به نام خدا
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)
لینک مشاهده مقاله در سایت ناشر
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
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)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Using the idea of the sparse representation to perform coarse- to-fine face recognition
Authors: Yong Xu , Qi Zhu a , Zizhu Fan , David Zhang d , Jianxun Mi a , Zhihui Lai
Abstract: In this paper, we propose a coarse-to-fine face recognition method. This method consists of two stages and works in a similar way as the well-known sparse representation method. The first stage determines a linear combination of all the training samples that is approximately equal to the test sample. This stage exploits the determined linear combination to coarsely determine candidate class labels of the test sample. The second stage again deter- mines a weighted sum of all the training samples from the candidate classes that is approximately equal to the test sample and uses the weighted sum to perform classification. The rationale of the proposed method is as follows: the first stage identifies the classes that are far from the test sample and removes themfromthe set of the training samples. Then the method will assign the test sample into one of the remaining classes and the classification problem becomes a simpler one with fewer classes. The proposed method not only has a high accuracy but also can be clearly interpreted.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: شناسایی چهره (Face Detection)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
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/forwardpre�xes. 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 con�rma-tion message, which may be followed, possibly after a fewdays, by a shipment noti�cation 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)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: A logical approach to fuzzy truth hedges
Authors: Francesc Esteva, Llus Godo, Carles Noguera
Abstract: The starting point of this paper are the works of H jek and Vychodil on the axiomatization of truth-stressing and-depressing hedges as expansions of H jek s BL logic by new unary con- nectives. They showed that their logics are chain-complete, but standard completeness was only proved for the expansions over G del logic. We propose weaker axiomatizations over an arbitrary core fuzzy logic which have two main advantages: (i) they preserve the standard completeness properties of the original logic and (ii) any subdiagonal (resp. super- diagonal) non-decreasing function on [0, 1] preserving 0 and 1 is a sound interpretation of the truth-stresser (resp. depresser) connectives. Hence, these logics accommodate most of the truth hedge functions used in the literature about of fuzzy logic in a broader sense.
Publish Year: 2013
Publisher: Information Sciences - Science Direct
موضوع: منطق فازی
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
