26,048 research outputs found

    Reason Maintenance - State of the Art

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    This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques

    Social Preferences and the Third Sector: Looking for a Microeconomic Foundation of the Local Development Path

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    The aim of the paper is to endorse the principle, recurrent in non-profit literature, that the third sector is an institution that supports the development process of economic systems. The third sector is considered as an institution that âÃÂÃÂfavors, transmits and cementsâÃÂàthe role of social preferences in a given economy and, in this way, it contributes to development. The paper thus considers two stances taken up in economic theory: (i) the theory of social preferences; (ii) the modern theory of development. These two stances do not exclusively and specifically refer to the third sector, and they generally follow parallel paths, rarely being aware of each other: in the paper, the third sector is assumed to form a bridge between them in that social preferences are supposed to be one of the driving forces in the change process of an economy.endogenous social preferences; third sector; local development

    Deep Learning based Recommender System: A Survey and New Perspectives

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    With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

    People on Drugs: Credibility of User Statements in Health Communities

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    Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

    A typology of propagation of technology and social preferences in the process of economic development: An input-output approach

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    In this paper we look at the interplay of technology and social preferences in different stages of economic development. We use a set of input-output tables for 32 different countries, published by OECD. The tables refer to the period 1996-2001 and were consolidated in 48 sectors so that structural comparisons were possible. Through the use of the fields of influence of structural change for partitioned input-output systems, we confirm that, for different levels of per capita GDP, technological progress is an important element to drive output growth. However, as an economy evolves, our dataset also confirm that the composition of final demand, which reveals social preferences in a static way, move away from agricultural and manufacturing to services activities. Such structural changes favor sectors with weaker output multipliers generating a force that helps driving income convergence among countries.Social Preferences; Economic Development; Input-Output
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