Abstract: A processor is configured to create a log data specific lexicon wherein each word in the log data specific lexicon corresponds to a weighted sentiment score with a binary polarity. The processor is further configured to assign a sentiment value to a log message based on the weighted sentiment scores of words appearing in the log message. The processor may further determine an alert type for the log message based on the sentiment value, a class indicating an issue the log message, and a priority of the log message, where the alert type is preconfigured with a set of alert type values of varying risk levels and wherein a system alert contains a predefined set of key performance indicators corresponding to the alert type.

Abstract: Dynamically determining respective threshold temperatures for data storage devices (DSDs) in a data storage system (DSS) slot involves determining a marginal temperature of a DSD (TMd) based on the difference between a current threshold temperature of a corresponding DSS slot (TTe) and a current temperature value of the DSD (TCd), and raising the current TTe to an updated TTe accordingly, thereby effectively permitting the DSD to operate at temperatures up to the updated TTe. Updating TTe may come after first determining whether the TMd is greater than a lower limit and/or less than an upper limit, both of which are based on an inherent threshold temperature value of the DSD (TTd). This approach can be applied to other DSDs housed in the same DSS enclosure, enabling varying each respective DSD operating temperature, in contrast to using a fixed operating temperature range for the entire enclosure.

Abstract: A method for facilitating workflows via an all-in-one voice feature is disclosed. The method includes receiving, via a user device, a request from a user, the request relating to an activation request for a workflow; authenticating the user via multi-factor authentication; providing, via the user device, audible prompts that relate to the workflow to obtain a response from the user, the response including a natural language response; parsing, by using a model, the obtained response to identify information necessary for execution of the workflow; determining, by using the model, whether additional data is required for the workflow based on the identified information; and automatically initiating, in real-time based on a result of the determining, the workflow using the identified information.

Abstract: A method and system for payment processing and provision of account services using a cloud containerized platform that improves reliability and scalability are provided. The method includes receiving, at the cloud containerized platform from a server, a request that relates to a user account that has been received by the server from the user; analyzing a content of the first request; selecting, based on a result of the analysis, an application for responding to the request; generating a response to the request by using the selected application; and transmitting the response to the server to be forwarded to the user. The application may include any one of a payment processing application, an account information inquiry application, an account statement generation application, an account update notification application, an account information routing application, and/or a new client onboarding application.

Abstract: Systems and processes disclosed herein perform an automatic remediation process. The automatic remediation process may be a log-based remediation process. Systems disclosed herein may obtain log files from an information management system and determine the occurrence of errors at the information management system based on error codes included in the logs. Further, the systems may access a knowledgebase to determine whether solutions for the errors have been previously generated. The solutions may include patches or hotfixes that can be applied to the information management system without removing user-access or stopping execution of the information management system. The systems may automatically update the information management system to address the errors. Alternatively, or in addition, the systems may alert a user, such as an administrator, of the existence of a solution to the error, and whether the solution may be applied without interrupting service or access to the information management system.

Abstract: Disclosed are systems and methods for proactively, instead of reactively, biasing parameters of a data storage device based on a spatial position in a storage enclosure. The method includes obtaining a spatial position for the data storage device in a storage enclosure. The method also includes proactively biasing one or more parameters for controlling the device memory, based on the spatial position. The spatial position has a corresponding thermal profile that is predetermined.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: creating a log data specific lexicon based on log data samples, each word in the log data specific lexicon corresponding to a weighted sentiment score with a binary polarity. A log message is obtained, and a sentiment value of the log message is assigned based on respective weighted sentiment scores of words appearing in the log message. The log message is classified for a class indicating an issue the log message addresses. An alert type for the log message is determined based on the sentiment value, the class, and a priority of the log message, and a system alert including a set of key performance indicators according to the alert type is produced to a user.

Abstract: A device may receive text data, audio data, and video data associated with a user, and may process the received data, with a first model, to determine a stress level of the user. The device may process the received data, with second models, to determine depression levels of the user, and may combine the depression levels to identify an overall depression level. The device may process the received data, with a third model, to determine a continuous affect prediction, and may process the received data, with a fourth model, to determine an emotion of the user. The device may process the received data, with a fifth model, to determine a response to the user, and may utilize a sixth model to determine a context for the response. The device may utilize seventh models to generate contextual conversation data, and may perform actions based on the contextual conversational data.

Abstract: Disclosed are systems and methods for proactively, instead of reactively, biasing parameters of a data storage device based on a spatial position in a storage enclosure. The method includes obtaining a spatial position for the data storage device in a storage enclosure. The method also includes proactively biasing one or more parameters for controlling the device memory, based on the spatial position. The spatial position has a corresponding thermal profile that is predetermined.

Abstract: Methods and systems for monitoring occupant health within a building. An illustrative method may include storing one or more illness behavioral models, capturing and storing over time a history of behaviors for each of a plurality of occupants of the building, identifying one or more current behaviors of a particular one of the plurality of occupants, determining whether the current behaviors of the occupant match one or more of the behaviors defined in the one or more illness behavioral models, determining whether the current behaviors of the occupant deviate from behaviors captured and stored in the history of behaviors for the occupant, and issuing an alert directing that the occupant undergo additional health screening when the current behaviors of the occupant match one or more of the behaviors defined in the illness behavioral models or deviate from behaviors captured and stored in the history of behaviors for the occupant.

Abstract: Systems and processes disclosed herein perform an automatic remediation process. The automatic remediation process may be a log-based remediation process. Systems disclosed herein may obtain log files from an information management system and determine the occurrence of errors at the information management system based on error codes included in the logs. Further, the systems may access a knowledgebase to determine whether solutions for the errors have been previously generated. The solutions may include patches or hotfixes that can be applied to the information management system without removing user-access or stopping execution of the information management system. The systems may automatically update the information management system to address the errors. Alternatively, or in addition, the systems may alert a user, such as an administrator, of the existence of a solution to the error, and whether the solution may be applied without interrupting service or access to the information management system.

Abstract: An example system can provide intelligent continuous learning by updating a machine learning model based on a new dataset. The system can utilize a transfer loss function that does not depend on old datasets used to train the existing model. The system can receive, on a graphical user interface (“GUI”), a selection of configuration criteria including threshold performance for automatic deployment. The new model can be created iteratively based on the configuration criteria. An evaluation of the new model over multiple iterations can be presented on the GUI. In an instance where the new model meets a deployment requirement selected on the GUI, a server can deploy the new model in place of the existing model.

Abstract: A processor may receive first issue data. The first issue data may be associated with input data entered by a user into a user interface on the issue submission application. The processor may analyze the first issue data. The processor may select a first set of prompted issue descriptions. The first set of prompted issue descriptions may be selected based on analyzing the first issue data. The processor may prompt the user to select a subset of the first set of prompted issue descriptions. The processor may receive from the user a selected subset of the first set of prompted issue descriptions. The processor may output an identified issue description. The identified issue description may be generated based on the selected subset of the first set of prompted issue descriptions.

Abstract: Systems and methods can implement a review process to evaluate changes to target code as part of development cycles for a continuous integration, continuous deployment pipeline for software-based products. The system can aggregate data and determine if the target code has been modified preliminarily and then intelligently determine where further review is needed before the changes are permanently implemented. To do this, a changeset including the preliminarily changed target code can be obtained from the aggregated data. The changeset can be tested with a prediction model based on feature data that characterizes aspects of a coding process carried out to generate the preliminary modification. The prediction model can provide an activation recommendation for the preliminary modification based on a plurality of risk factors determined from the testing. The prediction model can be trained, continuously, with training data that includes a plurality of data artifacts resulting from a code build processes.

Abstract: Methods and systems for temperature screening using a mobile device. In one example, a mobile temperature screening system may comprise a mobile device including a display, a memory, a processor, a thermal imaging camera, a visible light imaging camera, and a wireless communication capability. The processor may be configured to obtain a thermal image of a person and a visible light image of the person, determine a body temperature of the person based at least in part on the thermal image, and classify the person as having a normal body temperature when the body temperature of the person is below a threshold temperature. When the body temperature of the person exceeds the threshold temperature the processor may be configured to classify the person as having an elevated body temperature, generate a visual alert, and transmit the thermal image, the visible light image and the classification to a remote device.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: creating a log data specific lexicon based on log data samples, each word in the log data specific lexicon corresponding to a weighted sentiment score with a binary polarity. A log message is obtained, and a sentiment value of the log message is assigned based on respective weighted sentiment scores of words appearing in the log message. The log message is classified for a class indicating an issue the log message addresses. An alert type for the log message is determined based on the sentiment value, the class, and a priority of the log message, and a system alert including a set of key performance indicators according to the alert type is produced to a user.

Abstract: A method for monitoring a system is provided. The method includes: displaying, on a graphical user interface, an alert configuration screen that includes user-configurable alerting information items that relate to an application being executed by the system; receiving, from a user in response to the displaying of the alert configuration screen, a request for an alert that includes inputs with respect to the user-configurable alerting information items; monitoring the application to determine whether a current condition of the application conforms to the received inputs; when a determination is made that the current condition of the application conforms to the received inputs, generating the requested alert; and transmitting the generated alert to the user. The user-configurable alerting information items may include items relating to a content of the alert, items relating to a timing of the alert, and an item that relates to an intended recipient of the alert.

Abstract: Described are techniques for automated anomaly detection including a technique comprising training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure. The technique further comprises inputting aggregated time series data to the ensemble of deep learning models and identifying anomalies in the aggregated time series data based on respective portions of the aggregated time series data that are indicated as anomalous by a majority of deep learning models in the ensemble of deep learning models. The technique further comprises grouping the anomalies according to relationships between the anomalies and performing a mitigation action in response to grouping the anomalies.

Abstract: Techniques disclosed herein provide an approach for identifying application dependencies in a hybrid environment in which one or more applications run in operating system (OS)-less containers. One embodiment provides a computer-implemented method that includes monitoring network traffic at one or more host computer systems, wherein OS-less containers run in at least one of the host computer systems. The method further includes monitoring network traffic at virtual bridges to which the OS-less containers are attached, and identifying network dependencies based on the monitored network traffic at the host computer systems and the monitored network traffic at the virtual bridges. In addition, the method includes determining the application dependencies based on the identified network dependencies.

Abstract: Described are techniques for anomaly detection including a method comprising sorting a univariate data set in an numeric order and generating a second univariate data set based on the sorted univariate data set, where respective elements in the second univariate data set correspond to respective differences between consecutive elements in the sorted univariate data set. The method further comprises sorting the second univariate data set in numeric order and generating a third univariate data set that includes index values corresponding to respective differences in the sorted second univariate data set that are above a threshold. The method further comprises modifying the third univariate data set and defining a set of clusters based on the modified third univariate data set. The method further comprises clustering the sorted univariate data set according to the set of clusters and characterizing a new data point as anomalous in response to the clustering.