Inductive Data Flow Graphs

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Title: Inductive Data Flow Graphs

Authors: Azadeh Farzan Zachary Kincaid, Andreas Podelski

Abstract: The correctness of a sequential program can be shown by the annotation of its control flow graph with inductive assertions. We pro-pose inductive data flow graphs, data flow graphs with incorporated inductive assertions, as the basis of an approach to verifying concurrent programs. An inductive data flow graph accounts for a set of dependencies between program actions in interleaved thread executions, and therefore stands as a representation for the set of concurrent program traces which give rise to these dependencies. The approach first constructs an inductive data flow graph and then checks whether all program traces are represented. The size of the inductive data flow graph is polynomial in the number of data dependencies (in a sense that can be made formal); it does not grow exponentially in the number of threads unless the data dependencies do. The approach shifts the burden of the exponential explosion towards the check whether all program traces are represented, i.e., to a combinatorial problem (over finite graphs).   

Publish Year: 2013

Published in: ACM-SIGPLAN-SIGACT

Number of Pages: 14

موضوع: نظریه گراف (Graph Theory)

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Visualizing Uncertainty in Multi-resolution Volumetric Data Usin

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Title: Visualizing Uncertainty in Multi-resolution Volumetric Data Using Marching Cubes

Authors: J Ma,D Murphy,M Hayes,G Provan

Abstract: Data sets acquired from complex scientific simulation, high precision engineering experiment and high-speed computer network have been exponentially increased, and visualization and analysis of such large-scale of data sets have been identified as a significant challenge to the visualization com-munity. Over the past years many scientists have made at-tempt to address this problem by proposing various data reduction techniques. Consequently the size of data can be reduced and issues associated to the visualization can be improved (e.g. real-time interaction and visual overload).However, during the process of data reduction, the information of original data sets was approximated and potential errors were introduced. It leads to a new problem with regard to the integrity of the data and might mislead users for incorrect decision making. Therefore in this paper we aim to solve the problem by introducing three novel uncertainty visualization methods, which depict both the multi-resolution(MR) approximations of the original data set and the errors associated with each of its low resolution representations. As a result we faithfully represent the MR data sets and allow users to make suitable decisions from the visual output. We applied our techniques on a data set from medical domain to demonstrate their effectiveness and usability.   

Publish Year: 2012

Published in: AVI - ACM

Number of Pages: 8

موضوع: مصورسازی داده ها (Data Visualization)

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Tell Me More? The Effects of Mental Model Soundness on Personal

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Title: Tell Me More? The Effects of Mental Model Soundness on Personalizing an Intelligent Agent

Authors: Todd Kulesza, Simone Stumpf, Margaret Burnett, Irwin Kwan

Abstract: What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agents personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system’s reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system’s reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user’s intentions.   

Publish Year: 2012

Published in: CHI – ACM

موضوع: عاملهای هوشمند (Intelligent Agents) ، هوش مصنوعی (Artificial Intelligence)

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Sharding Social Networks

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Title: Sharding Social Networks

Authors: Quang Duong, Sharad Goel, Jake Hofman, Sergei Vassilvitskii

Abstract: Online social networking platforms regularly support hun-dreds of millions of users, who in aggregate generate sub-stantially more data than can be stored on any single phys-ical server. As such, user data are distributed, or sharded,across many machines. A key requirement in this setting israpid retrieval not only of a given user_s information, butalso of all data associated with his or her social contacts,suggesting that one should consider the topology of the so-cial network in selecting a sharding policy. In this paperwe formalize the problem of efficiently sharding large so-cial network databases, and evaluate several sharding strate-gies, both analytically and empirically. We find that randomsharding-the de facto standard-results in provably poorperformance even when frequently accessed nodes are repli-cated to many shards. By contrast, we demonstrate that onecan substantially reduce querying costs by identifying andassigning tightly knit communities to shards. In particular,our theoretical analysis motivates a novel, scalable shardingalgorithm that outperforms both random and location-basedsharding schemes.   

Publish Year: 2013

Published by: ACM-WSDM

موضوع: شبکه های اجتماعی (Social Netwroks)

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Threading Machine Generated Email

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Title: Threading Machine Generated Email

Authors: Nir Ailon, Zohar S Karnin, Edo Liberty, Yoelle Maarek

Abstract: Viewing email messages as parts of a sequence or a thread isa convenient way to quickly understand their context. Cur-rent threading techniques rely on purely syntactic methods,matching sender information, subject line, and reply/forwardprefixes. As such, they are mostly limited to personal con-versations. In contrast, machine-generated email, whichamount, as per our experiments, to more than 60% of theoverall email traffic, requires a different kind of threadingthat should reflect how a sequence of emails is caused bya few related user actions. For example, purchasing goodsfrom an online store will result in a receipt or a confirma-tion message, which may be followed, possibly after a fewdays, by a shipment notification message from an expressshipping service. In today_s mail systems, they will not bea part of the same thread, while we believe they should.In this paper, we focus on this type of threading that wecoin “causal threading”. We demonstrate that, by analyzingrecurring patterns over hundreds of millions of mail users,we can infer a causality relation between these two indi-vidual messages. In addition, by observing multiple causalrelations over common messages, we can generate “causalthreads” over a sequence of messages. The four key stagesof our approach consist of: (1) identifying messages that areinstances of the same email type or“template” (generated bythe same machine process on the sender side) (2) building acausal graph, in which nodes correspond to email templatesand edges indicate potential causal relations (3) learning acausal relation prediction function, and (4) automatically“threading” the incoming email stream. We present detailedexperimental results obtained by analyzing the inboxes of12.5 million Yahoo! Mail users, who voluntarily opted-in forsuch research. Supervised editorial judgments show thatwe can identify more than 70% (recall rate) of all “causalthreads”at a precision level of 90%. In addition, for a searchscenario we show that we achieve a precision close to 80%at 90% recall. We believe that supporting causal threads inPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.   

Publish Year: 2013

Publisher: ACM-WSDM

موضوع: یادگیری ماشین (Machine Learning)

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