International Business Machines Corporation

Celia Cintas

Komminist S Weldemariam

Aisha Walcott

Girmaw Abebe Tadesse

Victor Abayomi Akinwande

Sarbajit Kumar Rakshit

Sekou Lionel Remy

Komminist Weldemariam

Skyler Speakman

Charles Muchiri Wachira

William Ogallo

Michael S Gordon

Ramya Raghavendra

Naweed Aghmad Khan

David W Kaguma

Srihari Sridharan

Samuel Osebe

Joan Byamugisha

Arphaxade Wanjala Sifuna

Clifton Kipchirchir

With a trained, computerized discontinuation predictor machine learning component (MLC), predict, based on an input time series, a time when a subject will discontinue a course of medical treatment; with a trained, computerized pattern behavior extractor MLC, extract from said input time series the top k discriminatory sequences via discriminatory sub-sequence mining (said top k discriminatory sequences differentiate between first and second classes of interest to provide a hypothesis for downstream analysis of a cause of discontinuing said course of treatment). With a trained, causal effect estimator computerized MLC, determine a reason why said subject will discontinue said course of medical treatment, based on said top k discriminatory sequences and additional data; and with a computerized user interface, provide said time and said reason why to a responsible party to initiate an intervention.

Pattern discovery, prediction and causal effect estimation in treatment discontinuation

In an approach to improve platform modification by dynamically modifying content based on user feedback. Embodiments suggest one or more solutions and recommendations, via a user interface, to one or more users that comply with one or more standards, regulations, and resources of an organization. Further, embodiments verify the solutions and recommendations that comply with the one or more standards, regulations, and resources of the organization, and automatically generate a prototype solution. Additionally, embodiments provide, by the user interface, one or more augmented solutions for the one or more users, wherein augmented solutions are provided to the one or more users requiring a temporary solution; and label and cluster user feedback and associated solutions of the one or more users to provide effective solutions for future users based on a similarity of the feedback of the one or more users.

Dynamically modifying a platform based on user feedback

A supervised discriminator for detecting bio-markers in an audio sample dataset is trained and a denoising autoencoder is trained to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample of the audio sample dataset. A conditional auxiliary generative adversarial network (GAN) trained to generate the output audio sample with the same fidelity as the input audio sample, wherein the output audio sample is void of the bio-markers. The conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder are deployed in an audio processing system.

Obfuscating audio samples for health privacy contexts

An exemplary method includes vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations; a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds. An exemplary apparatus includes a vehicle; tires mounted to the vehicle; a speed sensor mounted to the vehicle; a vibration sensor mounted to the vehicle; and a processor connected in communication with the speed sensor and the vibration sensor. The processor is adapted to implement any of the method steps above.

Predicting tire imbalance and/or wheel misalignment

A computer system may create a language model corpus including multilingual alignment for training a combined language model and train (or pre-train) the combined language model. The computer system may create an adverse medication reaction corpus to include adverse medication reaction utterances and label an N-gram of an utterance in the adverse medication reaction utterances as a response to query, for multiple N-grams. The computer system may generate a code-mixed utterance model to perform code-mixed utterances in a turn by turn dialogue, by at least adding additional output layer including at least a start vector, language vector, and a query vector including at least the labeled N-gram, which are additional to the combined language model's predicted next words.

Goal-oriented conversation with code-mixed language

An illustrative embodiment includes a method for detecting whether a given item within input data is anomalous. The method includes: extracting activations at respective nodes of an autoencoder trained on the data, the activations comprising activations at the respective nodes for the given item within the data and for one or more other items within the data; calculating p-values corresponding to the respective nodes, wherein the p-value for a given node represents a proportion of the activations at the given node for the other items which are greater than the activations at the given node for the given item; determining at least one score at least in part by scanning for a subset of the respective nodes which maximizes a statistical scoring function applied to the corresponding p-values; and providing a visualization of at least the given item within the input data to a human user.

Identifying adversarial attacks with advanced subset scanning

Members of an audience of a visually dynamic event are clustered using a plurality of sources. A current point of interest (POI) of the visually dynamic event and a future POI of the visually dynamic event are identified across the member clusters. An effectiveness score for given content and a given member cluster is computed for the current point of interest and an effectiveness score for a given content and a given member cluster is computed for the future POI by tracking a position, a speed, and a direction of movement of the current POI. A location of a background area is determined and ranked for each member cluster based on the current point of interest. Electronic displays that correspond to the ranked background areas are identified and ranked. Content is distributed across the electronic displays for each current time period based on the ranked display areas.

Electronic display systems

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises generating a baseline prediction for each database in a plurality of databases, wherein the generated baseline prediction includes datapoints for each performance feature of a respective database in the plurality of databases; generating a linkage matrix using a dimension reduction algorithm in an equal-dimensional feature space based on a measured difference between actual performance datapoints of each respective database and respective datapoints associated with generated baseline prediction for each database in the plurality of databases; linking one or more databases in the plurality of databases based on the measured difference within the generated linkage matrix reaching a predetermined threshold of measured performance; and generating a design using the linked databases for a computer system capable of being displayed through a user interface.

Automated data linkages across datasets

A search query can be received. User parameters can be identified based on the search query. The search query can be refined to include the user parameters. A search result from a search for media content using the refined search query can be received. Based on at least one search result received from the search and based on the user parameters, an augmented media content can be generated. Playing of the augmented media content can be synchronized with a user's activity by controlling playing of the augmented media content while detecting the user's activity pace.

Automatic context aware composing and synchronizing of video and audio transcript

An embodiment for simulating a live video presentation in a recorded video is provided. The embodiment may include receiving a previously recorded online meeting. The embodiment may also include transcribing and indexing the transcription of the previously recorded online meeting. The embodiment may further include receiving audio content from a user. The embodiment may also include searching for a response to the audio content in the transcription. The embodiment may further include in response to determining the response is found in the transcription, generating a solution for the audio content from the transcription. The embodiment may also include integrating the generated solution into the previously recorded online meeting. The embodiment may further include updating one or more video frames of the previously recorded online meeting based on the generated solution.

Simulate live video presentation in a recorded video

In an aspect, during a presentation of a presentation material, viewers of the presentation material can be monitored. Based on the monitoring, new content can be determined for insertion into the presentation material. The new content can be automatically inserted to the presentation material in real time. In another aspect, during the presentation, a presenter of the presentation material can be monitored. The presenter's speech can be intercepted and analyzed to detect a level of confidence. Based on the detected level of confidence, the presenter's speech can be adjusted and the adjusted speech can be played back automatically, for example, in lieu of the presenter's original speech that is intercepted.

Dynamic creation and insertion of content

A contextual hashtag generation method, system, and computer program product include receiving content from an online source, identifying a set of contextual indicators for the content, determining an entity-desired outcome for the content, and generating a hashtag for the content using the set of contextual indicators while maximizing the entity-desired outcome.

Contextual hashtag generator

A method, system and apparatus of for guiding a physically impaired individual during an emergency, including receiving emergency data, determining or predicting the reactions of other people in response to the emergency data, interpreting in real-time the reactions of the other people to physically impaired person according to the impaired person profile, determining a type and modality of aid services to the physically impaired, and prompting the physically impaired person through one or more devices for aid services.

Interpreting reactions of other people for physically impaired during an emergency situation

A bias detection method, system, and computer program product include creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer, predicting a probability of overlapping data points between the applicant and the reviewer, building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer, and calculating a bias score via a deep learning model run over the enriched embeddings.

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Nairobi, Kenya

Celia Cintas