Abstract: A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.

Abstract: A device may receive data related to operations of a plurality of managed devices. The device may determine, after receiving the data, a multi-entity profile for the data. The device may determine, using the multi-entity profile, a set of sub-models for the data after determining the multi-entity profile. The set of sub-models may be associated with processing the data in a contextualized manner. The device may generate a model based on the set of sub-models. The device may perform one or more actions related to the plurality of managed devices or the at least one alert based on respective scores associated with the plurality of managed devices after generating the model.

Abstract: A device may receive utility usage data for multiple buildings across multiple locations. The device may process the utility usage data using a first set of models associated with performing at least one of: an intra-building anomaly detection for the utility usage data, a first grouping of the utility usage data based on characteristics of the utility usage data, or a second grouping of the utility usage data based on the multiple locations. The device may process first output from the first set of models using a second set of models associated with pre-processing the first output in association with identifying anomalies in the first grouping or in the second grouping. The device may process the first output and second output from the second set of models using a super model associated with identifying the anomalies. The device may perform, based on the score, one or more actions.

Abstract: A device may receive data related to operations of a plurality of managed devices. The device may determine, after receiving the data, a multi-entity profile for the data. The device may determine, using the multi-entity profile, a set of sub-models for the data after determining the multi-entity profile. The set of sub-models may be associated with processing the data in a contextualized manner. The device may generate a model based on the set of sub-models. The device may perform one or more actions related to the plurality of managed devices or the at least one alert based on respective scores associated with the plurality of managed devices after generating the model.

Abstract: A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.

Abstract: Embodiments of the present invention relate to the field of communications, and in particular, disclose a method and a device for generating and receiving an OOFDM signal, and a wavelength-division multiplexing system. The method for generating an OOFDM signal includes: converting a channel of serial high-speed data to N channels of parallel low-speed data; performing modulation mapping on the N channels of parallel low-speed data respectively to obtain N channels of information sequences; expanding the N channels of information sequences to 2N+2 channels of information sequences which have a Hermitian symmetric structure; performing inverse Fast Fourier Transform on the 2N+2 channels of information sequences, and then performing parallel-to-serial conversion to obtain an OFDM baseband signal; performing digital-to-analog conversion on the OFDM baseband signal to obtain an OFDM analog signal; and modulating the OFDM analog signal to an optical carrier to obtain an OOFDM signal.

Abstract: A method of expressing auxin synthetase gene specifically in cotton seed coat and fiber, which comprises constructing plant expression vector capable of expressing auxin synthetase gene specifically by fusing a tissue-specific promoter with an auxin synthetase gene, and then integrating the construct into a cotton genome. The method can significantly improve the yield and the quality of cotton fiber, thereby providing fiber with high quality for textile industry.