Abstract: A circuit, and method for using same comprising, a first intermediate memory communicatively coupled with a vector processor and a RAM, wherein the vector processor is communicatively coupled with the RAM, an address sequence memory to store non-linear RAM addresses corresponding to linear locations in the first intermediate memory, a data sequencer to read a first frame of data from the RAM to the first intermediate memory based on addresses stored in the address sequence memory, and the first intermediate memory to provide a linearized frame of data to the vector processor to execute a vector instruction

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Systems and methods for supporting dynamic disk growth within a storage appliance are provided. According to one embodiment, a portion of a logical size of each of multiple disks (e.g., hyperscale disks or Logical Unit Numbers (LUNs)) are provisioned for use by a storage system as backing for respective file system disks. To accommodate growth, block numbers for the file system disks are pre-allocated within a sparse space of a contiguous sequence of block numbers corresponding to a number of blocks represented by the logical size. Metadata is maintained for the file system disks regarding a range of the pre-allocated block numbers that are available for use. Responsive to a triggering condition, the provisioned portion of a disk is increased and subsequently, responsive to detecting a change in a size of the disk by the storage system, a size of the corresponding file system disk is updated within the metadata.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating an association between a graphic element and text for a messaging thread and providing an overlay including the graphic element based on providing a message to the messaging thread including the text. In particular, in one or more embodiments, the disclosed systems receive user selection of the text and the graphic element and provide the association between the text and the element to various client devices associated with the messaging thread. Accordingly, the disclosed systems provide instructions for each client device associated with the messaging thread to generate and provide an overlay for the messaging thread.

Abstract: An electronic device and method for contextual automated audio talkdown for remote guarding is provided. The electronic device detects a movement of an object in a physical area inside or in a vicinity of a built environment and device receives, based on the detected movement, a sequence of images of the physical area that include the object. The electronic device detects physical activities of the object that are associated with a behavior of an intruder based on application of a first AI model on the sequence of images and generates information that includes a textual description of the physical activities. The electronic device generates an audio alert based on the information and controls a playback of the audio alert via an audio reproduction device installed in a vicinity of the built environment. The playback includes a recitation of the textual description included in the information.

Abstract: Systems and methods for supporting dynamic disk growth within a virtual storage appliance are provided. According to one embodiment, a portion of a logical size of respective hyperscale disks provided by a hyperscaler are provisioned for use by a virtual storage system as backing for respective file system disks. To accommodate growth, block numbers for the file system disks are pre-allocated within a sparse space of a contiguous sequence of block numbers corresponding to a number of blocks represented by the logical size. Metadata is maintained for the file system disks regarding a range of the pre-allocated block numbers that are available for use. Responsive to a triggering condition, the provisioned portion of a hyperscale disk is increased and subsequently, responsive to detecting a change in a size of the hyperscale disk by the virtual storage system, a size of the corresponding file system disk is updated within the metadata.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Systems and methods for reducing the provisioned storage capacity of a disk or aggregate of disks of a storage appliance while the storage appliance continues to serve clients are provided. According to one embodiment, the size of the aggregate may be reduced by shrinking the file system of the storage appliance and removing a selected disk from the aggregate. When an identified shrink region includes the entire addressable PVBN space of the selected disk, the file system may be shrunk by relocating valid data from the selected disk elsewhere within the aggregate. After the valid data is relocated, the selected disk may be removed from the aggregate, thereby reducing the provisioned storage capacity of the aggregate by the size of the selected disk.

Abstract: Systems and methods for reducing the provisioned storage capacity of a disk or aggregate of disks of a storage appliance while the storage appliance continues to serve clients are provided. According to one embodiment, the size of the aggregate may be reduced by shrinking the file system of the storage appliance and removing a selected disk from the aggregate. When an identified shrink region is less than the entire addressable PVBN space of the selected disk, the file system may be shrunk by relocating valid data from the shrink region of the selected disk to one or more regions outside of the shrink region, mirroring data of the selected disk from outside of the shrink region to a smaller disk added to the aggregate, and then removing the selected disk after the mirrors are in sync, thereby reducing the provisioned storage capacity of the aggregate by the difference in size between the selected disk and the smaller disk.

Abstract: A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating an association between a graphic element and text for a messaging thread and providing an overlay including the graphic element based on providing a message to the messaging thread including the text. In particular, in one or more embodiments, the disclosed systems receive user selection of the text and the graphic element and provide the association between the text and the element to various client devices associated with the messaging thread. Accordingly, the disclosed systems provide instructions for each client device associated with the messaging thread to generate and provide an overlay for the messaging thread.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Systems, methods, client devices, and non-transitory computer-readable media are disclosed for utilizing an augmented reality (AR) data channel to enable shared augmented reality video calls which facilitate the sharing of and interaction with AR elements during video calls. For example, the disclosed systems can establish a video call between client devices that include a video (and audio) data channel and an AR data channel. Moreover, in one or more embodiments, the disclosed systems enable one of the client devices to transmit AR data (e.g., AR element identifiers, AR elements, object vectors, participant identifiers) through an AR data channel to cause the other client device to render an AR element on a video captured by the other client device during a video call. Indeed, the disclosed systems can enable AR environments, AR effects, AR-based activities, and/or individual AR elements during a video call utilizing an AR data channel.

Abstract: Presented herein are methods, non-transitory computer readable media, and devices triggering a metadata recovery process within a network storage system, which include: dividing metadata into metadata segments, wherein each of the metadata segments is tasked to perform a specific file system operation function, validating each of the metadata segments during the specific file system operation function; upon failure to validate at least one of the metadata segments, triggering an automatic repair process while maintaining the operation function tasked to the metadata segment, and upon finalizing the automatic repair process, resuming the specific file system operation function tasked to the metadata segment.

Abstract: Systems and methods for supporting dynamic disk growth within a virtual storage appliance are provided. According to one embodiment, a portion of a logical size of respective hyperscale disks provided by a hyperscaler are provisioned for use by a virtual storage system as backing for respective file system disks. To accommodate growth, block numbers for the file system disks are pre-allocated within a sparse space of a contiguous sequence of block numbers corresponding to a number of blocks represented by the logical size. Metadata is maintained for the file system disks regarding a range of the pre-allocated block numbers that are available for use. Responsive to a triggering condition, the provisioned portion of a hyperscale disk is increased and subsequently, responsive to detecting a change in a size of the hyperscale disk by the virtual storage system, a size of the corresponding file system disk is updated within the metadata.

Abstract: An artificial intelligence (AI) system is disclosed. The AI system provides an AI system lane processing chain, at least one AI processing block, a local memory, a hardware sequencer, and a lane composer. Each of the at least one AI processing block, the local memory coupled to the AI system lane processing chain, the hardware sequencer coupled to the AI system lane processing chain, and the lane composer is coupled to the AI system lane processing chain. The AI system lane processing chain is dynamically created by the lane composer.

Abstract: A system and method for determining sub-activities in videos and segmenting the videos is disclosed. The method includes extracting one or more batches from one or more videos and extracting one or more features from set of frames associated with the one or more batches. The method further includes generating a set of predicted codes and determining a cross-entropy loss, temporal coherence loss and a final loss. Further, the method includes categorizing the set of frames into one or more predefined clusters and generating one or more segmented videos based on the categorized set of frames, the determined final loss, and the set of predicted codes by using s activity determination-based ML model. The method includes outputting the generated one or more segmented videos on user interface screen of one or more electronic devices associated with one or more users.

Abstract: A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.

Abstract: Aspects of the present disclosure are presented for an AI system featuring specially designed AI hardware that incorporates security features to provide iron clad trust and security to run AI applications/solution models. Presented herein are various security features for AI processing, including: a trust and integrity verifier of data during operation of an AI solution model; identity and trust establishment between an entity and the AI solution model; secure isolation for a virtual AI multilane system; a real-time attack detection and prevention mechanism; and built in detection mechanisms related to rogue security attack elements insertion during manufacturing. Aspects also include security to implement an AI network interconnecting multiple user devices in an AI environment.

Abstract: Aspects of the present disclosure provide for a novel system and method where users can promote, subscribe, license, train collaboratively, and/or trade AI models in real-time over an AI trusted model network that represents a group of trusted users across platform agnostic ways. The system and methods described herein may effectively provide a marketplace to collaboratively develop and reliably disseminate developed AI models to other users who may have similar needs. The system may include an AI user device server (AI-UDS), an AI trusted model network (AI-TMN), an AI secure marketplace system (AI-SMPS), an AI collaborative training platform (AI-CTP), an AI secure transaction fulfillment system (AI-STFS), and an AI model license subscription management platform (AI-LSMP).