Abstract: A computer-implemented method according to one embodiment includes building a knowledge graph of resource vendors. The method further includes identifying resources that are in relatively short supply in a host system, and determining, using a matching algorithm, a first of the resource vendors in the knowledge graph from which to temporarily lease the identified resources to the host system. The first resource vendor includes the identified resources and satisfies policy prerequisites of the host system. A lease is caused to be issued, from the first resource vendor to the host system, for the identified resources. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: A computer-implemented method according to one embodiment includes building a knowledge graph of resource vendors. The method further includes identifying resources that are in relatively short supply in a host system, and determining, using a matching algorithm, a first of the resource vendors in the knowledge graph from which to temporarily lease the identified resources to the host system. The first resource vendor includes the identified resources and satisfies policy prerequisites of the host system. A lease is caused to be issued, from the first resource vendor to the host system, for the identified resources. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Generating a query response by receiving data for a non-user utterance, determining a question answering (QA) system response to the non-user utterance, receiving data for a user utterance responsive to the non-user utterance, determining a confidence score for the user utterance, determining a deviation between the user utterance and the QA system response, and providing the QA system response according to a combination of the deviation and the confidence score.

Abstract: An embodiment associates a user's post data that with a category from a predefined list of categories based on the post content. The embodiment analyzes, using machine learning, the post data for potentially sensitive content and generates a first sensitive data indicator identifying potentially sensitive information in the post data and an associated first confidence value. The embodiment generates explanatory data identifying a feature that contributed to the post data being identified as potentially sensitive, and generates a modified version of the post data that modifies the feature. The embodiment analyzes the modified post data for potentially sensitive content and generates a second sensitive data indicator and a second confidence value indicating that the post data is more likely to contain sensitive data than the modified post data. The embodiment alerts the user regarding the potentially sensitive data and recommends changing the post based on the modified feature value.

Abstract: Disclosed are techniques for faceted search queries through interactive graphical objects. A text search query corresponding to a faceted search is received from a user, which is then parsed for keywords. Attributes associated with the keywords are then extracted and used to generate a graphical representation corresponding to the results associated with the text search query. Further user input to refine the query is provided through manipulation of the graphical representation by the user (such as dragging a leg of a table to change the length of legs on a table) corresponding to modification of one or more facets of the faceted search, narrowing the available search results corresponding to the faceted search.

Abstract: Disclosed are methods, computer program products, and systems for maximizing renewals of purchase orders. One embodiment of the method may comprise utilizing a classifier machine learning model to identify metrics that are most relevant to whether customers will renew purchase orders, predicting respective risks of non-renewal for the purchase orders using the identified metrics, applying a tone analyzer natural language processing (NLP) model to determine current sentiments for respective customers, and recommending which of the respective customers to pursue with additional resources based the respectively determined sentiments and risks of non-renewal.

Abstract: Approaches presented herein enable dynamically determining a validity of a return. More specifically, a system obtains a return request from a customer, a transaction history of the customer, and a set of return policy rules. A generative adversarial network (GAN) trained to detect non-genuine returns is applied to the return request. The GAN uses, among other this, the transaction history of the customer and the set of return policy rules as parameters of the GAN. Based on an output of the GAN, at least one return processing action is recommended and implemented.

Abstract: A method for chronologically aggregating media content for a specific topic from media content providers includes receiving multi-type media content relating to the specific topic, where the multi-type media content includes at least a first type of media content and a second type of media content. Identifying a first statement in a first portion of the first type of media content that responds to a second statement in a second portion of the second type of media content. Determining a chronological sequence for the first portion of the first type of media content and the second portion of the second type of media content, where the first portion of the first type of media content occurs after the second portion of the first type of media content. Generating a final media content presentation file for the determined chronological sequence.

Abstract: A computer-implemented method, a computer system and a computer program product use artificial intelligence (AI) to extract information from a phisher. The method may include identifying a malicious email on a server. The malicious email comprises an attempt by the phisher to compromise a user. The method may also include generating an automated conversational agent that poses as the user. The method may further include transmitting a message to the phisher by the automated conversational agent. The message indicates that the user has been compromised. In addition, the method may include receiving a response from the phisher. Lastly, the method may include determining an intent of the phisher based on the response.

Abstract: A request is received from a user to search a document for at least one search term. The document is analyzed to determine contextual meanings associated with the at least one search term. An ordered set of contexts of the at least one search term is constructed. An option of disambiguation is then prompted to the user to select a context of the ordered set of contexts. Search results are presented to the user associated with the selected context of the at least one search term.

Abstract: A computer implemented method for selecting service providers includes receiving a set of client requirements and analyzing available service providers based on the received set of client requirements. The method additionally includes scoring the available service providers based on the analysis. The method further includes identifying one or more unstructured external data sources corresponding to the available service providers and analyzing the reliability of the one or more unstructured external data sources with respect to the available service providers. The method further includes adjusting the scoring of the service providers based, at least in part, on the data source reliability, and subsequently providing an optimal selection of service providers based on the adjusted scoring. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: A method, system, and computer program product for context-based machine learning model generation are provided. The method collects ground data for a set of machine learning model deployments associated with a set of problems. A knowledge graph is generated for the set of machine learning models based on the ground data. An initial set of hyperparameters are determined for a new problem based on the knowledge graph. A modified set of hyperparameters are generated for the new problem based on the initial set of hyperparameters. The method generates a machine learning model for the new problem based on the modified set of hyperparameters.

Abstract: Generating visual workflow representations by receiving data including text instructions, identifying actions in the instructions, generating a mapping of the actions according to a generative model, the mapping including an action sequence, providing the mapping to a user, receiving feedback from the user, altering the generative model according to the feedback, and generating a revised mapping according to the feedback.

Abstract: An approach is disclosed that ingests sets of metadata into an AI system utilizing a model trained to recognize a plurality of programming categories. Each of the sets of metadata corresponds to a computer programming language, and the ingested sets of metadata are stored in a corpus accessible by the AI system. A block of programming code is input to the AI system with the block including a number of computer instructions written in a computer programming language. Recommended programming languages are received from the AI system based on comparing the categories found in the block of programming code with the metadata corresponding to the recommended programming languages.

Abstract: A dataset comprising text-based messages can be accessed. Tokens for words and punctuation marks contained in the text-based messages can be generated. Each token corresponds to one word or one punctuation mark. A vector representation for each of a plurality of the tokens can be generated using natural language processing. A sequence of tokens corresponding to the text-based message can be generated for each of a plurality of the text-based messages in the dataset. Ones of the tokens that represent punctuation marks can be identified. An artificial neural network can be trained to predict use of the punctuation marks in sentence structures. The training uses the generated sequence of tokens and the vector representations for the tokens, in the sequence of tokens, that represent the punctuation marks.

Abstract: A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Abstract: Annotating programming code by receiving a first version of programming code, determining annotations for the code according to a first machine learning model, identifying a user, altering a generative model according a persona of the user, and generating altered annotations using the altered generative model.

Abstract: In an approach to mature a knowledge corpus using artificial intelligence (AI) and user collaboration, embodiments create, by an AI response system, a knowledge corpus based on retrieved data associated with a first user. Additionally, embodiments execute, by the AI response system, a search to locate one or more matching knowledge corpora based on a request of the first user, and identify, by an anonymous network, at least one cluster of one or more anonymous second users having respective search requests that substantially match the search request of the first user. Furthermore, embodiments execute an AI exchange between the identified one or more anonymous second users and the first user, and mature the knowledge corpus, via the AI response system, based on the AI exchange between the first user and the identified one or more anonymous second users.

Abstract: Disclosed are techniques for faceted search queries through interactive graphical objects. A text search query corresponding to a faceted search is received from a user, which is then parsed for keywords. Attributes associated with the keywords are then extracted and used to generate a graphical representation corresponding to the results associated with the text search query. Further user input to refine the query is provided through manipulation of the graphical representation by the user (such as dragging a leg of a table to change the length of legs on a table) corresponding to modification of one or more facets of the faceted search, narrowing the available search results corresponding to the faceted search.

Abstract: Generating a query response by receiving data for a non-user utterance, determining a question answering (QA) system response to the non-user utterance, receiving data for a user utterance responsive to the non-user utterance, determining a confidence score for the user utterance, determining a deviation between the user utterance and the QA system response, and providing the QA system response according to a combination of the deviation and the confidence score.