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  1. RecOn: Ontology Recommendation for Structureless Queries.Anila Sahar Butt, Armin Haller & Lexing Xie - 2016 - Applied ontology 11 (4):301-324.
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  • Künstliche Intelligenz: Chancen und Risiken.Mannino Adriano, David Althaus, Jonathan Erhardt, Lukas Gloor, Adrian Hutter & Thomas Metzinger - 2015 - Diskussionspapiere der Stiftung Für Effektiven Altruismus 2:1-17.
    Die Übernahme des KI-Unternehmens DeepMind durch Google für rund eine halbe Milliarde US-Dollar signalisierte vor einem Jahr, dass von der KI-Forschung vielversprechende Ergebnisse erwartet werden. Spätestens seit bekannte Wissenschaftler wie Stephen Hawking und Unternehmer wie Elon Musk oder Bill Gates davor warnen, dass künstliche Intelligenz eine Bedrohung für die Menschheit darstellt, schlägt das KI-Thema hohe Wellen. Die Stiftung für Effektiven Altruismus (EAS, vormals GBS Schweiz) hat mit der Unterstützung von Experten/innen aus Informatik und KI ein umfassendes Diskussionspapier zu den Chancen (...)
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  • Influential Nodes in the OBOR Fossil Energy Trade Network Based on D-S Theory: Detection and Evolution Analysis.Cuixia Gao, Simin Tao, Kehu Li & Yuyang He - 2022 - Complexity 2022:1-16.
    The structure formed by fossil energy trade among countries can be divided into multiple subcommodity networks. However, the difference of coupling mode and transmission mechanism between layers of the multirelationship network will affect the measurement of node importance. In this paper, a framework of multisource information fusion by considering data uncertainty and the classical network centrality measures is build. Then, the evidential centrality indicator is proposed, by integrating Dempster–Shafer evidence theory and network theory, to empirically identify influential nodes of fossil (...)
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  • A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge.Darren J. Edwards, Ciara McEnteggart & Yvonne Barnes-Holmes - 2022 - Frontiers in Psychology 13.
    Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining (...)
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  • Link Trustworthiness Evaluation Over Multiple Heterogeneous Information Networks.Meng Wang, Xu Qin, Wei Jiang, Chunshu Li & Guilin Qi - 2021 - Complexity 2021:1-11.
    Link trustworthiness evaluation is a crucial task for information networks to evaluate the probability of a link being true in a heterogeneous information network. This task can significantly influence the effectiveness of downstream analysis. However, the performance of existing evaluation methods is limited, as they can only utilize incomplete or one-sided information from a single HIN. To address this problem, we propose a novel multi-HIN link trustworthiness evaluation model that leverages information across multiple related HINs to accomplish link trustworthiness evaluation (...)
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  • Modeling law search as prediction.Faraz Dadgostari, Mauricio Guim, Peter A. Beling, Michael A. Livermore & Daniel N. Rockmore - 2021 - Artificial Intelligence and Law 29 (1):3-34.
    Law search is fundamental to legal reasoning and its articulation is an important challenge and open problem in the ongoing efforts to investigate legal reasoning as a formal process. This Article formulates a mathematical model that frames the behavioral and cognitive framework of law search as a sequential decision process. The model has two components: first, a model of the legal corpus as a search space and second, a model of the search process that is compatible with that environment. The (...)
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  • The Emergence of Informative Higher Scales in Complex Networks.Brennan Klein & Erik Hoel - 2020 - Complexity 2020:1-12.
    The connectivity of a network contains information about the relationships between nodes, which can denote interactions, associations, or dependencies. We show that this information can be analyzed by measuring the uncertainty contained in paths along nodes and links in a network. Specifically, we derive from first principles a measure known as effective information and describe its behavior in common network models. Networks with higher effective information contain more information in the relationships between nodes. We show how subgraphs of nodes can (...)
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  • Validating Research Performance Metrics Against Peer Rankings.S. Harnad - 2008 - Ethics in Science and Environmental Politics 8 (1):103-107.
  • Gaming Google: Some Ethical Issues Involving Online Reputation Management.Jo Ann Oravec - 2013 - Journal of Business Ethics Education 10:61-81.
    Using the search engine Google to locate information linked to individuals and organizations has become part of everyday functioning. This article addresses whether the “gaming” of Internet applications in attempts to modify reputations raises substantial ethical concerns. It analyzes emerging approaches for manipulation of how personally-identifiable information is accessed online as well as critically-important international differences in information handling. Itinvestigates privacy issues involving the data mining of personally-identifiable information with search engines and social media platforms. Notions of “gaming” and “manipulation” (...)
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  • Competition-Based Benchmarking of Influence Ranking Methods in Social Networks.Alexandru Topîrceanu - 2018 - Complexity 2018:1-15.
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  • An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks.Feifan Wang, Baihai Zhang, Senchun Chai & Yuanqing Xia - 2018 - Complexity 2018:1-10.
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  • AIRank: Author Impact Ranking Through Positions in Collaboration Networks.Jun Zhang, Yan Hu, Zhaolong Ning, Amr Tolba, Elsayed Elashkar & Feng Xia - 2018 - Complexity 2018:1-16.
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  • Exploiting the Block Structure of Theweb for Computing Pagerank.Christopher Manning with Sepandar D. Kamvar, Taher H. Haveliwala & and Gene H. Golub - manuscript
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  • Computing Pagerank Using Power Extrapolation.Christopher Manning - manuscript
    We present a novel technique for speeding up the computation of PageRank, a hyperlink-based estimate of the “importance” of Web pages, based on the ideas presented in [7]. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Power Extrapolation, accelerates the convergence of the Power Method by subtracting off the error along several nonprincipal eigenvectors from the current (...)
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  • BurstBiRank: Co-Ranking Developers and Projects in GitHub with Complex Network Structures and Bursty Interactions.Dengcheng Yan, Zhen Shao, Yiwen Zhang & Bin Qi - 2020 - Complexity 2020:1-12.
    With the wide adoption of social collaborative coding, more and more developers participate and collaborate on platforms such as GitHub through rich social and technical relationships, forming a large-scale complex technical system. Like the functionalities of critical nodes in other complex systems, influential developers and projects usually play an important role in driving this technical system to more optimized states with higher efficiency for software development, which makes it a meaningful research direction on identifying influential developers and projects in social (...)
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  • What Do Technology and Artificial Intelligence Mean Today?Scott H. Hawley & Elias Kruger - forthcoming - In Hector Fernandez (ed.), Sociedad Tecnológica y Futuro Humano, vol. 1: Desafíos conceptuales. Santiago, Chile: pp. 17.
    Technology and Artificial Intelligence, both today and in the near future, are dominated by automated algorithms that combine optimization with models based on the human brain to learn, predict, and even influence the large-scale behavior of human users. Such applications can be understood to be outgrowths of historical trends in industry and academia, yet have far-reaching and even unintended consequences for social and political life around the world. Countries in different parts of the world take different regulatory views for the (...)
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