Abstract: A method for classifying network traffic includes accessing processed network data that includes multiple groups. Each group includes multiple URL data objects and is associated with an entity. The method includes generating a dynamic intent score for each group by generating a comparison value for each URL data object within a group, selecting highest comparison values for the URL data objects within the group, generating the dynamic intent score by averaging the selected highest comparison values, and ranking the groups according to their respective dynamic intent scores. The comparison value for each URL data object is generated by scraping a webpage associated with a URL data object to generate a first scraped text data object, creating web embeddings by providing the scraped text data object to a machine learning module, and generating a comparison value by comparing the web embeddings with reference embeddings.

Abstract: A method includes receiving target input data that includes keyword input data and accessing processed network data that includes a plurality of groups. Each of the groups includes a plurality of URL data objects and is associated with an entity. The method includes generating a dynamic intent score for each group by, for each of the URL data objects, extracting keywords from a webpage associated with the URL data object that are similar to keywords of the target input data, comparing the extracted keywords with the target input data, generating a keyword comparison value for the URL data object, and generating the dynamic intent score based on the keyword comparison values. The method includes ranking the groups according to their respective dynamic intent scores, selecting a subset of the groups according to the ranking, and generating a target account list including the entities associated with the subset of the groups.

Abstract: A method including determining, for a given hardware link, whether a signal error rate for signals sent over the given hardware link is beyond a given threshold, when the signal error rate is beyond the given threshold, generating an error indication for the given hardware link, the error indication including a prediction that a hardware component associated with the given hardware link is likely to fail. Related apparatus and methods are also provided.

Abstract: A method including determining, for a given hardware link, whether a signal error rate for signals sent over the given hardware link is beyond a given threshold, when the signal error rate is beyond the given threshold, generating an error indication for the given hardware link, the error indication including a prediction that a hardware component associated with the given hardware link is likely to fail. Related apparatus and methods are also provided.

Abstract: Methods and devices for laser driver calibration are disclosed. The methods and devices disclose determining first and second bit error rates for use in calibrating the laser driver. The methods and devices also disclose that if the first bit error rate associated with a first initial value is above a predetermined bit error rate, increasing the first initial value until the first bit error rate is not above the predetermined bit error rate, and if the second bit error rate associated with a second initial value is above a predetermined bit error rate, decreasing the second initial value until the second bit error rate is not above the predetermined bit error rate. In addition, the methods and devices disclose setting a calibrated parameter for the laser driver based, at least in part, on the increased first initial value and the decreased second initial value.

Abstract: Methods and devices for laser driver calibration are disclosed. The methods and devices disclose determining first and second bit error rates for use in calibrating the laser driver. The methods and devices also disclose that if the first bit error rate associated with a first initial value is above a predetermined bit error rate, increasing the first initial value until the first bit error rate is not above the predetermined bit error rate, and if the second bit error rate associated with a second initial value is above a predetermined bit error rate, decreasing the second initial value until the second bit error rate is not above the predetermined bit error rate. In addition, the methods and devices disclose setting a calibrated parameter for the laser driver based, at least in part, on the increased first initial value and the decreased second initial value.