Abstract: Techniques are provided for software license optimization using machine learning-based user clustering. One method comprises obtaining key performance indicators indicating individual usage by a plurality of users of a software product; applying at least one function to the key performance indicators to obtain a plurality of time dependent features; processing the time dependent features using a machine learning model to cluster the users into a plurality of persona clusters; and determining a number of each available license type for the software product for the plurality of users based on the persona clusters. The key performance indicators comprise, for example, user behavioral data with respect to usage of the software product and/or performance data with respect to usage of the software product. One or more policies can be determined for managing an allocation of the available license types for the software product to the plurality of users.

Abstract: A method, system and computer-usable medium are disclosed for voice-based alerting of an individual wearing an obstructive listening device. Certain embodiments include detecting speech in an ambient environment in which the person wearing the obstructive listening device is present; determining whether the detected speech includes a name of the person wearing the obstructive listening device; if the detected speech includes the name of the person wearing the obstructive listening device determining whether the name of the person was spoken using a calling speech characteristic; and if the name of the person was spoken using a calling speech characteristic, automatically alerting the person wearing the obstructive listening device that another person in the ambient environment is calling for attention of the person wearing the obstructive listening device.

Abstract: Methods and systems for managing the utilization of cloud computing resources are described. The system monitors cloud computing resource utilization for a first set of active jobs to determine real-time utilization data. The system compares the real-time utilization data with historic utilization data to generate a utilization pattern and determines future cloud computing resource utilization for at least one future time period based on the utilization pattern and a second set of scheduled jobs. The system further generates a pricing matrix for utilizing the cloud computing resources during a future time period based on the determined future cloud computing resource utilization. The pricing matrix includes prices associated with utilization of the cloud computing resources for each of the at least one future time period. The system transmits the pricing matrix to one or more devices requesting utilization of the cloud computing resources.

Abstract: A system recommends the refactoring of microservices. The system determines a segments similarity score based on comparing first code segments, associated with a first microservice in an application, against second code segments, associated with a second microservice in the application. The system determines whether the segments similarity score satisfies a segments similarity threshold. The system determines microservices similarity scores based on comparing a size of similar code segments in the first code segments and the second code segments against sizes of the first microservice and the second microservice, if the segments similarity score satisfies the segments similarity threshold. The system determines whether any microservices similarity score satisfies a microservices similarity threshold. The system outputs a recommendation to merge the first microservice with the second microservice, if any microservices similarity score satisfies the microservices similarity threshold.

Abstract: A method includes monitoring data of one or more containers running on one or more container host devices, a given one of the containers providing operating-system level virtualization for running at least one application. The method also includes determining a first set of behavior metrics for the given container based on the monitoring data, the first set of behavior metrics characterizing current behavior of the given container. The method further includes generating a model characterizing normal operation of the at least one application running in the given container using a second set of behavior metrics obtained during a learning period, utilizing the model to detect one or more anomalies in the first set of behavior metrics characterizing the current behavior of the given container, generating an alert responsive to detecting one or more anomalies in the first set of behavior metrics, and delivering the alert to a client device.

Abstract: Flexible scheduling for backup jobs includes backup policies that allow for flexibility in scheduling execution time, and an automated data driven backup job execution scheduler. The scheduler balances load on the customer's and data protection system's resources using a dynamic pricing calculator that takes into account the real-time and predicted near-future status of the protection environment. The backup job scheduler includes a user interface that enables users to specify user-customized backup policies that enable flexibility in scheduling execution times and transparency in pricing.

Abstract: Deduplication of files or other data or objects in a manner that is aware of a format of the file and the application. A file may be chunked based on the format. The chunks are more consistent and lead to higher deduplication ratios. The file may be presented as a single file. However, the file is stored in chunks or subfiles and deduplication is performed with respect to the chunks. When the file is read, the file is rebuilt from its respective chunks. The files may also be compressed using differential compression, which leverages the content of similar files to compress a current file.

Abstract: A method and system for provisioning software licenses. Specifically, the method and system disclosed herein entail the intelligent provisioning of limited floating software licenses, available to an organization, based on cost analysis scoring measuring a risk associated with provisioning one of the floating software licenses to a license requestor. The cost analysis scoring may be derived from user productivity metrics, resource utilization metrics, user metadata, and project metrics.

Abstract: A system, method, and computer-readable medium are disclosed for improved searching of contact information that includes receiving a request for a member's contact information. An initial list of candidates is returned. Based on organizational charts, distances between candidates and a point of reference are calculated, calculation is made as to a number of messages that are exchanged between candidates and the point of reference. Scores are determined based on the calculations. The scores are aggregated, and a refined list of candidates' contact information based on the aggregated scores is returned.

Abstract: Predicting large data flushes by collecting usage data for system assets, analyzing the data using machine learning on each asset and the whole system to determine usage trends, predicting a next large data flush using a time-series model, and determining if a size of the predicted next flush size is too large relative to journal storage space in order to advance fast forward mode. Further, protecting history information by pausing distribution of data from journal volumes to replica volumes, taking storage-level snapshots of the replica and the journal volumes, storing a snapshot timestamp for each of the storage-level snapshots in a snapshot database prior to advancing the fast forward mode or un-pausing distribution.

Abstract: Systems and methods for providing data protection operations including cyber-threat protection operations. A sentiment analysis may be performed using language analysis to identify or determine a general or specific sentiment with or without intent to do harm. A score of the sentiment is then determined to assess risk. The data backup policy can be updated based on the assessed risk.

Abstract: Techniques are provided for sensor data compression in a multi-sensor Internet of Things environment. An exemplary method comprises obtaining sensor data from a plurality of sensors satisfying one or more of predefined sensor proximity criteria and predefined similar sensor type criteria; applying an image-based compression technique to the sensor data to generate compressed sensor data; and providing the compressed sensor data to a data center. The image-based compression technique comprises a discrete cosine transform technique, a video compression technique, and/or an auto-encoder deep learning technique that utilizes one or more over-fitted bidirectional recurrent convolutional neural networks. The sensor data is optionally normalized prior to being applied to the image-based compression technique.

Abstract: Systems and methods for detecting cost anomalies in a data protection system. Data is collected for assets of a data protection system operating in a cloud. The data often relates to cost and may constitute time series. The time series are then analyzed by performing a fitting competition using multiple models. The best fitting model is selected and the residuals are analyzes to find outliers and produce a normal zone for the signal. The outliers can identify cost anomalies that may reflect the health of the data protection system.

Abstract: Techniques are provided for software license optimization using machine learning-based user clustering. One method comprises obtaining key performance indicators indicating individual usage by a plurality of users of a software product; applying at least one function to the key performance indicators to obtain a plurality of time dependent features; processing the time dependent features using a machine learning model to cluster the users into a plurality of persona clusters; and determining a number of each available license type for the software product for the plurality of users based on the persona clusters. The key performance indicators comprise, for example, user behavioral data with respect to usage of the software product and/or performance data with respect to usage of the software product. One or more policies can be determined for managing an allocation of the available license types for the software product to the plurality of users.

Abstract: Application data is received from a plurality of monitored applications. The application data is parsed into a plurality of features describing an operation of the plurality of monitored applications. A counter associated with at least one of the plurality of features is incremented. A system health is derived for the plurality of monitored applications from the counter.

Abstract: Techniques are provided for data-driven ensemble-based malware detection. An exemplary method comprises obtaining a file; extracting metadata from the file; obtaining a plurality of malware detection procedures; selecting a subset of the plurality of malware detection procedures to apply to the file utilizing a likelihood that each of the plurality of malware detection procedures will result in a malware detection for the file based on the extracted metadata; applying the selected subset of the malware detection procedures to the file; and processing results of the subset of malware detection procedures using a machine learning model to determine a probability of the file being malware.

Abstract: Techniques are provided for detecting malicious software code embedded in image files, using machine learning. One method comprises obtaining metadata for an image file; applying the obtained metadata to at least one machine learning technique to classify the image file into at least one of a plurality of predefined classes, wherein the plurality of predefined classes comprises at least one malicious file class; and determining whether the image file comprises malicious software code based on the classification. The machine learning technique is trained using image files classified into at least one of the plurality of predefined classes. The machine learning technique employs a deep neural network and/or a convolutional neural network to classify the image file into the at least one predefined class.

Abstract: Techniques are provided for facial recognition using a high probability group database. One method comprises maintaining (i) a first database of facial images of individuals, and (ii) a second database of facial images comprising a subset of the individuals from the first database based on a probability of individuals appearing in sequences of image frames at a given time; applying a face detection algorithm to sequences of image frames to identify one or more faces in the sequences of images; and applying a facial recognition to at least one sequence of image frames using at least the second database to identify one or more individuals in the at least one sequence of image frames. The second database is comprised of facial images of: (i) individuals from multiple angles; (ii) individuals that appeared in prior image frames; and/or (iii) individuals that appeared in an image frame generated by a plurality of cameras.

Abstract: Techniques are provided for reinforcement learning-based evaluation of software product usage. One method comprises obtaining key performance indicators indicating software product usage by a user; determining, for a predefined time window: (i) a mean and/or a median of the obtained KPIs; (ii) an amount of time that the software product was active; and (iii) an amount of interactions by the user with a user interface; evaluating possible login states of the software product using at least one reinforcement learning agent, wherein the evaluating comprises (a) observing the plurality of possible login states, including a current state comprising a current login state of the software product, and (b) obtaining an expected utility score for changing from the current login state to a different login state of the software product; and determining whether to change from the current login state to a different login state of the software product based on the expected utility score.

Abstract: Techniques are provided for facial recognition using a high probability group database and a facial network of related persons.