Abstract: A goal of the disclosure is to provide real-time adjustment of a deep learning-based tracking system to track a moving individual without using a labeled set of training data. Disclosed are systems and methods for tracking a moving individual with an autonomous drone. Initialization video data of the specific individual is obtained. Based on the initialization video data, real-time training of an input neural network is performed to generate a detection neural network that uniquely corresponds to the specific individual. Real-time video monitoring data of the specific individual and the surrounding environment is captured. Using the detection neural network, target detection is performed on the real-time video monitoring data and a detection output corresponding to a location of the specific individual within a given frame of the real-time video monitoring data is generated.

Abstract: Disclosed are technologies for autonomous tracking. An initial coordinate of a beacon device carried by a user is registered as a dead reckoning waypoint with a drone configured to track the user. The drone receives IMU measurements from the beacon as the user moves. For each IMU measurement, a displacement vector characterizing user movement is calculated. Estimated beacon locations are calculated by dead reckoning, based on the displacement vectors and the dead reckoning waypoint. Later, an updated dead reckoning waypoint is calculated by obtaining the current location coordinate of the drone and performing optical triangulation to determine a relative position of the user with respect to the drone. The updated dead reckoning waypoint does not depend on previously estimated beacon locations, and accumulated IMU/estimation error is eliminated. Tracking continues, where subsequent estimated locations of the beacon are calculated by dead reckoning based on the updated dead reckoning waypoint.

Abstract: A computer-implemented method, according to one embodiment includes, for each repository data chunk in repository data that comprises a plurality of the repository data chunks, generating a corresponding set of repository distinguishing characteristics (RDCs). Each set of RDCs is generated by: applying a hash function to the respective input data chunk or repository data chunk to generate a plurality of hashes, each hash comprising a hash value and a hash position within the data chunk, applying a first function to the plurality of generated hashes to identify a first subset of hashes distributed across the data chunk, applying a second function to the hash positions of the hashes of the first subset to identify a second subset of the plurality of generated hashes, and defining the second subset of hashes as the set of RDCs.

Abstract: First and second screenshot images are obtained from a monitoring application provided on a first computing device. Each screenshot image comprises a plurality of content portions displayed by a communication application on the first computing device, and content boxing is performed to calculate a plurality of content boxes for the plurality of content portions. Each content box is classified as containing textual communication content or image communication content. Textual communications are extracted via Optical Character Recognition (OCR) and object identifiers are extracted from the image communications via image recognition. At least one shared content box present in both the first and second screenshot images is identified and used to temporally align the extracted textual communications. The temporally aligned textual communications are condensed into a textual communication sequence.

Abstract: A drone receives an activation command indicating a user's need for monitoring, and is deployed based on the activation command and a set of initial operational parameters. The drone autonomously navigates to a first position with respect to the user and performs a first configured action. A plurality of monitoring data signals corresponding to the user and surrounding environment is captured using sensors on the drone, and is wirelessly transmitted by the drone to a remote monitoring system. The monitoring data signals are continuously analyzed to generate updated operational parameters causing the drone to autonomously navigate to a second position and perform a second configured action. A third configured action is received by the drone from the remote monitoring system, wherein the third configured action is generated based on a threat analysis performed by the remote monitoring system on the monitoring data signals.

Abstract: Disclosed are technologies for autonomous tracking. An initial coordinate of a beacon device carried by a user is registered as a dead reckoning waypoint with a drone configured to track the user. The drone receives IMU measurements from the beacon as the user moves. For each IMU measurement, a displacement vector characterizing user movement is calculated. Estimated beacon locations are calculated by dead reckoning, based on the displacement vectors and the dead reckoning waypoint. Later, an updated dead reckoning waypoint is calculated by obtaining the current location coordinate of the drone and performing optical triangulation to determine a relative position of the user with respect to the drone. The updated dead reckoning waypoint does not depend on previously estimated beacon locations, and accumulated IMU/estimation error is eliminated. Tracking continues, where subsequent estimated locations of the beacon are calculated by dead reckoning based on the updated dead reckoning waypoint.

Abstract: A goal of the disclosure is to provide real-time adjustment of a deep learning-based tracking system to track a moving individual without using a labeled set of training data. Disclosed are systems and methods for tracking a moving individual with an autonomous drone. Initialization video data of the specific individual is obtained. Based on the initialization video data, real-time training of an input neural network is performed to generate a detection neural network that uniquely corresponds to the specific individual. Real-time video monitoring data of the specific individual and the surrounding environment is captured. Using the detection neural network, target detection is performed on the real-time video monitoring data and a detection output corresponding to a location of the specific individual within a given frame of the real-time video monitoring data is generated.

Abstract: A computer program product for searching a repository of binary uninterpretted data, according to one embodiment, includes a computer readable storage medium having program instructions executable by a computer to cause the computer to perform a method comprising: analyzing, by the computer, segments of each of the repository and input data to determine a repository segment that is similar to an input segment, the analyzing including searching an index of representation values of the repository data for matching representation values of the input in a time independent of a size of the repository and linear in a size of the input data; and analyzing, by the computer, the similar repository segment with respect to the input segment to determine their common data sections while utilizing at least some of the matching representation values for data alignment, in a time linear in a size of the input segment.

Abstract: First and second screenshot images are obtained from a monitoring application provided on a first computing device. Each screenshot image comprises a plurality of content portions displayed by a communication application on the first computing device, and content boxing is performed to calculate a plurality of content boxes for the plurality of content portions. Each content box is classified as containing textual communication content or image communication content. Textual communications are extracted via Optical Character Recognition (OCR) and object identifiers are extracted from the image communications via image recognition. At least one shared content box present in both the first and second screenshot images is identified and used to temporally align the extracted textual communications. The temporally aligned textual communications are condensed into a textual communication sequence.

Abstract: A drone receives an activation command indicating a user's need for monitoring, and is deployed based on the activation command and a set of initial operational parameters. The drone autonomously navigates to a first position with respect to the user and performs a first configured action. A plurality of monitoring data signals corresponding to the user and surrounding environment is captured using sensors on the drone, and is wirelessly transmitted by the drone to a remote monitoring system. The monitoring data signals are continuously analyzed to generate updated operational parameters causing the drone to autonomously navigate to a second position and perform a second configured action. A third configured action is received by the drone from the remote monitoring system, wherein the third configured action is generated based on a threat analysis performed by the remote monitoring system on the monitoring data signals.

Abstract: Exemplary method, system, and computer program product embodiments for scalable data deduplication working with small data chunk in a computing environment are provided. In one embodiment, by way of example only, for each small data chunk, a signature is generated based on a combination of a representation of characters used in selecting data to be deduplicated. A c-spectrum of the small data chunk being a sequence of representations of different characters ordered by a frequency of occurrence in the small data chunk, and an f-spectrum of the small data chunk being a corresponding sequence of frequencies of the different characters in the small data chunk.

Abstract: A plurality of server processor workers is configured for the distributed parallel processing of deduplicated data entities in a plurality of chunks. The deduplicated data processing rate is regulated using a rate control mechanism. The rate control mechanism incorporates a debt/credit algorithm specifying which of the plurality of workers processing the deduplicated data entities must wait for each of a plurality of calculated required sleep times, the calculated required sleep times being calculated as a best fit between a maximum allowable sleep time, a delta vector, and a limit per time vector, wherein the required sleep time is one of the plurality of calculated required sleep times.

Abstract: Various embodiments are provided for managing a global cache coherency in a distributed shared caching for a clustered file system (CFS). The CFS manages access permissions to an entire space of data segments by using the DSM module. In response to receiving a request to access one of the data segments, a calculation operation is performed for obtaining most recent contents of one of the data segments. The calculation operation performs one of providing the most recent contents via communication with a remote DSM module which obtains the one of the data segments from an associated external cache memory, instructing by the DSM module to read from storage the one of the data segments, and determining that any existing contents of the one of the data segments in the local external cache are the most recent contents.

Abstract: Real-time reduction of CPU overhead for data compression is performed by a processor device in a computing environment. Non-compressing heuristics are applied on a randomly selected data sample from data sequences for determining whether to compress the data sequences. A compression potential is calculated based on the non-compressing heuristics. The compression potential is compared to a threshold value. The data sequences are either compressed if the compress threshold is matched, compressed using Huffman coding if Huffman coding threshold is matched, or stored without compression.

Abstract: Various embodiments are provided for managing a global cache coherency in a distributed shared caching for a clustered file system (CFS). The CFS manages access permissions to an entire space of data segments by using the DSM module. In response to receiving a request to access one of the data segments, a calculation operation is performed for obtaining most recent contents of one of the data segments. Most recent contents are determined if ownership of the one of the data segments is possessed by a remote DSM module and the request to access one of the data segments is for shared permission and exists in the local external cache. The most recent contents are transported within the response if the response is in a remote external cache and has a valid permission for the one of the data segments otherwise reading from the one of the data segments.

Abstract: A computer-implemented method, according to one embodiment includes, for each repository data chunk in repository data that comprises a plurality of the repository data chunks, generating a corresponding set of repository distinguishing characteristics (RDCs). Each set of RDCs is generated by: applying a hash function to the respective input data chunk or repository data chunk to generate a plurality of hashes, each hash comprising a hash value and a hash position within the data chunk, applying a first function to the plurality of generated hashes to identify a first subset of hashes distributed across the data chunk, applying a second function to the hash positions of the hashes of the first subset to identify a second subset of the plurality of generated hashes, and defining the second subset of hashes as the set of RDCs.

Abstract: A computer program product for searching a repository of binary uninterpretted data, according to one embodiment, includes a computer readable storage medium having program instructions executable by a computer to cause the computer to perform a method comprising: analyzing, by the computer, segments of each of the repository and input data to determine a repository segment that is similar to an input segment, the analyzing including searching an index of representation values of the repository data for matching representation values of the input in a time independent of a size of the repository and linear in a size of the input data; and analyzing, by the computer, the similar repository segment with respect to the input segment to determine their common data sections while utilizing at least some of the matching representation values for data alignment, in a time linear in a size of the input segment.

Abstract: Systems and methods enabling search of a repository for the location of data that is similar to input data, using a defined measure of similarity, in a time that is independent of the size of the repository and linear in a size of the input data, and a space that is proportional to a small fraction of the size of the repository. The similar data segments thus located are further analyzed to determine their common (identical) data sections, regardless of the order and position of the common data sections in the repository and input, and in a time that is linear in the segment size and in constant space.

Abstract: Systems and methods enabling search of a repository for the location of data that is similar to input data, using a defined measure of similarity, in a time that is independent of the size of the repository and linear in a size of the input data, and a space that is proportional to a small fraction of the size of the repository. The similar data segments thus located are further analyzed to determine their common (identical) data sections, regardless of the order and position of the common data sections in the repository and input, and in a time that is linear in the segment size and in constant space.

Abstract: Various embodiments are provided for managing a global cache coherency in a distributed shared caching for a clustered file system (CFS). The CFS manages access permissions to an entire space of data segments by using the DSM module. In response to receiving a request to access one of the data segments, a calculation operation is performed for obtaining most recent contents of one of the data segments. The calculation operation performs one of providing the most recent contents via communication with a remote DSM module which obtains the one of the data segments from an associated external cache memory, instructing by the DSM module to read from storage the one of the data segments, and determining that any existing contents of the one of the data segments in the local external cache are the most recent contents.