Abstract

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin’s security lies in a proof-of-work scheme, which requires high computational resources at the miners. Despite advances in mobile technology, existing cryptocurrencies cannot be maintained by mobile devices due to their low processing capabilities. Mobile devices can only accommodate mobile applications (wallets) that allow users to exchange credits of cryptocurrencies. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin’s security with the social hardness of ensuring that all witnesses to a transaction are colluders. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed blockchain. We analyze LocalCoin for double spending for passive and active attacks and prove that under the assumption of sufficient number of users and properly selected tuning parameters the probability of double spending is close to zero. Extensive simulations on real mobility traces, realistic urban settings, and random geometric graphs show that the probability of success of one transaction converges to 1 and the probability of the success of a double spending attempt converges to 0.

Abstract

This paper explores Thompson sampling in the context of mechanism design for stochastic multi-armed bandit (MAB) problems. The setting is that of an MAB problem where the reward distribution of each arm consists of a stochastic component as well as a strategic component. Many existing MAB mechanisms use upper confidence bound (UCB) based algorithms for learning the parameters of the reward distribution. The randomized nature of Thompson sampling introduces certain unique, non-trivial challenges for mechanism design, which we address in this paper through a rigorous regret analysis. We first propose a MAB mechanism with deterministic payment rule, namely, TSM-D. We show that in TSMD, the variance of agent utilities asymptotically approaches zero. However, the game theoretic properties satisfied by TSM-D (incentive compatibility and individual rationality with high probability) are rather weak. As our main contribution, we then propose the mechanism TSM-R, with randomized payment rule, and prove that TSM-R satisfies appropriate, adequate notions of incentive compatibility and individual rationality. For TSM-R, we also establish a theoretical upper bound on the variance in utilities of the agents. We further show, using simulations, that the variance in social welfare incurred by TSM-D or TSM-R is much lower when compared to that of existing UCB based mechanisms. We believe this paper establishes Thompson sampling as an attractive approach to be used in MAB mechanism design.

Abstract

Automatic categorization of computer science research papers using just the abstracts, is a hard problem to solve. This is due to the short text length of the abstracts. Also, abstracts are a general discussion of the topic with few domain specific terms. These reasons make it hard to generate good representations of abstracts which in turn leads to poor categorization performance. To address this challenge, external Knowledge Bases (KB) like Wikipedia, Freebase etc. can be used to enrich the representations for abstracts, which can aid in the categorization task. In this work, we propose a novel method for enhancing classification performance of research papers into ACM computer science categories using knowledge extracted from related Wikipedia articles and Freebase entities. We use state-of-the-art representation learning methods for feature representation of documents, followed by learning to rank method for classification. Given the abstracts of research papers from the Citation Network Dataset containing 0.24 M papers, our method of using KB, outperforms a baseline method and the stateof-the-art deep learning method in classification task by 13.25% and 5.41% respectively, in terms of accuracy. We have also open-sourced the implementation of the project4.

Abstract

We describe our submission at TAC 2017 for extracting entities and relations of interest from drug labels. We employ an end-to-end relation extraction system which jointly extracts both entities and relations. The task of end-to-end relation extraction consists of identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Based on our earlier work (Pawar et al., 2017), a single neural network model (“All Word Pairs” model ie AWP-NN) is trained to assign an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. Moreover, we build an ensemble of multiple AWPNN models to achieve better performance than the individual models. We achieved 73.18% and 24.79% F-measures for entity and relation extraction, respectively.

Abstract

The purpose of this research is to address the issue of enhancing the Quality of Service(QoS) in IP networks, to make it more optimal in a given network of networks using a novel approach. In this approach, QoS is provided to the packet even when the packet crosses its access network into the backbone. It proposes a new design or set of features in the routers which maps and translates the QoS provisioning from one network to the other. This paper targets at making the quality of service more dynamic by attempting to provide QoS not just at ingress or egress routers but also in the backbone network. In addition, it discusses the economic aspects for upgraded network infrastructure and also comments on the scenarios in which the proposed schema would be helpful along with the placement of dynamic QoS aware routers in the network of networks.

Abstract

Developers need to comprehend a program before modifying it. In such cases,developers use cues to establish the relevance of a piece of information with a task. Beingfamiliar with different kinds of cues will help the developers to comprehend a programfaster. But unlike the experienced developers, novice developers fail to recognize therelevant cues, because (a) there are many cues and (b) they might be unfamiliar with theartifacts. However, not much is known about the developers’ choice of cue. Hence, weconducted two independent studies to understand the kind of cues used by the developersand how a tool influences their cue selection. First, from our user study on two commoncomprehension tasks, we found that developers actively seek the cues and their cue sourcechoices are similar but task dependent. In our second exploratory study, we investigatedwhether an integrated development environment (IDE) influences a developer’s cuechoices. By observing their interaction history while fixing bugs in Eclipse IDE, we foundthat the IDE’s influence on their cue choices was not statistically significant. Finally, as acase in point, we propose a novel task-specific program summarization approach to aidnovice developers in comprehending an unfamiliar program. Our approach used devel-opers cue choices to synthesize the summaries. A comparison of the synthesized sum-maries with the summaries recorded by the developers shows both had similar content.This promising result encourages us to explore task-specific cue models, which can aidnovice developers to accomplish complex comprehension tasks faster

Abstract

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by~ 18 F1 points.

Abstract

Scientific article recommendation problem deals with recommending similar scientific articles given a query article. It can be categorized as a content based similarity system. Recent advancements in representation learning methods have proven to be effective in modeling distributed representations in different modalities like images, languages, speech, networks etc. The distributed representations obtained using such techniques in turn can be used to calculate similarities. In this paper, we address the problem of scientific paper recommendation through a novel method which aims to combine multimodal distributed representations, which in this case are: 1. distributed representations of paper's content, and 2. distributed representation of the graph constructed from the bibliographic network. Through experiments we demonstrate that our method outperforms the state-of-the-art distributed representation methods in text and graph, by 29.6% and 20.4%, both in terms of precision and mean-average-precision respectively.

Abstract

Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc. Although the performance of the representation learning models has been better than the traditional baselines for such tasks, little is known about the elementary properties of a tweet encoded within these representations, or why particular representations work better for certain tasks. Our work presented here constitutes the first step in opening the black-box of vector embeddings for tweets.

Abstract

Automated segmentation of cortical and noncortical human brain structures has been hither to approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is0.844
0.031and0.743
0.019for datasets with the least(32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.