Abstract: A semantic search engine is enhanced to employ user preferences to customize answer output by, for a first user, extracting user preferences and sentiment levels associated with a first question; receiving candidate answer results of a semantic search of the first question; weighting the candidate answer results according to the sentiment levels for each of the user preferences; and producing the selected candidate answers to the first user. Optionally, user preferences and sentiment levels may be accumulated over different questions for the same user, or over different users for similar questions. And, supplemental information may be retrieved relative to a user preference in order to further tune the weighting per the preferences and sentiment levels.

Abstract: Systems and methods to determine image pixel status on a hardware display are provided. The systems and methods can determine a status of a uniquely identifiable measurement pixel of a first content item displayed on a display of a client device. The status can indicate that the measurement pixel is present in a viewable region of the display. The systems and methods can receive viewability variables from the client device. The viewability variables can include information regarding physical and electrical characteristics of the display, and can include information regarding data displayed on the display. The systems and methods can generate a view probability predictive model based on statuses and viewability variables determined from other client devices that previously displayed content items having measurement pixels.

Abstract: A first candidate answer to a time-sensitive question received by a question answering (QA) system is identified using a first document. A first temporal confidence subscore of the first candidate answer is then calculated using a first temporal confidence rule. A first general confidence score is then generated based on at least the first temporal confidence subscore. A second candidate answer to the question is identified using a second document. A second temporal confidence subscore of the second candidate answer is then calculated using a second temporal confidence rule. A second general confidence score is then generated based on at least the second temporal confidence subscore. The first general confidence score and the second general confidence score are then compared. Based on this comparison, a final answer is selected from the first candidate answer and the second candidate answer.

Abstract: Methods, apparatuses, and computer program products for dynamically recommending configuration changes to an operating system image are provided. Embodiments include monitoring, by an image deployment monitor, configurations of a plurality of deployed copies of a master operating system (OS) image; detecting, by the image deployment monitor, a change in one or more configurations of a first set of deployed copies of the plurality of deployed copies; and in response to detecting the change, generating, by the image deployment monitor, a configuration recommendation to recommend the change to an OS image.

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: Software that preserves information provided by a user in a first application utilizing a first interaction mode for use by a second application utilizing a second interaction mode, by performing the following steps: (i) generating a natural language log describing an interaction between a user and a first application, where the user interacts with the first application utilizing a first interaction mode; (ii) analyzing the natural language log using natural language processing to determine first user data; and (iii) utilizing the first user data by a second application, where the user interacts with the second application utilizing a second interaction mode different from the first interaction mode, and where the second application does not receive the first user data from the user via the second interaction mode.

Abstract: Software that preserves information provided by a user in a first application utilizing a first interaction mode for use by a second application utilizing a second interaction mode, by performing the following steps: (i) generating a natural language log describing an interaction between a user and a first application, where the user interacts with the first application utilizing a first interaction mode; (ii) analyzing the natural language log using natural language processing to determine first user data; and (iii) utilizing the first user data by a second application, where the user interacts with the second application utilizing a second interaction mode different from the first interaction mode, and where the second application does not receive the first user data from the user via the second interaction mode.

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: Mechanisms are provided for processing natural language text. The natural language text is parsed to generate a logical parse which is then analyzed to identify a solicitation node of the logical parse for which a user is able to provide additional data to assist in processing the natural language text. A clarifying question is output to a computing device associated with a user based on content of the solicitation node and a response to the clarifying question is received from the user. The natural language text is then processed based on the response to the clarifying question received from the user to thereby generate a result. The result of the processing of the natural language text is output to a source of the natural language text.

Abstract: A first candidate answer to a time-sensitive question received by a question answering (QA) system is identified using a first document. A first temporal confidence subscore of the first candidate answer is then calculated using a first temporal confidence rule. A first general confidence score is then generated based on at least the first temporal confidence subscore. A second candidate answer to the question is identified using a second document. A second temporal confidence subscore of the second candidate answer is then calculated using a second temporal confidence rule. A second general confidence score is then generated based on at least the second temporal confidence subscore. The first general confidence score and the second general confidence score are then compared. Based on this comparison, a final answer is selected from the first candidate answer and the second candidate answer.

Abstract: A semantic search engine is enhanced to employ user preferences to customize answer output by, for a first user, extracting user preferences and sentiment levels associated with a first question; receiving candidate answer results of a semantic search of the first question; weighting the candidate answer results according to the sentiment levels for each of the user preferences; and producing the selected candidate answers to the first user. Optionally, user preferences and sentiment levels may be accumulated over different questions for the same user, or over different users for similar questions. And, supplemental information may be retrieved relative to a user preference in order to further tune the weighting per the preferences and sentiment levels.

Abstract: A method, computer program product, and/or system dynamically appends a Uniform Resource Identifier (URI) fragment identifier to a Uniform Resource Locator (URL). A web browser on a client device displays a webpage, and accesses the HyperText Markup Language (HTML) coding for the webpage. HTML identification attributes are detected for the content that is being rendered within the web browser. A URI fragment identifier corresponding to the HTML identification attribute that is being rendered at the top of the web browser window is determined and is appended to the end of the URL of the webpage that is displayed in the URL box of the web browser. Detection of the content that is being rendered at the top of the web browser window occurs responsive to a command to scroll to a new section of the webpage.

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: In various embodiments the present disclosure provides a system and a method for estimating energy usage in a vehicle. The system includes hardware that perform certain operations that include receiving data associated with a route to be taken by the vehicle. The operations include determining, based on the data associated with the route and a state of the vehicle, an estimate corresponding to the energy that would be used by the vehicle when traversing a segment of the route.

Abstract: There are provided a system, a method and a computer program product for suggesting content to a group. The system identifies a group of users in a social network. The system receives inputs associated with the social network group. The system aggregates the received inputs. The system analyzes the aggregated inputs. The system searches, based on the aggregation and the analysis, one or more tailored content to be delivered to the group in Internet, one or more database and one or more data warehouse. The system delivers the tailored content to the group in the social network.

Abstract: A method of operation of an electronic system includes: receiving a trigger for a capturable input including an activation phase, a pre-activation phase and a post-activation phase; generating an interpretation from the capturable input based on a portion of the capturable input including an activation phase with at least a portion from the pre-activation phase or the post-activation phase; and issuing an operation based on the interpretation.

Abstract: Prioritizing facial recognition matches includes obtaining identification information and a facial image for each visitor entering the monitored environment, the monitored environment having a plurality of cameras at known locations including entry and exit points; obtaining itineraries of the visitors. Itineraries of the visitors are obtained, and based on the entry points and the itineraries of the visitors, likely routes of the visitors are determined through the monitored environment. Responsive to receiving an image captured by a first camera at a first location at an image capture time, the database records are sorted for facial recognition matching with the image from the first camera based on the visitors whose routes are likely to place the visitors in proximity to the first camera at the time of image capture.

Abstract: Mechanisms are provided, in association with a Question and Answer (QA) system, for implementing corpus identified reasoning when evaluating questions. An input question is received and an answer to the input question and supporting evidence for the answer are determined based on an evaluation of a corpus of information. The supporting evidence is analyzed to identify reasoning criteria specifying a reason as to why the answer is considered to be a correct answer by a provider of the supporting evidence. Rules are generated based on the identified reasoning criteria with the rules correlating the answer with the identified reasoning criteria and features of the input question. The rules are stored as entries in a reasoning data structure a subsequent question is processed based on the reasoning data structure.

Abstract: A method for adjusting search results based on viewed pages is provided. The method may include receiving at least one search request within a plurality of search requests. The method may also include identifying first and second variations of the at least one search request, wherein the first variation is different from the second variation. The method may further include searching for the first and second variations. Additionally, the method may include receiving a plurality of first and second variation search results. The method may also include presenting portions of the received plurality of first and second variation search results, wherein a first and second remainder of the first and second variations are not presented, respectively. The method may further include applying a criteria to adjust the first remainder and the second remainder to be presented, wherein the adjusting is responsive to detecting at least one user action.