Matches in ScholarlyData for { ?s ?p ?o. }
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- item-3 hasContent shyam-rajaram.
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- 649 creator boerkur-sigurbjoernsson.
- 649 creator roelof-van-zwol.
- 649 type InProceedings.
- 649 label "Faceted Exploration of Image Search Results".
- 649 sameAs 649.
- 649 abstract "This paper describes MediaFaces, a system that enables faceted exploration of media collections. The system processes semi-structured information sources to extract objects and facets, e.g. the relationships between two objects. Next, we rank the facets based on a statistical analysis of image search query logs, and the tagging behaviour of users annotating photos in Flickr. For a given object of interest, we can then retrieve the top-k most relevant facets and present them to the user. The system is currently deployed in production by Yahoo!'s image search engine. We present the system architecture, its main components, and the application of the system as part of the image search experience.".
- 649 hasAuthorList authorList.
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- 649 keyword "Faceted".
- 649 keyword "desktop search".
- 649 keyword "enterprise".
- 649 title "Faceted Exploration of Image Search Results".
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- item-1 hasContent roelof-van-zwol.
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- item-2 hasContent boerkur-sigurbjoernsson.
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- 66 creator adwait-ratnaparkhi.
- 66 creator dragomir-yankov.
- 66 creator scott-gaffney.
- 66 creator suju-rajan.
- 66 type InProceedings.
- 66 label "A Large Scale Active Learning System for Topical Categorization on the Web".
- 66 sameAs 66.
- 66 abstract "Many web applications such as ad matching systems, vertical search engines, and page categorization systems require the identification of a particular type or class of pages on the Web. The sheer number and diversity of the pages on the web, however, makes the problem of obtaining a good sample of the class of interest hard. In this paper, we describe a successfully deployed end-to-end system that starts from a manually collected biased training sample and makes use of several state-of-the-art machine learning systems working in tandem, including a powerful active learning component, in order to achieve a good classification system. The performance of the system is evaluated on the traffic to a real-world ad-matching platform and is shown to have significant reduction in editorial effort and labeling time, while maintaining pre-specified performance criteria.".
- 66 hasAuthorList authorList.
- 66 isPartOf proceedings.
- 66 keyword "Negative content filtering".
- 66 keyword "porn".
- 66 keyword "spam".
- 66 keyword "viruses".
- 66 title "A Large Scale Active Learning System for Topical Categorization on the Web".
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- item-1 hasContent suju-rajan.
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- item-2 hasContent dragomir-yankov.
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- item-4 hasContent adwait-ratnaparkhi.
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- 673 creator christopher-kruegel.
- 673 creator giovanni-vigna.
- 673 creator marco-cova.
- 673 type InProceedings.
- 673 label "Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code".
- 673 sameAs 673.
- 673 abstract "JavaScript is a browser scripting language that allows developers to create sophisticated client-side interfaces for web applications. However, JavaScript code is also used to carry out attacks against the user's browser and its extensions. These attacks usually result in the download of additional malware that takes complete control of the victim's platform, and are, therefore, called "drive-by downloads." Unfortunately, the dynamic nature of the JavaScript language and its tight integration with the browser make it difficult to detect and block malicious JavaScript code. This paper presents a novel approach to the detection and analysis of malicious JavaScript code. Our approach combines anomaly detection with emulation to automatically identify malicious JavaScript code and to support its analysis. We developed a system that uses a number of features and machine-learning techniques to establish the characteristics of normal JavaScript code. Then, during detection, the system is able to identify anomalous JavaScript code by emulating its behavior and comparing it to the established profiles. In addition to identifying malicious code, the system is able to support the analysis of obfuscated code and to generate detection signatures for signature-based systems. The system has been made publicly available and has been used by thousands of analysts.".
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- 673 keyword "Browser security issues".
- 673 keyword "defenses".
- 673 keyword "including attacks".
- 673 keyword "policy models".
- 673 title "Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code".
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- item-1 hasContent marco-cova.
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- item-2 hasContent christopher-kruegel.
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- item-3 hasContent giovanni-vigna.
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- 674 creator alessandro-bozzon.
- 674 creator marco-brambilla.
- 674 creator piero-fraternali.
- 674 creator stefano-ceri.
- 674 type InProceedings.
- 674 label "Liquid Query: Multi-domain Exploratory Search on the Web".