Abstract: Providing content from network servers by receiving a data request from a client performing an internet search on a search engine based on the data request, accessing URLs relevant to the request resulting from the search, inputting parsed text from the accessed URLs into a software model configured to identify terms of use in the URL, accessing a robots.txt of the URL to receive collection rules that define terms for collecting data in the URL, sending a data request to the URL, where the request includes the details of the requesting entity and an opt-out option for the URL, and collecting the data in the URLs.

Abstract: Providing content from network servers by receiving a data request from a client performing an internet search on a search engine based on the data request, accessing URLs relevant to the request resulting from the search, inputting parsed text from the accessed URLs into a software model configured to identify terms of use in the URL, accessing a robots.txt of the URL to receive collection rules that define terms for collecting data in the URL, sending a data request to the URL, where the request includes the details of the requesting entity and an opt-out option for the URL, and collecting the data in the URLs.

Abstract: A Diagnostic Trouble Code (DTC) rulebook generation system, the DTC rulebook generation system comprising a processing circuitry configured to: obtain: telematics trace data records and malfunction occurrence data records; and extract one or more ride records from the obtained telematics trace data records and the obtained malfunction occurrence data records, wherein at least one ride record of the ride records is for a given vehicle; label at least one ride record of the ride records as a healthy ride or a faulty ride, wherein a ride record associated with a given vehicle where no malfunction occurrence data records are associated with the given vehicle is labeled as a healthy ride, otherwise the ride record is labeled as a faulty ride; train one or more machine learning models on one or more subsets of the labeled ride records; determine one or more DTC rules utilizing at least one of the trained machine learning models.

Abstract: A system for delivering content over a communication, including an interface for receiving a request for transferring content, where the request specifies optimization types for transferring the content, multiple control units, where each control unit maintains an open communication channel with delivery peers that communicate with destination servers, and a peer manager communicating with the multiple control units and with the interface, the peer manager configured for selecting a specific delivery peer according to the optimization types and connection parameters included in the request and a set of rules, and sending a command to a selected control unit which communicates with the specific delivery peer selected to transfer the content from a specific destination server of the multiple destination servers, where the selected delivery peer transfers the content from a specific destination server.

Abstract: A model configuration selection system, the model configuration selection system comprising a processing circuitry configured to: (A) obtain: (a) one or more model configurations, each model configuration includes a set of parameters utilized to generate respective models, and (b) a training data-set comprising a plurality of unlabeled records, each unlabeled record including a collection of features describing a given state of a physical entity; (B) cluster the training data-set into two or more training data-set clusters using a clustering algorithm; (C) label (a) the unlabeled records of a subset of the training data-set clusters with a synthetic normal label, giving rise to a normal training data-set, and (b) the unlabeled records of the training data-set clusters not included in the subset with a synthetic abnormal label; (D) train, for each model configuration, using the normal training data-set, a corresponding model utilizing the corresponding set of parameters, each model capable of receiving the unl

Abstract: A system for delivering content over a communication, including an interface for receiving a request for transferring content, where the request specifies optimization types for transferring the content, multiple control units, where each control unit maintains an open communication channel with delivery peers that communicate with destination servers, and a peer manager communicating with the multiple control units and with the interface, the peer manager configured for selecting a specific delivery peer according to the optimization types and connection parameters included in the request and a set of rules, and sending a command to a selected control unit which communicates with the specific delivery peer selected to transfer the content from a specific destination server of the multiple destination servers, where the selected delivery peer transfers the content from a specific destination server.

Abstract: A system comprising a processing circuitry configured to: obtain: (a) one or more metadata representations, each representing metadata relating to a signal type of one or more signal types; (b) one or more signal sequences, each signal sequence is an ordered sequence of values associated with a given signal type of the one or more signal types; and (c) at least one new signal sequence, being a new ordered sequence of values, each associated with a label, the at least one new signal sequence is associated with corresponding at least one new signal type, not included in the one or more signal types; train a meta learner autoencoder, capable of mapping at least one given signal sequence and at least one respective metadata representation, being the metadata representation representing the signal type of the given signal sequence into a meta representation vector, wherein the trained meta learner autoencoder comprises a meta learner encoder and a meta learner decoder; determine, based on the metadata representati

Abstract: A model configuration selection system, the model configuration selection system comprising a processing circuitry configured to: (A) obtain: (a) one or more model configurations, each model configuration includes a set of parameters utilized to generate respective models, and (b) a training data-set comprising a plurality of unlabeled records, each unlabeled record including a collection of features describing a given state of a physical entity; (B) cluster the training data-set into two or more training data-set clusters using a clustering algorithm; (C) label (a) the unlabeled records of a subset of the training data-set clusters with a synthetic normal label, giving rise to a normal training data-set, and (b) the unlabeled records of the training data-set clusters not included in the subset with a synthetic abnormal label; (D) train, for each model configuration, using the normal training data-set, a corresponding model utilizing the corresponding set of parameters, each model capable of receiving the unl

Abstract: The present invention comprises a system and method for reduction of the influence of crosstalk, increase in and control over quality of service, increase in stability and reduction of power use in a system having multiple transmission lines. A novel crosstalk measurement method is introduced. Knowing the crosstalk, various algorithms may be employed, for example to reduce or eliminate its effects in order to guarantee a bit error rate equal to or less than the maximum allowed for each line. Similar methods are provided to minimize power consumption, or maximize related measures of line performance. Systems, devices, methods and techniques are provided that allow communication system to adapt transmission power margin, power spectral densities, and the like dynamically to changing subscriber's application needs in MIMO systems.