به نام خدا
Title: Menger’s theorem for fuzzy graphs
Authors: Sunil Mathew, MS Sunitha
Abstract: The concept of the strongest path plays a crucial role in fuzzy graph theory. In classical graph theory, all paths in a graph are strongest, with a strength value of one. In this article, we introduce Menger’s theorem for fuzzy graphs and discuss the concepts of strength- reducing sets and t-connected fuzzy graphs. We also characterize t-connected and t-arc connected fuzzy graphs.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: نظریه گراف (Graph Theory)– منطق فازی (Fuzzy Logic)
ایران سای – مرجع مقالات علمی فنی مهندسی
حامی دانش بومی ایرانیان
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: A survey of skyline processing in highly distributed environments
Authors: Katja Hose Akrivi Vlachou
Abstract: During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach have to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.
Publish Year: 2012
Published in: The VLDB Journal – Springer
موضوع: پردازش توزیع شده (Distributed Processing)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Efficient stochastic algorithms for document clustering
Authors: Rana Forsati, Mehrdad Mahdav, Mehrnoush Shamsfard, Mohammad Reza Meybodi
Abstract: Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Addition- ally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document data- sets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: الگوریتمهای تکاملی (Evolutionary Algorithms)- (Stochastic Algorithms)
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
