Abstract: System and method for event-based updating of user profiles UP of users U in a social network and formation of new communities based on a resonance condition. A database of user profiles UP with profile domains D based on self-reports as well as observed network behaviors is deployed. The user profiles UP of users U in groups G attending an event are re-compiled by an event-based updating module based on event responses ER. Further, the event-based updating module identifies a profile change ?P* in at least one of the profile domains D of user profiles UP, and the new community is formed by the event-based updating module from a subgroup SG whose profile change ?P* indicates a mutual resonance or consensus in their event responses ER.

Abstract: Annotating and classifying an image based on a user context includes determining a location data of an object captured in an image, determining an attribute data of the object, obtaining sensor data from sensors that are associated with the location data based on the attribute data, determining a recommended user context from one or more predefined user contexts based on a comparison of the location data, the attribute data, and the sensor data with location data, attribute data, and sensor data of one or more images associated with the one or more predefined user contexts, determining a recommended class of the captured image based on the recommended user context, selecting one or more annotation data from the location data, the attribute data, and the sensor data based on the recommended class or the recommended user context, and annotating the image with the one or more annotation data.

Abstract: A system and method for customizing promotional offers to be made available to an affinity group. The system monitors the activities of a member of an affinity group (user) on a social networking portal, and identifies the actions and the behavior of the user in respect of at least one promotional offer made available to him on the social networking portal. The system further identifies at least one promotional offer suitable for the member (user) of the affinity group, based on the monitored actions and behavior of the member of the affinity group. Further, the system also customizes the identified promotional offer in order to render it suitable for the members of the affinity group.

Abstract: Annotating and classifying an image based on a user context includes determining a location data of an object captured in an image, determining an attribute data of the object, obtaining sensor data from sensors that are associated with the location data based on the attribute data, determining a recommended user context from one or more predefined user contexts based on a comparison of the location data, the attribute data, and the sensor data with location data, attribute data, and sensor data of one or more images associated with the one or more predefined user contexts, determining a recommended class of the captured image based on the recommended user context, selecting one or more annotation data from the location data, the attribute data, and the sensor data based on the recommended class or the recommended user context, and annotating the image with the one or more annotation data.

Abstract: Annotating and classifying an image based on a user context includes determining a location data of an object captured in an image, determining an attribute data of the object, obtaining sensor data from sensors that are associated with the location data based on the attribute data, determining a recommended user context from one or more predefined user contexts based on a comparison of the location data, the attribute data, and the sensor data with location data, attribute data, and sensor data of one or more images associated with the one or more predefined user contexts, determining a recommended class of the captured image based on the recommended user context, selecting one or more annotation data from the location data, the attribute data, and the sensor data based on the recommended class or the recommended user context, and annotating the image with the one or more annotation data.

Abstract: A smart premise system has a router having a processor, connection to a data repository, a primary port to Internet connection on a primary path, and wired connection or wireless coupling to individual ones of a plurality of bandwidth-using devices forming a local premise network, bandwidth-using devices sharing bandwidth on the primary port, and software executing on the processor from a non-transitory medium, execution of the software providing a default bandwidth share and a set of rules for registered users and registered devices in the smart premise system, the default share managed by the router according to the default rules for each user of the system using a registered device, and a mechanism for individual users to monitor their own bandwidth share in real time, and to request additional bandwidth from the system.

Abstract: A router has a processor, a data repository, a primary port to Internet connection on a primary path, wired connection or wireless coupling to individual ones of a plurality of bandwidth-using devices forming a local premise network, the router and the bandwidth-using devices sharing bandwidth on the primary port, software executing on the processor from a non-transitory medium, execution of the software providing a mechanism for configuration, applying a priority level to individual ones of the bandwidth-using devices in the local premise network, monitoring bandwidth available on the primary path on a periodic or continuous basis, apportioning bandwidth share among the devices in the local premise according to the priority level of each device and the current bandwidth available, and managing receipt and transmission of packets to and from individual ones of the devices according to the bandwidth share apportioned.

Abstract: A system and method that computes a probability score indicating the probability that the user would prefer a particular item. The system prompts the user to review a particular item and subsequently prompts the user to reveal whether he liked the item or otherwise. The search engine also extracts the other review scores which were generated by the reviewers in respect of the item name specified by the user. Subsequently, the search engine also elicits the other review scores, preferably along with the corresponding reviews, in respect of the items reviewed by the reviewers who also reviewed the item specified by the user. The system further calculates probability scores indicating the probability that a user would prefer any of the items related to the reviews generated by the reviewers who also reviewed the item specified by the user. Accordingly the system generates recommendations based on the probability scores.

Abstract: A computer implemented system and method includes a recommendation engine that provides accurate recommendations based on an accurate analysis of the tastes and preferences of a user. The recommendation engine takes into consideration the information corresponding to the tastes and preferences of the users using information gathered from social networking profiles of the user as well as the reviews previously provided by the user. The recommendation engine collaborates this information with the review related information obtained from the reviewers in order to ascertain the recommendations that would match the preferences and tastes of the user.

Abstract: A method and system includes predicting energy consumption of a vehicle using a statistical model. The method includes obtaining a plurality of input vectors for plurality of points in time, wherein each input vector includes a plurality of variables with a weight vector. Thereafter, the energy level for each input vector is captured for each point in time. Subsequent to capturing the energy level, the method includes predicting a change in energy level of the vehicle using the statistical model.

Abstract: Automatically classifying multimedia content that is specific to a video production includes obtaining, from a video sensor embedded in a video capturing device that captures a video, a time series of location data, direction data, orientation data, and a position of the first user and the second user, identifying for any given duration in the video, corresponding first user data, corresponding second user data, and corresponding video sensor data, annotating the video with the corresponding first user data, the corresponding second user data, and the corresponding video sensor data to obtain an annotated multimedia content, performing a comparison of a data pattern of the annotated multimedia content with data patterns of a script stored in a database to obtain a recommended section for the annotated multimedia content, and automatically classifying the annotated multimedia content by associating the annotated multimedia content with the recommended section.

Abstract: A router has a processor, a data repository, wired connection or wireless coupling to individual ones of a plurality of power-using devices in a local premise, the router and the power-using devices drawing power from a primary source, and individual ones of the router and the power-using devices having switchable access to one or more alternative power sources, an Internet access connection, and software executing on the processor from a non-transitory medium. The software provides monitoring power provided by the primary source to the router and to individual ones of the power-using devices, receiving information regarding the primary power source from one or more network-connected servers, determining expected status of the primary power source by the software using the monitoring information and the information received from the Internet, and managing power usage by the router and the power-using devices according to the expected status determined.

Abstract: Annotating and classifying an image based on a user context includes determining a location data of an object captured in an image, determining an attribute data of the object, obtaining sensor data from sensors that are associated with the location data based on the attribute data, determining a recommended user context from one or more predefined user contexts based on a comparison of the location data, the attribute data, and the sensor data with location data, attribute data, and sensor data of one or more images associated with the one or more predefined user contexts, determining a recommended class of the captured image based on the recommended user context, selecting one or more annotation data from the location data, the attribute data, and the sensor data based on the recommended class or the recommended user context, and annotating the image with the one or more annotation data.

Abstract: A system and method for adaptive online learning management for creating and/or publishing customized, personalized, adaptive, and student-specific online course content to enable and provide an efficient learning environment to each student based on the dynamically changing self-evolving student's profile. A student's profile can be created based on factors such as demographic profile, psychographic profile, learning style, personality traits, interests, social networking profile, social media interactions, online interaction characteristics, social networking circle attributes, prerequisite knowledge assessments, social profile, skill, and performance of the student, wherein the student's profile can be processed with respect to a learning object repository to generate a defined set of student-specific learning objects that best suit the profile of the student. A online course content can accordingly be generated based on the defined set of student-specific learning objects.

Abstract: A system and method for creation and/or publication of student-specific adaptive personalized content and/or textbook to enable and provide an efficient learning environment to each based on the student's profile. A student's profile is created based on factors such as learning style, prerequisite knowledge, personality, interests, previous learning experiences, demographic and psychological, among other parameters. The student's profile is processed with respect to a learning object repository to generate a defined set of student-specific learning objects that best suit the profile of the student. A textbook and/or course content can accordingly be generated based on the defined set of student-specific learning objects. Such student-specific textbooks and/or course content can also be adapted/modified in real-time based on changes in the student profile and/or learning object repository.

Abstract: Observation-based user profiling and profile matching are provided. The network behavior of users of a computer-implemented social network are observed and used for user profiling. By observing network behavior instead of necessarily relying on user self-reported data, accurate and objective user profiles can be formed; user profiling is accomplished based on the observed network behaviors with or without the knowledge of the user being profiled. The observed network behaviors can include the customization of a visual graphic, a media preference, a communication preference, or a selection of words from a word list. The user profiles can be with respect to a domain and two or more users can be matched based on their profiles with respect to the same domain. User ratings and profile updating based on the ratings are also provided.

Abstract: Calibration and publication of user activity on a computer implemented social network is provided. Activity signals of user activity is measured by an activity monitor and calibrated by the user. The calibration includes assigning an activity signal with a personalized identifier for the activity for comparison with future measured activity signals. Near real-time publication of the activity on the social network allow users of the network to timely view user behavior. The publication on a social networking website includes the identifier if the activity has been calibrated. By calibrating and publishing user activity, users can connect to others based on the activity. The publication can be on newsfeed on a user profile for friends of the user to view. A user and the user's friends can communicate with each other, which allows the user to receive motivation from the friends and be accountable to the friends.

Abstract: Described herein are devices, systems, and methods for managing the power consumption of an automotive vehicle, and thereby for optimizing the power consumption of the vehicle. The devices and systems for managing the power consumption of the vehicle typically include power management logic that can calculate an applied power for the vehicle engine based on information provided from the external environment of the vehicle, the operational status of the vehicle, one or more command inputs from a driver, and one or more operational parameters of the vehicle.

Abstract: Described herein are devices, systems, and methods for managing the power consumption of an automotive vehicle, and thereby for optimizing the power consumption of the vehicle. The devices and systems for managing the power consumption of the vehicle typically include power management logic that can calculate an applied power for the vehicle engine based on information provided from the external environment of the vehicle, the operational status of the vehicle, one or more command inputs from a driver, and one or more operational parameters of the vehicle.

Abstract: System and method for event-based updating of user profiles UP of users U in a social network and formation of new communities based on a resonance condition. A database of user profiles UP with profile domains D based on self-reports as well as observed network behaviors is deployed. The user profiles UP of users U in groups G attending an event are re-compiled by an event-based updating module based on event responses ER. Further, the event-based updating module identifies a profile change ?P* in at least one of the profile domains D of user profiles UP, and the new community is formed by the event-based updating module from a subgroup SG whose profile change ?P* indicates a mutual resonance or consensus in their event responses ER.