Abstract: Techniques and solutions are provided for a source quality check service configured to analyze source text and identify issues in the source text. The source quality check service may identify the issues by performing a selected subset of checks with a centralized source quality check engine, and may be called from within one or more of an Integrated Development Environment (IDE), a build process, and/or a translation process to perform the selected subset of checks. The source quality check service may be further configured to output a report of the identified one or more issues.

Abstract: Techniques and solutions are provided for a source quality check service configured to analyze source text and identify issues in the source text. The source quality check service may identify the issues by performing a selected subset of checks with a centralized source quality check engine, and may be called from within one or more of an Integrated Development Environment (IDE), a build process, and/or a translation process to perform the selected subset of checks. The source quality check service may be further configured to output a report of the identified one or more issues.

Abstract: Techniques for model based data processing are proposed. In one example, a computer-implemented method is as follows. A first model is determined from a first category of data processing models and a second model is determined from a second category of data processing models. The second category can be different from the first category in some instances. Performance of a first combined model of the first and second models can be compared with performance of the first model. A target model for processing data can be selected from the first combined model and the first model based on the comparing.

Abstract: Embedding representation for a document is generated based on clustering words in the document. Representative clusters are selected and a weighted sum of the embeddings of the words in the selected clusters is determined as a document embedding. Documents are labeled based on document embeddings. A machine learning algorithm is trained using the documents. The machine learning algorithm predicts a label of a given document based on the given document's document embedding.

Abstract: Techniques for model based data processing are proposed. In one example, a computer-implemented method is as follows. A first model is determined from a first category of data processing models and a second model is determined from a second category of data processing models. The second category can be different from the first category in some instances. Performance of a first combined model of the first and second models can be compared with performance of the first model. A target model for processing data can be selected from the first combined model and the first model based on the comparing.

Abstract: Embedding representation for a document is generated based on clustering words in the document. Representative clusters are selected and weighted sum of the embeddings of the words in the selected clusters is determined as a document embedding. Documents are labeled based on document embeddings. A machine learning algorithm is trained using the documents. The machine learning algorithm predicts a label of a given document based on the given document's document embedding.

Abstract: A computer-implemented method, computer program product, and system for detecting work-piece defects. The computer-implemented method may include: receiving a first image of a training work-piece captured using a non-destructive imaging process and a second image of the training work-piece captured using a destructive imaging process; receiving an image of a work-piece captured using a non-destructive imaging process, wherein the work-piece is substantially similar to the training work-piece, and the non-destructive imaging process used to capture the work-piece is substantially similar to the non-destructive imaging process used to capture the training work-piece; matching the image of the work-piece to the first image of the training work-piece; and enhancing the image of the work-piece using the second image of the training work-piece, in response to the image of the work-piece matching the first image of the training work-piece.

Abstract: A computer-implemented method, computer program product, and system for detecting work-piece defects. The computer-implemented method may include: receiving a first image of a training work-piece captured using a non-destructive imaging process and a second image of the training work-piece captured using a destructive imaging process; receiving an image of a work-piece captured using a non-destructive imaging process, wherein the work-piece is substantially similar to the training work-piece, and the non-destructive imaging process used to capture the work-piece is substantially similar to the non-destructive imaging process used to capture the training work-piece; matching the image of the work-piece to the first image of the training work-piece; and enhancing the image of the work-piece using the second image of the training work-piece, in response to the image of the work-piece matching the first image of the training work-piece.

Abstract: Systems and techniques for blockchain-based reputation tracking in an online marketplace include and/or are configured for: receiving a rate reputation request, the rate reputation request relating to a transaction attempt between at least two entities in the online marketplace; determining a plurality of reputation factors; and determining a reputation adjustment for one or more of the entities participating in the transaction attempt. The reputation adjustment is based at least in part on one or more of the reputation factors, which include: validity of the transaction attempt to which the rate reputation request relates; historical proportion of transactions conducted between the at least two entities participating in the transaction attempt; historical proportion of reputation currency transferred between the entities participating in the transaction attempt; and/or confidence of the validator node with respect to the entities.