Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: Automated multi-round task-based conversation with a user is disclosed. In a first round conversation between a user and a data processing system involving first task(s), the data processing system cognitively identifies first entity(ies) and one or more first intent(s), based on a first input statement received by the data processing system from the user. In a subsequent second round conversation between the user and the data processing system involving second task(s) different from the first task(s), the data processing system cognitively identifies second entity(ies) and second intent(s), based on a second input statement received by the data processing system from the user. The data processing system reuses at least one of the first entity(ies) in the second round conversation.

Abstract: A machine-learning component of a conversational user-interface system retrieves records of human question-and-answer sessions from heterogenous sources. A sequence of image-processing operations converts each source into a set of grayscale images. Each image is segmented into disjoint sections as a function of the textures, edges, and contours that make up the image's content. Each section is tagged with a vector of parametric values that identify characteristics of the section's content from which may be inferred semantic meaning. A cognitive function intelligently analyzes the vectors to classify each section as containing a question, an answer to a question, non-textual media, or other types of content. Another cognitive function merges and organizes the sections into question-answer pairs and the vectors associated with each pair are stored in a corpus that is submitted to the self-learning user interface during a machine-learning training session.

Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: Automated multi-round task-based conversation with a user is disclosed. In a first round conversation between a user and a data processing system involving first task(s), the data processing system cognitively identifies first entity(ies) and one or more first intent(s), based on a first input statement received by the data processing system from the user. In a subsequent second round conversation between the user and the data processing system involving second task(s) different from the first task(s), the data processing system cognitively identifies second entity(ies) and second intent(s), based on a second input statement received by the data processing system from the user. The data processing system reuses at least one of the first entity(ies) in the second round conversation.

Abstract: A machine-learning component of a conversational user-interface system retrieves records of human question-and-answer sessions from heterogenous sources. A sequence of image-processing operations converts each source into a set of grayscale images. Each image is segmented into disjoint sections as a function of the textures, edges, and contours that make up the image's content. Each section is tagged with a vector of parametric values that identify characteristics of the section's content from which may be inferred semantic meaning. A cognitive function intelligently analyzes the vectors to classify each section as containing a question, an answer to a question, non-textual media, or other types of content. Another cognitive function merges and organizes the sections into question-answer pairs and the vectors associated with each pair are stored in a corpus that is submitted to the self-learning user interface during a machine-learning training session.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: A computing system performs file conversion upon format expiration. A computing system evaluates a risk of expiration of a first format associated with a first format object. The computing system determines if the risk of expiration is above a threshold. The computing system identifies in response to determining the risk of expiration is above the threshold, one or more files stored in the first format for conversion. The computing system converts one or more files stored in the first format to a second format. The computing system saves the one or more files in the second format.

Abstract: Disclosed is an apparatus and method for generating inverted organic solar cells and which required no electron selective layer, were fabricated and their power conversion efficiency was found to improve irreversibly with post-processing light soaking for a period. X-Ray photoelectron spectroscopy characterization further revealed segregation in surface composition at the interface and was found to explain the current density-voltage measurements. In addition, the light soaked devices were found to exhibit an extended lifetime as compared to conventional devices. Since no electron selective layer was required, light soaking may be considered as a cost-effective method to achieve efficient inverted organic solar cells.