Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

Personalizing a Web Page Outside of a Social Networking System with Content from the Social Networking System

Abstract: A user device requests a web page from a web server of a third-party website, which is separate from a social networking system. The web server from the third-party website sends a markup language document for the requested web page to the user device which includes an instruction for a browser application running on the user device to incorporate information obtained from the social networking system within the web page. Based on the instruction in the received markup language document, the user device requests personalized content from the social networking system, which generates the requested personalized content based on social information about the user. The user device then renders the web page with the personalized content contained in a frame and displays the rendered web page and the frame to the user.

Automatic categorization of entries in a contact list

Abstract: Entries within a participant list of an electronic communications system may be categorized automatically into one or more groups based on attributes of users represented in the participant list. Categorizing users includes maintaining a list of identifiers that are selected by a first user and for which online presence information is made perceivable through the list. The list includes one or more categories into which the identifiers are categorized. At least one attribute associated with an identifier for which online presence information is to be made perceivable through the list and attributes stored relative to at least one of the categories are accessed. The attributes associated with the identifier are compared to the stored attributes. Based on results of the comparison, at least one category within the list that corresponds to the identifier is identified, and the identifier is categorized into the identified category.

American attitudes toward nudges

Abstract: To successfully select and implement nudges, policy makers need a psychological understanding of who opposes nudges, how they are perceived, and when alternative methods (e.g., forced choice) might work better. Using two representative samples, we examined four factors that influence U.S. attitudes toward nudges – types of nudges, individual dispositions, nudge perceptions, and nudge frames. Most nudges were supported, although opt-out defaults for organ donations were opposed in both samples. “System 1” nudges (e.g., defaults and sequential orderings) were viewed less favorably than “System 2” nudges (e.g., educational opportunities or reminders). System 1 nudges were perceived as more autonomy threatening, whereas System 2 nudges were viewed as more effective for better decision making and more necessary for changing behavior. People with greater empathetic concern tended to support both types of nudges and viewed them as the “right” kind of goals to have. Individualists opposed both types of nudges, and conservatives tended to oppose both types. Reactant people and those with a strong desire for control opposed System 1 nudges. To see whether framing could influence attitudes, we varied the description of the nudge in terms of the target (Personal vs. Societal) and the reference point for the nudge (Costs vs. Benefits). Empathetic people were more supportive when framing highlighted societal costs or benefits, and reactant people were more opposed to nudges when frames highlighted the personal costs of rejection.

Grounded Video Description

Abstract: Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or "true" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video.