Abstract: An autonomous automated intent mining system extracts intents from stored conversations in a text-based chat corpus for use in an AI-driven dialog system. The intent mining system includes an intelligent pre-processor for extracting the conversation data from a computer memory and operating on the conversation data to identify relevant utterances. The system additionally includes a representation processor for operating on the identified relevant utterances to create a numerical representation. The system also includes a clustering processor for generating clusters of utterances and classifying each of the identified relevant utterances into a cluster based on the numerical representation of each utterance. Each generated cluster represents a unique intent for use in the AI-driven dialog system.

Abstract: An autonomous automated intent mining system extracts intents from stored conversations in a text-based chat corpus for use in an AI-driven dialog system. The intent mining system includes an intelligent pre-processor for extracting the conversation data from a computer memory and operating on the conversation data to identify relevant utterances. The system additionally includes a representation processor for operating on the identified relevant utterances to create a numerical representation. The system also includes a clustering processor for generating clusters of utterances and classifying each of the identified relevant utterances into a cluster based on the numerical representation of each utterance. Each generated cluster represents a unique intent for use in the AI-driven dialog system.

Abstract: Additional background information is used with a trained neural network based model to help classify whether text is a subtly and/or ambiguously offensive. This additional background information can come from different sources such as the article on which the comment was made, world knowledge about the external entities (e.g., Wikipedia, Urban Dictionary), phrases referenced in the text being classified, and, the context of the previous comments/text in the thread. The background information is retrieved based on key entities (e.g., people, places things) and/or key phrases in the comment. Sentence matrix encodings are built for both the comment and the background information. The background information encoding is used to condition the comment encoding. The background information encoding, and the conditioned comment encoding are fed to a trained multi-level perceptron to classify the comment as hate speech or non-hate speech.

Abstract: Storage space may be allocated from a non-reserved zone of a file system when the file system is not undergoing an online file system check. When the file system is undergoing an online file system check, storage space is allocated from a soft-reserved zone.

Abstract: A method of selecting an intervention includes, based on user content submitted to a platform, determining whether the content includes objectionable content. Based on the objectionable content, a content persona classification is associated with the objectionable content. Based on the content persona classification, an intervention is selected. The objectionable content may include, for example, content that has one or more characterizations as: subjective, misaligned, trigger, misguided, expressive, non-owner, offensive, prohibited, trigger product, trigger description, expired/invalid, and/or otherwise objectionable.

Abstract: Additional background information is used with a trained neural network based model to help classify whether text is a subtly and/or ambiguously offensive. This additional background information can come from different sources such as the article on which the comment was made, world knowledge about the external entities (e.g., Wikipedia, Urban Dictionary), phrases referenced in the text being classified, and, the context of the previous comments/text in the thread. The background information is retrieved based on key entities (e.g., people, places things) and/or key phrases in the comment. Sentence matrix encodings are built for both the comment and the background information. The background information encoding is used to condition the comment encoding. The background information encoding, and the conditioned comment encoding are fed to a trained multi-level perceptron to classify the comment as hate speech or non-hate speech.

Abstract: Methods and systems for automatically selecting a claim for fact checking. In a first stage, a claim can be classified as objective or subjective by an objectivity classifier. If the claim is classified as objective, then in a second stage, the claim is classified as being of public interest or not. If the claim is classified as both objective and of public interest, then the claim can be selected as worthy for fact checking. This approach reduces the workload of automated systems such as data-processing systems, which saves time in performing fact checking operations and results in greater system and network efficiencies.

Abstract: Dynamic updating of metadata associated with a file system is described herein. In accordance with an example, the updating includes creating a shadow file system metadata (FSM) index map in a shadow data block. The shadow data block is associated with a metadata storage of the file system and the metadata storage stores metadata files associated with the file system. The shadow FSM index map is associated with the metadata storage through an index map of the metadata storage, the index map being indicative of the metadata files in the metadata storage. The shadow FSM index map is updated to dynamically update the metadata associated with the file system, in the metadata storage.

Abstract: Example implementations relate to a storage system. An example implementation includes a storage system including a controller coupled to a storage medium. The controller is to receive block priority information from a file system via a communication agent. The block priority information and the file system are associated with the storage medium. Based on the block priority information, the controller is to selectively perform an operation on the storage medium. The operation includes a reconstruction operation, a data scrubbing operation, and a data migration operation.

Abstract: A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.

Abstract: A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.

Abstract: The disclosed embodiments illustrate method and system for medical data processing for generating personalized advisory information. The method includes receiving a request comprising a generic clinical care pathway report and medical data of a patient from a user-computing device. Further, the medical data comprises one or more factors associated with a patient profile and a population segment of the patient. The method further includes extracting one or more influence factors and one or more population segments to generate one or more knowledge bases. The method further includes comparing the one or more factors with the extracted one or more influence factors and the one or more population profiles. The method further includes generating a personalized advisory information. The method further includes rendering the generated personalized advisory information on an interactive user interface of the user-computing device over the communication network for selection by a medical practitioner.

Abstract: Dynamic updating of metadata associated with a file system is described herein. In accordance with an example, the updating includes creating a shadow file system metadata (FSM) index map in a shadow data block. The shadow data block is associated with a metadata storage of the file system and the metadata storage stores metadata files associated with the file system. The shadow FSM index map is associated with the metadata storage through an index map of the metadata storage, the index map being indicative of the metadata files in the metadata storage. The shadow FSM index map is updated to dynamically update the metadata associated with the file system, in the metadata storage.

Abstract: Storage space may be allocated from a non-reserved zone of a file system when the file system is not undergoing an online file system check. When the file system is undergoing an online file system check, storage space is allocated from a soft-reserved zone.

Abstract: Example implementations relate to a storage system. An example implementation includes a storage system including a controller coupled to a storage medium. The controller is to receive block priority information from a file system via a communication agent. The block priority information and the file system are associated with the storage medium. Based on the block priority information, the controller is to selectively perform an operation on the storage medium. The operation includes a reconstruction operation, a data scrubbing operation, and a data migration operation.

Abstract: Partial snapshot creation includes identifying a file having requested data for providing to a user device in response to a user request. The file is stored in a plurality of physical data blocks of a storage device. A logical offset range corresponding to the requested data is ascertained. The logical offset range corresponds to a virtual location of the requested data in a file system. Range of physical data blocks in which the requested data is stored is determined based on a mapping table and a file parameter. The mapping table includes a mapping between the logical offset range and the requested data. A data block mapping between a snapshot inode, having metadata associated with partial snapshot, and the range of physical data blocks is created for obtaining a partial snapshot of the requested data. The partial snapshot is rendered to the user device in response to the user request.

Abstract: Some examples described herein relate to storing file directories on a storage device. In an example, a portion of available free space on a storage device may be assigned for storing file directories only. Upon receipt of a request for allocating storage space to a file directory on the storage device, the assigned portion may be utilized to store the file directory.

Abstract: Some examples described herein relate to improving startup time of managed code. An example includes using a data or code from a previous execution of managed code, wherein the data or code from the previous execution of the managed code is stored in a byte addressable non-volatile memory, during a subsequent execution of the managed code.

Abstract: Some examples described herein relate to data deduplication. Redundancy information related to data may be recorded based upon a pre-defined rule. The redundancy information, which may be associated with the data, may be used during storage of the data in a storage system to determine that the data is redundant data of a previous data. An action related to the data may be performed.

Abstract: A method, and a corresponding system, for dynamically updating software while the software is running by automatically dividing a patch into a plurality of micro-updates using compiler analysis. The method includes providing the patch which includes updates to several variable units of the software, including functions, type definitions, and data stores. Next, an interference graph of the patch is generated by creating a node corresponding to each variable unit of the patch and creating connected components by adding edges connecting variable units having an impact expression with a non-zero intersection. The patch is divided into the micro-updates, where each micro-update corresponds to a connected component. The micro-updates are then applied to the software when the variable units of the micro-update are at a safepoint, and at least two of the micro-updates are applied at different times while the software is running.