Abstract: A method of providing a conversational agent for interacting with a user may include declaratively defining a task model of a task using a task modelling language, storing the task model in a computer-readable storage medium, generating a natural language grammar based on the task model, storing the natural language grammar in the computer-readable storage medium, receiving a user input from the user, interpreting the user input with a processor based on the task model and the natural language grammar, generating an agent response to the user input with the processor based on the task model, and communicating the agent response to the user.

Abstract: In some examples, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

Abstract: In some examples, collaborative learning-based cloud migration implementation may include identifying a migration agent that is to perform an application migration from a first cloud environment to a second cloud environment, and identifying a plurality of additional migration agents. A technical context and a migration flow context may be determined for the migration agent and for the plurality of additional migration agents. Executed allowed and error-response migration actions may be identified for states that are similar to a current state of the application migration, and a similarity between the migration agent and each of the migration agents that executed the allowed and error-response migration actions may be determined. A migration action that is to be performed may be identified based on a maximum relevance associated with the allowed and error-response migration actions. The identified migration action may be executed by the migration agent to perform the application migration.

Abstract: In some examples, migration context and flow graph based migration control may include ascertaining an application that is to be migrated from a physical environment to a cloud environment, and determining a migration issue associated with the migration of the application. Migration context and flow graph based migration control may further include identifying, from a historical issue database, a plurality of historical issues, determining, for the migration issue and the plurality of historical issues, unified proximities, sorting, based on the determined unified proximities, the historical issues, selecting, from the sorted historical issues, a topmost historical issue, and determining, from the topmost historical issue, a resolution associated with the topmost historical issue.

Abstract: In some examples, collaborative learning-based cloud migration implementation may include identifying a migration agent that is to perform an application migration from a first cloud environment to a second cloud environment, and identifying a plurality of additional migration agents. A technical context and a migration flow context may be determined for the migration agent and for the plurality of additional migration agents. Executed allowed and error-response migration actions may be identified for states that are similar to a current state of the application migration, and a similarity between the migration agent and each of the migration agents that executed the allowed and error-response migration actions may be determined. A migration action that is to be performed may be identified based on a maximum relevance associated with the allowed and error-response migration actions. The identified migration action may be executed by the migration agent to perform the application migration.

Abstract: In some examples, collective application portfolio migration control may include determining, for a plurality of applications that are to be clustered for migration to a cloud environment, a coupling coefficient that represents a type of coupling between pairs of applications from the plurality of applications, a proximity coefficient that represents application proximities, and a connectedness coefficient that represents application connections. A combined application relatedness coefficient may be determined based on the coupling coefficient, the proximity coefficient, and the connectedness coefficient. A portfolio graph may be generated based on the combined application relatedness coefficient to generate migration application clusters to duster the plurality of applications. Migration of the plurality of applications to the cloud environment may be controlled based on the migration application clusters.

Abstract: In some examples, migration context and flow graph based migration control may include ascertaining an application that is to be migrated from a physical environment to a cloud environment, and determining a migration issue associated with the migration of the application. Migration context and flow graph based migration control may further include identifying, from a historical issue database, a plurality of historical issues, determining, for the migration issue and the plurality of historical issues, unified proximities, sorting, based on the determined unified proximities, the historical issues, selecting, from the sorted historical issues, a topmost historical issue, and determining, from the topmost historical issue, a resolution associated with the topmost historical issue.

Abstract: A method of providing a conversational agent for interacting with a user may include declaratively defining a task model of a task using a task modelling language, storing the task model in a computer-readable storage medium, generating a natural language grammar based on the task model, storing the natural language grammar in the computer-readable storage medium, receiving a user input from the user, interpreting the user input with a processor based on the task model and the natural language grammar, generating an agent response to the user input with the processor based on the task model, and communicating the agent response to the user.

Abstract: A method of providing a conversational agent for interacting with a user may include declaratively defining a task model of a task using a task modelling language, storing the task model in a computer-readable storage medium, generating a natural language grammar based on the task model, storing the natural language grammar in the computer-readable storage medium, receiving a user input from the user, interpreting the user input with a processor based on the task model and the natural language grammar, generating an agent response to the user input with the processor based on the task model, and communicating the agent response to the user.

Abstract: In some examples, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

Abstract: A device may evaluate first data to identify a plurality of users, where each user, of the plurality of users, is assigned to complete a portion of a group task. The device may evaluate the first data to select a first user, of the plurality of users, for completion of a particular portion of the group task based on a first set of decision criteria. The device may provide, to the first user, information identifying the particular portion of the group task. The device may receive second data relating to the particular portion of the group task after providing information identifying the particular portion of the group task. The device may evaluate the first data to select a second user, of the plurality of users, based on a second set of decision criteria. The device may provide, to the second user, information associated with the second data.

Abstract: A method of providing a conversational agent for interacting with a user may include declaratively defining a task model of a task using a task modelling language, storing the task model in a computer-readable storage medium, generating a natural language grammar based on the task model, storing the natural language grammar in the computer-readable storage medium, receiving a user input from the user, interpreting the user input with a processor based on the task model and the natural language grammar, generating an agent response to the user input with the processor based on the task model, and communicating the agent response to the user.

Abstract: A device may evaluate first data to identify a plurality of users, where each user, of the plurality of users, is assigned to complete a portion of a group task. The device may evaluate the first data to select a first user, of the plurality of users, for completion of a particular portion of the group task based on a first set of decision criteria. The device may provide, to the first user, information identifying the particular portion of the group task. The device may receive second data relating to the particular portion of the group task after providing information identifying the particular portion of the group task. The device may evaluate the first data to select a second user, of the plurality of users, based on a second set of decision criteria. The device may provide, to the second user, information associated with the second data.

Abstract: A computer-implemented method affords multi-core and multi-level topical organization in social indexes. A corpus of articles is accessed. Each article includes online textual materials. A finite state pattern for a topic filters the articles as candidate articles, which are potentially on-topic. Similarity-based representations are formed for on-topic and off-topic core meanings of the topic. An aggregate score for each of the candidate articles is determined using the similarity-based representations to indicate whether the candidate article is sufficiently on-topic. The candidate articles are presented ordered by their aggregate scores. In a further embodiment, a hierarchy of topics is used to guide the presentation of articles from subtopics, with considerations of fairness of subtopic coverage, elimination of similarity-duplicates in articles, and article freshness.

Abstract: A computer-implemented method for providing robust topic identification in social indexes is described. Electronically-stored articles and one or more indexes are maintained. Each index includes topics that each relate to one or more of the articles. A random sampling and a selective sampling of the articles are both selected. For each topic, characteristic words included in the articles in each of the random sampling and the selective sampling are identified. Frequencies of occurrence of the characteristic words in each of the random sampling and the selective sampling are determined. A ratio of the frequencies of occurrence for the characteristic words included in the random sampling and the selective sampling is identified. Finally, for each topic, a coarse-grained topic model is built, which includes the characteristic words included in the articles relating to the topic and scores assigned to those characteristic words.

Abstract: A system and method for using banded topic relevance and time for article prioritization is provided. Articles of digital information and at least one social index are maintained. The social index includes topics that each relate to one or more of the articles. Fine-grained topic models matched to the digital information for each topic are retrieved. The articles are succinctly classified under the topics using the fine-grained topic models. Each of the articles is relevancy scored within the topic under which the article was classified. The articles are arranged into discrete bands by relevance score. The articles are temporally sorted within the discrete bands. The articles are presented within the discrete bands.

Abstract: A system and method for providing default hierarchical training for social indexing is provided. Articles of digital information for social indexing are maintained. A hierarchically-structured tree of topics is specified. Each topic includes a label that includes one or more words. Constraints inherent in the literal structure of the topic tree are identified. For each topic in the topic tree, a topic model that includes at least one term derived from the words in at least one of the labels is created. The topic models for the topic tree are evaluated against the constraints. Those of the topic models, which best satisfy the constraints are identified.

Abstract: A system and method for managing user attention by detecting hot topics in social indexes is provided. Articles of digital information and at least one social index are maintained. The social index includes topics that each relate to one or more of the articles. Topic models matched to the digital information are retrieved for each topic. The articles are classified under the topics using the topic models. Each of the topics in the social index is evaluated for hotness. A plurality of time periods projected from the present is defined. Counts of the articles appearing under each time period are evaluated. The topics exhibiting a rising curve in the count of the articles that increases with recency during the time periods are chosen. Quality of the articles within the topics chosen is analyzed. The topics including the articles having acceptable quality are presented.

Abstract: A computer-implemented method for providing multi-core and multi-level topical organization in social indexes is provided. A corpus of articles is accessed. Each article includes online textual materials. A finite state pattern is provided for a topic that filters the articles as candidate articles, which are potentially on-topic. Similarity-based representations for on-topic and off-topic core meanings of the topic are provided. An aggregate score for each of the candidate articles is determined using the similarity-based representations to indicate whether the candidate article is sufficiently on-topic. The candidate articles are presented ordered by their aggregate scores. In a further embodiment, a hierarchy of topics is provided and used to guide the presentation of articles from subtopics, with considerations of fairness of subtopic coverage, elimination of similarity-duplicates in articles, and article freshness.

Abstract: A system and method for using banded topic relevance and time for article prioritization is provided. Articles of digital information and at least one social index are maintained. The social index includes topics that each relate to one or more of the articles. Fine-grained topic models matched to the digital information for each topic are retrieved. The articles are succinctly classified under the topics using the fine-grained topic models. Each of the articles is relevancy scored within the topic under which the article was classified. The articles are arranged into discrete bands by relevance score. The articles are temporally sorted within the discrete bands. The articles are presented within the discrete bands.