Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Executive functions deficit in mild cognitive impairment.

Abstract: ObjectiveTo investigate whether patients diagnosed with amnestic mild cognitive impairment (MCI) have also impairment in attention/executive functions, and therefore to clarify whether all subcomponents of executive control are equally affected in MCI.BackgroundMCI refers to the transitional state b

Unsupervised query segmentation using generative language models and wikipedia

Abstract: In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. We use a generative query model to recover a query's underlying concepts that compose its original segmented form. The model's parameters are estimated using an expectation-maximization (EM) algorithm, optimizing the minimum description length objective function on a partial corpus that is specific to the query. To augment this unsupervised learning, we incorporate evidence from Wikipedia.Experiments show that our approach dramatically improves performance over the traditional approach that is based on mutual information, and produces comparable results with a supervised method. In particular, the basic generative language model contributes a 7.4% improvement over the mutual information based method (measured by segment F1 on the Intersection test set). EM optimization further improves the performance by 14.3%. Additional knowledge from Wikipedia provides another improvement of 24.3%, adding up to a total of 46% improvement (from 0.530 to 0.774).

Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords

Abstract: We investigate the "generalized second price" auction (GSP), a new mechanism which is used by search engines to sell online advertising that most Internet users encounter daily. GSP is tailored to its unique environment, and neither the mechanism nor the environment have previously been studied in the mechanism design literature. Although GSP looks similar to the Vickrey-Clarke-Groves (VCG) mechanism, its properties are very different. In particular, unlike the VCG mechanism, GSP generally does not have an equilibrium in dominant strategies, and truth-telling is not an equilibrium of GSP. To analyze the properties of GSP in a dynamic environment, we describe the generalized English auction that corresponds to the GSP and show that it has a unique equilibrium. This is an ex post equilibrium that results in the same payoffs to all players as the dominant strategy equilibrium of VCG.

System and method for context based query augmentation

Abstract: A system and method for context based query augmentation. A question is received over a network from a questioning user comprising an identification of a user and at least one question criteria. A first query is formulated so as to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data so as to identify user context data relevant to question criteria. The question is modified using the user context data to create at least one modified question having at least one additional criteria based on the user context data. A second query is formulated so as to search, via the network, for knowledge data, user profile data, social network data, spatial data, temporal data and topical data so as to identify knowledge data relevant to the identified user and the modified question criteria. The knowledge data is transmitted, over the network, to the questioning user.

Vision: automated security validation of mobile apps at app markets

Abstract: Smartphones and "app" markets are raising concerns about how third-party applications may misuse or improperly handle users' privacy-sensitive data. Fortunately, unlike in the PC world, we have a unique opportunity to improve the security of mobile applications thanks to the centralized nature of app distribution through popular app markets. Thorough validation of apps applied as part of the app market admission process has the potential to significantly enhance mobile device security. In this paper, we propose AppInspector, an automated security validation system that analyzes apps and generates reports of potential security and privacy violations. We describe our vision for making smartphone apps more secure through automated validation and outline key challenges such as detecting and analyzing security and privacy violations, ensuring thorough test coverage, and scaling to large numbers of apps.