Abstract: Methods and systems for emulating an application include generating a log template to match one or more patterns in a set of application logs collected from an original application. Semantic state representations are learned for the original application from the log template. A classifier is trained to predict a next action template based on a sequence of prior action templates. A regressor is trained to generate a parameter value for a template based on a sequence of prior action templates and a particular semantic state of the original application.

Abstract: Methods and systems for emulating an application include generating a log template to match one or more patterns in a set of application logs collected from an original application. Semantic state representations are learned for the original application from the log templates. A classifier is trained to predict a next action template based on a sequence of prior action templates. A regressor is trained to generate a parameter value for a template based on a sequence of prior action templates and particular semantic state of the original application.

Abstract: Techniques are described regarding log data comprehension in a computing environment. An associated computer-implemented method includes inducting data into a knowledge base associated with a log comprehension machine learning knowledge model in order to configure a common log schema. The method further includes extracting at least one key-value pair from a log file including input data associated with at least one downstream application and deriving at least one value feature and any key signal associated with the at least one key-value pair. The method further includes applying the log comprehension machine learning knowledge model in order to compare data associated with the at least one key-value pair extracted from the log file with knowledge base node key-value pair data. The method further includes creating mapping results formatted according to the common log schema and compatible with the at least one downstream application based upon the model application.

Abstract: In various examples, a computer-implemented method includes: receiving, by one or more processing devices, acoustic content data; receiving, by the one or more processing devices, acoustic data for a target environment; training, by the one or more processing devices, a neural network model on the acoustic data for the target environment to extract features of the target environment; using, by the one or more processing devices, the neural network model to transfer the features of the target environment to the acoustic content data; constructing, by the one or more processing devices, the acoustic content data with the transferred features of the target environment; and outputting, by the one or more processing devices, via a user interface (UI), information on and configurable options for the training of the neural network model on the acoustic data for the target environment.

Abstract: Systems, methods and/or computer program products for controlling and visualizing topic modeling results using a topic modeling interface. The interface allows user directed exploration, understanding and control of topic modeling algorithms, while offering both semantic summaries and/or structure attribute explanations about results. Explanations and differentiations between results are presented using metrics such as cohesiveness and visual displays depicting hierarchical organization. Through user-manipulation of features of the interface, iterative changes are implemented through user-feedback, adjusting parameters, broadening or narrowing topic results, and/or reorganizing topics by splitting or merging topics. As users trigger visual changes to results being displayed, users can compare and contrast output from the topic modeling algorithm.

Abstract: A circuit board and a camera module. A first board body of the circuit board can slide under the action of a drive force so as to drive a photosensitive chip to displace relative to a lens component to achieve optical anti-shake. An output pin of a second board body of the circuit board can be electrically connected to a motherboard of a mobile terminal, and the second board body is fixed on a base.

Abstract: Systems, methods and/or computer program products for controlling and visualizing topic modeling results using a topic modeling interface. The interface allows user directed exploration, understanding and control of topic modeling algorithms, while offering both semantic summaries and/or structure attribute explanations about results. Explanations and differentiations between results are presented using metrics such as cohesiveness and visual displays depicting hierarchical organization. Through user-manipulation of features of the interface, iterative changes are implemented through user-feedback, adjusting parameters, broadening or narrowing topic results, and/or reorganizing topics by splitting or merging topics. As users trigger visual changes to results being displayed, users can compare and contrast output from the topic modeling algorithm.

Abstract: An embodiment of the present invention relates to a camera module comprising: a housing comprising a through hole; a lens module disposed in the through hole; a connector penetrating the housing and extending to an outside from an inside of the housing; a first substrate unit located inside the housing and having an image sensor mounted thereon; a second substrate unit disposed inside the housing and connected to the connector; and a flexible substrate for electrically connecting the first substrate and the second substrate unit, wherein the flexible substrate is coupled to a lower surface of the first substrate unit and a side surface of the second substrate.

Abstract: Embodiments relate to a system, program product, and method for inducing creativity in an artificial neural network (ANN) having an encoder and decoder. Neurons are automatically selected and manipulated from one or more layers of the encoder. An encoded vector is sampled for an encoded image. Decoder neurons and a corresponding activation pattern are evaluated with respect to the encoded image. The decoder neurons that correspond to the activation pattern are selected, and an activation setting of the selected decoder neurons is changed. One or more novel data instances are automatically generated from an original latent space of the selectively changed decoder neurons.

Abstract: An embodiment of the present invention relates to a camera module comprising: a housing comprising a through hole; a lens module disposed in the through hole; a connector penetrating the housing and extending to an outside from an inside of the housing; a first substrate unit located inside the housing and having an image sensor mounted thereon; a second substrate unit disposed inside the housing and connected to the connector; and a flexible substrate for electrically connecting the first substrate and the second substrate unit, wherein the flexible substrate is coupled to a lower surface of the first substrate unit and a side surface of the second substrate.

Abstract: An embodiment of the present invention relates to a camera module comprising: a housing comprising a through hole; a lens module disposed in the through hole; a connector penetrating the housing and extending to an outside from an inside of the housing; a first substrate unit located inside the housing and having an image sensor mounted thereon; a second substrate unit disposed inside the housing and connected to the connector; and a flexible substrate for electrically connecting the first substrate and the second substrate unit, wherein the flexible substrate is coupled to a lower surface of the first substrate unit and a side surface of the second substrate.

Abstract: A method for obtaining information and status about a monitored system by adaptively analyzing log messages is provided. A log analyzer receives log messages generated by a monitored system. The log analyzer identifies static and variable portions in the received log messages. The log analyzer generates a template based on the identified static and variable portions of the received log messages. The log analyzer computes a metric for the generated template based on a number of log messages that fall within the template. The log analyzer reports a status in the monitored system based on the computed metric.

Abstract: A method for obtaining information and status about a monitored system by adaptively analyzing log messages is provided. A log analyzer receives log messages generated by a monitored system. The log analyzer identifies static and variable portions in the received log messages. The log analyzer generates a template based on the identified static and variable portions of the received log messages. The log analyzer computes a metric for the generated template based on a number of log messages that fall within the template. The log analyzer reports a status in the monitored system based on the computed metric.

Abstract: Techniques for assessing the proficiency of artificial intelligence agents and users in a given knowledge domain are described. A plurality of proficiency agents can be initialized with a plurality of proficiency scores, by performing a plurality of assessments between pairs of proficiency agents selected from the plurality of proficiency agents. A first client device associated with a first user is matched with a first proficiency agent of the plurality of proficiency agents, based on a first proficiency score associated with the first user and a second proficiency score of the plurality of proficiency scores corresponding to the first proficiency agent. Assessments results of an assessment performed between the first client device and the first proficiency agent are received, and a rating system update function is used to update the first proficiency score and the second proficiency score, based on the assessment results.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving text input via a chat interface of a tutor application, identifying, by at least one classifier applied to the text input, a concept in the text input, mapping the concept in the text input to at least one of a visual action and a first visual object, generating, based on a first machine learning (ML) model, a first program code statement corresponding to the at least one of the visual action and the first visual object, and executing the first program code statement to modify a visualization interface of the tutor application based on the text input received via the chat interface.

Abstract: Embodiments relate to a system, program product, and method for inducing creativity in an artificial neural network (ANN) having an encoder and decoder. Neurons are automatically selected and manipulated from one or more layers of the encoder. An encoded vector is sampled for an encoded image. Decoder neurons and a corresponding activation pattern are evaluated with respect to the encoded image. The decoder neurons that correspond to the activation pattern are selected, and an activation setting of the selected decoder neurons is changed. One or more novel data instances are automatically generated from an original latent space of the selectively changed decoder neurons.

Abstract: A method and system of providing education content to a student is provided. A topic to be taught to a student is received. A user model of the student is identified. An electronic content model for the topic is determined. One or more presentation templates are identified based on the electronic content model. A concept map is determined based on the one or more presentation templates. A presentation template is selected from the one or more presentation templates. A segment of an educational content of the topic is provided to a user device of the student, based on the selected presentation template.

Abstract: An embodiment of the present invention relates to a camera module comprising: a housing comprising a through hole; a lens module disposed in the through hole; a connector penetrating the housing and extending to an outside from an inside of the housing; a first substrate unit located inside the housing and having an image sensor mounted thereon; a second substrate unit disposed inside the housing and connected to the connector; and a flexible substrate for electrically connecting the first substrate and the second substrate unit, wherein the flexible substrate is coupled to a lower surface of the first substrate unit and a side surface of the second substrate.

Abstract: Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.

Abstract: Method and apparatus for generating natural language observations using sequence-to-sequence models are provided. The method includes receiving a first electronic document and dynamically generating, without requiring user intervention, a first natural language observation corresponding to a first portion of the first electronic document by processing the first portion of the first electronic document using a first sequence-to-sequence model. A second natural language observation is received for the first portion of the first electronic document, and the generated first natural language observation and the authoritative natural language observation are provided. The method further includes receiving an evaluation of the first natural language observation and the second natural language observation, and refining the first sequence-to-sequence model based on the evaluation of the first natural language observation and second natural language observation.