Abstract: According to embodiments, methods, systems, and computer program products are provided for generating material compositions including receiving, by a processor, a plurality of material inputs that collectively form one or more known material compositions, assigning a vector space value to each of the plurality of material inputs, wherein each vector space value is unique for each material input, receiving a first constraint and a second constraint, and generating a transformed material composition based on the vector space value, the first constraint, and the second constraint.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: Identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns may include receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content. A first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations may be generated. Information associated with a target user in the given organization may be received. A customized learning plan for the target user in the given organization may be generated based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: According to embodiments, methods, systems, and computer program products are provided for generating material compositions including receiving, by a processor, a plurality of material inputs that collectively form one or more known material compositions, assigning a vector space value to each of the plurality of material inputs, wherein each vector space value is unique for each material input, receiving a first constraint and a second constraint, and generating a transformed material composition based on the vector space value, the first constraint, and the second constraint.

Abstract: A system and method dynamically provisions resources in a virtual environment. A current resource requirement is determined based on a current workload demand using one or more computer systems providing resources and access to the resources. The method and system includes comparing the current resource requirement with a current resource allocation using an engine communicating with resources. The engine is configured to allocate the resources, and the engine determines the resource requirement responsive to communications with a plurality of library instances. The library exposes a single-node interface for use by a user-application. The current resource allocation is modified based on the comparison of the current resource requirement with the current resource allocation, and in response to the current resource requirement, using the engine.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: Embodiments relate to estimating closeness of topics based on graph analytics. A graph that includes a plurality of nodes and edges is accessed. Each node in the graph represents a topic and each edge represents a known association between two topics. A statistical traversal experiment is performed on the graph. A strength of relations between any two topics represented by nodes in the graph is inferred based on statistics extracted from the statistical traversal experiment.

Abstract: Enterprise learning system may receive as input learning goals for a learner. Learning goals may also be specified by job-roles. The system may output course-sequence recommendations, sequence-associations to learning goals and learning-goal recommendations. A semantic analysis may automatically relate learning-goals to learning assets and job-roles. Computational semantic relation-finding may leverage available knowledge-bases. A sequence-recommendation recommends course or learning asset sequences to the learner based on historical usage, the learner's current and/or desired job-roles and learning goals.

Abstract: Embodiments relate to estimating closeness of topics based on graph analytics. A graph that includes a plurality of nodes and edges is accessed. Each node in the graph represents a topic and each edge represents a known association between two topics. A statistical traversal experiment is performed on the graph. A strength of relations between any two topics represented by nodes in the graph is inferred based on statistics extracted from the statistical traversal experiment.

Abstract: A computing device identifies one or more contextual variables within one or more social media messages. The computing device determines a contextual influence value based on the one or more social media messages. The computing device determines an appetite level. The computing device determines an unadjusted expected value of pleasantness based on the determined contextual influence value and the determined appetite level.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: A method that comprises receiving a plurality of inputs including data a plurality of multiple business repositories, generating from the plurality of inputs a corpus graph as a statistical relational network, inferring a plurality of new relations between informational elements of the statistical relational network, and generating a plurality of summaries of the plurality of new relations. Further, each summary describes the informational elements of the statistical relational network associated with a corresponding risk-relation.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: A tool for computational generation of organizational mentoring relationships. The tool determines a mentor pool and a mentee pool based, at least in part, on per-person domain metric data for each person in a general pool. The tool determines a plurality of per-metric ranked mentor lists for each of the one or more mentees in the mentee pool. The tool determines a per-mentee fused rank list for each of the one or more mentees in the mentee pool. The tool determines, based, at least in part, on the per-mentee fused rank list for each of the one or more mentees in the mentee pool, one or more cross-organizational mentorship assignments. The tool establishes, based, at least in part, on the one or more cross-organizational mentorship assignments, at least one mentor-mentee relationship for each of the one or more mentees in the mentee pool.

Abstract: A method and system are provided for implementing enhanced flash storage control using reinforcement learning to provide enhanced performance metrics. A flash controller, such as a Reinforcement Learning (RL) flash controller, is coupled to a flash storage. The flash controller defines a feature set of flash parameters determined by a predefined one of a plurality of optimization metrics. The optimization metric is adapted dynamically based upon system workload and system state. The flash controller employing the feature set including at least one feature responsive to erase operations; computes a current system state responsive to the employed feature set; selects actions at each time step by sensing the computed current system state for performing an action to maximize a long term reward, and moves to another state in the system while obtaining a short-term reward for the performed action.

Abstract: A hybrid encryption scheme for storing data lines in a memory includes identifying data lines determined to be frequently accessed, and encrypting the data lines using a first encryption scheme. The hybrid encryption scheme also includes encrypting data lines determined not to be frequently accessed using a second encryption scheme.

Abstract: An aspect of this invention is a method for providing a PreSET region in a memory device wherein the PreSET region includes one or more lines of the memory device which have been PreSET; performing a write operation on one or more out of place lines of the memory device by writing to the PreSET region instead of writing to an in place line of the memory device; and storing in an indirection table a mapping of each of a respective plurality of logical pages of the memory device to a corresponding physical page of a plurality of physical pages of the memory device, wherein the indirection table keeps track of the one or more out of place lines.

Abstract: A method for analyzing a virtual discussion. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

Abstract: A memory device may be equipped with quick erase capability to secure the contents of the memory device. The quick erase capability may effectively permanently disable access to data stored in the memory device instantaneously upon a command being issued, making all previous data written to the memory device unreadable. The quick erase capability may allow use of the memory device for new write operations and for reading the newly written data immediately once the erase command is received and executed. The quick erase capability may begin a physical erase process of data not newly written without altering other aspects of the quick erase. Aspects may be accomplished with one or more bits per row in a memory device.