International Business Machines Corporation

Martin Gleize

Pierpaolo Tommasi

Yufang Hou

Debasis Ganguly

Francesca Bonin

Ambrish Rawat

Charles Arthur Jochim

Embodiments for providing enhanced data anonymity protection by a processor are disclosed. Selected portions of data intended for distribution in a communication channel or currently distributed on one or more data sources having a potential for revealing identify of a user in a public domain (and/or private domain) may be identified, where an assessment is provided indicating a current status of an amount of data currently exposing the identity of the user in the public domain. The selected portions of the data may be transformed into anonymous data by applying a one or more data corrective operations to prevent further exposure of the identity of the user into the public domain.

Data anonymity protector

Submitting a data space to a provider can risk exposing sensitive information in the data space. To protect the sensitive information, the data space can be analyzed to identify features that are relevant to sensitive information and features that are relevant to a goal task. Features that are relevant to the sensitive information but are not relevant to the goal task can be pruned from the data space, and the pruned data space can be transmitted to the provider.

Sensitive-data-aware encoding

Methods, computer program products and/or systems are provided that perform the following operations: obtaining a collection of objects; constructing a weighted graph based on the collection of objections, wherein the weighted graph preserves neighborhood semantics of objects of the collection of objects; generating partitions of nodes in the weighted graph of a fixed maximum size utilizing combinatorial partitioning; generating a vector for each node based on the partitions of nodes in the weighted graph; determining vector representations for objects in this collection and eventually applying this vector representation of the objects to gain efficiency (e.g., in terms of computation time and memory requirements) for use in downstream tasks such as recommendation.

Discrete representation learning

A method for automatically detecting inappropriate content on a computing application is provided. The method may include, in response to detecting an attempt to post content on the computing application, parsing the content to identify parts of the content. The method may further include determining whether the parts of the content comprises inappropriate content by applying algorithms to the parts of the content based on information associated with one or more computing applications and by predicting whether potential viewers of the one or more parts of the content view the one or more parts as inappropriate. The method may further include, in response to determining that the one or more parts of the content includes inappropriate content based on the applied algorithms and the potential viewers, generating and displaying feedback and providing suggestions for editing the inappropriate content on the attempted post.

Computer application content detection and feedback

Embodiments for assisting pronunciation correction are described. A representation of a user pronunciation of an utterance is received. A representation of a target pronunciation of the utterance is identified. The representation of the user pronunciation of the utterance is compared to the representation of the target pronunciation of the utterance. A recommendation associated with correcting the user pronunciation of the utterance is generated based on the comparing of the representation of the user pronunciation of the utterance to the representation of the target pronunciation of the utterance and information associated with the user.

Methods and systems for assisting pronunciation correction

Tailoring textual content to a target audience by receiving an input of a user, wherein the input of the user includes textual data, identifying a target audience of the textual data based at least in part on the input of the user, determining a style of the target audience, wherein the style is a variety of language used by the target audience, generating a modification recommendation to the textual data of the input of the user based at least in part on the textual data and the determined style.

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Dublin, Ireland

Martin Gleize