Abstract: A person's drawing capability is used as an authentication credential. During a training phase, a user is asked to hand draw various reference shapes such as a rectangle, flower, etc. These user drawings for a given shape are input to a training discriminator (with an “authentic” label) along with drawings automatically generated from a latent sample (with a “not authentic” label), and the training discriminator computes positive discrimination vectors for this shape that are unique to this user. Thereafter, when the user wants access to a resource (such as an online account, a mobile computing device, or an electronic document), the user is presented with a drawing challenge for one of the reference shapes, and they draw a corresponding challenge image. An image vector for the challenge image is generated, and if the image vector falls within the positive discrimination vectors, access to the resource is granted.

Abstract: Embodiments relate to a system, computer program product, and method to merge two or more heterogeneous datasets. Seed attributes of each dataset that is the subject of the merge are identified. The seed attributes are derived from candidate attributes of the respective datasets. A correlation is assessed to create a set of mergeable attributes and a set of non-mergeable attributes. A cohesiveness characteristic is leveraged to iteratively identify one or more attributes from the set of non-mergeable attributes, and to amend the set of mergeable attributes with the one or more attributes identified in the set of non-mergeable attributes. A merged dataset based on the amended set of mergeable attributes and representing non-trivial similarities between the first and second dataset is formed as output.

Abstract: A computer-implemented process of intelligent dataset slicing within a network having a plurality of microservices is disclosed. Handshaking between a first microservice and a second microservice is initiated. A vertical reduction of a dataset is required by the second microservice and from the first microservice. A first slice of the dataset generated by the vertical reduction is received by the second microservice and from the first microservice. The sliced dataset is evaluated by the second microservice. The vertical reduction is terminated by the second microservice based upon the evaluating.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the management of word and image relationships in a combined vector space. More specifically, the system, computer program product, and method disclosed herein facilitate establishing one or more continuous semantic relationships with word vectors, sentence vectors, and image vectors within an image-word combined vector space with N-dimensional coordinates. In general, the word vectors, sentence vectors, and image vectors are generated within their respective domains that are resident within the image-word combined vector space. Such domains of word vectors, sentence vectors, and image vectors include inherent semantic relationships between respective members of each domain that facilitate establishing continuous, formal, virtual relationships between respective word vectors, image vectors, and sentence vectors within the image-word combined vector space.

Abstract: Embodiments relate to a system, program product, and method for use with an intelligent computer platform to create and apply textual data in vector format, and more specifically to apply context to the vector representation. Both context and document vectors are generated and assessed, with a calculated distance between the vectors corresponding to a weight. Word vectors are generated with associated word pairs and frequencies. A word vector generation model is trained. Utilization of the trained model generates one or more context sensitive word vector representations. A summarized sentence document is created and returned through application of the context sensitive word vectors.

Abstract: In an approach to summarizing contextually sensitive text documents based on long short-term memory networks, one or more computer processors create a model based on a vectorized document corpus. The one or more computer processors determine one or more contextually relevant sections utilizing the created model. The one or more computer processors generate a relevancy level for each of the one or more respective determined sections. The one or more computer processors prioritize each of the one or more determined sections based on the respective relevancy level. The one or more computer processors generate one or more textual summaries based on the prioritized sections, wherein the generated one or more textual summaries are aggregated into a document.

Abstract: A person's drawing capability is used as an authentication credential. During a training phase, a user is asked to hand draw various reference shapes such as a rectangle, flower, etc. These user drawings for a given shape are input to a training discriminator (with an “authentic” label) along with drawings automatically generated from a latent sample (with a “not authentic” label), and the training discriminator computes positive discrimination vectors for this shape that are unique to this user. Thereafter, when the user wants access to a resource (such as an online account, a mobile computing device, or an electronic document), the user is presented with a drawing challenge for one of the reference shapes, and they draw a corresponding challenge image. An image vector for the challenge image is generated, and if the image vector falls within the positive discrimination vectors, access to the resource is granted.

Abstract: An approach is provided in which an information handling system uses historical time durations of deprecated resources to compute an increased probability window. The increased probability window corresponds to an increase in probability that a currently active resource is likely to be active at a future point in time. Next, the information handling system identifies a set of active resources that have active time durations within the increased probability window and, in turn, marks the set of resources as a set of forecasted active resources. In turn, the information handling system generates a resource cost forecast based on the set of forecasted active resources.

Abstract: A method includes providing a server executing computer code embodying the chat-bot, receiving, via the server, a requirement for customization of a main computer code, the main computer code including modules, and determining, by the chat-bot, top-k modules of the main computer code potentially affected given the requirement for customization. The determination of the top-k modules includes representing each of the modules of the main computer code in an embedding vector space, representing the requirement for customization as a vector in the embedding space, and fetching the top-k modules nearest the requirement for customization in the embedding space. The method further includes determining a question, communicating the question to an entity providing the requirement for customization, receiving a response to the question, and filtering the top-k modules using the response to determine a set of the modules associated with the requirement for customization.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the management of word and image relationships in a combined vector space. More specifically, the system, computer program product, and method disclosed herein facilitate establishing one or more continuous semantic relationships with word vectors, sentence vectors, and image vectors within an image-word combined vector space with N-dimensional coordinates. In general, the word vectors, sentence vectors, and image vectors are generated within their respective domains that are resident within the image-word combined vector space. Such domains of word vectors, sentence vectors, and image vectors include inherent semantic relationships between respective members of each domain that facilitate establishing continuous, formal, virtual relationships between respective word vectors, image vectors, and sentence vectors within the image-word combined vector space.

Abstract: A method includes providing a server executing computer code embodying the chat-bot, receiving, via the server, a requirement for customization of a main computer code, the main computer code including modules, and determining, by the chat-bot, top-k modules of the main computer code potentially affected given the requirement for customization. The determination of the top-k modules includes representing each of the modules of the main computer code in an embedding vector space, representing the requirement for customization as a vector in the embedding space, and fetching the top-k modules nearest the requirement for customization in the embedding space. The method further includes determining a question, communicating the question to an entity providing the requirement for customization, receiving a response to the question, and filtering the top-k modules using the response to determine a set of the modules associated with the requirement for customization.

Abstract: Embodiments relate to a system, computer program product, and method to merge two or more heterogeneous datasets. Seed attributes of each dataset that is the subject of the merge are identified. The seed attributes are derived from candidate attributes of the respective datasets. A correlation is assessed to create a set of mergeable attributes and a set of non-mergeable attributes. A cohesiveness characteristic is leveraged to iteratively identify one or more attributes from the set of non-mergeable attributes, and to amend the set of mergeable attributes with the one or more attributes identified in the set of non-mergeable attributes. A merged dataset based on the amended set of mergeable attributes and representing non-trivial similarities between the first and second dataset is formed as output.

Abstract: Embodiments are directed to a system, computer program product, and method for application of cognitive processing to a communication, and selectively transmitting the communication based on the cognitive processing. Natural language understanding (NLU) decomposes the communication to identify content and keywords. A dictionary determined to be contextually related to the communication is identified to support and enable a multi-dimensional analysis of the communication content. The communication is subject to dynamic filtering with support of the dictionary and dictionary content, and the dictionary is subject to a selective amendment based on the dynamic processing. A response is generated from the filtering, and the response is subject to selective transmission.

Abstract: In an approach to summarizing contextually sensitive text documents based on long short-term memory networks, one or more computer processors create a model based on a vectorized document corpus. The one or more computer processors determine one or more contextually relevant sections utilizing the created model. The one or more computer processors generate a relevancy level for each of the one or more respective determined sections. The one or more computer processors prioritize each of the one or more determined sections based on the respective relevancy level. The one or more computer processors generate one or more textual summaries based on the prioritized sections, wherein the generated one or more textual summaries are aggregated into a document.

Abstract: Embodiments are directed to a system, computer program product, and method for application of cognitive processing to a communication, and selectively transmitting the communication based on the cognitive processing. Natural language understanding (NLU) decomposes the communication to identify content and keywords. A dictionary determined to be contextually related to the communication is identified to support and enable a multi-dimensional analysis of the communication content. The communication is subject to dynamic filtering with support of the dictionary and dictionary content. A response is generated from the filtering, and is subject to selective transmission.

Abstract: Embodiments relate to a system, program product, and method for use with an intelligent computer platform to create and apply textual data in vector format, and more specifically to apply context to the vector representation. Both context and document vectors are generated and assessed, with a calculated distance between the vectors corresponding to a weight. Word vectors are generated with associated word pairs and frequencies. A word vector generation model is trained. Utilization of the trained model generates one or more context sensitive word vector representations. A summarized sentence document is created and returned through application of the context sensitive word vectors.

Abstract: Embodiments relate to a system, program product, and method for use with an intelligent computer platform to create and apply textual data in vector format, and more specifically to apply context to the vector representation. Both context and document vectors are generated and assessed, with a calculated distance between the vectors corresponding to a weight. Word vectors are generated with associated word pairs and frequencies. A word vector generation model is trained. Utilization of the trained model generates one or more context sensitive word vector representations. A summarized sentence document is created and returned through application of the context sensitive word vectors.

Abstract: Aspects of the present invention disclose a method for regeneration of live stream data lost during an outage. The method includes one or more processors identifying a data feed of a live stream. The method further includes applying a cognitive model to the data feed of the live stream. The method further includes modifying parameters of the cognitive model based at least in part on a modified weight, wherein the cognitive model performs one or more calculations to generate the modified weight based at least in part on a set of training data of the data feed. The method further includes identifying an outage in the data feed of the live stream. The method further includes generating data corresponding to the outage in the data feed of the live stream, wherein the generated data is based at least in part on the modified weight of the set of training data.

Abstract: An approach is provided in which an information handling system uses historical time durations of deprecated resources to compute an increased probability window. The increased probability window corresponds to an increase in probability that a currently active resource is likely to be active at a future point in time. Next, the information handling system identifies a set of active resources that have active time durations within the increased probability window and, in turn, marks the set of resources as a set of forecasted active resources. In turn, the information handling system generates a resource cost forecast based on the set of forecasted active resources.

Abstract: Embodiments are directed to a system, computer program product, and method for application of cognitive processing to a communication, and selectively transmitting the communication based on the cognitive processing. Natural language understanding (NLU) decomposes the communication to identify content and keywords. A dictionary determined to be contextually related to the communication is identified to support and enable a multi-dimensional analysis of the communication content. The communication is subject to dynamic filtering with support of the dictionary and dictionary content. A response is generated from the filtering, and is subject to selective transmission.