Abstract: Low-level nodes (LLNs) that are communicatively connected to one another each have sensing capability and processing capability. High-level nodes (HLNs) that are communicatively connected to one another and to the LLNs each have processing capability more powerful than the processing capability of each LLN. The LLNs and the HLNs perform processing based on sensing events captured by the LLNs. The processing is performed by the LLNs and the HLNs to minimize data communication among the LLNs and the HLNs, and to provide for software-defined sensing.

Abstract: Secure multi-party information retrieval is disclosed. One example is a system including a query processor to request secure retrieval of candidate terms similar to a query term. A collection of information processors, where a given information processor receives the request and generates a random permutation. A plurality of data processors, where a given data processor generates clusters of a plurality of terms in a given dataset, where the clusters are based on similarity scores for pairs of terms, and selects a representative term from each cluster. The given information processor determines similarity scores between a secured query term received from the query processor and secured representative terms received from the given data processor, where the secured terms are based on the permutation, and the given data processor filters, without knowledge of the query term, the candidate terms of the plurality of terms based on the determined similarity scores.

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: Incremental update of a neighbor graph via an orthogonal transform based indexing is disclosed. One example is a system including a hash transform module to apply an orthogonal transform to a data object in a data stream, and to associate the data object with a collection of ordered hash positions. An indexing module retrieves an index of ordered key positions, where each key position is indicative of data objects in the data stream that have a hash position at the key position. A neighbor determination module determines a ranked collection of neighbors for the data object in a neighbor graph, where the ranking is based on the index. A graph update module incrementally updates the neighbor graph by including the data object as a neighbor for a selected sub-plurality of data objects in the ranked collection.

Abstract: Low-level nodes (LLNs) that are communicatively connected to one another each have sensing capability and processing capability. High-level nodes (HLNs) that are communicatively connected to one another and to the LLNs each have processing capability more powerful than the processing capability of each LLN. The LLNs and the HLNs perform processing based on sensing events captured by the LLNs. The processing is performed by the LLNs and the HLNs to minimize data communication among the LLNs and the HLNs, and to provide for software-defined sensing.

Abstract: Low-level nodes (LLNs) that are communicatively connected to one another each have sensing capability and processing capability. High-level nodes (HLNs) that are communicatively connected to one another and to the LLNs each have processing capability more powerful than the processing capability of each LLN. The LLNs and the HLNs perform processing based on sensing events captured by the LLNs. The processing is performed by the LLNs and the HLNs to minimize data communication among the LLNs and the HLNs, and to provide for software-defined sensing.

Abstract: Interactive sequential pattern mining is disclosed. One example is a system including a sequence miner, and an interaction processor. A sequence database is received, the sequence database including a plurality of input sequences, where each sequence of the plurality of input sequences is an ordered list of events, and each event in the list of events includes at least one item. The sequence miner mines the sequence database for a plurality of candidate sequence patterns, the mining based on an interaction with a user. The interaction processor processes the interaction with the user, the interaction based on domain relevance of the plurality of candidate sequence patterns to the user.

Abstract: Audio capture systems and methods applicable to a large interactive device (LID) are provided. An audio capture method can include relating captured ambient audio provided by a first user with media presented to the first user via the LID. The method can also include indicating related media displayed on the LID based upon an audio input provided by the first user.

Abstract: Incremental update of a neighbor graph via an orthogonal transform based indexing is disclosed. One example is a system including a hash transform module to apply an orthogonal transform to a data object in a data stream, and to associate the data object with a collection of ordered hash positions. An indexing module retrieves an index of ordered key positions, where each key position is indicative of data objects in the data stream that have a hash position at the key position. A neighbor determination module determines a ranked collection of neighbors for the data object in a neighbor graph, where the ranking is based on the index. A graph update module incrementally updates the neighbor graph by including the data object as a neighbor for a selected sub-plurality of data objects in the ranked collection.

Abstract: Secure multi-party information retrieval is disclosed. One example is a system including a query processor to request secure retrieval of candidate terms similar to a query term. A collection of information processors, where a given information processor receives the request and generates a random permutation. A plurality of data processors, where a given data processor generates clusters of a plurality of terms in a given dataset, where the clusters are based on similarity scores for pairs of terms, and selects a representative term from each cluster. The given information processor determines similarity scores between a secured query term received from the query processor and secured representative terms received from the given data processor, where the secured terms are based on the permutation, and the given data processor filters, without knowledge of the query term, the candidate terms of the plurality of terms based on the determined similarity scores.

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method includes extracting features related to a plurality of users and a plurality of items and computing a correction parameter score for each of a plurality of user-item pair combinations. The method further includes computing a user response value for a user-item pair combination by applying a generalized linear model to the features of the user-item pair combination and using the correction parameter score for the user-item pair combination in the generalized linear model.

Abstract: Low-level nodes (LLNs) that are communicatively connected to one another each have sensing capability and processing capability. High-level nodes (HLNs) that are communicatively connected to one another and to the LLNs each have processing capability more powerful than the processing capability of each LLN. The LLNs and the HLNs perform processing based on sensing events captured by the LLNs. The processing is performed by the LLNs and the HLNs to minimize data communication among the LLNs and the HLNs, and to provide for software-defined sensing.

Abstract: An example method includes determining a region of interest of an interactive display. The method also includes determining a time since utilization of a user interface element exceeds a threshold. The method includes determining timing information for content in a region of the interactive display other than the region of interest, determining the content is stale based on the timing information exceeding a threshold, and removing the content from the interactive display based on the content being stale. The method includes automatically moving the user interface element progressively over a period of time from an initial location to the region other than the region of interest. The method further includes providing another user interface element capable of selecting a previous time since utilization, receiving input selecting the previous time since utilization, and moving the user interface element to a historic location associated with the previous time since utilization.

Abstract: Interactive sequential pattern mining is disclosed. One example is a system including a sequence miner, and an interaction processor. A sequence database is received, the sequence database including a plurality of input sequences, where each sequence of the plurality of input sequences is an ordered list of events, and each event in the list of events includes at least one item. The sequence miner mines the sequence database for a plurality of candidate sequence patterns, the mining based on an interaction with a user. The interaction processor processes the interaction with the user, the interaction based on domain relevance of the plurality of candidate sequence patterns to the user.

Abstract: Audio capture systems and methods applicable to a large interactive device (LID) are provided. An audio capture method can include relating captured ambient audio provided by a first user with media presented to the first user via the LID. The method can also include indicating related media displayed on the LID based upon an audio input provided by the first user.

Abstract: A device to detect a portable device associated with a user accessing an accessible region of the device with a sensor, identify the portable device and log the user into the device with a profile of the user if the accessible region is accessed by the portable device, and generate a session on the device based on the profile of the user.

Abstract: Image display systems and methods are provided. A plurality of pixel groups, each including a plurality of independent pixels, forms a display. A plurality of lenses (1251-N) forming an optical array and aligned along at least one axis (130), can be disposed proximate the display. Each of the plurality of lenses can provide a limited viewing arc (1501-N) for each of the plurality of independent pixels. A sample system can also include a detector (160) to detect the presence and location of a first user (165) and a second user (170) and logic (175), that when executed by a processor (180) coupled to the display, routes a first display signal (1451) based upon the viewing arc of a first portion of pixels; and routes a second display signal (1452) based upon the viewing arc of a second portion of pixels.

Abstract: Example embodiments disclosed herein relate to displaying private user content (420) of a first user (430) on a large interactive display (410) of a multi-user interface. The visibility of the private user content is obstructed based on identifying a second user (440).