Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Architecture that monitors interaction data (e.g., search queries, query results and click-through rates), and provides users with links to other users that fall into similar categories with respect to the foregoing monitored activities (e.g., providing links to individuals and groups that share common interests and/or profiles). A search engine can be interactively coupled with one or more social networks, and that maps individuals and/or groups within respective social networks to subsets of categories associated with searches. A database stores mapped information which can be continuously updated and reorganized as links within the system mapping become stronger or weaker. The architecture can comprise a social network system that includes a database for mapping search-related information to an entity of a social network, and a search component for processing a search query for search results and returning a link to an entity of a social network based on the search query.

Abstract: In one embodiment, datasets are stored in a catalog. The datasets are enriched by establishing relationships among the domains in different datasets. A user searches for relevant datasets by providing examples of the domains of interest. The system identifies datasets corresponding to the user-provided examples. The system them identifies connected subsets of the datasets that are directly linked or indirectly linked through other domains. The user provides known relationship examples to filter the connected subsets and to identify the connected subsets that are most relevant to the user's query. The selected connected subsets may be further analyzed by business intelligence/analytics to create pivot tables or to process the data.

Abstract: Some examples include high-performance query processing of real-time and offline temporal-relational data. Further, some implementations include processing streaming data events by annotating individual events with a first timestamp (e.g., a “sync-time”) and second timestamp that may identify additional event information. The stream of incoming data events may be organized into a sequence of data batches that each include multiple data events. The individual data batches in the sequence may be processed in a non-decreasing “sync-time” order.

Abstract: In one embodiment, datasets are stored in a catalog. The datasets are enriched by establishing relationships among the domains in different datasets. A user searches for relevant datasets by providing examples of the domains of interest. The system identifies datasets corresponding to the user-provided examples. The system them identifies connected subsets of the datasets that are directly linked or indirectly linked through other domains. The user provides known relationship examples to filter the connected subsets and to identify the connected subsets that are most relevant to the user's query. The selected connected subsets may be further analyzed by business intelligence/analytics to create pivot tables or to process the data.

Abstract: A system and method infer true labels for multiple items. The inferred labels are generated from judgments. Multiple judges select the judgments from a specified choice of labels for each item. The method includes determining a characterization of judge expertise and item difficulties based on the judgments. The method also includes determining, using maximum entropy, a probability distribution over the specified choice of labels for each judge and item, based on the judgments. The method further includes selecting improved labels for the items from the specified choice such that the entropy over the probability distribution is reduced. The improved labels represent an improvement from the judgments toward the true labels. Additionally, the method includes performing iterative procedure to determine the true labels, the characterizations of judge expertise and the labeling difficulties.

Abstract: Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution.

Abstract: A system and method infer true labels for multiple items. The inferred labels are generated from judgments. Multiple judges select the judgments from a specified choice of labels for each item. The method includes determining a characterization of judge expertise and item difficulties based on the judgments. The method also includes determining, using maximum entropy, a probability distribution over the specified choice of labels for each judge and item, based on the judgments. The method further includes selecting improved labels for the items from the specified choice such that the entropy over the probability distribution is reduced. The improved labels represent an improvement from the judgments toward the true labels. Additionally, the method includes performing iterative procedure to determine the true labels, the characterizations of judge expertise and the labeling difficulties.

Abstract: A reliable automated malware classification approach with substantially low false positive rates is provided. Graph-based local and/or global file relationships are used to improve malware classification along with a feature selection algorithm. File relationships such as containing, creating, copying, downloading, modifying, etc. are used to assign malware probabilities and simultaneously reduce the false positive and false negative rates on executable files.

Abstract: Provided is mechanism for guiding computer searches (e.g., web, Internet, intranet . . . ). Machine learning and reasoning mechanisms are employed together with a search engine to facilitate intelligent guidance of queries and results based on a query and responses to computer generated inquires. Also, disclosed is a mechanism that enables users to validate computer-based information. Users can receive a validity metric associated with computer generated or provided results. Validation systems and methods are disclosed to facilitate determining the results including those that employ humans (e.g., referrals, voting . . . ) and/or automated ways (e.g., source analysis, data mining . . . ).

Abstract: A document-term matrix may be generated based on a corpus. A term representation matrix may be generated based on modifying a plurality of elements of the document-term matrix based on antonym information included in the corpus. Similarities may be determined based on a plurality of elements of the term representation matrix.

Abstract: Various embodiments promote the discoverability of data that can be contained within a database. In one or more embodiments, data within a database is organized in a structure having a schema. The structure and data can be processed in a manner that renders one or more pseudo-documents each of which constitutes a sub-structure that can be indexed. Once produced and indexed, the pseudo-documents constitute a set of searchable objects each of which relationally points back to its associated structure within the database. Searches can now be performed against the pseudo-documents which, in turn, returns a set of search results. The set of search results can include multiple sub-sets of pseudo-documents, each sub-set of which is associated with a different structure.

Abstract: A system enables metadata to be gathered about a data store beginning from the creation and generation of the data store, through subsequent use of the data store. This metadata can include keywords related to the data store and data appearing within the data store. Thus, keywords and other metadata can be generated without owner/creator intervention, with enough semantic meaning to make a discovery process associated with the data store much easier and efficient. Usage of or communication regarding a data store are monitored and keywords are extracted from the usage or communication. The keywords are then written to otherwise associated with metadata of the data store. During searching, keywords in the metadata are made available to be used to attempt to match query terms entered by a searcher.

Abstract: A data broker observes datasets that are opened or created by a user. The data broker looks for related datasets in a data catalog. If a related dataset is found, the data broker asks the user if they want to access the related dataset. If the user is interested, then the data broker asks the data owner if they are willing to share access to the related dataset with the user. The data owner may deny access, allow access, or request the user's identity. If the user does not want to provide his or her identity, then access to the related dataset is denied. If the user does provide his or her identity, then the data owner determines whether or not to share the data with that user. Once the owner approves sharing the related dataset, then the dataset or a link to the dataset is sent to the user.

Abstract: In one embodiment, datasets are stored in a catalog. The datasets are enriched by establishing relationships among the domains in different datasets. A user searches for relevant datasets by providing examples of the domains of interest. The system identifies datasets corresponding to the user-provided examples. The system them identifies connected subsets of the datasets that are directly linked or indirectly linked through other domains. The user provides known relationship examples to filter the connected subsets and to identify the connected subsets that are most relevant to the user's query. The selected connected subsets may be further analyzed by business intelligence/analytics to create pivot tables or to process the data.

Abstract: A reliable automated malware classification approach with substantially low false positive rates is provided. Graph-based local and/or global file relationships are used to improve malware classification along with a feature selection algorithm. File relationships such as containing, creating, copying, downloading, modifying, etc. are used to assign malware probabilities and simultaneously reduce the false positive and false negative rates on executable files.

Abstract: A model for mapping the raw text representation of a text object to a vector space is disclosed. A function is defined for computing a similarity score given two output vectors. A loss function is defined for computing an error based on the similarity scores and the labels of pairs of vectors. The parameters of the model are tuned to minimize the loss function. The label of two vectors indicates a degree of similarity of the objects. The label may be a binary number or a real-valued number. The function for computing similarity scores may be a cosine, Jaccard, or differentiable function. The loss function may compare pairs of vectors to their labels. Each element of the output vector is a linear or non-linear function of the terms of an input vector. The text objects may be different types of documents and two different models may be trained concurrently.

Abstract: Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution.

Abstract: Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution.

Abstract: In an Item-based system, Images (e.g., JPEG, TIFF, bitmap, and so on) are treated as core platform objects (“Image Items” or, more simply, “Images”) and exist in an “Image Schema” that provides an extensible representation of an Image in the system—that is, the characteristics of an Image and how that Image relates to other Items (including but not limited to other Images) in the system. To this end, the Image Schema defines the properties, behaviors, and relationships for Images in the system, and the Schema also enforces rules about Images, for example, what data specific Images must contain, what data specific Images may optionally contain, how specific Images can be extended, and so on and so forth.