Abstract: A system and a method to map attack paths in a visualization interface may include storing in a memory asset inventory indicating application assets, attack vector parameters configured to indicate vulnerabilities of one or more of the application assets, and asset mapping information. A processor may determine multiple vulnerable assets in the application assets based at least in part upon the attack vector parameters. Further, the processor may obtain security parameters from a security framework indicating one or more attack techniques, associate each of the vulnerable assets to one or more of the security parameters, and generate a visual interface showing the vulnerable assets and the security parameters. The processor may determine an attack path connecting the vulnerable assets based at least in part upon the asset mapping information, and map the attack path to the application layers and the security parameters in the visual interface.

Abstract: According to one or more embodiments of the disclosure, a first autonomous mobile robot (AMR) encounters a second AMR, while navigating a location. The first AMR receives, from the second AMR, a task list of the second AMR. The first AMR determines an adjustment to the task list of the second AMR, based in part on a comparison between the task list of the second AMR and a task list maintained by the first AMR. The first AMR sends, to the second AMR, the adjustment to the task list of the second AMR.

Abstract: In one embodiment, a video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video data.

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: In one embodiment, a video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video data.

Abstract: In one embodiment, a video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video data.

Abstract: According to one or more embodiments of the disclosure, a first autonomous mobile robot (AMR) encounters a second AMR, while navigating a location. The first AMR receives, from the second AMR, a task list of the second AMR. The first AMR determines an adjustment to the task list of the second AMR, based in part on a comparison between the task list of the second AMR and a task list maintained by the first AMR. The first AMR sends, to the second AMR, the adjustment to the task list of the second AMR.

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 reasoning engine executed by a device, identifies one or more structural breaks in a time series for a particular metric regarding a computer network. The reasoning engine associates the one or more structural breaks in the time series data with a network event. The reasoning engine determines, using symbolic reasoning, a root cause for the network event based on a symbolic knowledge base maintained by the reasoning engine. The reasoning engine provides an indication of the determined root cause for the network event to one or more devices.

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: In one embodiment, a video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video data.

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 method includes obtaining text from a user, applying the text to a deep learning neural network to generate a plurality of bias coordinates defining a point in an embedded space, and, in response to determining that at least one of the plurality of bias coordinates exceeds a threshold, providing an indication of bias to the user.

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 method includes obtaining text from a user, applying the text to a deep learning neural network to generate a plurality of bias coordinates defining a point in an embedded space, and, in response to determining that at least one of the plurality of bias coordinates exceeds a threshold, providing an indication of bias to the user.

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.