Abstract: This method for determining a skin colour of a region of interest of a user, comprises: —importing, from an image library (LIB) of a user computational device (APP), at least one image (IMk) comprising a representation of the region of interest of the user; —performing processing (TS) of said at least one image (IMk) with a machine-learning model (AI_TS) suitable for providing, for each imported image (IMk), a numerical value (ClrEstm_k) representative of the skin colour of the region of interest present in said at least None imported image (IMk); —performing evaluation (EVAL) of the skin colour of the region of interest on the basis of said one or more numerical values (ClrEstm_k) of each imported image.

Abstract: The invention relates to a network entity for controlling a respective operation mode of a plurality of physical base stations of a wireless communication network 100. The network entity comprises processing circuitry configured to: obtain a respective current data load of each of a plurality of physical base stations in an active operation mode; estimate a respective current data load of each of a plurality of virtual base stations based on the respective current data loads of the plurality of physical base stations; predict a respective data load of each of the plurality of virtual base stations; and adjust, based on the respective data loads of the plurality of virtual base stations, the operation mode of one or more of the plurality of physical base stations to the idle operation mode.

Abstract: In some embodiments of the present disclosure, one or more machine learning models are trained to accurately estimate skin color in one or more images regardless of the lighting conditions. In some embodiments, the models can then be used to estimate a skin color in a new image, and that estimated skin color can be used for a variety of purposes. For example, the skin color may be used to generate a recommendation for a foundation shade that accurately matches the skin color, or a recommendation for another cosmetic product that is complimentary with the estimated skin color. Thus, the need for an in-person test of the product is eliminated.

Abstract: The present application is directed to a method and system for determining at least one physical and/or chemical characteristic of a keratinous surface of a user, the method comprising the steps of: —receiving data corresponding to at least one image of the keratinous surface, —processing the image by applying at least one machine learning model to said image, —returning at least one numerical value corresponding to a grade of the characteristic of the keratinous surface to be determined.

Abstract: In some embodiments of the present disclosure, one or more machine learning models are trained to accurately estimate skin color in one or more images regardless of the lighting conditions. In some embodiments, the models can then be used to estimate a skin color in a new image, and that estimated skin color can be used for a variety of purposes. For example, the skin color may be used to generate a recommendation for a foundation shade that accurately matches the skin color, or a recommendation for another cosmetic product that is complimentary with the estimated skin color. Thus, the need for an in-person test of the product is eliminated.

Abstract: In some embodiments of the present disclosure, one or more machine learning models are trained to accurately estimate skin color in one or more images regardless of the lighting conditions. In some embodiments, the models can then be used to estimate a skin color in a new image, and that estimated skin color can be used for a variety of purposes. For example, the skin color may be used to generate a recommendation for a foundation shade that accurately matches the skin color, or a recommendation for another cosmetic product that is complimentary with the estimated skin color. Thus, the need for an in-person test of the product is eliminated.

Abstract: The invention relates to a network entity for controlling a respective operation mode of a plurality of physical base stations of a wireless communication network 100. The network entity comprises processing circuitry configured to: obtain a respective current data load of each of a plurality of physical base stations in an active operation mode; estimate a respective current data load of each of a plurality of virtual base stations based on the respective current data loads of the plurality of physical base stations; predict a respective data load of each of the plurality of virtual base stations; and adjust, based on the respective data loads of the plurality of virtual base stations, the operation mode of one or more of the plurality of physical base stations to the idle operation mode.

Abstract: The present disclosure involves systems, software, and computer implemented methods for monitoring processes running on a platform as a service architecture. One example method includes extracting one or more electronic job postings from a plurality of job posting websites. For each extracted electronic job posting, data associated with the extracted electronic job posting is automatically normalized into a normalized electronic job posting in a structured format. Each normalized electronic job posting is automatically enriched to create an enriched electronic job posting based on data sources external to the job posting websites. Each enriched electronic job posting is stored in a job posting repository.

Abstract: The present disclosure involves systems, software, and computer implemented methods for monitoring processes running on a platform as a service architecture. One example method includes extracting one or more electronic job postings from a plurality of job posting websites. For each extracted electronic job posting, data associated with the extracted electronic job posting is automatically normalized into a normalized electronic job posting in a structured format. Each normalized electronic job posting is automatically enriched to create an enriched electronic job posting based on data sources external to the job posting websites. Each enriched electronic job posting is stored in a job posting repository.