Abstract: This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.

Abstract: The techniques disclosed herein enable systems to train machine learning models using benign augmentation to enabled resistance various data poisoning attacks. This is achieved by first training a machine learning model using an initial dataset that is trustworthy and originates from a known source. The initial dataset is then modified to include known attack triggers such as syntactic paraphrasing to generate an augmented dataset. The augmented dataset is then used to train a robust machine learning model based using the initially trained machine learning model. The resultant robust machine learning model is then enabled to detect and resist attacks captured by the augmented dataset. The robust machine learning model can be retrained using an untrusted dataset that includes various compromised inputs in conjunction with the augmented dataset. Retraining results in an updated robust machine learning model that can learn and resist various data poisoning attacks on the fly.

Abstract: Technologies described herein relate to receiving data from a social networking application, wherein the data is generated by a first individual, and executing an entity extraction algorithm to extract at least one entity from the received data. A hyperlink is displayed on a computing device utilized by a second individual, wherein the hyperlink is based at least in part upon the at least one entity, and the hyperlink is configured to direct the first individual to a search engine and is further configured to cause the search engine to execute a search that pertains to the at least one entity.

Abstract: A method described herein includes acts of extracting at least one entity from an arbitrary web page being viewed by an individual on a computing device and comparing the at least one entity with social network data of the individual, wherein the social network data comprises a plurality of messages generated by members of a social network of the individual. The method further includes the acts of identifying at least one message in the plurality of messages based at least in part upon the comparing of the at least one entity with the social network data of the individual and causing the at least one message to be displayed on the web page in conjunction with the at least one entity.

Abstract: Technologies pertaining to the creation of personalized data for an individual, including personalized data based on a social networking participant's social networking data, entity extraction, and automatic collage generation are described herein. In one implementation, the personalized data is used to provide a personalized, social-based replacement for a default UI element. In one implementation, a disambiguated named entity is extracted from user generated content in a social networking application, a socially interesting photomontage is automatically generated based on photographs from a social networking application, and the photomontage is rendered with hotspots containing socially relevant annotations.

Abstract: Techniques that may be used for detecting a primary content (e.g., a web page) that the user is viewing and presenting one or more pieces of supplemental content (e.g., social media data) together with the primary content. The supplemental content presented to the user together with the primary content may be content that is matched to the primary content and therefore detected to be relevant to the user. Detection of primary content and matching to supplemental content may be carried out based on a comparison of entities related to the primary and supplemental content. In some embodiments, an analysis of the primary content for entities may include ordering entities according to significance in the primary content and selecting top entities for comparison. Also, in some embodiments, multiple pieces of supplemental content may be displayed to a user categorized based on entity.

Abstract: Techniques that may be used for detecting a primary content (e.g., a web page) that the user is viewing and presenting one or more pieces of supplemental content (e.g., social media data) together with the primary content. The supplemental content presented to the user together with the primary content may be content that is matched to the primary content and therefore detected to be relevant to the user. Detection of primary content and matching to supplemental content may be carried out based on a comparison of entities related to the primary and supplemental content. In some embodiments, an analysis of the primary content for entities may include ordering entities according to significance in the primary content and selecting top entities for comparison. Also, in some embodiments, multiple pieces of supplemental content may be displayed to a user categorized based on entity.

Abstract: Systems, methods, and computer storage media are provided for analyzing a large amount of social media data from a large population of social media users and constructing correlational data models between one or more events that occur within each user's timeline. Social media posts directed to personal experiences of a large number of social media users are extracted. Event timelines are generated for each of the social media users, based on their personal experiences. The event timelines are analyzed with a particular event of interest to measure correlations between events occurring within the timelines and the particular event of interest. Using the measured correlations, a correlational data model is thereby constructed. The correlational data model may be used for application to decision-making calculations by one or more systems in an active or passive manner.

Abstract: Various technologies described herein pertain to disambiguation of a mention of an ambiguous entity in a document. A set of candidate entities can be retrieved from an entity knowledge base based upon the mention of the ambiguous entity, where each of the candidate entities has a respective entity feature representation. Moreover, a document feature representation can be generated based upon features of the document and the respective entity feature representations of the candidate entities. A processor can be caused to select a subset of features from the document feature representation based upon a measure of how discriminative the features from the document feature representation are for disambiguating the mention of the ambiguous entity. A disambiguated result for the mention of the ambiguous entity can be determined based upon the subset of the features. The disambiguated result can be an unknown entity or one of the candidate entities.

Abstract: Systems, methods, and computer storage media are provided for analyzing a large amount of social media data from a large population of social media users and constructing correlational data models between one or more events that occur within each user's timeline. Social media posts directed to personal experiences of a large number of social media users are extracted. Event timelines are generated for each of the social media users, based on their personal experiences. The event timelines are analyzed with a particular event of interest to measure correlations between events occurring within the timelines and the particular event of interest. Using the measured correlations, a correlational data model is thereby constructed. The correlational data model may be used for application to decision-making calculations by one or more systems in an active or passive manner.

Abstract: An edge computing platform that provides on-demand delivery of Rich Internet Applications and other applications is disclosed. One embodiment includes an optional manager node and content distribution network (CDN) that include one or more compute nodes. The CDN collects information pertaining to execution of a software application. The CDN aggregates the information and transfers the aggregated information to the manager node. The manager node analyzes the information from the CDN and transfers results of the analysis to the CDN. The CDN receives a software application that is designed to be dynamically updated when executed at the clients. The CDN modifies the software application based on the information from the manager node. The CDN receives a request that pertains to the software application from a client device. The CDN transfers at least a portion of the modified software application to the client.

Abstract: Technologies described herein relate to receiving data from a social networking application, wherein the data is generated by a first individual, and executing an entity extraction algorithm to extract at least one entity from the received data. A hyperlink is displayed on a computing device utilized by a second individual, wherein the hyperlink is based at least in part upon the at least one entity, and the hyperlink is configured to direct the first individual to a search engine and is further configured to cause the search engine to execute a search that pertains to the at least one entity.

Abstract: A method described herein includes acts of receiving data from a social networking application, wherein the data is generated by a first individual, and executing an entity extraction algorithm to extract at least one entity from the received data. The method also includes automatically causing a hyperlink to be displayed on a computing device utilized by a second individual, wherein the hyperlink is based at least in part upon the at least one entity, wherein the hyperlink is configured to direct the first individual to a search engine and is further configured to cause the search engine to execute a search that pertains to the at least one entity.

Abstract: Various technologies described herein pertain to using social activity data to personalize ranking of results returned by a computing operation for a user. For each of the results returned by the computing operation, a respective first affinity of the user to a corresponding result and a respective second affinity of the user to the corresponding result can be calculated and used for ranking the results. The respective first affinity of the user to the corresponding result can be calculated based on correlations between social activity data of the user and social activity data of a first group of historical users that clicked the corresponding result. Moreover, the respective second affinity of the user to the corresponding result can be calculated based on correlations between the social activity data of the user and social activity data of a second group of historical users that skipped the corresponding results.

Abstract: Various technologies described herein pertain to disambiguation of a mention of an ambiguous entity in a document. A set of candidate entities can be retrieved from an entity knowledge base based upon the mention of the ambiguous entity, where each of the candidate entities has a respective entity feature representation. Moreover, a document feature representation can be generated based upon features of the document and the respective entity feature representations of the candidate entities. A processor can be caused to select a subset of features from the document feature representation based upon a measure of how discriminative the features from the document feature representation are for disambiguating the mention of the ambiguous entity. A disambiguated result for the mention of the ambiguous entity can be determined based upon the subset of the features. The disambiguated result can be an unknown entity or one of the candidate entities.

Abstract: Sharing of user preferences is described. In an embodiment a user preference associated with a user is shared with a group of users in order to improve the relevance of results they receive. A database is used to store information detailing a number of groups of users extracted from a social network graph, where the social network graph describes connections between users. On receipt of a user preference associated with a user, a group of users containing the user is selected and the user preference is then shared with everyone in the selected group. In a further embodiment, the groups of users in the database may comprise cohesive groups of users and an extended group associated with each cohesive group. When selecting a group to share preference data with, a cohesive group containing the user is first selected and then the preference data is shared with the corresponding extended group.

Abstract: Various technologies described herein pertain to using social activity data to personalize ranking of results returned by a computing operation for a user. For each of the results returned by the computing operation, a respective first affinity of the user to a corresponding result and a respective second affinity of the user to the corresponding result can be calculated and used for ranking the results. The respective first affinity of the user to the corresponding result can be calculated based on correlations between social activity data of the user and social activity data of a first group of historical users that clicked the corresponding result. Moreover, the respective second affinity of the user to the corresponding result can be calculated based on correlations between the social activity data of the user and social activity data of a second group of historical users that skipped the corresponding results.

Abstract: A system described herein includes a receiver component that receives a dataset that is stored in a computer-readable medium of a computing device, wherein the dataset includes a plurality of queries issued by users to a search engine and a plurality of search results selected by the users upon issuing the plurality of queries. A distribution determiner component determines click distributions over the search results selected by the users with respect to the plurality of queries. A labeler component labels at least two queries in the plurality of queries as being substantially similar to one another based at least in part upon the click distributions over the search results selected by the users with respect to the plurality of queries.

Abstract: Computer-executable instructions comprising some or all of a program can be delivered to a client for execution on a real-time basis such that the client receives anew the computer-executable instructions for each new execution of the program. Such an environment enables instrumentation instructions to be inserted into the computer-executable instructions after a request and prior to the delivery of the computer-executable instructions. The inserted instrumentation instructions can be spread across multiple deliveries of the same computer-executable instructions, and they can be modified to account for information received from previously inserted instrumentation instructions. The instrumentation instructions can be inserted as part of the server process, the client process, or as part of a proxy server that can be used at the discretion of the program developer.

Abstract: Techniques that may be used for detecting a primary content (e.g., a web page) that the user is viewing and presenting one or more pieces of supplemental content (e.g., social media data) together with the primary content. The supplemental content presented to the user together with the primary content may be content that is matched to the primary content and therefore detected to be relevant to the user. Detection of primary content and matching to supplemental content may be carried out based on a comparison of entities related to the primary and supplemental content. In some embodiments, an analysis of the primary content for entities may include ordering entities according to significance in the primary content and selecting top entities for comparison. Also, in some embodiments, multiple pieces of supplemental content may be displayed to a user categorized based on entity.