Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, grouping the index records into a number of groups without prior knowledge of a key distribution for dividing the one or more index records into key ranges for parallel processing thereof; and processing the groups of index records in parallel.

Abstract: A question-and-answer system directed to a specific domain optimally utilizes reference documents that are semantically complete for that domain. Semantic completeness of a document is assessed using quality control questions (provided by subject matter experts) applied to the Q&A system followed by analysis of the proposed answers. That analysis is carried out using a cogency module having a feedforward neural network which receives metadata features of the document such as document ownership, document priority, and document type. A domain-optimized corpus for the Q&A system is built by so assessing multiple documents in a document collection, and adding each reference document that is reported as being semantically complete to the domain-optimized corpus. Thereafter, the deep learning question-and-answer system can receive a natural language query from a user, find a responsive answer in the documents while applying the domain-optimized corpus, and provide that answer to the user.

Abstract: A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include generating a user profile specifying a plurality of attribute values for a plurality of principle attributes, by processing a corpus of electronic documents using a first trained machine learning model. In an embodiment, the method further comprises generating a provider profile specifying a plurality of attribute values for the plurality of principle attributes, for each of a plurality of providers, by processing a respective corpus of electronic documents associated with each respective provider using a second trained machine learning model. A plurality of match coefficients based on comparing the user profile and the plurality of provider profiles are determined. Finally, one or more online interactions between the user and the target provider are influenced based on the determined match coefficients.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include receiving, from a first user of a plurality of users, a first set of electronic documents, where each electronic document in the first set of electronic documents corresponds to a respective user in the plurality of users. The methods also include identifying a plurality of user profiles, where each of the plurality of user profiles was generated by processing a corpus of electronic documents associated with each respective user using a first trained machine learning model. The methods include determining a plurality of match coefficients, based on comparing a plurality of user profiles associated with each respective user in the plurality of users, filtering the first set of electronic documents based on the plurality of match coefficients, and providing the filtered first set of electronic documents to the first user.

Abstract: A question-and-answer system directed to a specific domain optimally utilizes reference documents that are semantically complete for that domain. Semantic completeness of a document is assessed using quality control questions (provided by subject matter experts) applied to the Q&A system followed by analysis of the proposed answers. That analysis is carried out using a cogency module having a feedforward neural network which receives metadata features of the document such as document ownership, document priority, and document type. A domain-optimized corpus for the Q&A system is built by so assessing multiple documents in a document collection, and adding each reference document that is reported as being semantically complete to the domain-optimized corpus. Thereafter, the deep learning question-and-answer system can receive a natural language query from a user, find a responsive answer in the documents while applying the domain-optimized corpus, and provide that answer to the user.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: A computer-implemented mechanism is disclosed. The mechanism includes receiving a data signal, and comparing the data signal to one or more predefined patterns to determine one or more long/short term predictor scores. A discount factor is generated in response to the long/short term predictor scores. A set of expected rewards is generated. The set of expected rewards correspond to an action set specific to the data signal. The set of expected rewards are generated according to reinforced learning. The set of expected rewards are adjusted based on the discount factor. A selected action is selected from the action set based on the set of expected rewards. The selected action is initiated.

Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, grouping the index records into a number of groups without prior knowledge of a key distribution for dividing the one or more index records into key ranges for parallel processing thereof; and processing the groups of index records in parallel.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, wherein the active key range is determined based on any of: a type of the index records, a horizontal pointer to a next index record, and a low key value for each index record, and grouping the index records into a number of groups for parallel processing thereof.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: A resource reservoir is provided. The resource reservoir is a pool of resources that are allocated to and shared between a plurality of reserve virtual machines. A reserve virtual machine of the plurality of reserve virtual machines corresponds to a primary virtual machine from a plurality of primary virtual machines. Each reserve virtual machine of the plurality of reserve virtual machines fulfills a resource requirement from the resource reservoir. The sum of the resources requirements of the plurality of reserve virtual machines is greater than a sum of total resources within the resource reservoir. In response to determining a decrease in performance of the primary virtual machine, at least a portion of workload of the primary virtual machine is applied to the corresponding reserve virtual machine. The resources used by the reserve virtual are indicated as unavailable to the remaining reserve virtual machines from the resource reservoir.

Abstract: A resource reservoir is provided. The resource reservoir is a pool of resources that are allocated to and shared between a plurality of reserve virtual machines. A reserve virtual machine of the plurality of reserve virtual machines corresponds to a primary virtual machine from a plurality of primary virtual machines. Each reserve virtual machine of the plurality of reserve virtual machines fulfills a resource requirement from the resource reservoir. The sum of the resources requirements of the plurality of reserve virtual machines is greater than a sum of total resources within the resource reservoir. In response to determining a decrease in performance of the primary virtual machine, at least a portion of workload of the primary virtual machine is applied to the corresponding reserve virtual machine. The resources used by the reserve virtual are indicated as unavailable to the remaining reserve virtual machines from the resource reservoir.

Abstract: Techniques for managing a plurality of threads on a multi-threading processing core. Embodiments provide an instruction count threshold condition that determines how many countable instructions of a thread the multi-threading processing core will execute before context switching to another one of the plurality of threads. A first plurality of instructions for a first one of the plurality of threads is processed on the multi-threading processing core. Embodiments determine, for each of the first plurality of instructions, whether the instruction is a countable instruction, wherein at least one of the first plurality of instructions is not a countable instruction. A count of the countable instructions is maintained. Upon determining that the instruction count threshold condition is satisfied, based on the maintained count, embodiments context switch the multi-threading processing core to process a second plurality of instructions for a second one of the plurality of threads.

Abstract: Techniques for managing a plurality of threads on a multi-threading processing core. Embodiments provide an instruction count threshold condition that determines how many countable instructions of a thread the multi-threading processing core will execute before context switching to another one of the plurality of threads. A first plurality of instructions for a first one of the plurality of threads is processed on the multi-threading processing core. Embodiments determine, for each of the first plurality of instructions, whether the instruction is a countable instruction, wherein at least one of the first plurality of instructions is not a countable instruction. A count of the countable instructions is maintained. Upon determining that the instruction count threshold condition is satisfied, based on the maintained count, embodiments context switch the multi-threading processing core to process a second plurality of instructions for a second one of the plurality of threads.

Abstract: A resource reservoir is provided. The resource reservoir is a pool of resources that are allocated to and shared between a plurality of reserve virtual machines. A reserve virtual machine of the plurality of reserve virtual machines corresponds to a primary virtual machine from a plurality of primary virtual machines. Each reserve virtual machine of the plurality of reserve virtual machines fulfills a resource requirement from the resource reservoir. The sum of the resources requirements of the plurality of reserve virtual machines is greater than a sum of total resources within the resource reservoir. In response to determining a decrease in performance of the primary virtual machine, at least a portion of workload of the primary virtual machine is applied to the corresponding reserve virtual machine. The resources used by the reserve virtual are indicated as unavailable to the remaining reserve virtual machines from the resource reservoir.

Abstract: Virtual Machine (VM) images in a virtualized environment are updated through the use of patches. A virtualization data processing system includes a hypervisor that manages a VM image. The hypervisor is configured to retrieve a patch for an instance of the VM image from a secure site. The hypervisor blocks all other network access to the VM image.

Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, wherein the active key range is determined based on any of: a type of the index records, a horizontal pointer to a next index record, and a low key value for each index record, and grouping the index records into a number of groups for parallel processing thereof.