Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.

Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.

Abstract: Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.

Abstract: Systems and techniques that facilitate automated recommendation of microservice decomposition strategies for monolithic applications are provided. In various embodiments, a community detection component can detect a disjoint code cluster in a monolithic application based on a code property graph characterizing the monolithic application. In various aspects, the code property graph can be based on a temporal code evolution of the monolithic application. In various embodiments, a topic modeling component can identify a functional purpose of the disjoint code cluster based on a business document corpus corresponding to the monolithic application. In various embodiments, a microservices component can recommend a microservice to replace the disjoint code cluster based on the functional purpose.

Abstract: Methods, systems, and computer program products for factchecking artificial intelligence models using blockchain are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and at least one set of data related to the at least one artificial intelligence model; determining a set of characteristics based at least in part on the at least one artificial intelligence model and the at least one set of data; selecting one of a plurality of networks based at least in part on a target deployment of the at least one artificial intelligence model to verify the set of characteristics; generating a report based at least in part on verifying the set of characteristics using the selected network, wherein the report establishes a threshold level of trust for the at least one artificial intelligence model; and storing the report on a blockchain.

Abstract: Methods, systems, and computer program products for assisting dependency migration are provided herein. A computer-implemented method includes determining differences between a first version of a dependency used by a software application and each of a plurality of upgrade candidates, the plurality of upgrade candidates comprising at least one of: (i) one or more newer versions of the dependency and (ii) a substitute dependency; identifying, based on the determined differences for a given one of the upgrade candidates, one or more sections of code of the software application that need to be patched in order to be compatible with the given upgrade candidate; and generating a modified version of the software application for the given upgrade candidate that comprises one or more patches for at least a portion of the identified one or more sections of code.

Abstract: Methods, systems and computer program products for natural language context embedding are provided herein. A computer-implemented method includes extracting a document anatomy and document elements from a given structured document, identifying semantic references in the given structured document, and generating an ontology comprising (i) a hierarchy of concepts and (ii) relations connecting the concepts, each concept comprising attributes for a document element. The computer-implemented method also includes generating natural language text context for a given document element by utilizing the ontology to combine (i) attributes of a given concept corresponding to the given document element with (ii) attributes of another concept, the other concept corresponding to another document element, the other concept being connected to the given concept by at least one relation.

Abstract: Methods, systems, and computer program products for assisting dependency migration are provided herein. A computer-implemented method includes determining differences between a first version of a dependency used by a software application and each of a plurality of upgrade candidates, the plurality of upgrade candidates comprising at least one of: (i) one or more newer versions of the dependency and (ii) a substitute dependency; identifying, based on the determined differences for a given one of the upgrade candidates, one or more sections of code of the software application that need to be patched in order to be compatible with the given upgrade candidate; and generating a modified version of the software application for the given upgrade candidate that comprises one or more patches for at least a portion of the identified one or more sections of code.

Abstract: One embodiment provides a method, including: receiving a training dataset to be utilized for training a deep-learning model; identifying a plurality of aspects of the training dataset, wherein each of the plurality of aspects corresponds to one of a plurality of categories of operations that can be performed on the training dataset; measuring, for each of the plurality of aspects, an amount of variance of the aspect within the training dataset; creating additional data to be incorporated into the training dataset, wherein the additional data comprise data generated for each of the aspects having a variance less than a predetermined amount, wherein the data generated for an aspect results in the corresponding aspect having an amount of variance at least equal to the predetermined amount; and incorporating the additional data into the training dataset.

Abstract: Systems and techniques that facilitate automated recommendation of microservice decomposition strategies for monolithic applications are provided. In various embodiments, a community detection component can detect a disjoint code cluster in a monolithic application based on a code property graph characterizing the monolithic application. In various aspects, the code property graph can be based on a temporal code evolution of the monolithic application. In various embodiments, a topic modeling component can identify a functional purpose of the disjoint code cluster based on a business document corpus corresponding to the monolithic application. In various embodiments, a microservices component can recommend a microservice to replace the disjoint code cluster based on the functional purpose.

Abstract: Embodiments of the present invention provide a method, a computer program product, and a system for generating a haptic signal representing a fabric composition. Embodiments of the present invention can be used to generate a haptic signal that is based on a user selection. For example, embodiments of the present invention can combine characteristic signals corresponding to a plurality of textiles to generate the haptic signal for output to a haptic device. Embodiments of the present invention can be used to recommend similar fabric compositions based upon similarity between a characteristic signal of a fabric composition and the haptic signal.

Abstract: Embodiments describe an approach for automatically generating feedback for an online forum. Embodiments determine if a user is using a solution to a problem, wherein the solution is posted on an online forum, and responsive to determining the user is using the solution, capturing environment information associated with the user's computing device. Additionally, embodiments determine if the solution solved the problem, and responsive to determining the solution solved the problem, automatically generate feedback associated with the solution, wherein the feedback comprises the environment information and information detailing that the solution solved the problem.

Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.

Abstract: One embodiment provides a method, including: receiving input of a learning session that is being conducted by an educator, being provided to at least one user, and being related to a subject; determining, using a knowledge base, that at least one topic relevant to the subject of the learning session is incomplete, wherein the determining comprises building a knowledge subgraph of the learning session and comparing the built knowledge subgraph to at least a portion of the knowledge base; generating at least one question to be asked of the educator relevant to the at least one incomplete topic; identifying, using at least one natural language text classifier model, a location within the learning session to ask the generated at least one question; and providing, to the educator, an output corresponding to the at least one question at the identified location within the learning session.

Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.

Abstract: Embodiments describe an approach for automatically generating feedback for an online forum. Embodiments determine if a user is using a solution to a problem, wherein the solution is posted on an online forum, and responsive to determining the user is using the solution, capturing environment information associated with the user's computing device. Additionally, embodiments determine if the solution solved the problem, and responsive to determining the solution solved the problem, automatically generate feedback associated with the solution, wherein the feedback comprises the environment information and information detailing that the solution solved the problem.

Abstract: One embodiment provides a method, including: providing, at a collaborative deep learning model authoring tool, a dialog window that (i) receives user inputs discussing deep learning model aspects and (ii) provides recommendations from the collaborative deep learning model authoring tool; providing, at the collaborative deep learning model authoring tool, a consensus view indicating (i) a conflicting aspect identified as an aspect where more than one user selected a different aspect and (ii) the aspect selected for implementation within the deep learning model based upon that aspect having the most user selections; providing, at the collaborative deep learning model authoring tool, a model view displaying layers of the deep learning model based upon (i) aspects selected by the users in the dialog window and (ii) the aspect selected for implementation in the consensus view; and providing, at the collaborative deep learning model authoring tool, a deployment view that displays an execution of the deep learning m

Abstract: Methods, systems and computer program products for natural language context embedding are provided herein. A computer-implemented method includes extracting a document anatomy and document elements from a given structured document, identifying semantic references in the given structured document, and generating an ontology comprising (i) a hierarchy of concepts and (ii) relations connecting the concepts, each concept comprising attributes for a document element. The computer-implemented method also includes generating natural language text context for a given document element by utilizing the ontology to combine (i) attributes of a given concept corresponding to the given document element with (ii) attributes of another concept, the other concept corresponding to another document element, the other concept being connected to the given concept by at least one relation.

Abstract: One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: using an electronic device to engage in an interactive session between a user and a virtual assistant; receiving, at the electronic device, audio input from the user, wherein the audio input comprises a problem-solving query corresponding to a request by the user for assistance in solving a problem related to at least one object; parsing the audio input to identify at least one annotated video file corresponding to the at least one object and the problem-solving query; determining a state of the object and a location in the at least one annotated video file corresponding to the state of the object; and providing, to the user and based on the location in the at least one annotated video file, instructional output related to the problem-solving query.

Abstract: Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.