Abstract: Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Abstract: Systems, methods, and computer-readable for multi-temporal scale analysis include obtaining two or more timescales associated with one or more images. A context associated with a monitoring objective is obtained, based on real time analytics or domain specific knowledge. The monitoring objective can include object detection, event detection, pattern recognition, or other. At least a subset of timescales for performing a differential analysis on the one or more images is determined based on the context. Multi timescale surprise detection and clustering are performed using the subset of timescales to determine whether any alerts are to be generated based on entropy based surprises. A set of rules can be created for the monitoring objective based on the differential analytics and alerts or entropy based surprises, if any.

Abstract: Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Abstract: Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Abstract: Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Abstract: Systems, methods, and computer-readable for multi-temporal scale analysis include obtaining two or more timescales associated with one or more images. A context associated with a monitoring objective is obtained, based on real time analytics or domain specific knowledge. The monitoring objective can include object detection, event detection, pattern recognition, or other. At least a subset of timescales for performing a differential analysis on the one or more images is determined based on the context. Multi timescale surprise detection and clustering are performed using the subset of timescales to determine whether any alerts are to be generated based on entropy based surprises. A set of rules can be created for the monitoring objective based on the differential analytics and alerts or entropy based surprises, if any.

Abstract: In one embodiment, a device in a network receives a time-slotted channel hopping (TSCH) communication schedule. The TSCH communication schedule is divided into a plurality of macrocells, each macrocell comprising a plurality of TSCH cells. The device receives a packet from a routing protocol child node of the device during a particular macrocell of the TSCH communication schedule that is associated with propagation of the packet through the network. In response to receiving the packet, the device claims a token associated with the particular macrocell that authorizes the device to transmit during one or more cells of the macrocell. The device transmits the received packet to a second node in the network during the authorized one or more cells of the particular macrocell.

Abstract: In one embodiment, a device in a network receives a time-slotted channel hopping (TSCH) communication schedule. The TSCH communication schedule is divided into a plurality of macrocells, each macrocell comprising a plurality of TSCH cells. The device receives a packet from a routing protocol child node of the device during a particular macrocell of the TSCH communication schedule that is associated with propagation of the packet through the network. In response to receiving the packet, the device claims a token associated with the particular macrocell that authorizes the device to transmit during one or more cells of the macrocell. The device transmits the received packet to a second node in the network during the authorized one or more cells of the particular macrocell.

Abstract: A system for providing content to a user, comprising a transmitter 502 for transmitting to a user's receiver/decoder 2000 guide data relating to a plurality of items of content, and each of said items of content to which said guide data relates, prior to commencement of a period of availability of said items of content to the user; and a receiver/decoder 2100 comprising means 2016 for receiving said guide data and said items of content, means 3120 for generating a display of said guide data to enable a user to select at least one of said items of content for storage, means 2100 for storing the selected at least one item of content upon receipt thereof, and means 3120 for enabling the user to access the stored at least one item of content upon request following commencement of said period.

Abstract: A system for providing content to a user, comprising a transmitter 502 for transmitting to a user's receiver/decoder 2000 guide data relating to a plurality of items of content, and each of said items of content to which said guide data relates. prior to commencement of a period of availability of said items of content to the user; and a receiver/decoder 2100 comprising means 2016 for receiving said guide data and said items of content, means 3120 for generating a display of said guide data to enable a user to select at least one of said items of content for storage, means 2100 for storing the selected at least one item of content upon receipt thereof, and means 3120 for enabling the user to access the stored at least one item of content upon request following commencement of said period.

Abstract: A method for retrieving metadata for use in a content guide is disclosed. The method includes: crawling one or more crawlable data sources; storing metadata extracted from the one or more crawlable data sources in an indexed cache; receiving a search request from a client according to search criteria, the search request requesting metadata for use in the content guide; searching a subset of the indexed cache according to the search criteria; extracting metadata from the indexed cache as results of the searching; identifying relevant metadata in the results, the relevant metadata including metadata suitable for use by the client in the content guide; and transmitting the relevant metadata to the client for use in the content guide.

Abstract: A method for selecting advertisement in a Set Top Box (STB) of a television network comprises receiving advertisement selection information for one or a plurality of advertisement associations. Each advertisement association associates an advertisement space with an advertisement. The advertisement selection information corresponds for each advertisement association to a value of probability for selecting the advertisement for the advertisement space. A selection of an advertisement for a determined advertisement space is triggered and a random value or a pseudo-random value generated. An advertisement is selected using the random value or the pseudo-random value, by selecting a determined advertisement association that associates an advertisement with the determined advertisement space and for which the corresponding advertisement selection information has a value of probability that relates to the random value or the pseudo-random value.

Abstract: A content sharing system, for implementation in a requesting peer, to receive at least a part of a chunk from a serving peer, the chunk being part of a content item, the requesting peer being operationally connected to a plurality of peers including the serving peer via a communications network, the content item being media content originally broadcast in a media stream by a Headend to at least some of the peers, the system including a metadata module to receive chunk metadata identifying the location of the chunk based on an identifier in the media stream originally broadcast by the Headend, a content transfer module to request the at least part of the chunk from the serving peer based on the chunk metadata, and receive the at least part of the chunk from the serving peer. Related apparatus and methods are also described.

Abstract: A method for identifying additional content information to a content consumer, is disclosed. The additional content information relates to a content item selected for consumption by the content consumer. The method comprises: receiving data published by a content provider (203,205), wherein said data describes content information (207) available from said content provider; indexing said data thereby producing indexed data; storing said indexed data in a database (217); receiving a search request from a content consumer, said search request requesting said additional content information; searching said database (217) for data describing said additional content information; and identifying said additional content information by returning data identified during said searching to said content consumer.