Abstract: A method for manufacturing a semiconductor device and a semiconductor device produced thereby. For example and without limitation, various aspects of this disclosure provide methods for manufacturing a semiconductor device, and semiconductor devices produced thereby, that comprise forming an interposer including a reinforcement layer.

Abstract: An apparatus and method for analyzing time series data on the basis of machine learning are provided. According to the disclosed embodiments, it is possible to effectively augment time series data, which is a target to be learned, according to characteristics of the time series data, thereby solving a problem of overfitting a machine learning model due to limited training data and a problem of deterioration of prediction accuracy due to imbalance of distribution of time series data and improving reliability of time series data analysis. In addition, according to the disclosed embodiments, it is possible to effectively set an optimal parameter for augmenting time series data.

Abstract: A method for model customization according to an embodiment includes providing a user with prediction results of each of a plurality of pre-trained natural language processing models for a document subjected to analysis selected from a document set including a plurality of documents, acquiring user feedback on the prediction results from the user, generating a plurality of augmented documents from at least one of the plurality of documents based on data attributes of each of the plurality of documents and the user feedback; and retraining at least one of the plurality of natural language processing models, using training data including the plurality of augmented documents.

Abstract: A method for model customization according to an embodiment includes providing a user with prediction results of each of a plurality of pre-trained natural language processing models for a document subjected to analysis selected from a document set including a plurality of documents, acquiring user feedback on the prediction results from the user, generating a plurality of augmented documents from at least one of the plurality of documents based on data attributes of each of the plurality of documents and the user feedback; and retraining at least one of the plurality of natural language processing models, using training data including the plurality of augmented documents.

Abstract: An apparatus and method for analyzing time series data on the basis of machine learning are provided. According to the disclosed embodiments, it is possible to effectively augment time series data, which is a target to be learned, according to characteristics of the time series data, thereby solving a problem of overfitting a machine learning model due to limited training data and a problem of deterioration of prediction accuracy due to imbalance of distribution of time series data and improving reliability of time series data analysis. In addition, according to the disclosed embodiments, it is possible to effectively set an optimal parameter for augmenting time series data.

Abstract: A method and apparatus for detecting and managing faults, which can consider both causes from a device where a failure has occurred and causes from other devices as the causes of the failure, is provided. The method and apparatus may provide fault detect managing which divide analysis target data into a normal section and a faulty section and can thus perform fault detection and management using correlation coefficients that can distinctly show a failure.