Matches in ScholarlyData for { ?s ?p ?o. }
- item-1 hasContent hugo-zaragoza.
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- item-2 hasContent jeffrey-pound.
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- item-3 hasContent peter-mika.
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- 1065 creator c-lee-giles.
- 1065 creator daniel-kifer.
- 1065 creator jian-pei.
- 1065 creator prasenjit-mitra.
- 1065 creator qi-he.
- 1065 type InProceedings.
- 1065 label "Context-aware Citation Recommendation".
- 1065 sameAs 1065.
- 1065 abstract "When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a context-aware approach. The core idea is to design a novel non-parametric probabilistic model which can measure the context-based relevance between a citation context and a document. Our approach can recommend citations for a context effectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.".
- 1065 hasAuthorList authorList.
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- 1065 keyword "Personalization".
- 1065 keyword "recommendation systems".
- 1065 title "Context-aware Citation Recommendation".
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- item-1 hasContent qi-he.
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- item-2 hasContent jian-pei.
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- item-3 hasContent daniel-kifer.
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- item-4 hasContent prasenjit-mitra.
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- item-5 hasContent c-lee-giles.
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- 108 creator adrian-mettler.
- 108 creator akshay-krishnamurthy.
- 108 creator david-wagner.
- 108 type InProceedings.
- 108 label "Fine-Grained Privilege Separation for Web Applications".
- 108 sameAs 108.
- 108 abstract "We present a web application programming model that simplifies reasoning about security considerations and verifying security properties of web applications. In our model, applications can easily be divided into privilege-separated components, enabling rich security policies to be enforced. The model is designed to help external reviewers check these security properties. Privilege separation of web applications is accomplished by using an object-capability language and interfaces that expose limited, explicitly specified privileges to web application components. This approach restricts what each component of the application can do and quarantines buggy or compromised application code. This also provides a better way to safely integrate third-party, less trusted code into a web application. We have implemented a prototype of this model, based upon the Java Servlet framework, and used it to build a web mail application. Our experience with this example suggests that the approach is viable and helpful at establishing application-level security properties.".
- 108 hasAuthorList authorList.
- 108 isPartOf proceedings.
- 108 keyword "Fine-grained sharing".
- 108 keyword "access control".
- 108 keyword "privacy".
- 108 keyword "security".
- 108 title "Fine-Grained Privilege Separation for Web Applications".
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- item-1 hasContent akshay-krishnamurthy.
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- item-2 hasContent adrian-mettler.
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- item-3 hasContent david-wagner.
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- 109 creator kristen-lefevre.
- 109 creator lujun-fang.
- 109 type InProceedings.
- 109 label "Privacy Wizards for Social Networking Sites".
- 109 sameAs 109.
- 109 abstract "Privacy is an enormous problem in online social networking sites. While sites such as Facebook allow users fine-grained control over who can see their profiles, it is difficult for average users to specify this kind of detailed policy. In this paper, we propose a template for the design of a social networking privacy wizard. The intuition for the design comes from the observation that real users conceive their privacy preferences (which friends should be able to see which information) based on an implicit set of rules. Thus, with a limited amount of user input, it is usually possible to build a machine learning model that concisely describes a particular user's preferences, and then use this model to configure the user's privacy settings automatically. As an instance of this general framework, we have built a wizard based on an active learning paradigm called uncertainty sampling. The wizard iteratively asks the user to assign privacy "labels" to selected ("informative") friends, and it uses this input to construct a classifier, which can in turn be used to automatically assign privileges to the rest of the user's (unlabeled) friends. To evaluate our approach, we collected detailed privacy preference data from 45 real Facebook users. Our study revealed two important things. First, real users tend to conceive their privacy preferences in terms of communities, which can easily be extracted from a social network graph using existing techniques. Second, our active learning wizard, using communities as features, is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools.".
- 109 hasAuthorList authorList.
- 109 isPartOf proceedings.
- 109 keyword "Access control".
- 109 keyword "rights management".
- 109 keyword "security of social content".
- 109 title "Privacy Wizards for Social Networking Sites".
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- item-1 hasContent lujun-fang.
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