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

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

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

This paper analyzes, compares and looks at tradeoffs in different load distribution schemes for consistent hashing in DHTs(distributed hash tables). Different traffic patterns, including an adversarial pattern, were constructed to test the load distribution. A simulator was made for each load distribution scheme and the load on each node was recorded for each traffic pattern. It was shown that increasing the number of consistent hash rings doesn't change the load distribution characteristics of the DHT in simple traffic scenarios, but in the case of adversarial traffic, increasing the number of hash rings leads to an improvement of around 15% in the load distribution statistics

Abstract

In this work we propose a novel representation learning model which computes semantic representations for tweets accurately. Our model systematically exploits the chronologically adjacent tweets ('context') from users' Twitter timelines for this task. Further, we make our model user-aware so that it can do well in modeling the target tweet by exploiting the rich knowledge about the user such as the way the user writes the post and also summarizing the topics on which the user writes. We empirically demonstrate that the proposed models outperform the state-of-the-art models in predicting the user profile attributes like spouse, education and job by 19.66%, 2.27% and 2.22% respectively.

Abstract

Graph-OLAP is an online analytical framework which allows us to obtain various projections of a graph, each of which helps us view the graph along multiple dimensions and multiple levels. Given a series of snapshots of a temporal heterogeneous graph, we aim to find interesting projections of the graph which have anomalous evolutionary behavior. Detecting anomalous projections in a series of such snapshots can be helpful for an analyst to understand the regions of interest from the temporal graph. Identifying such semantically related regions in the graph allows the analyst to derive insights from temporal graphs which enables her in making decisions. While most of the work on temporal outlier detection is performed on nodes, subgraphs and communities, we are the first to propose detection of evolutionary graph cuboid outliers. Further, we perform this detection in a query sensitive manner. Thus, an evolutionary graph cuboid outlier is a projection (or cuboid) of a snapshot of the temporal graph such that it contains an unexpected number of matches for the query with respect to other cuboids both in the same snapshot as well as in the other snapshots. Identifying such outliers is challenging because (1) the number of cuboids per snapshot could be large, and (2) number of snapshots could itself be large. We model the problem by predicting the outlier score for each cuboid in each snapshot. We propose to build subspace ensemble regression models to learn (a) the behavior of a cuboid across different snapshots, and (b) the behavior of all the cuboids in a given snapshot. Experimental results on both synthetic and real datasets show the effectiveness of the proposed algorithm in discovering evolutionary graph cuboid outliers.

Abstract

Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. In this paper, we introduce learning sub-word level representations in our LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in our architecture enables us to learn the information about sentiment value of important morphemes. This also seems to work well in highly noisy text containing misspellings as shown in our experiments which is demonstrated in morpheme-level feature maps learned by our model. Also, we hypothesize that encoding this linguistic prior in the Subword-LSTM architecture leads to the superior performance. Our system attains accuracy 4-5% greater than traditional approaches on our dataset, and also outperforms the available system for sentiment analysis in Hi-En code-mixed text by 18%.

Abstract

Research in social media analysis is experiencing a recent surge with a large number of works applying representation learning models to solve high-level syntactico-semantic tasks such as sentiment analysis, semantic textual similarity computation, hashtag prediction and so on. Although the performance of the representation learning models are better than the traditional baselines for the tasks, little is known about the core properties of a tweet encoded within the representations. Understanding these core properties would empower us in making generalizable conclusions about the quality of representations. Our work presented here constitutes the first step in opening the black-box of vector embedding for social media posts, with emphasis on tweets in particular. In order to understand the core properties encoded in a tweet representation, we evaluate the representations to estimate the extent to which it can model each of those properties such as tweet length, presence of words, hashtags, mentions, capitalization, and so on. This is done with the help of multiple classifiers which take the representation as input. Essentially, each classifier evaluates one of the syntactic or social properties which are arguably salient for a tweet. This is also the first holistic study on extensively analysing the ability to encode these properties for a wide variety of tweet representation models including the traditional unsupervised methods (BOW, LDA), unsupervised representation learning methods (Siamese CBOW, Tweet2Vec) as well as supervised methods (CNN, BLSTM).

Abstract

The problem of providing optimal assignment for backend storage is a central problem in the design of cloud systems. It has taken a further central role as a result of growing heterogeneity from emerging Software Defined Storage systems. In this paper, we propose a solution to optimal IO Workload assignment using statistical modelling to estimate measures of performance such as Throughput, IOPS, et al. The proposed system uses support vector regression to estimate the performance of individual IO Workloads on each available SDS system for optimal assignment. As a proof of concept, we demonstrate our solution in a heterogeneous environment comprising of HDFS, GlusterFS, and Ceph. We first show the accuracy of estimation of throughput and IOPS with values of coefficient of determination over 0.65 in all cases. We further show the analysis of using this regression model to classify workloads to respective SDS backend that will maximize throughput.

Abstract

With improving efficiency and cost effectiveness of public cloud systems, there has been a growing trend to complement in-house cloud environment with them. We propose an open solution to the problem of distributing jobs in a hybrid environment, as a substitute to hadoop systems which are cloud specific. The proposed system, MultiStack, is a big data orchestration platform for deploying big data jobs across multiple cloud providers. The specific architecture elaborated in this paper uses Amazon Web Services as the public cloud provider and Openstack as the private cloud framework. Our solution supports complete Hadoop ecosystem tools - hive, pig, Hbase, oozie etc. and on-demand scaling of Hadoop clusters. The proposed framework aims at reducing the Job completion time on workloads along with decrease in cost using Spot Instance provisioning compared to on-demand provisioning. This is achieved by providing two modes of operation: Proactive scheduling and Reactive scheduling, which takes into account user providing job characteristics(eg memory, cpu, etc), quota limitation and business objectives. From our experiments, we conclude that Multistack is able to reduce average job completion time by 30-37% with minimal increase in cost.