Abstract: A method for a language model applies in-context learning to detect problematic text and reformulate the problematic text to correct problematic text by (a) receiving, in the language model, a user generated text example; (b) determining if the user generated text example is a problematic text having a determined classification; (c) reformulating the user generated text example if the text example is a problematic text having the determined classification; (d) outputting the user generated text example if the text example is determined to be not a problematic text having the determined classification; and (e) outputting the reformulated text example if the text example is determined to be a problematic text having the determined classification.

Abstract: There is disclosed a computer-implemented method for detecting machine-generated documents in a collection of documents including machine-generated and human-authored documents. The computer-implemented method includes computing a set of long-repeated substrings (such as super-maximal repeats) with respect to the collection of documents and using a subset of the long-repeated substrings to designate documents containing the subset of the repeated substrings as machine-generated. The documents designated as machine-generated serve as positive examples of machine-generated documents and a set of documents including at least one human-authored document serves as negative examples of machine-generated documents. A plurality of classifiers are trained with a dataset including both the positive and negative examples of machine-generated documents. Classified output of the classifiers is then used to detect an extent to which a given document of the dataset is machine-generated.

Abstract: There is disclosed a computer-implemented method for detecting machine-generated documents in a collection of documents including machine-generated and human-authored documents. The computer-implemented method includes computing a set of long-repeated substrings (such as super-maximal repeats) with respect to the collection of documents and using a subset of the long-repeated substrings to designate documents containing the subset of the repeated substrings as machine-generated. The documents designated as machine-generated serve as positive examples of machine-generated documents and a set of documents including at least one human-authored document serves as negative examples of machine-generated documents. A plurality of classifiers are trained with a dataset including both the positive and negative examples of machine-generated documents. Classified output of the classifiers is then used to detect an extent to which a given document of the dataset is machine-generated.

Abstract: A computer-implemented system and method for identifying passenger trips on a transportation network are described. The method includes acquiring transaction data for a collection of passengers boarding at stops on a transportation network. The network includes a plurality of routes. Route sequences are identified, based on the transaction data, each route sequence including at least two trip segments by a passenger. Each pair of trip segments of an identified route sequence are spaced by a transfer. For each identified route sequence, using a trip planner, the route sequence is classified as a multi-segment trip that includes at least one transfer or a multi-trip journey. A route sequence which is classified as a multi-trip journey is partitioned into at least two trips, each trip being a multi-segment trip or a single-segment trip.

Abstract: A system and method are provided for predicting passenger loads at vehicle stops on a transportation network. The method includes providing a classifier which has been trained to predict passenger loads at vehicle stops on a transportation route, based on reconstructed passenger loads for vehicle stops on the route. Transaction data is acquired for passengers boarding at vehicle stops on the transportation route. Reconstructed passenger loads are computed for vehicle stops on the route based on the transaction data. With the trained classifier, a passenger load for at least one of the vehicle stops on the transportation route is predicted, based on the reconstructed passenger load for the vehicle stop.

Abstract: A computer-implemented system and method for identifying passenger trips on a transportation network are described. The method includes acquiring transaction data for a collection of passengers boarding at stops on a transportation network. The network includes a plurality of routes. Route sequences are identified, based on the transaction data, each route sequence including at least two trip segments by a passenger. Each pair of trip segments of an identified route sequence are spaced by a transfer. For each identified route sequence, using a trip planner, the route sequence is classified as a multi-segment trip that includes at least one transfer or a multi-trip journey. A route sequence which is classified as a multi-trip journey is partitioned into at least two trips, each trip being a multi-segment trip or a single-segment trip.