International Business Machines Corporation

Sridevi Kannan

Sathya Santhar

Sarbajit Kumar Rakshit

Samuel Mathew Jawaharlal

Kothagorla Lakshmana Rao

Suvedhahari Velusamy

Reji Jose

A computer implemented method, system and product to predict an incremental degradation of a battery of a transportation vehicle while traveling on a route using a number of battery degradation parameters and a current condition of the battery as inputs in an artificial intelligence enabled system trained using historical battery degradation parameters. An equivalent carbon footprint is calculated based on the incremental degradation of the battery of the transportation vehicle. An equivalent carbon footprint tax is calculated based on the equivalent carbon footprint. The equivalent carbon footprint tax is assessed against the transportation vehicle.

Calculating carbon footprint while traversing route based on predicted incremental battery degradation

An embodiment for factcheck streaming in a virtual reality (VR) collaboration is provided. The embodiment may include receiving a video of a VR collaboration and one or more inputs from a user. The embodiment may also include identifying a pre-determined boundary of the VR collaboration. The embodiment may further include executing a factcheck of one or more items of content. The embodiment may also include identifying at least one factually inaccurate item of content. The embodiment may further include in response to determining the at least one factually inaccurate item of content is able to be replaced, generating, by a pre-trained generative adversarial network (GAN) model, at least one factually accurate item of content. The embodiment may also include replacing the at least one factually inaccurate item of content with the generated at least one factually accurate item of content in the VR collaboration.

Factcheck streaming in VR collaboration

A computer implemented method predicts a next frame. A number of processor units receives a sequence of frames for a live video stream for an activity. The number of processor units predicts a next frame for the live video stream for using the sequence of frames for the activity. The number of processor units determines a set of advertisement spaces in the next frame predicted for live video stream.

Next event and frame prediction system for live video

Provided is a method, system, and computer program product for using a generative adversarial network to visually generate object deformation predictions. A processor may identify, based on an analysis of image data, a plurality of objects that are in a first stack formation. The processor may determine a load distribution of each object of the plurality of objects in relation to a subset of objects of the plurality of objects in the first stack formation. The processor may generate, using a generative adversarial network (GAN) algorithm and based on the load distribution for each object, a visualization depicting deformation of each object of the plurality of object in relation to the subset of objects of the plurality of objects in the first stack formation. The processor may display the visualization depicting the deformation of each object of the plurality of objects to a user.

Predicting object deformation using a generative adversarial network model

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.

Contextual priority based multimedia modification

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.

Generative adversarial network based digital image enhancement through distributed resources

A computer-implemented method for predicting a next frame for a video is provided. Aspects include obtaining the video having a sequence of frames, obtaining a plurality of machine learning models (MLMs) that are trained to perform next frame prediction, and inputting a subset of the sequence of frames into each of the plurality of MLMs. Aspects also include calculating, for the subset of the sequence of frames, a score for each of the plurality of MLMs and generating, using one of the plurality of the MLMs having a highest score, the next frame, where the next frame is a frame immediately subsequent to the subset of the sequence of frames.

Predicting a next frame for a video using ensembling

An embodiment for transmitting and regenerating compressed media is provided. The embodiment may include receiving an image selected by a sender to be transmitted. The embodiment may also include sending the image to a pre-trained generative adversarial network (GAN) model. The embodiment may further include identifying a first set of one or more pixels in the image assigned to one or more high priority Class of Service (CoS) groups and a second set of one or more pixels in the image assigned to one or more low priority CoS groups. The embodiment may also include compressing the one or more pixels in the second set. The embodiment may further include transmitting the one or more pixels in the first set and the compressed one or more pixels in the second set to a recipient in an order from a highest priority CoS group to a lowest priority CoS group.

Compressed media transmission and high quality regeneration

A method, computer program product, and computer system for predicting a class of a given input to multiple models. Class parameters of the models are received. A process that predicts the class of a given input to the models is performed using the class parameters of the models. The process is (i) a model ensemble process predicting that the class of the given input is a class that maximizes a function of class accuracy parameters selected from combinations of the class parameters, (ii) a first threshold process based on a requirement that the class of the given input be predicted by running a model whose class accuracy parameter is at least a specified threshold, or (iii) a second threshold process based on a requirement that a specified class be predicted by a running of all models whose class accuracy parameter is at least the specified threshold.

Class prediction based on class accuracy of multiple models

One or more user interactions directed to a set of one or more voice-controlled devices in an environment are received by a first connected device. A first input to a first voice-controlled device of the set of voice-controlled devices is detected based on the user interactions. A potential second input to the set of voice-controlled devices is determined in response to the first input and based on an activity model. A deviation from the potential second input is monitored for, in response to the first input and from the user interactions. An activity anomaly in the environment is identified based on the monitoring. A correction action is performed in response to the activity anomaly.

Reactive voice device management

Provided is a method, system, and computer program product for determining a degree of impact caused by space debris. A processor may receive data from a data stream of one or more satellites, the data including observational data of one or more objects in orbit. The processor may determine, based on an analysis of the observational data, that the one or more objects are space debris. The processor may identify, based on the analyzed observational data, ownership of the one or more objects that are determined to be space debris. The processor may determine, using one or more smart contracts, a degree of impact caused by the one or more objects that are space debris, where the degree of impact is attributed to an identified owner of the one or more objects.

Automated determination of cost ownership of space debris

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.

Text summarization with emotion conditioning

Embodiments herein describe maneuvering a spacecraft using a solar sail when sunlight is not available. Smaller satellites may rely solely on solar sails in order to maneuver to different locations (e.g., different orbits) to adjust for orbital decay, avoid collisions with other satellites, or to avoid space junk. However, solar sails cannot rely on the sun when orbiting on the dark side of a planet (e.g., when in the earth's shadow). When a spacecraft should maneuver but the sun is not available as a power source, the embodiments herein describe identifying other spacecraft within line-of-sight (LOS) of the spacecraft and using these spacecraft to direct lasers (or reflecting sunlight if available) at the spacecraft to maneuver it to a desired path (e.g., a new orbit).

Maneuvering spacecraft having solar sails

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.

Training an environment generator of a generative adversarial network (GAN) to generate realistic environments that incorporate reinforcement learning (RL) algorithm feedback

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.

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Chennai, India

Sridevi Kannan