adequately understands the definitions of “class,” labour and production. In India’s specific context, the coming together of the concerns and imaginations of the industrial worker and the Adivasi peasant/farmer can facilitate this conversation between “red” and “green” imaginations.

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

During production of voiced speech, there exists impulse-like excitations due to abrupt closure of vocal folds. These impulselike excitations are often referred as epochs or glottal closure instants (GCIs). The zero frequency filtering (ZFF) method exploits the properties of impulse-like excitation by passing a speech signal through the resonator whose pole pair is located at 0 Hz. As the resonator is unstable, the polynomial growth/decay is observed in the filtered signal, thus requiring a trend removal operation. It is observed that the length of the window for trend removal operation is critical in speech signals where there are more fluctuations in the fundamental frequency (F0). In this paper, a simple finite impulse response (FIR) implementation is proposed. The FIR filter is designed by placing large number of zeros at fs 2 Hz (fs represents the sampling frequency), closer to the unit circle, in the z-plane. Experimental results show that the proposed method is robust and computationally less complex when compared to the ZFF method. Index Terms: Impulse-like excitation, ep

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.

Abstract

We describe a fully Bayesian approach to grapheme-to-phoneme conversion based on the joint-sequence model (JSM). Usually, standard smoothed n-gram language models (LM, e.g. Kneser-Ney) are used with JSMs to model graphone sequences (joint grapheme-phoneme pairs). However, we take a Bayesian approach using a hierarchical Pitman-Yor-Process LM. This provides an elegant alternative to using smoothing techniques to avoid over-training. No held-out sets and complex parameter tuning is necessary, and several convergence problems encountered in the discounted Expectation-Maximization (as used in the smoothed JSMs) are avoided. Every step is modeled by weighted finite state transducers and implemented with standard operations from the OpenFST toolkit. We evaluate our model on a standard data set (CMUdict), where it gives comparable results to the previously reported smoothed JSMs in terms of phoneme-error rate while requiring a much smaller training/testing time. Most importantly, our model can be used in a Bayesian framework and for (partly) un-supervised training.