Abstract: A content item on a content management system can be shared using a generated sharing link. The sharing link can be a custom network address, such as a uniform resource locator (URL), which allows the content item to be accessed without authentication. The sharing index and a content path of the content items can be listed in a sharing index used to identify the content upon a request initiated by the sharing link. The content management system can generate a content link to a content item upon receiving a sharing input from a user indicating the user wants to share a content item. Alternatively, in some embodiments, sharing links can be pre-generated by the content management system and stored on the client device. The sharing link can stored directly to a data buffer on the client device, such as a clipboard, where it can be accessed by the user.

Abstract: Systems and methods for the crowdsourced clustering of data items in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining categories for a set of source data includes obtaining a set of source data, determining a plurality of subsets of the source data, where a subset of the source data includes a plurality of pieces of source data in the set of source data, generating a set of pairwise annotations for the pieces of source data in each subset of source data, clustering the set of source data into related subsets of source data based on the sets of pairwise labels for each subset of source data, and identifying a category for each related subset of source data based on the clusterings of source data and the source data metadata for the pieces of source data in the group of source data.

Abstract: Systems and methods for distributed data annotation in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a distributed data annotation server system includes a storage device configured to store source data, one or more annotators, annotation tasks and a processor, wherein a distributed data annotation application configures the processor to receive source data including one or more pieces of source data, select one or more annotators, create one or more annotation tasks for the selected annotators and source data, request one or more annotations for the source data using the annotation tasks, receive annotations, determine source data metadata for at least one piece of source data using the received annotations, generate annotator metadata for at least one annotator using the received annotations and the source data, and estimate the ground truth for the source data using the source data metadata and the annotator metadata.

Abstract: Systems and methods for determining annotator performance in the distributed annotation of source data in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for clustering annotators includes obtaining a set of source data, determining a training data set representative of the set of source data, obtaining sets of annotations from a set of annotators for a portion of the training data set, for each annotator determining annotator recall metadata based on the set of annotations provided by the annotator for the training data set and determining annotator precision metadata based on the set of annotations provided by the annotator for the training data set, and grouping the annotators into annotator groups based on the annotator recall metadata and the annotator precision metadata.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.

Abstract: A content item on a content management system can be shared using a generated sharing link. The sharing link can be a custom network address, such as a uniform resource locator (URL), which allows the content item to be accessed without authentication. The sharing index and a content path of the content items can be listed in a sharing index used to identify the content upon a request initiated by the sharing link. The content management system can generate a content link to a content item upon receiving a sharing input from a user indicating the user wants to share a content item. Alternatively, in some embodiments, sharing links can be pre-generated by the content management system and stored on the client device. The sharing link can stored directly to a data buffer on the client device, such as a clipboard, where it can be accessed by the user.

Abstract: A content item on a content management system can be shared using a generated sharing link. The sharing link can be a custom network address, such as a uniform resource locator (URL), which allows the content item to be accessed without authentication. The sharing index and a content path of the content items can be listed in a sharing index used to identify the content upon a request initiated by the sharing link. The content management system can generate a content link to a content item upon receiving a sharing input from a user indicating the user wants to share a content item. Alternatively, in some embodiments, sharing links can be pre-generated by the content management system and stored on the client device. The sharing link can stored directly to a data buffer on the client device, such as a clipboard, where it can be accessed by the user.

Abstract: Embodiments are provided for content item classification. In some embodiments, an image for classification is received, a compact representation for the image having values indicative of pixel values within the received image is generated, a plurality of angle measurements for possible edges of at least one potential document within the received image are determined, and the image is classified using said compact representation and said plurality of angle measurements.

Abstract: Systems and methods for determining annotator performance in the distributed annotation of source data in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for clustering annotators includes obtaining a set of source data, determining a training data set representative of the set of source data, obtaining sets of annotations from a set of annotators for a portion of the training data set, for each annotator determining annotator recall metadata based on the set of annotations provided by the annotator for the training data set and determining annotator precision metadata based on the set of annotations provided by the annotator for the training data set, and grouping the annotators into annotator groups based on the annotator recall metadata and the annotator precision metadata.

Abstract: Systems and methods for the crowdsourced clustering of data items in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining categories for a set of source data includes obtaining a set of source data, determining a plurality of subsets of the source data, where a subset of the source data includes a plurality of pieces of source data in the set of source data, generating a set of pairwise annotations for the pieces of source data in each subset of source data, clustering the set of source data into related subsets of source data based on the sets of pairwise labels for each subset of source data, and identifying a category for each related subset of source data based on the clusterings of source data and the source data metadata for the pieces of source data in the group of source data.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.

Abstract: Systems and methods for determining annotator performance in the distributed annotation of source data in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for clustering annotators includes obtaining a set of source data, determining a training data set representative of the set of source data, obtaining sets of annotations from a set of annotators for a portion of the training data set, for each annotator determining annotator recall metadata based on the set of annotations provided by the annotator for the training data set and determining annotator precision metadata based on the set of annotations provided by the annotator for the training data set, and grouping the annotators into annotator groups based on the annotator recall metadata and the annotator precision metadata.

Abstract: Systems and methods for the crowdsourced clustering of data items in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining categories for a set of source data includes obtaining a set of source data, determining a plurality of subsets of the source data, where a subset of the source data includes a plurality of pieces of source data in the set of source data, generating a set of pairwise annotations for the pieces of source data in each subset of source data, clustering the set of source data into related subsets of source data based on the sets of pairwise labels for each subset of source data, and identifying a category for each related subset of source data based on the clusterings of source data and the source data metadata for the pieces of source data in the group of source data.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.

Abstract: Embodiments are provided for content item classification.

Abstract: Embodiments are provided for content item classification. In some embodiments, an image for classification is received, a compact representation for the image having values indicative of pixel values within the received image is generated, a plurality of angle measurements for possible edges of at least one potential document within the received image are determined, and the image is classified using said compact representation and said plurality of angle measurements.

Abstract: Embodiments are provided for content item classification.

Abstract: Systems and methods for the crowdsourced clustering of data items in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining categories for a set of source data includes obtaining a set of source data, determining a plurality of subsets of the source data, where a subset of the source data includes a plurality of pieces of source data in the set of source data, generating a set of pairwise annotations for the pieces of source data in each subset of source data, clustering the set of source data into related subsets of source data based on the sets of pairwise labels for each subset of source data, and identifying a category for each related subset of source data based on the clusterings of source data and the source data metadata for the pieces of source data in the group of source data.

Abstract: A content item on a content management system can be shared using a generated sharing link. The sharing link can be a custom network address, such as a uniform resource locator (URL), which allows the content item to be accessed without authentication. The sharing index and a content path of the content items can be listed in a sharing index used to identify the content upon a request initiated by the sharing link. The content management system can generate a content link to a content item upon receiving a sharing input from a user indicating the user wants to share a content item. Alternatively, in some embodiments, sharing links can be pre-generated by the content management system and stored on the client device. The sharing link can stored directly to a data buffer on the client device, such as a clipboard, where it can be accessed by the user.

Abstract: Systems and methods for distributed data annotation in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a distributed data annotation server system includes a storage device configured to store source data, one or more annotators, annotation tasks and a processor, wherein a distributed data annotation application configures the processor to receive source data including one or more pieces of source data, select one or more annotators, create one or more annotation tasks for the selected annotators and source data, request one or more annotations for the source data using the annotation tasks, receive annotations, determine source data metadata for at least one piece of source data using the received annotations, generate annotator metadata for at least one annotator using the received annotations and the source data, and estimate the ground truth for the source data using the source data metadata and the annotator metadata.