Abstract: An embodiment includes detecting a set of anomalies recorded during a first predefined window of time in log entries for a computer environment. The embodiment also includes generating cluster data representative of a cluster of anomalies from among the set of anomalies, where the cluster is formed using a lattice clustering algorithm that spatially distinguishes the cluster of anomalies from other anomalies in the set of anomalies. The embodiment also includes composing an explanation using log templates generated from log entries associated with the cluster of anomalies.

Abstract: Provided is a method, computer program product, and system for automatically assigning robotic devices to users based on need using predictive analytics. A processor may monitor activities performed by one or more users. The processor may determine, based on the monitoring, a set of activities that require assistance from a robotic device when being performed by the one or more users. The processor may match the set of activities to a set of capabilities related to a plurality of robotic devices. The processor may identify, based on the matching, a first robotic device that is capable of assisting the one or more users in performing a first activity of the set of activities. The processor may deploy the first robotic device to assist the one or more users in performing the first activity.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: training of a machine learning model for predicting a rating in certain content rating systems based on training data. The machine learning model includes a plurality of maturity classifiers corresponding to individual features of the previously rated contents. An input content is obtained and features of the input content are extracted by use of respective content analysis tools and subsequently classified by the maturity classifiers of the machine learning model.

Abstract: Detecting system log anomalies by receiving multivariate time-series system log data an multivariate metric data, inferring system metrics from the system log data and metric data, receiving a metric causal graph including causal relationships between system metrics, determining a univariate variation score for the system metrics, determining a causal variation score for the multivariate time series system metric data, according to the causal graph, determining an overall activity score according to the univariate variation score, and causal variation score, and altering a review window duration according to the activity score.

Abstract: A method, a computer program product, and a system of dynamically managing permissions of storage blocks. The method includes predicting at least one storage block that will be accessed by a user on a storage device and predicting a time window when the storage block will be accessed the user. The predictions can be performed by a machine learning model trained using the historical accesses and access times of the user. The method also includes granting the user an access to the storage block during the time window and monitoring whether the storage block is accessed by the user. The method also includes determining, based on the monitoring, that the user accessed the storage block, and revoking the access to the storage block granted to the user after a predetermined access time.

Abstract: Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines.

Abstract: In an approach to improve multi-data center environments by minimizing the environmental impact of workloads in multi-data center environments embodiments migrate at least a portion of one or more workloads between one or more data centers automatically to maximize a usage of renewable energy based on a predetermined threshold score of input power and a combination of renewal energy sources. Further, embodiments dictate, by a policy engine, where at least a portion of the one or more workloads can be hosted. Additionally, embodiments control, by a scheduling engine, how, when, and where at least a portion of the one or more workloads will migrate, and perform data replication to migrate data between a plurality of data center locations.

Abstract: Embodiments can include receiving, by a data processing system, digital teaching content monitoring, by the data processing system, user gaze of the digital teaching content.

Abstract: Systems, methods and/or computer program products maximizing answer coherence of virtual agents responding to multi-part user queries, using relationships among extracted chunks corresponding to support document(s). Relationships are extracted by mining entities from text and images of support document(s) and mapping entities to an otology. Induced subgraphs for chunks, based on extracted entities are created and compared. Depending on amounts of overlap and/or relationships between subgraphs, relationships among chunks are identified. Chunks relationships can be complementary, contained within one another, instances of one another, or not related at all. For a given multi-part query, a query answer graph is created comprising answer nodes corresponding to potential answer candidates associated with each chunk.

Abstract: Digital teaching content ordering includes receiving, by a data processing system, digital teaching content and any corresponding annotations and content heuristic(s) from a teacher or other presenter, the digital teaching content including text and/or image(s). The data processing system monitors student gaze of the digital teaching content during a class and can cognitively skip some of the digital teaching content during the class based on the monitored student gaze and content heuristic(s). The system can also cognitively modify the digital teaching content outside of class based, in part, on the monitoring and the content heuristic(s). A hypergraph of the digital teaching content may also be received, the cognitively skipping being further based on the hypergraph, and the cognitively modifying being further based on a heat map built using the hypergraph and the student gaze.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of data logs; generating a data model using analyzed time series data from the identified data logs; detecting anomalies within the generated data model; constructing a causal graph using the detected anomalies and retrieved domain knowledge; computing a severity value for the detected anomalies with the constructed causal graph; assigning the detected anomaly to a classification based on a function vector, wherein the computed severity value is a function vector; and automatically modifying a function of a computing device based on the function vector of the assigned, detected anomaly, wherein a modification addresses the detected anomaly located at a center of the constructed casual graph.

Abstract: One or more computer processors determine a storage strategy for each chunked data block in a training dataset based on a respective computed usefulness score and a series of usefulness thresholds, wherein the storage strategy comprises RAID strategies that include striping, mirroring, parity, and double parity. The one or more computer processors distribute each data block in the training dataset according to the respective determined storage strategy.

Abstract: An embodiment of the invention may include a method, computer program product and system for deployment of microservices within a shared pool of configurable computing resources. An embodiment may include creating a dependency map for a plurality of microservices of an application deployed on the shared pool of configurable computing resources. An embodiment may include identifying attributes, with associated values, for each microservice of the plurality of microservices and identifying eligible deployment locations within the shared pool of configurable computing resources. An embodiment may include creating a bipartite graph based on the plurality of microservices and the identified eligible deployment locations. An embodiment may include applying bipartite matching to the shared pool of configurable computing resources based on the created bipartite graph.

Abstract: A system and method is provided to selectively process and store tables of a relational database by calculating an overall data sensitivity score for each table based on predefined attribute rules; performing column-wise splitting of at least one of the tables into a first table and a second table based on the overall data sensitivity score of each table, thereby generating a total number of relational database tables; storing a first subset of the total number of relational database tables in a private cloud storage database in a distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables; and storing a second subset of the total number of relational database tables in a public cloud storage database of the distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables.

Abstract: One embodiment of the invention provides a method of training a chatbot. The method comprises identifying one or more chat logs that exhibit a trait. The method further comprises identifying one or more labels associated with the trait based on the one or more chat logs. The method further comprises training the chatbot to generate a response that models the trait based on the one or more chat logs. The method further comprises labeling the chatbot with the one or more labels.

Abstract: Digital photograph filters and associated settings are recommended by an image classification model. A plurality of images and metadata associated with each of the plurality of images are received. Human interaction scores for each of the images are received. Training data is generated classifying the images using the associated metadata and human interaction scores. The training data is used to derive an image classification model for predicting a human interaction score for an image having an unknown human interaction score. At least one recommended image capture setting using the image classification model is determined in response to determining that a user is preparing to capture an image with a device. The recommended image capture setting is displayed on a display of the device.

Abstract: One or more processors compile user preference and activity information associated with a mobile device operated by the user. The installation of a first application (app) on the mobile device operated by the user is confirmed. Responsive to detecting availability of an update to the first application, and confirmation accepting installation of the update, dynamically embedding a message within a new feature included in the update of the first application. A message is selected from one or more available messages to dynamically embed within the new feature included in the update of the first application, based on profile and activity information collected about the first user, and responsive to receiving a confirmation to receive the update of the first application, the message selected from the one or more available messages, is presented to the user, embedded within the new feature of the update to the first application.

Abstract: Localizing a faulty microservice in a microservice architecture is achieved by developing healthy execution sequence data for comparison to execution sequences during system failures. Oftentimes the faulty microservice does not emit a failure signal. Frequent sub-sequences arising from log template time series data during healthy execution facilitates localization of faulty services when there is no failure signal from the faulty service.

Abstract: Provided is a method, system, and computer program product for storing images across multiple distributed computing systems according to image sensitivity. The method comprises identifying an image and analyzing the image to identify sensitive information in the image. The method further comprises splitting the image into a sensitive portion and a non-sensitive portion. The method further comprises storing the sensitive portion of the image in a first distributed computing system and storing the non-sensitive portion of the image in a second distributed computing system.

Abstract: Provided is a method, computer program product, and drying system for determining a dryness level of a garment. A processor of a controller may receive location data from a position sensor. The location data indicates a position of one or more garments on a drying rack. The processor may register the location of the one or more garments on a virtual map of the drying rack displayed on a user interface of the controller. The processor may receive dryness data for the one or more garments from a moisture sensor. The processor may compare the dryness data to a dryness threshold. In response to the dryness data meeting the dryness threshold, the processor may push a notification to a user.