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

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.

Abstract

In this research paper several interesting themes related to Hopfield network are explored and concrete results are derived. For instance, convergence in partial parallel mode of operation, convergence when synaptic weight matrix, W is row diagonally dominant case. Also, structured recurrent Hopfield network with companion matrix as W is studied. The dynamics when the activation function is a step function, the state space is asymmetric hypercube are also studied. Finally several experimental results are derived.

Abstract

The fifth generation (5G) networks employing mil- limeter wave (mmwave) technology will gain the advantages of short distance directive propagation and more spectrum for communication. Device-to-Device communication (D2D) when integrated with mmWave technology can reach the demand for high capacity. In this paper, resource allocation for underlay D2D in outdoor mmWave scenario is presented. We emphasize on fair distribution of resources in a cell while maximizing the spectral efficiency. Also, we discuss the aspects in ad-hoc and cellular communication using the same scheme based on the requirement. The problem is formulated mathematically to maximize sum rate and an approach for resource allocation is proposed. This work aims to achieve higher system capacity with the proposed resource allocation scheme that does not have exclusivity in resource allocation and allows more than one user to share the same resource block without degrading the system spectral efficiency. However, to reduce the complexity, an upper limit on the number of users per resource block is imposed which is adaptive to the traffic environment. Results show that the proposed scheme can significantly enhance the system performance in terms of fairness and spectral efficiency.

Abstract

Fifth generation (5G) cellular networks would include millimeter wave (mmWave) for communication which gains the advantage of extensive frequency reuse and directive propagation. The recent trends in device-to-device (D2D) communication have escalated the importance of proximity based services for next generation cellular systems. The integration of mmWave and D2D technology will improve the performance and spectral efficiency greatly. In areas with high call traffic density, D2D communication can operate in overlay mode giving the advantage of low complexity interference avoidance technique. The complexity is decreased as the resources to overlay mode are dedicated resulting in no interference from cellular user unlike in underlay mode. Such a scenario is considered in this paper where interference management between D2D devices sharing same radio resources plays a key role in system performance. Thus, the inband resources for D2D operation selectively switch to overlay mode in based on the call traffic. Two problems are addressed in this paper. Firstly, assigning the resources for overlay mode which is solved using quadratic programming. Next, the problem is formulated to maximize system capacity in overlay mode which is solved using a heuristic algorithm which is simulated. The proposed scheme has optimal performance for dense network in controlled overlay operation for 5G networks. The scheme also overcomes the problem of spectrum wastage in overlay mode by its selective operation.

Abstract

This paper presents a technique for coronary artery disease (CAD) detection through photoplethysmography (PPG). This work is aimed at developing a non-invasive, inexpensive screening technique suitable for home monitoring. Time domain analysis of PPG signal and its second derivative has been carried out to extract distinguishing features. Support Vector Machine based classifier has been used to classify CAD patients. ICU patient data from MIMIC-II dataset has been used for performance evaluation. Sensitivity of 85% and specificity of 78% has been achieved for the analysed data.