Abstract: A system, method, and computer program product for implementing digital script modification is provided. The method includes generating image sequences associated with textual content of a digital story. Multiple contextual dimensions are identified within the textual content and a group of dimensions are selected. The image sequences in combination with the group of dimensions are expanding or contracted and image sequences are altered based on detected interactions with the group of dimensions. Dimensions are extracted from the group of dimensions during presentation of the digital story and a scriptwriter is enabled to modify the dimensions. The image sequences are modified and a hardware interface device is enabled to interact with various image sequences and alter the multiple contextual dimensions. The textual content of the digital story is dynamically altered.

Abstract: A processor may initiate a recording. The processor may segment the recording into one or more segments. The processor may determine, based on the identification of a primary object in a first segment of the recording, a first bit rate for the first segment of the first recording. The processor may preload one or more subsequent segments that include the primary object at the first bit rate. The processor may preload each of the one or more subsequent segments with a secondary object at a second bit rate. The second bit rate may be lower than the first bit rate. The processor may display the recording to the user.

Abstract: A determination is made via a computational device of an activity being performed by an entity in a virtual reality environment. An electronic subscriber identification module (eSIM) of a communications device of the entity is reconfigured in the virtual reality environment, in response to the determination of the activity being performed by the entity in the virtual reality environment.

Abstract: A method, computer program, and computer system are provided for text summarization that maintains emotional content. Data corresponding to text to be summarized and a target emotion to be maintained in the text to be summarized is received. The target emotion is encoded as an emotion probability vector. One or more words that correspond to the target emotion that is encoded in the emotion probability vector are identified from a dictionary. A text summary to be associated with the text to be summarized is generated based on the one or more identified words.

Abstract: A method, computer program, and computer system are provided for correcting holographic content to be displayed on a foldable mobile device. The method, computer program, and computer system may include identifying holographic content to be displayed on two display regions of a foldable mobile device. An angle between the two display regions of the foldable mobile device is calculated, and a determination is made as to whether the holographic content on each of the two display regions overlaps based on the calculated angle. The holographic content to be displayed is updated based on applying one or more geometric transformations to the holographic content corresponding to determining the holographic content overlaps, and the updated holographic content is displayed as a three-dimensional holographic image.

Abstract: A video file is detected by a computer system. The video file that is to be provided to one or more client devices. The video file contains a video stream that includes a plurality of video images. A first video image of the plurality of video images is reconstructed based on a first machine learning technique. The first machine learning technique is based on one or more video images that occur temporally before the first video image in the video stream. A reconstruction status of the first video image of the plurality of video images is identified based on the video file and based on a second machine learning technique. An altered video file is generated in response to the reconstruction status and based on the video file.

Abstract: Image data encryption by receiving first image data corresponding to a first image having a first image size, compressing the first image data, yielding second image data corresponding to a second image having a second image size, augmenting the second image data yielding third image data corresponding to a third image having the first image size, determining coordinates of a location of the second image within the third image, encrypting the third image data according to the coordinates, providing the encrypted third image data to a decoder by a first communications channel, and providing the coordinates of the second image within the third image to the decoder by a second communications channel.

Abstract: A computer-implemented method for optimizing reinforcement learning based on a stability of a reinforcement learning state is disclosed. The computer-implemented method includes determining whether a stability of a next reinforcement learning state of a reinforcement learning problem is above a predetermined threshold. The computer-implemented method further includes responsive to determining that the stability of the next reinforcement state is below the predetermined threshold, determining a stability of an alternate next reinforcement learning state of the reinforcement learning problem. The computer-implemented method further includes responsive to determining that the stability of the next reinforcement state is above the predetermined threshold, transitioning from a current reinforcement learning state to the next reinforcement learning state based, at least in part, on determining that the stability of the next reinforcement learning state is above the predetermined threshold.

Abstract: According to one embodiment, a method, computer system, and computer program product for digital image enhancement is provided. The embodiment may include receiving a digital image being navigated by a user. The embodiment may include identifying an image content navigation pattern of the user and a context of the digital image. Based on the image content navigation pattern and context, the embodiment may include identifying one or more objects of the digital image on which the user will perform a predicted zoom-in action. The embodiment may include identifying one or more distributed computing resources for digital image enhancement of the identified one or more objects. The embodiment may include performing digital image enhancement of the identified one or more objects. Upon the user performing a zoom-in action on an identified object which matches a predicted zoom-in action, the embodiment may include displaying a digitally enhanced image of the identified object.

Abstract: A method includes receiving model data, training a plurality of supervised models using the model data, each of the plurality of supervised models including a plurality of layers, slicing each of the plurality of supervised models into individual layers of the plurality of layers, calculating accuracy of feature detection of each of the individual layers of each of the plurality of supervised models, and combining a sequence of the individual layers taken from different models of the plurality of supervised models into a composite model based on the calculated accuracy of feature detection of each of the individual layers of each of the plurality of supervised models.

Abstract: A computer-implemented method for multimedia modification is disclosed. The computer-implemented method includes classifying one or more objects detected within a user's field of view through an augmented reality environment. The computer-implemented method further includes determining a context of the user based, at least in part, on the one or more classified objects detected within the user's field of view. The computer-implemented method further includes generating a priority score for the one or more classified objects based, at least in part, on the context of the user. The computer-implemented method further includes modifying an object detected within the user's field of view based, at least in part, on the priority score of the object.

Abstract: A computer-implemented method according to one embodiment includes causing an environment generator of a Generative Adversarial Network (GAN) to generate realistic training environments, and causing a first discriminator of the GAN to determine whether the realistic training environments are real or fake. In response to a determination that an accuracy of the first discriminator at determining whether the realistic training environments are real or fake is within a predetermined range, the environment generator is caused to generate a first realistic environment. The method further includes causing the first realistic environment to be shared with an agent of a reinforcement learning (RL) algorithm and a second discriminator, and receiving, from the agent of the RL algorithm and the second discriminator, feedback associated with the first realistic environment. The environment generator is caused to generate a second realistic environment based on the feedback associated with the first realistic environment.

Abstract: An approach for state-based dynamic multi-dimensional goal reinforcement learning may be provided. The approach may include measuring a reward for an action. The reward may be a dimensional vector of a number of sub-goal dimensions for achieving a final goal. The approach may also include, determining a temporal difference. The temporal difference can be the difference between the reward from an action taken in response to an immediately prior state and the measured reward. The approach may also include updating a Q-table for the action. Updating the Q-table can be based on the first state and the measured reward. Further, the approach may also include predicting a goal for a first state. The prediction can be based on a Q-value from the updated Q-table. Within the approach, the prediction can be based on a deep neural network configured to output a probability.

Abstract: Provided is a system and method for moving a virtual object within virtual space in response to an external input supplied by a user. A machine learning model may predict a movement of the virtual object and implement such movement in a next frame of the virtual space. An example operation may include one or more of receiving a measurement of an external input of a user with respect to a virtual object displayed in virtual space, predicting, via execution of a machine learning model, a movement of the virtual object in the virtual space in response to the external input of the user based on the measurement of the external input of the user, and moving the virtual object in the virtual space based on the predicted movement of the virtual object by the machine learning model.

Abstract: A computer-implemented method, according to one embodiment, includes: determining whether a predetermined version of a source script is available. In response to determining that a predetermined version of the source script is available, it is used to condition a first processor, and instructions are sent to the conditioned first processor to generate a translated copy of the source script by translating the words in the source script from a source language to a target language. Instructions are also sent to a second processor to determine a distribution of metrics associated with the speech of each of the actors in the source audio file. The distributions are used to condition a third processor, and instructions are sent to the conditioned third processor to generate an audio file that includes words spoken in the target language. Furthermore, instructions are sent to merge the generated audio file with the video file.

Abstract: An approach for generating an optimized video of a speaker, translated from a source language into a target language with the speaker's lips synchronized to the translated speech, while balancing optimization of the translation into a target language. A source video may be fed into a neural machine translation model. The model may synthesize a plurality of potential translations. the translations may be received by a generative adversarial network which generates video for each translation and classifies the translations as in-sync or out of sync. A lip-syncing score may be for each of the generated videos that are classified as in-sync.

Abstract: A system, method, and computer program product for implementing automated digital text optimization is provided. The method includes receiving during execution of an Internet search process, digital textual content comprising embedded words, morphemes, and phrases. A specified format for generating a summary for the digital textual content is identified and probability distribution attributes are generated with respect to target vocabulary of the digital textual content. Content of the digital textual content is replaced with replacement content and an automated digital summary of the digital textual content is generated thereby enabling optimized operational functionality of a recurrent neural network (RNN) encoder-decoder hardware device. A decoded version of the automated digital summary is presented via a specialized graphical user interface.

Abstract: A system, method, and computer program product for implementing digital script modification is provided. The method includes generating image sequences associated with textual content of a digital story. Multiple contextual dimensions are identified within the textual content and a group of dimensions are selected. The image sequences in combination with the group of dimensions are expanding or contracted and image sequences are altered based on detected interactions with the group of dimensions. Dimensions are extracted from the group of dimensions during presentation of the digital story and a scriptwriter is enabled to modify the dimensions. The image sequences are modified and a hardware interface device is enabled to interact with various image sequences and alter the multiple contextual dimensions. The textual content of the digital story is dynamically altered.

Abstract: Systems, methods, and computer programming products leveraging cognitive computing and machine learning to dynamically establish circuits within conference calls merging in new callers. The newly established circuits are optimized and established based on location attributes of the new caller and conference call participants, as well as the calling plans and/or costs associated with establishing the circuits to the different conference call participants. New circuits merging in new callers can be prioritized to establish endpoints of the circuit with optimal participants at nearby locations or the same location as the new caller, rather than establishing the endpoints between the new caller and a target participant of the conference call as the default endpoints. Optimized circuits being established between new callers and existing conference call participants can be provisioned entirely within the PSTN of a single service provider or can span across multiple service provider networks.

Abstract: A computer-implemented includes streaming a first segment of multimedia data to a buffer on a first computing device at a first bitrate, wherein the first segment includes a first set of video frames encoded at a first resolution. The computer-implemented method further includes streaming a second segment of the multimedia data to the buffer on the first computing device at a second bitrate, wherein the second segment includes a second set of video frames encoded at a second resolution. The computer-implemented method further includes determining that a first predetermined threshold is reached. The computer-implemented method further includes streaming, in response to determining that the first predetermined threshold is reached, a third segment of multimedia data to the buffer on the first computing device, wherein the third segment includes a first set of pixels that are logically related to the second set of video frames of the second segment.