Abstract: Systems and methods for generating predicted preferences are disclosed. The method includes implementing, with a computing device having a processor and a non-transitory computer-readable memory, a conjoint architecture comprising: an autoencoder trained to transform input data including one or more choices and one or more features into a latent representation, and a choice classification network trained to predict one or more predicted preferences from the latent representation extracted by the autoencoder. The method further includes outputting, from the choice classification network, the one or more predicted preferences.

Abstract: A method for use of elicited factors to inform intervention suggestions is described. The method includes identifying a demographic of a user according to a background of the user. The method also includes predicting factors including an associated set of barriers and/or motivators regarding a preference according to the demographic of the user. The method further includes predicting a plurality of interventions to shift the preference of the user according to the associated set of barriers and/or motivators. The method also includes presenting a selected one of the plurality of determined interventions to the user.

Abstract: Systems and methods disclosed herein present topic-centric visualizations of collaboration data. An example method includes: obtaining a set of topics based on an analysis of collaboration data and displaying an interactive visualization that includes first UI elements that correspond to each topic (first UI elements corresponding to similar topics are positioned close together). In response to receiving a specified time period for the interactive visualization, the method includes: identifying a subset of the plurality of persons that are associated with the specified time period. The method additionally includes: obtaining a first set of connections between the set of topics and the subset and a second set of connections between related persons in the subset. The method further includes: updating the interactive visualization to include second UI elements corresponding to each respective person of the subset and visual representations of each connection in the first and second sets.

Abstract: Systems and methods disclosed herein associate images with business venues. An example method includes: receiving a first image and retrieving textual reviews and stored images that are associated with one or more candidate business venues. The method further includes: detecting, using trained visual detectors, a plurality of business-aware concepts in the first image and assessing likelihood that detected business-aware concepts are in the first image. The method additionally includes: (i) generating a first representation of the first image based on the likelihoods and one or more term vectors for high-scoring concepts and (ii) receiving second representations of each candidate based on the retrieved textual reviews and stored images. In accordance with determining that the first representation is most similar to a respective second representation of a first candidate, the method includes: (i) associating the first image with the first candidate and (ii) providing an indication of the association.

Abstract: Systems and methods disclosed herein present topic-centric visualizations of collaboration data. An example method includes: obtaining a set of topics based on an analysis of collaboration data and displaying an interactive visualization that includes first UI elements that correspond to each topic (first UI elements corresponding to similar topics are positioned close together). In response to receiving a specified time period for the interactive visualization, the method includes: identifying a subset of the plurality of persons that are associated with the specified time period. The method additionally includes: obtaining a first set of connections between the set of topics and the subset and a second set of connections between related persons in the subset. The method further includes: updating the interactive visualization to include second UI elements corresponding to each respective person of the subset and visual representations of each connection in the first and second sets.

Abstract: Systems and methods disclosed herein associate images with business venues. An example method includes: receiving a first image and retrieving textual reviews and stored images that are associated with one or more candidate business venues. The method further includes: detecting, using trained visual detectors, a plurality of business-aware concepts in the first image and assessing likelihood that detected business-aware concepts are in the first image. The method additionally includes: (i) generating a first representation of the first image based on the likelihoods and one or more term vectors for high-scoring concepts and (ii) receiving second representations of each candidate based on the retrieved textual reviews and stored images. In accordance with determining that the first representation is most similar to a respective second representation of a first candidate, the method includes: (i) associating the first image with the first candidate and (ii) providing an indication of the association.

Abstract: Techniques for training and using linked event detection systems and transforming source-identified stopwords are provided. A training corpus of source identified stories and a reference language is determined. Optionally, stopwords for source-identified stories are transformed based on statistical analysis of parallel verified and un-verified transformations. Reference language and non-reference language terms are selectively included in source-pair term frequency-inverse story frequency models. Optionally, incremental source-identified term frequency-inverse story frequency models are determined. Selected terms are weighted and similarity metrics determined. Associated source-pair statistics, computed in part from a training corpus, are combined with the values of each similarity metric in the set of similarity metrics to form a similarity vector. Similarity vectors and verified link label information are used to determine a predictive model.

Abstract: An integrated method for modeling the handoff between cameras for tracking a specific individual, including: creating a representation of overlaps, gaps, and allowable movement among the fields of view of the cameras, wherein the representation is modeled as states in a Hidden Markov Model (HMM); training the HMM using video of people walking through the fields of view of the cameras; selecting a person to be tracked; and identifying the best camera area using the HMM.

Abstract: A system, a computer readable storage medium including instructions, and method for generating genre models used to identify genres of a document. For each document image in a set of document images that are associated with one or more genres, the document image is segmented into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable, and features of the document image and the plurality of tiles are computed. At least one genre classifier is trained to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.

Abstract: Demographic information of an Internet user is predicted based on an analysis of accessed web pages. Web pages accessed by the Internet user are detected and mapped to a user path vector which is converted to a normalized weighted user path vector. A centroid vector identifies web page access patterns of users with a shared user profile attribute. The user profile attribute is assigned to the Internet user based on a comparison of the vectors. Bias values are also assigned to a set of web pages and a user profile attribute can be predicted for an Internet user based on the bias values of web pages accessed by the user. User attributes can also be predicted based on the results of an expectation maximization process. Demographic information can be predicted based on the combined results of a vector comparison, bias determination, or expectation maximization process.

Abstract: Demographic information of an Internet user is predicted based on an analysis of accessed web pages. Web pages accessed by the Internet user are detected and mapped to a user path vector which is converted to a normalized weighted user path vector. A centroid vector identifies web page access patterns of users with a shared user profile attribute. The user profile attribute is assigned to the Internet user based on a comparison of the vectors. Bias values are also assigned to a set of web pages and a user profile attribute can be predicted for an Internet user based on the bias values of web pages accessed by the user. User attributes can also be predicted based on the results of an expectation maximization process. Demographic information can be predicted based on the combined results of a vector comparison, bias determination, or expectation maximization process.

Abstract: Systems and computer-implemented methods for use in body pose estimation are provided. Training data is obtained, where the training data includes observation vector data and corresponding pose vector data for a plurality of images. The observation vector data is representative of the images in observation space. The pose vector data is representative of the same images in pose space. Based on the training data, a model is computed that includes parameters of mapping from the observation space to latent space, parameters of mapping from the latent space to the pose space, and parameters of the latent space. The latent space has a lower dimensionality than the observation space and the pose space.

Abstract: A system, a computer readable storage medium including instructions, and method for generating genre models used to identify genres of a document. For each document image in a set of document images that are associated with one or more genres, the document image is segmented into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable, and features of the document image and the plurality of tiles are computed. At least one genre classifier is trained to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.

Abstract: Techniques for new event detection are provided. For a new story and a corpus of stories, story-pairs based on the new story and each corpus story are determined. Adjustments to the importance of terms are determined based on story characteristics associated with each story. Story characteristics are based on direct or indirect characteristics. Direct story characteristics include authorship, language associated with a story and the like. Indirect story characteristics may include derived characteristics such as an ROI category characteristic, a same ROI characteristic, a same event-same source characteristic, an average story similarity characteristic or any other known or later developed characteristic associated with a story. Adjustments to the inter-story similarity metrics are then determined based on story characteristics and/or a weighting function.

Abstract: Systems and computer-implemented methods for use in body pose estimation are provided. Training data is obtained, where the training data includes observation vector data and corresponding pose vector data for a plurality of images. The observation vector data is representative of the images in observation space. The pose vector data is representative of the same images in pose space. Based on the training data, a model is computed that includes parameters of mapping from the observation space to latent space, parameters of mapping from the latent space to the pose space, and parameters of the latent space. The latent space has a lower dimensionality than the observation space and the pose space.

Abstract: One aspect of the invention is that of efficiently and incrementally adding new terms to an already trained probabilistic latent semantic analysis (PLSA) model.

Abstract: An integrated method for modeling the handoff between cameras for tracking a specific individual, including: creating a representation of overlaps, gaps, and allowable movement among the fields of view of the cameras, wherein the representation is modeled as states in a Hidden Markov Model (HMM); training the HMM using video of people walking through the fields of view of the cameras; selecting a person to be tracked; and identifying the best camera area using the HMM.

Abstract: Techniques for determining interactive topic-based summarization are provided. A text to be summarized is segmented. Discrete keyword, key-phrase, n-gram, sentence and other sentence constituent based summaries are generated based on statistical measures for each text segment. Interactive topic-based summaries are displayed with human sensible omitted text indicators such as alternate colors, fonts, sounds, tactile elements or other human sensible display characteristics useful in indicating omitted text. Individual and/or combinations of discrete keyword, key-phrase, n-gram, sentence, noun phrase and sentence constituent based summaries are dynamically displayed to provide an overview of topic and subtopic development within a text. A hierarchical and interactive display of texts based on the use of discrete sentence constituent based summaries which associates expansible and contractible displayed text provides contextualized access to an interactive topic-based text summary and to an original text.

Abstract: Foreground segmentation in real world dynamic scenes, including under various lighting and shadow conditions, is disclosed. It may be used with one or multiple cameras for various automated tasks, such as classification of moving object, tracking moving objects, and event detection in various indoor or outdoor environments. Pixel to pixel subtraction is performed on each frame, followed by a feature-level based foreground segmentation to properly validate the foreground pixels. In this step, for each pixel in the image, a neighborhood of pixels is selected, and the aggregate change in the neighborhood image is used to classify foreground and background pixels. Normalized cross correlation is then applied to the neighborhood of each pixel that was confirmed to be foreground pixel.

Abstract: Techniques for determining sentence based interactive topic-based summarization are provided. A text to be summarized is segmented. Discrete keyword, key-phrase, n-gram, sentence and other sentence constituent based summaries are generated based on statistical measures for each text segment. Interactive topic-based summaries are displayed with human sensible omitted text indicators such as alternate colors, fonts, sounds, tactile elements or other human sensible display characteristics useful in indicating omitted text. Individual and/or combinations of discrete keyword, key-phrase, n-gram, sentence, noun phrase and sentence constituent based summaries are dynamically displayed to provide an overview of topic and subtopic development within a text.