Abstract: A cold spray lubricant for forming a coating includes an alloy feedstock consisting of a metal powder such as copper or nickel, and a solid lubricant, such as tungsten disulfide (WS2) powder or molybdenum disulfide (MoS2).

Abstract: Utilizing social information for recommending an application includes providing an application recommendation system based on social characterizations, and responsive to a user searching for an application meeting a criteria, utilizing the application recommendation system by searching for applications meeting the criteria, characterizing the applications according to a social proximity factor to the user, and presenting the applications ordered by the social proximity factor.

Abstract: A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

Abstract: A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: A visualization of literary elements of a work of literature, such as a novel or short story, is generated from meta-data records representing a digital work of literature including literary elements (humor, drama, adventure, etc.), characters, and plot devices related to a position within the work of literature where each appears. A significance level is determined for each of the elements, characters and plot devices at each position within the work of literature, and these are plotted into a sequential graph having position (e.g. timeline) axis and a significance level axis. The sequential graph is then output for printing or display. Human-generated and machine-generated meta-data may be ingested equally well by the method. Colors, line thickness, and a broken line patterns may be employed for greater visual meaning. And, the sequential graph may be annotated according to segments (e.g. chapters, sections) and dominant genre within each segment.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: Methods and apparatus, including computer program products and a computer system, implement and use techniques for preserving referential integrity. A first input receiving component receives a first input selecting a target file within a first directory. A second input receiving component receives a second input selecting all files referenced by the target file. A transfer component automatically performs a moving or copying operation of all referenced files from a first directory to a second directory when a moving or copying operation of the target file is performed.

Abstract: Reducing a negative social interaction includes receiving a response to a post from a user, the response includes content to be posted on an activity stream of a social network, analyzing the content of the response to determine a negative response risk to the post, analyzing a profile of the user to determine a tendency of the user to respond negatively in responses, and executing, based on the negative response risk and the tendency, an action for the response to reduce negative responses directed towards the post.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: Arrangements relate to automatically taking notes in a virtual meeting. The virtual meeting has meeting content that includes a plurality of meeting content streams. One or more of the meeting content streams is in a non-text format. The one or more meeting content streams in a non-text format can be converted into text. As a result, the plurality of meeting content streams is in text format. The text of the plurality of meeting content streams can be analyzed to identify a key element within the text. Consolidated system notes that include the key element can be generated.

Abstract: Arrangements relate to automatically taking notes in a virtual meeting. The virtual meeting has meeting content that includes a plurality of meeting content streams. One or more of the meeting content streams is in a non-text format. The one or more meeting content streams in a non-text format can be converted into text. As a result, the plurality of meeting content streams is in text format. The text of the plurality of meeting content streams can be analyzed to identify a key element within the text. Consolidated system notes that include the key element can be generated.

Abstract: Mechanisms for clarifying an input question are provided. A question is received for generation of an answer. A set of candidate answers is generated based on an analysis of a corpus of information. Each candidate answer has an evidence passage supporting the candidate answer. Based on the set of candidate answers, a determination is made as to whether clarification of the question is required. In response to a determination that clarification of the question is required, a request is sent for user input to clarify the question. User input is received from the computing device in response to the request and at least one candidate answer in the set of candidate answers is selected as an answer for the question based on the user input.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: A search engine is enhanced to employ user preferences to customize answer output by, receiving a first plurality of candidate answers and weights to a first search query for a first user from a first stage of a non-semantic search engine routing and processing path; augmenting the weights for the plurality of candidate answers according to a set of preference coefficients according to a first set of preferences for the first user; selecting one or more of the first plurality of candidate answers according to the augmented weights; and producing an output including the selected one or more candidate answers.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: A method, system and computer-usable medium are disclosed for analyzing and deducing criteria-related content for evaluation in a system capable of answering questions. A criteria text is processed to identify criteria. The criteria text is then analyzed to identify a set of criteria-related content associated with the criteria, which in turn is processed to determine its relationship to the criteria. Once the relationship has been determined, the set of criteria-related content is annotated accordingly. The set of criteria-related content is then used in accordance with its annotation when processing the criteria for evaluation.

Abstract: A method, system and computer-usable medium are disclosed for analyzing and deducing criteria-related content for evaluation in a system capable of answering questions. A criteria text is processed to identify criteria. The criteria text is then analyzed to identify a set of criteria-related content associated with the criteria, which in turn is processed to determine its relationship to the criteria. Once the relationship has been determined, the set of criteria-related content is annotated accordingly. The set of criteria-related content is then used in accordance with its annotation when processing the criteria for evaluation.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: There are provided a system, a method and a computer program product for creating the social group whose participants are involved in an online conversation. The system aggregates data associated with the online conversation and a group profile, of the social group. The system determines, based on the online conversation data and the group profile, a context of the online conversation and a goal of the social group. The system receives, based on the determined context and the goal, a first bidding from each provider, the first bidding associated with the determined context and the goal, the each provider providing one or more of: goods, services or discounts associated with the context and the goal.