Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99


Any time

2017 ( 2 )

2016 ( 2 )

2015 ( 3 )

2014 ( 17 )

Custom range...

Search Results: 1 - 10 of 220 matches for " Smriti Bhagat "
All listed articles are free for downloading (OA Articles)
Page 1 /220
Display every page Item
International Journal of Microbiology Research , 2011,
Abstract: Solid-state fermentation for lipase production from Rhizopus oryzae KG-10, using different low cost available oilcakes JOC (Jatropha oil cake), TOC (Teesi oil cake), MOC (Mustard oil cake), GOC (Groundnut oil cake) was carried and itwas found that the fungus produced significant amount of lipase utilizing oil cakes as substrate. Among the four substratesused crude enzyme extracted from MOC medium showed highest activity of 170 IU. Activity of enzyme extracted from mediumcontaining JOC, TOC and GOC were assayed to be 80 IU, 60 IU and 60 IU respectively. Total protein of the crude enzymeextracted from the different medium was estimated by Lowry’s method. Total protein content of extracts from MOC, JOC, TOCand GOC medium were 32 mg/ml, 30 mg/ml, 31.2 mg/ml and 26.4 mg/ml respectively. Thus it could be seen that in aboutsame amount of extracellular proteins the activity was maximum in case of MOC, suggesting it to be the best substrate
Node Classification in Social Networks
Smriti Bhagat,Graham Cormode,S. Muthukrishnan
Computer Science , 2011, DOI: 10.1007/978-1-4419-8462-3_5
Abstract: When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled. These labels can indicate demographic values, interest, beliefs or other characteristics of the nodes (users). A core problem is to use this information to extend the labeling so that all nodes are assigned a label (or labels). In this chapter, we survey classification techniques that have been proposed for this problem. We consider two broad categories: methods based on iterative application of traditional classifiers using graph information as features, and methods which propagate the existing labels via random walks. We adopt a common perspective on these methods to highlight the similarities between different approaches within and across the two categories. We also describe some extensions and related directions to the central problem of node classification.
Finding Heavy Paths in Graphs: A Rank Join Approach
Mohammad Khabbaz,Smriti Bhagat,Laks V. S. Lakshmanan
Computer Science , 2011,
Abstract: Graphs have been commonly used to model many applications. A natural problem which abstracts applications such as itinerary planning, playlist recommendation, and flow analysis in information networks is that of finding the heaviest path(s) in a graph. More precisely, we can model these applications as a graph with non-negative edge weights, along with a monotone function such as sum, which aggregates edge weights into a path weight, capturing some notion of quality. We are then interested in finding the top-k heaviest simple paths, i.e., the $k$ simple (cycle-free) paths with the greatest weight, whose length equals a given parameter $\ell$. We call this the \emph{Heavy Path Problem} (HPP). It is easy to show that the problem is NP-Hard. In this work, we develop a practical approach to solve the Heavy Path problem by leveraging a strong connection with the well-known Rank Join paradigm. We first present an algorithm by adapting the Rank Join algorithm. We identify its limitations and develop a new exact algorithm called HeavyPath and a scalable heuristic algorithm. We conduct a comprehensive set of experiments on three real data sets and show that HeavyPath outperforms the baseline algorithms significantly, with respect to both $\ell$ and $k$. Further, our heuristic algorithm scales to longer lengths, finding paths that are empirically within 50% of the optimum solution or better under various settings, and takes only a fraction of the running time compared to the exact algorithm.
Leveraging Side Observations in Stochastic Bandits
Stephane Caron,Branislav Kveton,Marc Lelarge,Smriti Bhagat
Computer Science , 2012,
Abstract: This paper considers stochastic bandits with side observations, a model that accounts for both the exploration/exploitation dilemma and relationships between arms. In this setting, after pulling an arm i, the decision maker also observes the rewards for some other actions related to i. We will see that this model is suited to content recommendation in social networks, where users' reactions may be endorsed or not by their friends. We provide efficient algorithms based on upper confidence bounds (UCBs) to leverage this additional information and derive new bounds improving on standard regret guarantees. We also evaluate these policies in the context of movie recommendation in social networks: experiments on real datasets show substantial learning rate speedups ranging from 2.2x to 14x on dense networks.
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization
Smriti Bhagat,Udi Weinsberg,Stratis Ioannidis,Nina Taft
Computer Science , 2013,
Abstract: Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of initiative in filling out their online profiles. We illustrate a new threat in which a recommender learns private attributes of users who do not voluntarily disclose them. We design both passive and active attacks that solicit ratings for strategically selected items, and could thus be used by a recommender system to pursue this hidden agenda. Our methods are based on a novel usage of Bayesian matrix factorization in an active learning setting. Evaluations on multiple datasets illustrate that such attacks are indeed feasible and use significantly fewer rated items than static inference methods. Importantly, they succeed without sacrificing the quality of recommendations to users.
The Shapley Value in Knapsack Budgeted Games
Smriti Bhagat,Anthony Kim,S. Muthukrishnan,Udi Weinsberg
Computer Science , 2014,
Abstract: We propose the study of computing the Shapley value for a new class of cooperative games that we call budgeted games, and investigate in particular knapsack budgeted games, a version modeled after the classical knapsack problem. In these games, the "value" of a set $S$ of agents is determined only by a critical subset $T\subseteq S$ of the agents and not the entirety of $S$ due to a budget constraint that limits how large $T$ can be. We show that the Shapley value can be computed in time faster than by the na\"ive exponential time algorithm when there are sufficiently many agents, and also provide an algorithm that approximates the Shapley value within an additive error. For a related budgeted game associated with a greedy heuristic, we show that the Shapley value can be computed in pseudo-polynomial time. Furthermore, we generalize our proof techniques and propose what we term algorithmic representation framework that captures a broad class of cooperative games with the property of efficient computation of the Shapley value. The main idea is that the problem of determining the efficient computation can be reduced to that of finding an alternative representation of the games and an associated algorithm for computing the underlying value function with small time and space complexities in the representation size.
Modeling Non-Progressive Phenomena for Influence Propagation
Vincent Yun Lou,Smriti Bhagat,Laks V. S. Lakshmanan,Sharan Vaswani
Computer Science , 2014,
Abstract: Recent work on modeling influence propagation focus on progressive models, i.e., once a node is influenced (active) the node stays in that state and cannot become inactive. However, this assumption is unrealistic in many settings where nodes can transition between active and inactive states. For instance, a user of a social network may stop using an app and become inactive, but again activate when instigated by a friend, or when the app adds a new feature or releases a new version. In this work, we study such non-progressive phenomena and propose an efficient model of influence propagation. Specifically, we model in influence propagation as a continuous-time Markov process with 2 states: active and inactive. Such a model is both highly scalable (we evaluated on graphs with over 2 million nodes), 17-20 times faster, and more accurate for estimating the spread of influence, as compared with state-of-the-art progressive models for several applications where nodes may switch states.
Privacy Tradeoffs in Predictive Analytics
Stratis Ioannidis,Andrea Montanari,Udi Weinsberg,Smriti Bhagat,Nadia Fawaz,Nina Taft
Computer Science , 2014,
Abstract: Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data. Can a privacy-conscious user benefit from personalization while simultaneously protecting her private attributes? We study this question in the context of a rating prediction service based on matrix factorization. We construct a protocol of interactions between the service and users that has remarkable optimality properties: it is privacy-preserving, in that no inference algorithm can succeed in inferring a user's private attribute with a probability better than random guessing; it has maximal accuracy, in that no other privacy-preserving protocol improves rating prediction; and, finally, it involves a minimal disclosure, as the prediction accuracy strictly decreases when the service reveals less information. We extensively evaluate our protocol using several rating datasets, demonstrating that it successfully blocks the inference of gender, age and political affiliation, while incurring less than 5% decrease in the accuracy of rating prediction.
Manik Sharma,Smriti Smriti
International Journal of Computers & Technology , 2012,
Abstract: Parallel processing is a technique of executing the multiple tasksconcurrently on different processors. Parallel processing is usedto solve the complex problems that require vast amount ofprocessing time. Task scheduling is one of the major problemsof parallel processing. The objective of this study is to analyzethe performance of static (HLFET) and dynamic (DLS) BNPparallel scheduling algorithm for allocating the tasks ofdistributed database over number of processors. In the wholestudy the focus will be given on measuring the impact ofnumber of processors on different metrics of performance likemakespan, speed up and processor utilization by using HLFETand DLS BNP task scheduling algorithms.
Comparative Analysis of Image Enhancement Techniques for Ultrasound Liver Image
Smriti Sahu
International Journal of Electrical and Computer Engineering , 2012, DOI: 10.11591/ijece.v2i6.1513
Abstract: Liver cancer is the sixth most common malignant tumour in the world and the third most common cause of cancer-related deaths worldwide. To diagnose such liver diseases, In this paper comparison has been made for various image enhancement techniques that are applied to liver ultrasound image. Three types of liver ultrasound images used are normal, benign and malignant liver images. The techniques, which are compared on the basis of two evaluation parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) including, Contrast Stretching, Shock Filter, Histogram Equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE).Such a comparison would be helpful in determining the best suited method for clinical diagnosis. It also has been observed that the Shock filter gives the better performance than others for liver ultrasonic image analysis.
Page 1 /220
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.