Abstract: A network-based data store maintaining storage volumes for file systems at storage clients may implement dynamic storage volume scaling. A scaling event may be detected according to a specified scaling policy for a storage volume maintained at a network-based data store that is mounted at a storage client and configured for a file system. In a least some embodiments, the scaling policy for the storage volume may be received from the storage client. In response to detecting the storage event, storage maintaining the storage volume may be modified according to the scaling policy to scale the storage volume. An indication of the scaling event may be sent to the storage client in order to update the file system at the storage client so that the file system may utilize the scaled storage volume.

Abstract: Systems and methods include a cloud hub located in a premises, and the cloud hub comprises adapters configured for coupling to premises devices. The cloud hub is configured as a gateway for the premises devices. The system includes a virtual gateway located in a cloud server environment and coupled to the cloud hub. The virtual gateway is configured as a server-side abstraction of the cloud hub. The cloud hub and the virtual gateway are configured as an automation platform that maintains state data of the premises devices, controls interaction among the premises devices, and monitors and manages the premises devices.

Abstract: A photograph sharing system is described. The photograph sharing system allows for contributors to upload photographs of an event. Contributors or other application users may select an uploaded photograph in a desired section and digitally zoom in on a desired individual in that section and then download the zoomed in photograph.

Abstract: A system and methods comprise a touchscreen at a premises. The touchscreen includes a processor coupled to a security system at the premises. User interfaces are presented via the touchscreen. The user interfaces include a security interface that provides control of functions of the security system and access to data collected by the security system, and a network interface that provides access to network devices. A camera at the premises is coupled to the touchscreen via a plurality of interfaces. A security server at a remote location is coupled to the touchscreen via a plurality of channels and a plurality of protocols. The security server comprises a client interface through which remote client devices exchange data with the touchscreen and the security system.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: Disclosed implementations display information about entities in a structured way. In one aspect, a method is performed at a server system comprising memory and processors. The server receives a request from a client device for an information page associated with an entity belonging to a viewable media category. The server identifies predefined information types associated with the viewable media category and populates a first region of a search result page with information about the entity corresponding to the predefined information types. Responsive to determining that the first entity is available for streaming or downloading, the server populates a second region of the search result page with a location-based information type that is distinct from the set of predefined information types and populated using information about a location from which the first entity can be streamed or downloaded. The server transmits the search result page to the client device.

Abstract: A warning monitoring system includes: a warning information generation unit configured to receive a warning and a ripple warning generated due to the warning, each including a device name and sent by a device configuring a network, determine respective types of the warning and the ripple warning based on messages included in the warning and the ripple warning, and generate warning information in which the warning and the ripple warning are associated with the types; a network configuration database in which configuration information about the network is accumulated; and an interrelation analysis unit including a prior rule that indicates a generation manner of the ripple warning different depending on the type, and configured to acquire specific connection destination information by searching the network configuration database by using target information defined by the prior rule and the device name as keys, and relate the warning and the ripple warning.

Abstract: A service data transmission method and apparatus are provided. The method includes: obtaining, by an information control center, first information from a service server, where the first information includes service information and/or application information; obtaining, by the information control center, second information from user equipment, where the second information includes at least one of service information, mobility information, behavior information, and status information of the user equipment; and controlling, by the information control center, transmission of service data of the user equipment based on the first information and/or the second information. In the embodiments of the present disclosure, transmission of the service data can be controlled based on specific information that is about the service data and that is obtained by the information control center, and different transmission quality is provided for different types of service data.

Abstract: The present disclosure generally relates to a feedback processing service that can receive customer input, as customer feedback corresponds to a service context. The feedback processing service aggregates semantically similar feedback as a cluster. Then, the feedback processing service can prioritize each of the clusters by ranking each of the clusters.

Abstract: A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Abstract: A method begins by a drive unit affiliated with farm equipment receiving data from the farm equipment to produce agricultural data. The method continues with the drive unit determining a filtering constraint based on one or more parameters selected from a plurality of lists of agricultural parameters and filtering the agricultural data based on the filtering constraint to produce filtered agricultural data. The method continues with the drive unit determining processing of the filtered agricultural data and executing the processing of the filtered agricultural data.

Abstract: A method for load balancing application requests across a multi-cluster container orchestration system includes receiving a load-balancing configuration for a multi-cluster service managing access to a set of destination clusters hosting a software application deployed by a user. The multi-cluster service uses the load-balancing configuration to load balance application level traffic across the set of destination clusters. Each destination cluster includes at least one container executing the software application and a respective geographical region. The method also includes receiving an application level request directed toward the software application hosted across the destination clusters. The application level request is received from a client and includes a host name and a geographical location associated with the client. The method also includes routing the request to one of the clusters based on the geographical location of the request and the respective geographical locations of the clusters.

Abstract: The disclosure is related to adaptive transcoding of video streams from a camera. A camera system includes a camera and a base station connected to each other in a first communication network, which can be a wireless network. When a user requests to view a video from the camera, the base station obtains a video stream from the camera, transcodes the video stream, based on one or more input parameters, to generate a transcoded video stream, and transmits the transcoded video stream to a user device. The base station can transcode the video stream locally, e.g., within the base station, or in a cloud network based on transcoding location factors. Further, the camera system can also determine whether to stream the video to the user directly from the base station or from the cloud network based on streaming location factors.

Abstract: A computer-implemented method and a system are provided for providing enrichment information using Hypertext Transfer Protocol Secure (HTTPS). The method includes: extracting and storing enrichment information received in a client handshake initiator packet for negotiating an encryption protocol for a network session of a Hypertext Transfer Protocol Secure (HTTPS) packet stream at a termination of a security protocol in a network; and obtaining one or more Hypertext Transfer Protocol (HTTP) requests by decrypting the Hypertext Transfer Protocol Secure (HTTPS) packet stream of the network session, and adding the enrichment information as a header to at least the first HTTP request being routed towards a server.

Abstract: The disclosure includes embodiments for adaptive sensor data sharing by a connected vehicle. A method includes calculating, by a processor of an ego vehicle, a view angle overlap between a first sensor of the ego vehicle and a second sensor of a roadway device, wherein the view angle overlap is an amount of sensor view overlap shared by first sensor and the second sensor. The method includes determining an amount of ego sensor data to share with the roadway device based on the view angle overlap. The method includes building a sharing message that includes the amount of ego sensor data in a payload of the sharing message. The method includes transmitting, by a communication unit of the ego vehicle, the sharing message to the roadway device.

Abstract: A method for predicting a time-to-failure of a target storage device may include training a machine learning scheme with a time-series dataset, and applying the telemetry data from the target storage device to the machine learning scheme which may output a time-window based time-to-failure prediction. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include applying a data quality improvement framework to a time-series dataset of operational and failure data from multiple storage devices, and training the scheme with the pre-processed dataset. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include training the scheme with a first portion of a time-series dataset of operational and failure data from multiple storage devices, testing the machine learning scheme with a second portion of the time-series dataset, and evaluating the machine learning scheme.

Abstract: A method for detecting fraud rings involves clustering unknown users into unknown user clusters based on a grouping attribute. The method further involves, for each of the unknown user clusters, determining aggregated features including at least one quantification of at least one homogeneity attribute across the unknown users in the unknown user cluster. The method also involves, for each of the unknown user clusters, determining a predictive suspiciousness score based on the aggregated features, determining that at least one of the unknown user clusters is suspicious based on the determined predictive suspiciousness scores, and taking a protective action for the at least one suspicious unknown user cluster.

Abstract: Consolidating events to execute objects to extract, transform, and load data from source systems to a structured data store. An event manager process executing on a server runtime utilizes one or more event properties to determine which events can be consolidated to reduce unnecessary processor utilization.

Abstract: Techniques for generating and monitoring service level objectives (SLOs) are disclosed. The techniques include an SLO platform performing: storing a first SLO definition of a first SLO including a first error budget for a first metric associated with a first service; storing a second SLO definition of a second SLO including a second error budget for a second metric associated with a second service; obtaining first telemetry data from a first data source associated with the first service; obtaining second telemetry data from a second data source associated with the second service; monitoring the first SLO at least by computing the first metric based on the first telemetry data and evaluating the first metric against the first error budget; and monitoring the second SLO at least by computing the second metric based on the second telemetry data and evaluating the second metric against the second error budget.

Abstract: Handling unacceptable behavior by a participant in a video conference includes detecting the unacceptable behavior by the participant by applying machine learning to data about the participant received from one or more capturing devices and by using a predetermined list of bad habits, determining recognition accuracy for the unacceptable behavior, and providing a response to the unacceptable behavior that varies according to the recognition accuracy. The machine learning may include an initial training phase that, prior to deployment, is used to obtain a general recognition capability for each item on the predetermined list of bad habits. The one or more capturing devices may include a laptop with a camera and a microphone, a mobile device, autonomous cameras, add-on cameras, headsets, regular speakers, smart watches, wristbands, smart rings, and wearable sensors, smart eyewear, heads-up displays, headbands, and/or smart footwear.