Abstract: Certain embodiments described herein are generally directed to processing domain objects in a distributed system using logical sharding. In some embodiments, a central control plane (CCP) node receives a domain object. In some embodiments, if the CCP node determines that the domain object is not already present in a shared data store and that the CCP node is the logical master of the domain object, the CCP node generates a status based on the domain object, and stores the status and domain object in the shared data store. In some embodiments, the shared data store notifies the plurality of CCP nodes of the stored status and domain object.

Abstract: Data processing methods, live broadcasting methods and devices are disclosed. An example data processing method may comprise converting audio and video data into broadcast data in a predetermined format, and performing speech recognition on audio data in the audio and video data, and adding the text information obtained from speech recognition into the broadcast data. In real time, text information obtained from speech recognition according to the audio data can be inserted.

Abstract: In a disclosed embodiment, a method for communication in a network includes receiving, at a first device registered to the network, a physical layer (PHY) frame that includes a PHY header and a MAC header. The PHY frame may further include a MAC payload. The PHY header includes a destination address field. The method further includes comparing a network address of the first device to the destination address field to determine whether the destination address field stores a value having the same number of bits as the network address. When the comparison indicates that the value stored by the destination address field does not have the same number of bits as the network address, the method skips decoding the MAC header and the MAC payload.

Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.

Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

Abstract: Techniques are disclosed to optimize feature selection in generating betas for a feature dictionary of a neuro-linguistic Cognitive AI System. A machine learning engine receives a sample vector of input data to be analyzed by the neuro-linguistic Cognitive AI System. The neuro-linguistic Cognitive AI System is configured to generate multiple betas for each of a plurality of sensors. The machine learning engine identifies a sensor specified in the sample vector and selects optimization parameters for generating betas based on the identified sensor.

Abstract: Copolymers formed by copolymerization of: (1) one or more hydrofluoroolefin monomer(s) selected from the group consisting of hydrofluoroethylenes, hydrofluoropropenes, hydrofluorobutenes, hydrofluoropentenes and combinations of these; (2) one or more aromatic vinyl ester monomer(s); (3) one or more vinyl ester monomer(s); and (4) one or more vinyl ether monomer(s), wherein at least a portion of said vinyl ether monomer is a hydroxyl group-containing vinyl ether monomer.

Abstract: A process of producing an endcapped fluorocopolymer including the forming of a fluorocopolymer by copolymerization of: (1) one or more hydrofluoroolefin monomer(s) selected from the group consisting of hydrofluoroethylenes, hydrofluoropropenes, hydrofluorobutenes, hydrofluoropentenes and combinations of these; (2) one or more vinyl ester monomer(s); and (3) one or more vinyl ether monomer(s), wherein at least a portion of said vinyl ether monomer is a hydroxyl group-containing vinyl ether monomer; and at the end of copolymerization, reacting a radical transfer agent, preferably methanol, with the fluorocopolymer to produce the endcapped fluorocopolymer, preferably containing ether end groups.

Abstract: The embodiments of the present application include acquiring a monitoring region and each observation point therein; partitioning the monitoring region into N layers of grids according to a seismic source positioning accuracy, wherein a side length of a grid cell of an i-th layer of grid is D/2i-1, i=1, . . . N, and D is an initial side length of the grid cell and not more than a double of a distance between the respective observation points; searching all nodes in a first layer of grid to acquire a node satisfying a preset condition therefrom; from i=2, determining and searching nodes satisfying a first preset requirement in the i-th layer of grid, to acquire a node satisfying the preset condition therefrom, until a search in an N-th layer of grid is completed, wherein a node satisfying the preset condition acquired in the N-th layer of grid is a seismic source point location.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: The present disclosure discloses a pre-roll advertisement playing method, a terminal, and a video server, and relates to the field of multimedia technologies. The method includes: after detecting a play operation of an offline video, determining a current network connection status of a terminal; when the terminal is in a first connection state, determining, among multiple pre-roll advertisements obtained by the terminal, a specified pre-roll advertisement corresponding to the offline video; and playing the specified pre-roll advertisement in a process of playing the offline video. When playing an offline video in a non-WiFi connection state, the terminal may additionally play a pre-roll advertisement. Therefore, an audience range of the pre-roll advertisement is expanded, and an application range is relatively wide.

Abstract: A method for selecting a bottomhole assembly (BHA) includes inputting casing while drilling BHA parameters, wellbore parameters, and casing while drilling operating parameters, performing a dynamic simulation of a first BHA based on the casing while drilling BHA parameters, wellbore parameters, and casing while drilling operating parameters, and presenting a first set of performance data of the first BHA calculated from the dynamic simulation.

Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include receiving data for an object within the scene and determining whether the object has remained substantially stationary within the scene for at least a threshold period. If the object is determined to have remained stationary for at least the threshold period, a rareness score is calculated for the object to indicate a likelihood of the object being stationary to an observed degree at an observed location. The rareness score may use a learning model to take into account previous stationary and/or non-stationary behavior of objects within the scene. In general, the learning model may be updated based on observed stationary and/or non-stationary behaviors of the objects. If the rareness score meets reporting conditions, the stationary object event may be reported.

Abstract: An approach for an adaptive, performance-oriented, and compression-assisted encryption scheme implemented on a host computer to adaptively improve utilization of CPU resources is provided. The method comprises queueing a new data packet and determining a size of the new data packet. Based on historical data, a plurality of already encrypted data packets is determined. Based on information stored for the plurality of already encrypted data packets, an average ratio of compression for the plurality of already encrypted data packets is determined. Based on the average ratio of compression, a throughput of compression value and a throughput of encryption value, a prediction whether compressing the new data packet will reduce a CPU load is derived. If it is determined that compressing the new data packet will improve utilization of the CPU resources, then a compressed new data packet is generated by compressing the new data packet.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.

Abstract: Methods, apparatus and articles of manufacture to use artificial intelligence to define encryption and security policies in a software defined data center are disclosed. Example apparatus include a language parser to parse a natural language statement into a policy statement that defines a distributed network encryption policy or a distributed network security policy. Example apparatus also include a comparator to compare the policy statement to a set of reference policy templates and a template configurer to select a first policy template from the set of reference policy templates in response to the comparator determining the first policy template corresponds to the policy statement. A policy distributor distributes a policy rule defined by the first policy template for enforcement at network nodes of a software defined data center. The policy rule is a distributed network encryption policy rule or a security policy rule.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: Certain embodiments described herein are generally directed to enabling a group of host machines within a network to securely communicate an unknown unicast packet. In some embodiments, a key policy is defined exclusively for the secure communication of unknown unicast packets. The key policy is transmitted by a central controller to the group of host machines for negotiating session keys among each other when communicating unknown unicast packets.

Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.

Abstract: Exemplary methods, apparatuses, and systems include a central controller receiving a request to generate a new encryption key for a security group to replace a current encryption key for the security group. The security group includes a plurality of hosts that each encrypt and decrypt communications using the current encryption key. In response to receiving the request, the central controller determines that a threshold period following generation of the current encryption key has not expired. In response to determining that the threshold period has not expired, the central controller delays execution of the request until the expiration of the threshold period. In response to the expiration of the threshold period, the central controller executes the request by generating the new encryption key, storing a time of creation of the new encryption key, and transmitting the new encryption key to the plurality of hosts.