Abstract: Technologies for automated context-aware media curation include a computing device that captures context data associated with media objects. The context data may include location data, proximity data, behavior data of the user, and social activity data. The computing device generates inferred context data using one or more cognitive or machine learning algorithms. The inferred context data may include semantic time or location data, activity data, or sentiment data. The computing device updates a user context model and an expanded media object graph based on the context data and the inferred context data. The computing device selects one or more target media objects using the user context model and the expanded media object graph. The computing device may present context-aware media experiences to the user with the target media objects. Context-aware media experiences may include contextual semantic search and contextual media browsing. Other embodiments are described and claimed.

Abstract: Technologies for automated context-aware media curation include a computing device that captures context data associated with media objects. The context data may include location data, proximity data, behavior data of the user, and social activity data. The computing device generates inferred context data using one or more cognitive or machine learning algorithms. The inferred context data may include semantic time or location data, activity data, or sentiment data. The computing device updates a user context model and an expanded media object graph based on the context data and the inferred context data. The computing device selects one or more target media objects using the user context model and the expanded media object graph. The computing device may present context-aware media experiences to the user with the target media objects. Context-aware media experiences may include contextual semantic search and contextual media browsing. Other embodiments are described and claimed.

Abstract: A data processing system includes components for providing a pleasant user experience. Those components may include a family interaction engine that provides a family channel. The family interaction engine may provide for creation of a user group. The family channel may present content of interest to multiple users in the user group. When a user is detected near the data processing system, the family interaction engine may automatically present content of interest to that user. When used for presenting media content, the data processing system may also cause supplemental data to automatically be presented, wherein the supplemental data is relevant to the media content and to a predetermined interest of the user. The data processing system may also provide a ranked list of applications for potential activation by the user. The applications may be ordered based on the current context. Other embodiments are described and claimed.

Abstract: One embodiment provides an apparatus. The apparatus includes a processor; at least one peripheral device coupled to the processor; a memory coupled to the processor; a generic sentiment model and a first domain training corpus stored in memory; and a hybrid sentiment analyzer logic stored in memory and to execute on the processor. The hybrid sentiment analyzer logic includes a sentiment lexicon generator logic to generate a domain sentiment lexicon based, at least in part, on the first domain training corpus and to store the domain sentiment lexicon in memory, a lexicon-based sentiment classifier logic to generate an annotated training corpus unsupervisedly, based, at least in part, on the domain sentiment lexicon and to store the annotated training corpus in memory, and a model-based sentiment adaptor logic to adapt the generic sentiment model based, at least in part, on the annotated training corpus to generate an adapted sentiment model and to store the adapted sentiment model in memory.

Abstract: A container (e.g., a garbage bin or document disposal bin) includes a sensor for sensing a quantity indicative of the amount of items deposited into the container, and a local controller capable of wireless communication with a remote controller for sending the sensed data to the remote controller. The sensor may be mounted on the cover or at the bottom of the container. The sensor placed on the cover may be a range finder that measures the distance between the container and its content. The sensor that is placed at the bottom of the container may be sensitive to the weight of the container and its contents. The sensor may be, for example, a reflective IR range finder, or a pressure sensor, such as a force sensing resistor. In addition, the sensed quantity may be the presence of a gaseous compound, such as hydrogen sulfide, ammonia and methane.

Abstract: An air duct cover provides a sensor platform that contains a rich set of sensors. In addition, the air duct cover may include an energy harvesting capability, thereby increasing efficiency in an HVAC system and sustaining the sensors on the sensor platform. The air duct cover may also integrate a smoke detection device. As power is provided by the air duct cover, maintenance requirements for the smoke detection device are substantially eliminated. The air duct cover may also serve as an air quality control device, with dust, smoke, and carbon monoxide (CO) sensors that allow it to detect the amount of dust flowing through and to serve as a smoke detector.

Abstract: A smart facility management platform (SFMP) includes wireless sensors installed in containers for waste disposals and containers holding supplies, so that the use status of these monitored containers may be automatically transmitted to a remote management service over a computer network, e.g., cloud service. Facility managers and supervisors may monitor the use status in real time and accordingly schedule on-demand cleanup operations, based on criteria set up in the SFMP. The SFMP may further have an event generation capability for notifying the supervisors and janitors through application programs running on mobile devices (“mobile apps”), SMS messages or email, when a service condition arises (e.g., garbage can fills up, towel dispenser runs out of towels, sensor has a low-battery condition, or any malfunction). In addition, the SFMP also allows better management of inventory for facility management companies and reduces wastage of supplies, such as garbage bags and air filters.

Abstract: A method may include detecting motion of a mobile device and inferring from the motion a first activity of a user of the mobile device. The method may also include evaluating a signal quality of a radio signal received by a signal module and altering an operational mode of the signal module based on the signal quality and the first activity.

Abstract: A data processing system includes components for providing a pleasant user experience. Those components may include a family interaction engine that provides a family channel. The family interaction engine may provide for creation of a user group. The family channel may present content of interest to multiple users in the user group. When a user is detected near the data processing system, the family interaction engine may automatically present content of interest to that user. When used for presenting media content, the data processing system may also cause supplemental data to automatically be presented, wherein the supplemental data is relevant to the media content and to a predetermined interest of the user. The data processing system may also provide a ranked list of applications for potential activation by the user. The applications may be ordered based on the current context. Other embodiments are described and claimed.

Abstract: Techniques for pose estimation and false positive filtering for gesture recognition are described. For example, a method may comprise receiving data from one or more sensors indicating motion of an electronic device, determining if the motion comprises a gesture motion using one or more statistical gesture recognition algorithms, determining a start pose and an end pose for the gesture motion, determining if the start pose and end pose of the gesture motion correspond to a start pose and end pose of a gesture model corresponding to the gesture motion, and triggering a gesture event if the start pose and end pose of the gesture motion match the start pose and end pose of the gesture model. Other embodiments are described and claimed.

Abstract: A method may include detecting motion of a mobile device and inferring from the motion a first activity of a user of the mobile device. The method may also include evaluating a signal quality of a radio signal received by a signal module and altering an operational mode of the signal module based on the signal quality and the first activity.

Abstract: A method, apparatus, and article containing computer instructions are described. Embodiments may use accelerometer data regarding forward motion by a wearer of a three-axis on-body accelerometer. Embodiments may further measure an acceleration due to gravity on each axis x, y, z of the accelerometer and use the direction of gravity to associate or align the x axis of the accelerometer with gravity. Embodiments may then use the acceleration not due to gravity to identify the forward motion and associate or align the forward direction with the y axis. The remaining direction may be identified as the sideways direction, which may be associated or aligned with the z axis. Additional activities may then be performed using the now-known orientation of the accelerometer. Other embodiments are described and claimed.

Abstract: A method, apparatus, and article containing computer instructions are described. Embodiments may use accelerometer data regarding forward motion by a wearer of a three-axis on-body accelerometer. Embodiments may further measure an acceleration due to gravity on each axis x, y, z of the accelerometer and use the direction of gravity to associate or align the x axis of the accelerometer with gravity. Embodiments may then use the acceleration not due to gravity to identify the forward motion and associate or align the forward direction with the y axis. The remaining direction may be identified as the sideways direction, which may be associated or aligned with the z axis. Additional activities may then be performed using the now-known orientation of the accelerometer. Other embodiments are described and claimed.

Abstract: A state classifier uses learning obtained from a plurality of training algorithms each adapted to differentiate between states of physical orientation of an object in response to input data from an tri-axial accelerometer. At least two of the training algorithms are trained using data from an accelerometer mounted at a non-ideal angle. The classifier is trained to distinguish between the desired states from data collected from an tri-axial accelerometer device mounted at a plurality of respective angles with respect to a optimal axis on the object, wherein the angles are in the range of ?180 degrees to +180 degrees. The classifier may include a plurality of classifiers and a decision fusion module used to combine the decisions from the respective classifiers to ascertain a state.

Abstract: A state classifier uses learning obtained from a plurality of training algorithms each adapted to differentiate between states of physical orientation of an object in response to input data from an tri-axial accelerometer. At least two of the training algorithms are trained using data from an accelerometer mounted at a non-ideal angle. The classifier is trained to distinguish between the desired states from data collected from an tri-axial accelerometer device mounted at a plurality of respective angles with respect to a optimal axis on the object, wherein the angles are in the range of ?180 degrees to +180 degrees. The classifier may include a plurality of classifiers and a decision fusion module used to combine the decisions from the respective classifiers to ascertain a state.