Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

Abstract: The described technology is generally directed towards a machine learning feature recommender, for use in connection with a feature store. By collecting data and recommending machine learning features to users based on collected data, embodiments can facilitate data scientists' discovery of features that have been used by their colleagues and that are likely to make their machine learning models more performant. The disclosed machine learning feature recommender can reduce the effort involved in developing machine learning models.

Abstract: A method, computer-readable medium, and apparatus for classifying IP network traffic are disclosed. For example, a method may include a processor for applying a regularized probabilistic model to internet protocol network traffic for performing geolocation, wherein the internet protocol network traffic contains internet protocol address information, detecting an outlier in the internet protocol network traffic, wherein the outlier is associated with a transaction over a network, and performing a remedial action to address the outlier.

Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: A trust-less system for sharing event information among autonomous nodes may include: a plurality of autonomous nodes, each of which creates an event related to a condition, a first ledger configured to collect and store a record of each event; a second ledger configured to store reputation information for the plurality of autonomous nodes; and a bookmaker module operable to determine a reward and a penalty for each event. The bookmaker module may be operable to modify the reputation information based on a status of the condition determined by subsequent events created by autonomous nodes where the subsequent events either validate or invalidate the condition.

Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: A trust-less system for sharing event information among autonomous nodes may include: a plurality of autonomous nodes, each of which creates an event related to a condition, a first ledger configured to collect and store a record of each event; a second ledger configured to store reputation information for the plurality of autonomous nodes; and a bookmaker module operable to determine a reward and a penalty for each event. The bookmaker module may be operable to modify the reputation information based on a status of the condition determined by subsequent events created by autonomous nodes where the subsequent events either validate or invalidate the condition.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving first user-generated input from a recruiter device. The recruiter device is associated with a manager of a group. Further embodiments include generating a first smart contract between an applicant and the manager according to the first user-generated input. The generating of the first smart contract comprises generating the first smart contract on a blockchain. Additional embodiments can include providing the applicant access to the group according to the first smart contract, receiving second user-generated input from the recruiter device, and generating a second smart contract between a record keeper and the manager according to the second user-generated input. The generating of the second smart contract comprises generating the second smart contract on the blockchain. Also, embodiments can include providing the record keeper access to the group according to the second smart contract. Other embodiments are disclosed.

Abstract: In a dataset exchange environment in which datasets are available for exchange or transformation, a dataset validation platform may be configured to update a cryptographically signed record based on each dataset that is available via the data exchange environment. The dataset validation platform may be further configured to control access to the datasets based on whether a request to access a particular dataset is compliant with an availability requirement of the particular dataset. The dataset validation platform may be further configured to update the cryptographically signed record based on requests to access the datasets, transformations that are based on the datasets, or modifications to the availability requirement of the datasets, such as a modification to a privacy limitation or other availability requirement indicating a criteria for usage of the requested dataset.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving first user-generated input from a recruiter device. The recruiter device is associated with a manager of a group. Further embodiments include generating a first smart contract between an applicant and the manager according to the first user-generated input. The generating of the first smart contract comprises generating the first smart contract on a blockchain. Additional embodiments can include providing the applicant access to the group according to the first smart contract, receiving second user-generated input from the recruiter device, and generating a second smart contract between a record keeper and the manager according to the second user-generated input. The generating of the second smart contract comprises generating the second smart contract on the blockchain. Also, embodiments can include providing the record keeper access to the group according to the second smart contract. Other embodiments are disclosed.

Abstract: A trust-less system for sharing event information among autonomous nodes may include: a plurality of autonomous nodes, each of which creates an event related to a condition, a first ledger configured to collect and store a record of each event; a second ledger configured to store reputation information for the plurality of autonomous nodes; and a bookmaker module operable to determine a reward and a penalty for each event. The bookmaker module may be operable to modify the reputation information based on a status of the condition determined by subsequent events created by autonomous nodes where the subsequent events either validate or invalidate the condition.

Abstract: A method, computer-readable medium, and apparatus for classifying IP network traffic are disclosed. For example, a method may include a processor for applying a regularized probabilistic model to internet protocol network traffic for performing geolocation, wherein the internet protocol network traffic contains internet protocol address information, detecting an outlier in the internet protocol network traffic, wherein the outlier is associated with a transaction over a network, and performing a remedial action to address the outlier.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving first user-generated input from a recruiter device. The recruiter device is associated with a manager of a group. Further embodiments include generating a first smart contract between an applicant and the manager according to the first user-generated input. The generating of the first smart contract comprises generating the first smart contract on a blockchain. Additional embodiments can include providing the applicant access to the group according to the first smart contract, receiving second user-generated input from the recruiter device, and generating a second smart contract between a record keeper and the manager according to the second user-generated input. The generating of the second smart contract comprises generating the second smart contract on the blockchain. Also, embodiments can include providing the record keeper access to the group according to the second smart contract. Other embodiments are disclosed.