Abstract: The disclosed embodiments illustrate methods and systems for processing social media data for content recommendation to a user. The method includes extracting a set of entity data from the social media data of the user. The method further includes extracting semantic data of each entity data in the extracted set of entity data from one or more knowledge databases over a communication network. The method further includes generating a user-interest vector of the user. The user-interest vector of the user is generated based on at least a mapping of the extracted semantic data of each entity data with one or more leaf nodes in an interest taxonomy. The generated user-interest vector is further utilized for recommending targeted content to the user.

Abstract: The disclosed embodiments illustrate methods of data processing for training a target domain classifier to label text segments. The method includes identifying a set of common keywords with same label from a set of source keywords and a set of target keywords. The method includes training a first classifier, based on the set of common keywords, to label a first set of target text segments. The method includes training a second classifier based on at least a subset of the labeled first set of target text segments. The method includes training a third classifier, based on the first classifier and the second classifier, to label a second set of target text segments, wherein a subset of the labeled second set of target text segments is utilized for re-training the second classifier. The method further includes determining labels of another plurality of target text segments based on the re-trained second classifier.

Abstract: Disclosed are embodiments of methods and systems for predicting a health condition of a first human subject. The method comprises receiving a measure of one or more physiological parameters associated with the first human subject. The method estimates one or more latent variables based on a first count indicative of a number of the plurality of d-vines, a second count indicative of a number of the one or more records, a first value that is representative of a number of the one or more records clustered into a d-vine from the plurality of d-vines, and a second value that is representative of a parameter utilizable to predict a third value. The method generates the plurality of d-vines based on the estimated one or more latent variables. The method predicts health condition of the first human subject by utilizing a trained classifier based on the estimated one or more latent variables.

Abstract: A method for trip validation includes receiving signals, on a vehicle, from one of a plurality of spaced stationary beacons on a transportation route, the signals including localization data for the respective beacon. A passenger identifier is received from a mobile communication device of a passenger on the vehicle via short range communication. Encrypted transaction data is generated, based on the user identifier, localization data, and a timestamp. The encrypted transaction data is transmitted to passenger mobile communication devices which relay the data to an associated server for processing. The method allows the location of a public transportation device to be identified without the need to provide GPS localization equipment on the vehicle.

Abstract: Disclosed are embodiments of methods and systems for predicting a health condition of a first human subject. The method comprises extracting a historical data including physiological parameters of second human subjects. Thereafter, a first distribution of a first physiological parameter is determined based on a marginal cumulative distribution of a rank transformed historical data. Further, a second distribution of a second physiological parameter is determined based on the first distribution and a first conditional cumulative distribution of the rank transformed historical data. Further, a latent variable is determined based on the first and the second distributions. Thereafter, one or more parameters of at least one bivariate distribution, corresponding to a D-vine copula, are estimated based on the latent variable. Further, a classifier is trained based on the D-vine copula. The classifier is utilizable to predict the health condition of the first human subject based on his/her physiological parameters.

Abstract: A store profile generation system includes a mobile base and an image capture assembly mounted on the base. The assembly includes at least one image capture device for acquiring images of product display units in a product facility, product labels being associated with the product display units which include product-related data. A control unit acquires the images captured by the at least one image capture device at a sequence of locations of the mobile base in the product facility. The control unit extracts the product-related data from the acquired images and constructs a store profile indicating locations of the product labels throughout the product facility, based on the extracted product-related data. The store profile can be used for generating new product labels for a sale in an appropriate order for a person to match to the appropriate locations in a single pass through the store.

Abstract: A system for determining a labor flexibility level for a document management system that meets a performance level of a full-flexibility level and is within one or more threshold limits includes a document management system having a plurality of print production devices, an electronic device in communication with the document management system, and a computer-readable storage medium having one or more programming instructions. The document management system is associated with a plurality of operators who each possess one or more skill sets for operating one or more of the print production devices.

Abstract: Systems and methods of modeling irregularly sampled time series signals with unknown temporal dynamics are disclosed wherein a temporal difference variable (TDV) is introduced to model irregular time differences between subsequent measurements. A hierarchical model is designed comprising two linear dynamical systems that model the effects of evolving TDV on temporal observations. All the parameters of the model, including the temporal dynamics, are statistically estimated using historical data.

Abstract: A dialog device comprises a natural language interfacing device (chat interface or a telephonic device), and a natural language output device (the chat interface, a display device, or a speech synthesizer outputting to the telephonic device). A computer stores natural language dialog conducted via the interfacing device and constructs a current utterance word-by-word. Each word is chosen by applying a plurality of language models to a context comprising concatenation of the stored dialog and the current utterance thus far. Each language model outputs a distribution over the words of a vocabulary. A recurrent neural network (RNN) is applied to the distributions to generate a mixture distribution. The next word is chosen using the mixture distribution. The output device outputs the current natural language utterance after it has been constructed by the computer.

Abstract: The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Abstract: A method and system are disclosed for dynamically estimating an origin-destination matrix. An origin-destination matrix is initialized with a set of origin stops and destination stops. Validation sequences are acquired for a set of travelers on a transportation system which include a plurality of the origin stops and respective timestamps. Corresponding destination stops may be known or inferred. For each validation sequence, a set of subsequences is generated, each including a respective one of the origin stops and the associated timestamp. Subsequences which, in combination, constitute a valid transfer trip are identified. For a combination of subsequences constituting a valid transfer trip, the method includes determining whether the valid transfer trip is a multi-goal trip for which there is least a first destination stop with an intermediate goal and a second destination stop with a final goal. The origin-destination matrix is updated, based on the determination.

Abstract: According to exemplary methods, automated image processing of images from order locations of a multi-lane drive-thru system is continually performed using an automated ordering system. It is automatically determined if a vehicle is present at any of the order locations based on the automated image processing. It is automatically determined if an order has been initiated into the automated ordering system. When the order has been initiated while the vehicle is present at any of the order locations, an automated sequencing processing is automatically begun. The automated sequencing processing determines an order pick-up sequence for picking up orders at pickup windows of the multi-lane drive-thru system. When the order has been initiated while the vehicle is not present at any of the order locations, a potential out-of-sequence warning is automatically associated with the order and output from the automated ordering system.

Abstract: A method and a system are provided for identifying one or more locations for placement of one or more replenishment stations for one or more vehicles. The method comprises receiving a historical demand data at a plurality of existing replenishment stations within a pre-defined area. The method identifies one or more point of interest locations within the pre-defined area based on a map data. Further, the method receives traffic information between a plurality of road intersections within the pre-defined area. Based on an aggregation of a first demand prediction, a second demand prediction, and a third demand prediction, the method predicts a replenishment demand at a plurality of locations. The method further identifies the one or more locations from the plurality of locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the plurality of locations and a pre-defined threshold.

Abstract: The disclosed embodiments illustrate methods and systems for predicting requirements of a user for resources. The method includes transforming a message posted by the user into a first message vector. The method further includes categorizing one or more first message vectors into one or more categories. The method further includes transforming each of the categorized first message vectors into one or more second message vectors using a wavelet transform technique. The method further includes determining, for each of the categorized first message vectors, a first score based on at least a probability distribution of one or more coefficients associated with each associated feature. The method further includes selecting a predefined number of features based on at least the first score. The method further includes training one or more classifiers on the selected predefined number of features to identify at least the one or more needs of the user.

Abstract: A computer-implemented system and method for providing multi-locational curbside valet parking service is provided. A multiplicity of motor vehicle parking spaces are managed by a server. A plurality of smart parking devices and vehicle occupancy sensors are interfaced with the server. Use of a plurality of the parking spaces is reserved for a valet service. A reservation request for the use of the plurality of the parking spaces is accepted for use by the valet service for a period of time. Occupancy of the reserved parking spaces by a motor vehicle is sensed through the vehicle occupancy sensor. A valet drop-off location of at least one of the parking spaces that is reserved for the use of the valet service and is not occupied by a motor vehicle is identified. The valet drop-off location and a location of a lock box are provided to the motorist through a user interface.

Abstract: Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.

Abstract: A dynamic digital signage system based on measured customer behaviors through video analytics.

Abstract: The disclosed embodiments illustrate methods and systems for generating recommendations for client process execution of one or more client processes corresponding to a plurality of clients of an organization. The method comprises retrieving an event log including event data captured during execution of one or more processes in the organization to service a plurality of clients of a predefined type. The event log is analyzed across the plurality of clients to determine cross-clientele information including a process compliance deviation between an observed and an expected client process execution of the one or more processes. Thereafter, a set of root-causes of the process compliance deviation is determined based on process models of the one or more processes and/or decision rules of the organization. Further, one or more recommendations for the client process execution of the one or more processes of the organization are generated, based on the set of root-causes.

Abstract: A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.

Abstract: Methods and systems for preventing vehicle theft. A video stream of a parking area wherein a vehicle is parked can be captured, the video stream provided by a theft notification service to which the vehicle is pre-registered. The vehicle in the video stream can be identified. The video stream is then analyzed to detect motion with respect to the vehicle. An alert can then be generated if motion is detected.