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.

Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

Abstract: Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.

Privacy skyline: privacy with multidimensional adversarial knowledge

Abstract: Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. This problem is further complicated by the fact that, in addition to the published data, the adversary may also have access to other resources (e.g., public records and social networks relating individuals), which we call external knowledge. A robust privacy criterion should take this external knowledge into consideration. In this paper, we first describe a general framework for reasoning about privacy in the presence of external knowledge. Within this framework, we propose a novel multidimensional approach to quantifying an adversary's external knowledge. This approach allows the publishing organization to investigate privacy threats and enforce privacy requirements in the presence of various types and amounts of external knowledge. Our main technical contributions include a multidimensional privacy criterion that is more intuitive and flexible than previous approaches to modeling background knowledge. In addition, we provide algorithms for measuring disclosure and sanitizing data that improve computational efficiency several orders of magnitude over the best known techniques.

Who moderates the moderators?: crowdsourcing abuse detection in user-generated content

Abstract: A large fraction of user-generated content on the Web, such as posts or comments on popular online forums, consists of abuse or spam. Due to the volume of contributions on popular sites, a few trusted moderators cannot identify all such abusive content, so viewer ratings of contributions must be used for moderation. But not all viewers who rate content are trustworthy and accurate. What is a principled approach to assigning trust and aggregating user ratings, in order to accurately identify abusive content? In this paper, we introduce a framework to address the problem of moderating online content using crowdsourced ratings. Our framework encompasses users who are untrustworthy or inaccurate to an unknown extent --- that is, both the content and the raters are of unknown quality. With no knowledge whatsoever about the raters, it is impossible to do better than a random estimate. We present efficient algorithms to accurately detect abuse that only require knowledge about the identity of a single 'good' agent, who rates contributions accurately more than half the time. We prove that our algorithm can infer the quality of contributions with error that rapidly converges to zero as the number of observations increases; we also numerically demonstrate that the algorithm has very high accuracy for much fewer observations. Finally, we analyze the robustness of our algorithms to manipulation by adversarial or strategic raters, an important issue in moderating online content, and quantify how the performance of the algorithm degrades with the number of manipulating agents.

Acute pancreatitis in a cohort of 129 patients referred for severe hypertriglyceridemia.

Abstract: Objectives The aim of this study was to assess retrospectively the prevalence and the predictive factors of acute pancreatitis (AP) in a population of patients referred in our endocrinology department for evaluation of very high triglyceride (TG) levels. Methods One hundred twenty-nine patients (119 with type IV phenotypes and 10 with type V phenotypes according to Fredrickson's classification) were referred to our hospital between 2000 and 2005. Results Twenty-six subjects (20.2% of the population) presented with AP. This population was significantly younger at diagnosis of hyperlipidemia (32 vs 40 years, P 150 mg/L, or Balthazar score >C) was observed in 71.5% of the patients. Conclusions Twenty percent of patients with severe hypertriglyceridemia experience at least 1 attack of AP. Pancreatitis seems to occur in young patients at higher levels of TG than previously thought (85% of patients >30 g/L) and is associated with a severe clinical course.