Abstract: Techniques herein balance the need for flexibility with the need accuracy, using a reactive approach to viral spam detection. After content (e.g., a social media platform news feed or timeline post) is created, interaction activity (e.g., content views) with the content is monitored. Based on the monitoring of the interactivity activity, it is determined whether a reactive viral spam analysis condition is satisfied for the content (e.g., because the number of content views exceeds a threshold). In response to determining that the reactive viral spam analysis condition is satisfied, a determination is made whether the content is or is not viral spam. If the content is determined to be viral spam, then it may be reported or flagged for further action (e.g., take down after manual confirmation).

Abstract: Embodiments of copypasta filtering system technologies cluster digital content items into copypasta clusters, extract a first feature set from the digital content items in the copypasta clusters, apply a first set of filters to the first feature set, and based on output of the first set of filters, divide the copypasta clusters into first intent copypasta clusters and possible second intent copypasta clusters. A second feature set is extracted from the digital content items in the possible second intent copypasta clusters. A second set of filters is applied to the second feature set. Based on output of the second set of filters, second intent copypasta clusters are created.

Abstract: In an example embodiment, a scalable hybrid approach for sequence modeling of online network interactions is provided. This hybrid approach combines generative modeling, including determining salient aspects of a distribution and estimating the confidence in this determination, along with discriminative modeling, which allows for scalability to provide a scalable and robust approach to model any user-generated sequence in a social network.

Abstract: Embodiments of copypasta filtering system technologies cluster digital content items into copypasta clusters, extract a first feature set from the digital content items in the copypasta clusters, apply a first set of filters to the first feature set, and based on output of the first set of filters, divide the copypasta clusters into first intent copypasta clusters and possible second intent copypasta clusters. A second feature set is extracted from the digital content items in the possible second intent copypasta clusters. A second set of filters is applied to the second feature set. Based on output of the second set of filters, second intent copypasta clusters are created.

Abstract: Techniques for selecting models using greedy search on validation metrics are disclosed herein. A system generates corresponding predictions for a validation dataset using a plurality of prediction models. The system selects one of the prediction models for inclusion in an ensemble set based on the selected prediction model generating more correct predictions for the validation dataset than the other prediction models, and then removes the selected prediction model from the plurality of prediction models to form a reduced plurality of prediction models.

Abstract: Methods, systems, and computer programs are presented for detecting near duplicates and partial matches of videos. One method includes an operation for receiving a video containing frames. For each frame, keypoints are determined within the frame. For each keypoint, a horizontal gradient vector is calculated based on a horizontal gradient at the keypoint and a vertical gradient vector is calculated based on a vertical gradient at the keypoint. The horizontal and vertical gradients are binary vectors. Further, a keypoint description is generated for each keypoint based on the horizontal gradient vector and the vertical gradient vector. Further, the frames are matched to frames of videos in a video library based on the keypoint descriptions of the keypoints in the frame in the videos in the video library. Further, a determination is made if the video has near duplicates in the video library based on the matching.

Abstract: Technologies for generating a multi-modal representation of an image based on the image content are provided. The disclosed techniques include receiving an image, to be classified, that comprises one or more embedded text characters. The one or more embedded text characters are identified from the image and a first machine learning model is used to generate a text vector that represents a numerical representation of the one or more embedded text characters. A second machine learning model is used to generate an image vector that represents a numerical representation of the graphical portion of the image. The text vector and the image vector are used as input to generate a multi-modal vector that contains information from both the text vector and the image vector. The image may be classified into one of a plurality of image classifications based upon the information in the multi-modal vector.

Abstract: Techniques for selecting models using greedy search on validation metrics are disclosed herein. A system generates corresponding predictions for a validation dataset using a plurality of prediction models. The system selects one of the prediction models for inclusion in an ensemble set based on the selected prediction model generating more correct predictions for the validation dataset than the other prediction models, and then removes the selected prediction model from the plurality of prediction models to form a reduced plurality of prediction models.

Abstract: Methods, systems, and computer programs are presented for detecting near duplicates and partial matches of videos. One method includes an operation for receiving a video containing frames. For each frame, keypoints are determined within the frame. For each keypoint, a horizontal gradient vector is calculated based on a horizontal gradient at the keypoint and a vertical gradient vector is calculated based on a vertical gradient at the keypoint. The horizontal and vertical gradients are binary vectors. Further, a keypoint description is generated for each keypoint based on the horizontal gradient vector and the vertical gradient vector. Further, the frames are matched to frames of videos in a video library based on the keypoint descriptions of the keypoints in the frame in the videos in the video library. Further, a determination is made if the video has near duplicates in the video library based on the matching.

Abstract: Technologies for generating a multi-modal representation of an image based on the image content are provided. The disclosed techniques include receiving an image, to be classified, that comprises one or more embedded text characters. The one or more embedded text characters are identified from the image and a first machine learning model is used to generate a text vector that represents a numerical representation of the one or more embedded text characters. A second machine learning model is used to generate an image vector that represents a numerical representation of the graphical portion of the image. The text vector and the image vector are used as input to generate a multi-modal vector that contains information from both the text vector and the image vector. The image may be classified into one of a plurality of image classifications based upon the information in the multi-modal vector.

Abstract: A reliability aware negative bit-line write assist (RA-NBL) circuit comprises a coupling capacitor to provide a negative bump for write assist, and a control input generator control charging of the coupling capacitor, such that the negative bump is high at a low voltage, and the negative bump is low at a high voltage.

Abstract: A reliability aware negative bit-line write assist (RA-NBL) circuit comprises a coupling capacitor to provide a negative bump for write assist, and a control input generator control charging of the coupling capacitor, such that the negative bump is high at a low voltage, and the negative bump is low at a high voltage.

Abstract: An image compression circuit includes an encoder configured to compress a current frame and to output current frame compressed data and a current frame bitstream; a decoder configured to decode the previous frame bitstream and to output previous frame compressed data; a frame memory controller configured to write the current frame bitstream to a frame memory and simultaneously read a previous frame bitstream from the frame memory; a dynamic capacitance compensation controller configured to output a previous frame reference value based on the current frame, the current frame compressed data, and the previous frame compressed data; and an overdrive circuit configured to generate a current overdriven frame including an overdrive pixel value for a current pixel based on a pixel value of the current pixel in the current frame and the previous frame reference value.