Abstract: This document describes systems, methods, devices, and other techniques for performing content moderation. In some implementations, a computing device receives input data in relation to an electronic document. The computing device generates, based on the received input data, data representing one or more features of the electronic document and analyzes the generated data representing one or more features of the electronic document to determine one or more reliability scores indicating respective measures of reliability of the electronic document. The reliability scores include one or more of (i) a content reliability score, (ii), an author reliability score, and (iii) a domain reliability score. The computing device indicates, based on one or more of the reliability scores, whether the electronic document is reliable or not.

Abstract: There is a need for solutions for more efficient predictive data analysis systems. This need can be addressed, for example, by a system configured to obtain, for each predictive task of a plurality of predictive tasks, a plurality of per-model inferences; generate, for each predictive task, a cross-model prediction based on the plurality of per-model inferences for the predictive task; and generate, based on each cross-model prediction associated with a predictive task, a cross-prediction for the particular predictive task, wherein determining the cross-prediction comprises applying one or more probabilistic updates to the cross-model prediction for the particular predictive task and each probabilistic update is determined based on the cross-model prediction for a related predictive task of the one or more related predictive tasks.

Abstract: Provided is a method for correcting errors in a data transmission network, comprising: transmitting a plurality of uncoded information packets across a network path; transmitting a plurality of coded packets for recovering information packets lost in transmission across said network path, the coded packets being temporally interspersed among said uncoded information packets, wherein the coded packets are encoded based on information packets transmitted prior to a previously transmitted coded packet; and determining the interspersion of the coded packets according to a packet loss rate.

Abstract: A method is disclosed for transmitting data between a source node and destination node connected via multiple paths of a heterogeneous network, at least one of the paths delivering packets with a non-deterministic delivery time. Data is divided into frames, each frame comprising a number of packets, where processing by the destination node of an information packet p is conditional on receipt of the data for any information packet i where i<p. A number of sequential transmission slots s, each for transmitting a given packet p of a frame, are allocated for each network path k. A set D of possible assignments of packets to transmission slots for a frame are provided. The method comprises determining an assignment x with minimum in-order delivery delay of said packets based on expected arrival times of packets to their destination and expected reordering delay for packets. Packets are then transmitted in accordance with the determined assignment x.

Abstract: A device receives source data, target data, external data, and a target task, and generates features of and differentiators between the source data and the target data. The device identifies a set of mappings between the source data and the target data based on the features and the differentiators, and determines different clusters of the source data based on the external data, the features, and the differentiators. The device generates, based on the external data, a set of artificial intelligence (AI) models as candidates to perform the target task, and generates a performance measure for the set of AI models based on the features, the differentiators, and the external data. The device refines the set of mappings, and identifies an AI model, from the set of AI models, to perform the target task based on the different clusters of the source data and based on the performance measure.

Abstract: A device receives source data, target data, external data, and a target task, and generates features of and differentiators between the source data and the target data. The device identifies a set of mappings between the source data and the target data based on the features and the differentiators, and determines different clusters of the source data based on the external data, the features, and the differentiators. The device generates, based on the external data, a set of artificial intelligence (AI) models as candidates to perform the target task, and generates a performance measure for the set of AI models based on the features, the differentiators, and the external data. The device refines the set of mappings, and identifies an AI model, from the set of AI models, to perform the target task based on the different clusters of the source data and based on the performance measure.

Abstract: Provided is a method for correcting errors in a data transmission network, comprising: transmitting a plurality of uncoded information packets across a network path; transmitting a plurality of coded packets for recovering information packets lost in transmission across said network path, the coded packets being temporally interspersed among said uncoded information packets, wherein the coded packets are encoded based on information packets transmitted prior to a previously transmitted coded packet; and determining the interspersion of the coded packets according to a packet loss rate.

Abstract: A device receives source data, target data, external data, and a target task, and generates features of and differentiators between the source data and the target data. The device identifies a set of mappings between the source data and the target data based on the features and the differentiators, and determines different clusters of the source data based on the external data, the features, and the differentiators. The device generates, based on the external data, a set of artificial intelligence (AI) models as candidates to perform the target task, and generates a performance measure for the set of AI models based on the features, the differentiators, and the external data. The device refines the set of mappings, and identifies an AI model, from the set of AI models, to perform the target task based on the different clusters of the source data and based on the performance measure.

Abstract: This document describes systems, methods, devices, and other techniques for performing content moderation. In some implementations, a computing device receives input data in relation to an electronic document. The computing device generates, based on the received input data, data representing one or more features of the electronic document and analyzes the generated data representing one or more features of the electronic document to determine one or more reliability scores indicating respective measures of reliability of the electronic document. The reliability scores include one or more of (i) a content reliability score, (ii), an author reliability score, and (iii) a domain reliability score. The computing device indicates, based on one or more of the reliability scores, whether the electronic document is reliable or not.

Abstract: Provided is a method for correcting errors in a data transmission network, comprising: transmitting a plurality of uncoded information packets across a network path; transmitting a plurality of coded packets for recovering information packets lost in transmission across said network path, the coded packets being temporally interspersed among said uncoded information packets, wherein the coded packets are encoded based on information packets transmitted prior to a previously transmitted coded packet; and determining the interspersion of the coded packets according to a packet loss rate.

Abstract: A method is disclosed for transmitting data between a source node and destination node connected via multiple paths of a heterogeneous network, at least one of the paths delivering packets with a non-deterministic delivery time. Data is divided into frames, each frame comprising a number of packets, where processing by the destination node of an information packet p is conditional on receipt of the data for any information packet i where i<p. A number of sequential transmission slots s, each for transmitting a given packet p of a frame, are allocated for each network path k. A set D of possible assignments of packets to transmission slots for a frame are provided. The method comprises determining an assignment x with minimum in-order delivery delay of said packets based on expected arrival times of packets to their destination and expected reordering delay for packets. Packets are then transmitted in accordance with the determined assignment x.

Abstract: Provided is a method for correcting errors in a data transmission network, comprising: transmitting a plurality of uncoded information packets across a network path; transmitting a plurality of coded packets for recovering information packets lost in transmission across said network path, the coded packets being temporally interspersed among said uncoded information packets, wherein the coded packets are encoded based on information packets transmitted prior to a previously transmitted coded packet; and determining the interspersion of the coded packets according to a packet loss rate.