Abstract: Systems, computer-implemented methods, and computer program products to facilitate modernization of an application are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a determination component that determines one or more relevant surrounding contexts for a raw entity. The computer executable components also can comprise a matching component that matches the one or more relevant surrounding contexts with one or more known surrounding contexts of one or more known entities. The computer executable components further can comprise a type identification component that identifies an entity type for the raw entity based on the matching of the one or more relevant surrounding contexts with the one or more known surrounding contexts.

Abstract: In an approach to augmenting a caption dataset by leveraging a denoising autoencoder to sample and generate additional captions from the ground truth captions, one or more computer processors generate a plurality of new captions utilizing an autoencoder fed with one or more noisy captions, wherein the autoencoder is trained with a dataset comprising a plurality of ground truth captions. The one or more computer processors calculate an importance weight for each new caption in the plurality of generated new captions as compared to a plurality of associated ground truth captions based on a consensus metric. The one or more computer processors train a caption model with the generated plurality of new captions and associated calculated weights.

Abstract: Approaches presented herein enable fault detection. More specifically, implementation code of one or more functions is identified from source code. The implementation code of the one or more functions is converted to corresponding Abstract Syntax Trees (ASTs). The implementation code of the one or more functions is represented as a first plurality of sets of AST paths over the ASTs. Classification results for the one or more functions are generated with a classifier based on the first plurality of sets of AST paths for the implementation code of the one or more functions. Each of the classification results indicates a probability of having at least one fault in a corresponding function of the one or more functions. Fault detection results of the source code are generated based on the classification results.

Abstract: The present invention relates to a method, computer system, and computer program product for data processing based on a response strategy. According to the method, a performance value of a server is determined in response to receiving at least one request to the server. A response strategy for the at least one request is determined based on the determined performance value. At least one response is provided to the at least one request according to the determined response strategy.

Abstract: In an approach to improving the image captioning performance of low-resource languages by leveraging multimodal inputs, one or more computer processors encode an image utilizing an image encoder, wherein the image is contained within a triplet comprising the image, one or more high-resource captions, and one or more low-resource captions. The one or more computer processors generate one or more high-resource captions utilizing the encoded image and the triplet inputted into a high-resource decoder. The one or more computer processors encode the one or more generated high-resource captions utilizing a high-resource encoder. The one or more computer processors add adaptive cycle consistency constraints on a set of calculated attention weights associated the triplet. The one or more computer processors generate one or more low-resource captions by simultaneously inputting the encoded image, the encoded high-resource caption, and the triplet into a trained low-resource decoder.

Abstract: Techniques are provided for training machine learning model. According to one aspect, a training data is received by one or more processing units. The machine learning model is trained based on the training data, wherein the training comprises: optimizing the machine learning model based on stochastic gradient descent (SGD) by adding a dynamic noise to a gradient of a model parameter of the machine learning model calculated by the SGD.

Abstract: An approach is provided that receives multimedia content and extracts a set of metadata from the content. The extraction of metadata includes performing image analysis on the multimedia content. The approach then analyzes the set of metadata with the analysis resulting in a set of regulations that apply to the multimedia content. The approach compares the set of metadata to the set of regulations and allows publication of the multimedia content when the comparison reveals that the multimedia content is in compliance with the set of regulations, and inhibits publication of the multimedia content when the multimedia content fails to comply with the set of regulations.

Abstract: Techniques are provided for training, by a system operatively coupled to a processor, an attention weighted recurrent neural network encoder-decoder (AWRNNED) using an iterative process based on one or more paragraphs of agent sentences from respective transcripts of one or more conversations between one or more agents and one or more customers, and based on one or more customer response sentences from the respective transcripts, and generating, by the system, one or more groups respectively comprising one or more agent sentences and one or more customer response sentences selected based on attention weights of the AWRNNED.

Abstract: A computer implemented method, computer system and computer program product are provided for aspect-based sentiment analysis. According to the method, at least one sentence and at least one aspect are received by one or more processing units. At least one opinion snippet of the at least one sentence is determined based on the at least one sentence and the at least one aspect, through a trained aspect-sentence fusion model, by one or more processing units. A sentiment prediction of the at least one opinion snippet is calculated by one or more processing units for the at least one aspect.

Abstract: Approaches presented herein enable fault detection. More specifically, implementation code of one or more functions is identified from source code. The implementation code of the one or more functions is converted to corresponding Abstract Syntax Trees (ASTs). The implementation code of the one or more functions is represented as a first plurality of sets of AST paths over the ASTs. Classification results for the one or more functions are generated with a classifier based on the first plurality of sets of AST paths for the implementation code of the one or more functions. Each of the classification results indicates a probability of having at least one fault in a corresponding function of the one or more functions. Fault detection results of the source code are generated based on the classification results.

Abstract: A computer-implemented method for Continues Integration and Continues Deployment (CICD) pipeline security check is provided according to embodiments of the present disclosure. In the method, a plurality of events is executed sequentially to create a CICD pipeline. The plurality of events is monitored. Moreover, a security status of the CICD pipeline is determined based on the monitored events and a model for predicting the security status of the CICD pipeline.

Abstract: A virtual change database system that supports iterative and parallel database application development is disclosed. The system stores a common set of base physical data and a plurality of sets of virtual changes. Each set of virtual changes is associated with a database object. A database application may access a database object in the database by using the virtual version of the object to extract the object's data content from the common base physical data. The database system present a first query response to (i) a first application based on the set of base physical data and (ii) a first set of virtual changes for a particular database object, while also presenting a second query response to a second application based on the set of base physical data and a second, different set of virtual changes for the particular database object.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate modernization of an application are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a determination component that determines one or more relevant surrounding contexts for a raw entity. The computer executable components also can comprise a matching component that matches the one or more relevant surrounding contexts with one or more known surrounding contexts of one or more known entities. The computer executable components further can comprise a type identification component that identifies an entity type for the raw entity based on the matching of the one or more relevant surrounding contexts with the one or more known surrounding contexts.

Abstract: Using a natural language processing model, a historical defect report comprising a defect description in narrative text form is parsed. Within a code repository, source code associated with the historical defect report is identified. From the historical defect report and the source code, a logging rule comprising a defect type, logging placement information corresponding to the defect type, and logging format information corresponding to the defect type is generated. By parsing a new defect report using the natural language processing model, the new defect report reporting a defect in new source code, it is determined that the logging rule applies to the new defect report. Logging source code generating logging output when executed is placed within the new source code according to the logging rule. Execution of the new source code including the logging source code is caused, generating the logging output.

Abstract: In an approach to augmenting caption datasets, one or more computer processors sample a ratio lambda from a probability distribution based on a pair of datapoints contained in a dataset, wherein each datapoint in the pair of datapoints comprises an image and an associated caption; extend the dataset by generating one or more new datapoints based on the sampled ratio lambda for each pair of datapoints in the dataset, wherein the sampled ratio lambda incorporates an interpolation of features associated with the pair of datapoints into the generated one or more new datapoints; identify one or more objects contained within a subsequent image utilizing an image model trained utilizing the extended dataset; generate a subsequent caption for one or more identified objects contained within the subsequent image utilizing a language generating model trained utilizing the extended dataset.

Abstract: An approach is provided that receives multimedia content and extracts a set of metadata from the content. The extraction of metadata includes performing image analysis on the multimedia content. The approach then analyzes the set of metadata with the analysis resulting in a set of regulations that apply to the multimedia content. The approach compares the set of metadata to the set of regulations and allows publication of the multimedia content when the comparison reveals that the multimedia content is in compliance with the set of regulations, and inhibits publication of the multimedia content when the multimedia content fails to comply with the set of regulations.

Abstract: In an approach to improving the image captioning performance of low-resource languages by leveraging multimodal inputs, one or more computer processors encode an image utilizing an image encoder, wherein the image is contained within a triplet comprising the image, one or more high-resource captions, and one or more low-resource captions. The one or more computer processors generate one or more high-resource captions utilizing the encoded image and the triplet inputted into a high-resource decoder. The one or more computer processors encode the one or more generated high-resource captions utilizing a high-resource encoder. The one or more computer processors add adaptive cycle consistency constraints on a set of calculated attention weights associated the triplet. The one or more computer processors generate one or more low-resource captions by simultaneously inputting the encoded image, the encoded high-resource caption, and the triplet into a trained low-resource decoder.

Abstract: In an approach to augmenting caption datasets, one or more computer processors sample a ratio lambda from a probability distribution based on a pair of datapoints contained in a dataset, wherein each datapoint in the pair of datapoints comprises an image and an associated caption; extend the dataset by generating one or more new datapoints based on the sampled ratio lambda for each pair of datapoints in the dataset, wherein the sampled ratio lambda incorporates an interpolation of features associated with the pair of datapoints into the generated one or more new datapoints; identify one or more objects contained within a subsequent image utilizing an image model trained utilizing the extended dataset; generate a subsequent caption for one or more identified objects contained within the subsequent image utilizing a language generating model trained utilizing the extended dataset.

Abstract: In an approach to augmenting a caption dataset by leveraging a denoising autoencoder to sample and generate additional captions from the ground truth captions, one or more computer processors generate a plurality of new captions utilizing an autoencoder fed with one or more noisy captions, wherein the autoencoder is trained with a dataset comprising a plurality of ground truth captions. The one or more computer processors calculate an importance weight for each new caption in the plurality of generated new captions as compared to a plurality of associated ground truth captions based on a consensus metric. The one or more computer processors train a caption model with the generated plurality of new captions and associated calculated weights.

Abstract: Embodiments of the present disclosure relate to generation of sentence representation. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words, the sentence graph comprising nodes representing the words and edges connecting the nodes to indicate relationships between the words. Word representations for the plurality of words are determined based on the sentence graph by applying a graph convolution operation on respective sets of neighbor nodes for respective ones of the nodes, a set of neighbor nodes for a node having edges connected with the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.