Abstract: Three-dimensional printing of objects is facilitated. For an object to be printed in three-dimensions (3D), one or more contact points relating to the object are determined. A contact point is at least one location on a base of a 3D printer in which a portion of the object is to contact the base based on printing the portion of the object. A centralized location of the base at which one or more movable blocks of the base are to be moved to position the one or more contact points at the centralized location to facilitate printing of the one or more portions of the object is also determined. A print plan to be used to print the object in three-dimensions is created, in which the print plan includes one or more instructions to print at least the one or more portions of the object at the centralized location.

Abstract: Three-dimensional printing of objects is facilitated. For an object to be printed in three-dimensions (3D), one or more contact points relating to the object are determined. A contact point is at least one location on a base of a 3D printer in which a portion of the object is to contact the base based on printing the portion of the object. A centralized location of the base at which one or more movable blocks of the base are to be moved to position the one or more contact points at the centralized location to facilitate printing of the one or more portions of the object is also determined. A print plan to be used to print the object in three-dimensions is created, in which the print plan includes one or more instructions to print at least the one or more portions of the object at the centralized location.

Abstract: Data protection activity time in a backup environment can be predicted with a computer-implemented method. The method includes gathering datasets of data points from a range of backup components of a backup system and constructing input features for a predictive machine learning model, where the input features relate to backup parameters of the backup system. The method also includes training the predictive machine learning model to predict data protection activity times for the backup system by using the gathered datasets and applying the input features to the model. The method also includes deploying the trained predictive machine learning model to predict a data protection activity time of a backup system formed of a set of the backup components.

Abstract: Aspects of the present disclosure relate to light-based 3D printing. A computer aided design (CAD) representation of an object is obtained. Mirror and lighting requirements of a light-based 3D printer containing at least one mirror and at least one light source are determined based on the CAD representation of the object. Mirror and lighting characteristics of the at least one mirror and the at least one light source are adjusted based on the determined mirror and lighting requirements, the adjusting resulting in a first 3D light-based printer configuration. A 3D printing process is then initiated, wherein initiating the 3D printing process includes exposing a photosensitive material within the light-based 3D printer to light based on the first light-based 3D printer configuration.

Abstract: Computer-generated speech based on a device feed includes generating a corpus for robotic use by receiving first information representative of a user's speech in different environments at different times and second information representative of environmental conditions of different locations at the different times. The first and second information of corresponding different environments and different locations for each of the different times is combined with third information received from external data sources. A plurality of annotated combined datasets including the first information, the second information, and the third information is generated for each of the different times in a repository. The plurality of annotated combined datasets is correlated to create training data that is subsequently processed using a predetermined machine learning model.

Abstract: Aspects of the present disclosure relate to light-based 3D printing. A computer aided design (CAD) representation of an object is obtained. Mirror and lighting requirements of a light-based 3D printer containing at least one mirror and at least one light source are determined based on the CAD representation of the object. Mirror and lighting characteristics of the at least one mirror and the at least one light source are adjusted based on the determined mirror and lighting requirements, the adjusting resulting in a first 3D light-based printer configuration. A 3D printing process is then initiated, wherein initiating the 3D printing process includes exposing a photosensitive material within the light-based 3D printer to light based on the first light-based 3D printer configuration.

Abstract: Systems, methods, and computer program products leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences. The collected user data and user-defined parameters define a user profile, providing insight into career or educational options best suited to the user. Further educational and career information may be collected and presented to the user, including enrollment information from one or more colleges and universities, specific educational programs aligned with the user's defined profile, and descriptions of courses and curriculum.

Abstract: Coordinated storage media verification is provided by checking usage of a plurality of storage media, maintaining a storage media check database indicating media verification to be performed for one or more of the plurality of storage media, where the maintaining includes determining and comparing a time since a last load of a selected storage medium to a verification frequency threshold and determining whether media verification of the selected storage medium is to be performed, coordinating media verification of at least one storage medium of the plurality of storage media based on the storage media check database, which coordinating includes selecting at least one storage medium to be verified in an ascertained window of time, and initiating media verification of the selected at least one storage medium.

Abstract: Coordinated storage media verification is provided by checking usage of a plurality of storage media, maintaining a storage media check database indicating media verification to be performed for one or more of the plurality of storage media, where the maintaining includes determining and comparing a time since a last load of a selected storage medium to a verification frequency threshold and determining whether media verification of the selected storage medium is to be performed, coordinating media verification of at least one storage medium of the plurality of storage media based on the storage media check database, which coordinating includes selecting at least one storage medium to be verified in an ascertained window of time, and initiating media verification of the selected at least one storage medium.

Abstract: Data protection activity time in a backup environment can be predicted with a computer-implemented method. The method includes gathering datasets of data points from a range of backup components of a backup system and constructing input features for a predictive machine learning model, where the input features relate to backup parameters of the backup system. The method also includes training the predictive machine learning model to predict data protection activity times for the backup system by using the gathered datasets and applying the input features to the model. The method also includes deploying the trained predictive machine learning model to predict a data protection activity time of a backup system formed of a set of the backup components.

Abstract: Systems, methods, and computer program products leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences. The collected user data and user-defined parameters define a user profile, providing insight into career or educational options best suited to the user. Further educational and career information may be collected and presented to the user, including enrollment information from one or more colleges and universities, specific educational programs aligned with the user's defined profile, and descriptions of courses and curriculum.