Abstract: An apparatus for a communication device, the apparatus may include a processor configured to: obtain channel metrics for a plurality of radio communication channels, each obtained channel metric is associated with a respective radio communication channel of the plurality of radio communication channels, generate a plurality of channel hopping sequences, each channel hopping sequence is representative of an allocation of the plurality of radio communication channels for a plurality of time slots, wherein a number of time slots allocated for each radio communication channel within each channel hopping sequence is based on the respective obtained channel metric, and select one of the plurality of channel hopping sequences based on a predefined criterion to communicate with a further communication device.

Abstract: The current invention relates to a computer-implemented method for determining a category of a request provided by a user by means of a user device; said user device comprising connection means and means for receiving a request description relating to said request from said user; the method comprising a plurality of steps among which: receiving, from said user, said request description, by means of said device; uploading said request description to a server, said server having access to a database comprising a plurality of previously categorized requests each comprising a category and a vocabulary comprising a plurality of word vector representations; identifying, by said server, a plurality of component words belonging to a natural language text string comprised in said request description; obtaining, for at least one of said plurality of component words, an associated word vector representation from said vocabulary; and determining a request vector, based on said at least one obtained word vector representa

Abstract: A device may receive historic temporal data identifying events associated with a system, and may perform block bootstrapping of the hierarchical time series data, based on a hyperparameters, to generate blocks of data points of the historic time series data. The device may process the blocks of data points, with a plurality of different machine learning models, to calculate predictions, and may apply weights to the predictions to generate weighted predictions. The device may aggregate the weighted predictions to generate aggregated predictions, and may apply final weights to the aggregated predictions to generate weighted aggregated predictions. The device may aggregate the weighted aggregated predictions to generate a final prediction, and may perform one or more actions based on the final prediction.

Abstract: A device may receive historic temporal data identifying events associated with a system, and may perform block bootstrapping of the hierarchical time series data, based on a hyperparameters, to generate blocks of data points of the historic time series data. The device may process the blocks of data points, with a plurality of different machine learning models, to calculate predictions, and may apply weights to the predictions to generate weighted predictions. The device may aggregate the weighted predictions to generate aggregated predictions, and may apply final weights to the aggregated predictions to generate weighted aggregated predictions. The device may aggregate the weighted aggregated predictions to generate a final prediction, and may perform one or more actions based on the final prediction.

Abstract: The current invention relates to a computer-implemented method for determining a category of a request provided by a user by means of a user device; said user device comprising connection means and means for receiving a request description relating to said request from said user; the method comprising a plurality of steps among which: receiving, from said user, said request description, by means of said device; uploading said request description to a server, said server having access to a database comprising a plurality of previously categorized requests each comprising a category and a vocabulary comprising a plurality of word vector representations; identifying, by said server, a plurality of component words belonging to a natural language text string comprised in said request description; obtaining, for at least one of said plurality of component words, an associated word vector representation from said vocabulary; and determining a request vector, based on said at least one obtained word vector representa

Abstract: A computer-implemented method determines a category of a request provided by a user by means of a user device. The user device includes connection means and means for receiving a request description relating to said request from said user. The method includes receiving, from the user, the request description, by means of the device, and uploading the request description to a server. The server has access to a database which includes a number of previously categorized requests each including a category and a vocabulary, which includes a number of word vector representations. The method further includes identifying, by the server, a number of component words belonging to a natural language text string included in the request description; obtaining, for at least one of the component words, an associated word vector representation from the vocabulary, and determining a request vector, based on at least one obtained word vector representation.

Abstract: Systems, methods and computer readable media for detecting customization of an application running on a customer's environment are described. An application's original source can maintain a master hash registry for an application. The master hash registry includes valid and invalid hash codes for all objects in the application across all versions of the application. This master hash registry may be provided to the customer. A customization detection system loads a master hash registry to memory. The customization detection system may then retrieve an application object from the application, generate hash values for the object and compare these values with the object's master hash registry values to determine whether the application object is new or whether it has been customized in a supportable or unsupportable manner. The customization detection system may then set the object's customization status based on the results of the comparison.

Abstract: Systems, methods and computer readable media for detecting customization of an application running on a customer's environment are described. An application's original source can maintain a master hash registry for an application. The master hash registry includes valid and invalid hash codes for all objects in the application across all versions of the application. This master hash registry may be provided to the customer. A customization detection system loads a master hash registry to memory. The customization detection system may then retrieve an application object from the application, generate hash values for the object and compare these values with the object's master hash registry values to determine whether the application object is new or whether it has been customized in a supportable or unsupportable manner. The customization detection system may then set the object's customization status based on the results of the comparison.