Abstract: Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Abstract: Briefly, embodiments disclosed herein may relate to formulating synthetic questions, such as in response to a search query, for example. Candidate synthetic questions may be presented to a user who may initiate a search at least in part by selecting one or more candidate synthetic questions, for example, in an embodiment.

Abstract: Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.

Abstract: Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).

Abstract: Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).

Abstract: A system and method for learning a new user's interests in the absence of historical data includes: generating a user interface on which a user interacts in a session; formulating questions such that a user response to the questions indicates a preference; presenting the questions to the user on the user interface; receiving responses from the user, the responses indicating user preferences; and populating a new user profile with the user preferences. The steps of presenting, receiving, and populating are repeated until the session terminates.

Abstract: Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).

Abstract: Briefly, embodiments disclosed herein may relate to formulating synthetic questions, such as in response to a search query, for example. Candidate synthetic questions may be presented to a user who may initiate a search at least in part by selecting one or more candidate synthetic questions, for example, in an embodiment.

Abstract: Deriving a user profile includes: generating a user interface on which a user interacts in a session; formulating questions such that a user response to the questions indicates a preference; presenting the questions to the user on the user interface; receiving responses from the user, the responses indicating user preferences; and populating a user profile with the user preferences. The steps of presenting, receiving, and populating are repeated until the session terminates.

Abstract: Disclosed herein is a mechanism, which may be in a community-driven system, to assist a user, or member of the community, to articulate a request, such as a question, that is likely to receive a response, e.g., an answer to a question posed, from the user community. A machine learning approach may be used to generate a model, which model may be trained using previously-posted requests, e.g., questions, and response, e.g., answers. The generated model may be used to make a prediction, e.g., to predict a potential number of answers for a given question, which prediction may also have an accompanying confidence score. A prediction generated by the model may be presented to the user as the user is providing the input, e.g., typing a question. Such prediction may be generated for a partial question, e.g., as the question is being typed, as well as a completed question.

Abstract: A method for software processing includes extracting from software code under test respective items of meta-information pertaining to elements of the software code and receiving respective quality data regarding the elements of software code. The quality data and meta-information are processed in a computer so as to assign respective metrics to the items of the meta-information responsively to the quality data regarding the elements of the software code to which the items pertain. At least some of the meta-information is presented to a user in accordance with the assigned metrics.

Abstract: A method for monitoring performance includes monitoring packet traffic on at least one socket that is associated with an application running on a computer and is communicated over a data network. First and second instances of a performance indicator of the packet traffic are measured respectively during first and second monitoring intervals. An alert is issued upon making a determination that a change between the first instance and the second instance exceeds a predetermined threshold.

Abstract: Read-ahead of data blocks in a storage system is performed based on a policy. The policy is stochastically selected from a plurality of policies in respect to probabilities. The probabilities are calculated based on past performances, also referred to as rewards. Policies which induce better performance may be given precedence over other policies. However, the other policies may be also utilized to reevaluate them. A balance between exploration of different policies and exploitation of previously discovered good policies may be achieved.

Abstract: A method, system, and computer usable program product for detecting a no progress state of an application are provided in the illustrative embodiments. A resource usage and an output of a set of applications are monitored. The resource usage and the output are measured to determine a resource usage value and an output value at a given time. A determination is made whether the resource usage value is included in a sub-range of a resource usage scale and the output value is included in a sub-range of an output scale where the sub-range of the resource usage scale corresponds to the sub-range of the output scale.

Abstract: A system, and computer usable program product for condition based detection of a no progress state of an application are provided in the illustrative embodiments. A resource usage and an output of a set of applications including the application are monitored. The resource usage and the output are measured to determine a resource usage value and an output value at a first time. A present condition is detected under which the set of applications is executing. A determination is made whether the resource usage value is included in a sub-range of a resource usage scale and the output value is included in a sub-range of an output scale where the sub-range of the resource usage scale corresponds to the sub-range of the output scale for the present condition. The no progress state of the application is detected if the determination is negative.

Abstract: A method for condition based detection of a no progress state of an application are provided in the illustrative embodiments. A resource usage and an output of a set of applications including the application are monitored. The resource usage and the output are measured to determine a resource usage value and an output value at a first time. A present condition is detected under which the set of applications is executing. A determination is made whether the resource usage value is included in a sub-range of a resource usage scale and the output value is included in a sub-range of an output scale where the sub-range of the resource usage scale corresponds to the sub-range of the output scale for the present condition. The no progress state of the application is detected if the determination is negative.

Abstract: Read-ahead of data blocks in a storage system is performed based on a policy. The policy is stochastically selected from a plurality of policies in respect to probabilities. The probabilities are calculated based on past performances, also referred to as rewards. Policies which induce better performance may be given precedence over other policies. However, the other policies may be also utilized to reevaluate them. A balance between exploration of different policies and exploitation of previously discovered good policies may be achieved.

Abstract: A stress of a computerized system may be detected based on actions performed by the computerized system in respect to a storage system, such as comprising a secondary storage. The stress detection may be performed automatically based on an age of data blocks accessed by the storage system. The stress may take into account a number of accesses to the storage system by the computerized system. Stress detection may be performed automatically based on a decision procedure.