Abstract: Disclosed herein is an indwelling urinary catheter having a guidewire anchoring mechanism. The indwelling urinary catheter includes a catheter tube having a proximal opening, a distal opening, a catheter tube lumen with the catheter tube having a first wall thickness. The guidewire anchoring mechanism is configured to transition between an insertion state and an anchored state. The guidewire anchoring mechanism can include a proximal portion having a top cap, a collapsible section having a second wall thickness less than the first wall thickness, and a distal portion including a holder cap coupled to a holder. The holder can be coupled to the catheter tube, the distal portion can be in communication with the proximal portion, and a guidewire can be coupled to the proximal portion, the guidewire extending through the catheter tube lumen to the distal portion.

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: 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: Detecting log data anomalies by pre-processing log data entries, identifying sequences of the log data entries, generating at least one key performance indicator (KPI) for the log data entries according to the sequences, training at least one machine learning (ML) model according to the KPI, and identifying a log data entry anomaly according to the machine learning model.

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: A computer receives a stream of discrete log data entries containing at least one unique entry, for the computer to identify anomalies in machine log data. The computer generates a log data sentiment analyzer with a lexicon customized to identify a tone of content for each of said log data entries. The computer assigns a unique message ID to the unique messages and collects data attributes of the unique entries. For each unique entry, the computer identifies an entry tone or sentiment and at least one additional unique entry attribute. The computer generates a time series analysis of the identified entry sentiment and the additional attributes and conducts statistical analysis of the attributes using at least one deep learning analysis model to identify historical anomalies in the log data attributes, using the identified anomalies indicate trouble in a system associated with the collected log data.

Abstract: A contact tracing system and method identifies which persons at a facility may have been exposed to a possibly contagious person. In some examples, multiple sensors, such as access card readers and cameras with video and thermographic imaging, monitor individuals at the facility. For each person, the system collects data pertaining to an individual's location and/or temperature at discrete points in time. In addition to collecting data throughout the day, data is also collected over an extended period of days or weeks. Based on data recorded during the extended periods, the system determines each person's typical schedule and travel pattern through the facility. When one or more sensors detect a possibly contagious person by sensing a fever, or even a gradually rising body temperature, the system searches for person's whose typical schedule and travel patterns overlap with those of the contagious person. The system then automatically notifies people accordingly.

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: 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: 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: 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: 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: A computer system for generating a labeling term for a set of data entries may include one or more processors having instructions to obtain a set of data entries and identify a set of unique terms. The program instructions further include instructions to determine a frequency of the unique terms and select a first a subset of unique terms based on the frequency. The program instructions further include instructions to form a set of exclusive groups using the unique terms in the first subset and select a second subset of exclusive groups according to a frequency of each exclusive group. The program instructions further include instructions to form distinct terms from the second subset of exclusive groups and designate a label to a set of data entries using the distinct terms. A computer program product and method corresponding to the above computer system are also disclosed herein.

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: 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: 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.

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: A surveillance system having one or more cameras, each configured with a field-of-view (FOV) in a map displayed on a screen of a panel viewer. An entity may be tagged within an FOV of a current camera of the one or more cameras by clicking or drawing with a cursor on a screen showing the entity within the FOV, which results in a closed geometrical line around at least a portion of the entity displayed within the FOV. When the entity moves, the geometrical outline may move with the entity from an FOV of one camera to an FOV of another camera. One or more adjacent cameras having FOV's that are close to, adjacent to or overlapping to the FOV of the current camera, may be loaded with the map on the screen of the panel viewer.

Abstract: An embodiment of the present invention is directed to a code review tool. A system that implements a T24 code review tool comprises: a memory component; an interactive interface that receives one or more user inputs; a processor, coupled to the memory component and the interactive interface, the processor configured to perform the steps comprising: retrieve components of an application; initiate automated code review function for the components; identify code review comments prior to code compilation; display comments with severity for code review; and confirm and initiate code compilation process.

Abstract: A computer system for generating a labeling term for a set of data entries may include one or more processors having instructions to obtain a set of data entries and identify a set of unique terms. The program instructions further include instructions to determine a frequency of the unique terms and select a first a subset of unique terms based on the frequency. The program instructions further include instructions to form a set of exclusive groups using the unique terms in the first subset and select a second subset of exclusive groups according to a frequency of each exclusive group. The program instructions further include instructions to form distinct terms from the second subset of exclusive groups and designate a label to a set of data entries using the distinct terms. A computer program product and method corresponding to the above computer system are also disclosed herein.