Abstract: Embodiments are directed to managing skill proficiencies. Declared skills may be determined based on a job description and natural language processing (NLP) actions declared in one or more extraction models. An inference prompt for a large language model (LLM) may be generated based on the job description such that the job description and the declared skills may be included in the inference prompt. The LLM may be trained with the inference prompt to generate a response such that the inference prompt may be iteratively updated based on validations of the response. The LLM may be retrained with the updated inference prompt to generate an updated response that includes the inferred skills that may be separate from the declared skills. A job profile that corresponds to the job description may be updated to include the declared skills and the separate inferred skills.

Abstract: Embodiments are directed to managing courses. Course information for a course may be provided. Skill terms may be determined for the course based on the course information and skill models. Candidate skills may be determined based on the skill terms and a unified skill dictionary such that the skill terms may be mapped to the candidate skills based on the unified skill dictionary. A summary of the course information and the candidate skills may be displayed to a subject matter expert. if the subject matter expert confirms that a candidate skill may be taught by the course, the candidate skill may be associated with a course profile for the course that includes the confirmed candidate skills and proficiency scores for the confirmed skills.

Abstract: Embodiments are directed to managing skill proficiencies by using multiple evaluators to identify overlapping skills. A first job description and a second job description that each respectively describe features of a first job and a second job may be provided. Activities associated with the first job may be determined based on the first job description and activities associated with the second job may be determined based on the second job description. The first activities may be compared with the second activities based on matching models such that each activity that may be included in the first job description and included in the second job description may be considered shared activities. Shared skills may be determined based on the shared activities and may be associated with the job seeker, the first job, the second job, or the like.

Abstract: The present invention provides a fluid measurement apparatus, including a pressure sensing element and a data processing element. The pressure sensing element is disposed in direct contact with a measured fluid, the pressure sensing element is a sealed element, and includes a first surface and a second surface that are disposed opposite to each other, the measured fluid directly impacts the first surface and the second surface, and pressure values generated when the first surface and the second surface are impacted by the measured fluid are detected. The data processing element is connected to the pressure sensing element, and a flow velocity and/or a flow rate of the measured fluid are/is calculated by using the pressure value detected by the pressure sensing element. The present invention resolves a problem that the fluid measurement apparatus cannot operate normally due to clogging of a pressure tap hole.

Abstract: A computing system can receive location data from a computing device of a driver. Based at least in part on the location data, execute a location-based feasibility model to determine that one or more anomalous locational attributes are present, where the location-based feasibility model outputs a probability that the computing device of the respective driver is performing location-spoofing. Based on the probability indicating that the computing device of the driver is performing location-spoofing, the system associates a data set with a driver profile of the respective driver.

Abstract: Embodiments are directed to managing skill proficiencies. Declared skills may be determined based on a job description and natural language processing (NLP) actions declared in one or more extraction models. An inference prompt for a large language model (LLM) may be generated based on the job description such that the job description and the declared skills may be included in the inference prompt. The LLM may be trained with the inference prompt to generate a response such that the inference prompt may be iteratively updated based on validations of the response. The LLM may be retrained with the updated inference prompt to generate an updated response that includes the inferred skills that may be separate from the declared skills. A job profile that corresponds to the job description may be updated to include the declared skills and the separate inferred skills.

Abstract: Embodiments are directed to determining interpersonal or behavioral skills based on course information in course offerings. One or more learning objectives may be determined from course information associated with a course such that each learning objective may be associated with a learning objective narrative. A skills model may be employed to determine one or more soft skills based on the one or more learning objective narratives. The one or more soft skills may be associated with a course profile that corresponds to the course. In response to a quality score associated with the skills model being less than a threshold value, the skills model may be retrained with one or more reference models having one or more portions based on machine learning.

Abstract: Embodiments are directed to managing data for a predictive learner recommendation platform. A platform that includes applications hosted in an application layer may be provided. The applications may be employed to provide a request to determine a pathway prediction for a learner such that the pathway prediction may be associated with a role offered by employers. Prediction engines associated with the request may be determined based on the service layer interface and the request such that the request may be provided to the determined prediction engines via the service layer interface. The prediction engines may be employed to generate the pathway prediction based on a learner profile that corresponds with the learner, a role success profile that corresponds to the employers, and models that are trained to predict matches between the learner profile and the role success profile.

Abstract: Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.

Abstract: The present invention relates to the technical field of optical communications, and relates to an optical amplification method and an amplifier, and in particular, to a self-adaptive wave band amplification method and an amplifier. The present invention consists of a master amplifying unit and a slave amplifying unit, and can autonomously detect the service signal wave band range of an optical transmission line, and according to the detection result, the two amplifying units do not need to perform scheduling or configuration from the level of network management, and perform direct interaction and action from the bottom layer to implement self-adaptive on, off and adjustment in real time. On one hand, power consumption is reduced, and energy is saved; and on the other hand, the performance is optimized, and an optimal optical amplification index is obtained.

Abstract: Embodiments are directed to managing data for unified graph representation of skills and acumen. Information associated with one or more subjects may be classified to provide profile information that conforms to a unified schema. Fields of the profile information may be classified as facts, fact-relationships, actions, skills, or skill-relationships based on the unified schema. A plurality of profile graphs may be generated based on map models and the facts, the fact-relationships, the actions, the skills, or the skill-relationships such that the map models include one or more directives for associating the facts, the fact-relationships, the actions, the skills, or the skill-relationships with one or more nodes or one or more edges in the plurality of profile graphs. In response to query information provided by one or more analysis applications, classifying a portion of the plurality of profile graphs based on the query information.

Abstract: A computing system can receive location data from a computing device of a driver. Based at least in part on the location data, execute a location-based feasibility model to determine that one or more anomalous locational attributes are present, where the location-based feasibility model outputs a probability that the computing device of the respective driver is performing location-spoofing. Based on the probability indicating that the computing device of the driver is performing location-spoofing, the system associates a data set with a driver profile of the respective driver.

Abstract: Embodiments are directed to managing data for unified graph representation of skills and acumen. Information associated with one or more subjects may be classified to provide profile information that conforms to a unified schema. Fields of the profile information may be classified as facts, fact-relationships, actions, skills, or skill-relationships based on the unified schema. A plurality of profile graphs may be generated based on map models and the facts, the fact-relationships, the actions, the skills, or the skill-relationships such that the map models include one or more directives for associating the facts, the fact-relationships, the actions, the skills, or the skill-relationships with one or more nodes or one or more edges in the plurality of profile graphs. In response to query information provided by one or more analysis applications, classifying a portion of the plurality of profile graphs based on the query information.

Abstract: A computing system can receive location data from computing devices of drivers, each of the computing devices operating a designated application associated with an application service. The system can determine a set of locational attributes of a respective driver and determine whether one or more anomalous locational attributes are present in the set of locational attributes of the respective driver. In response to determining that one or more anomalous locational attributes are present, the system can associate a data set with a driver profile of the respective driver.

Abstract: Embodiments are directed to managing courses. Course information for a course may be provided. Skill terms may be determined for the course based on the course information and skill models. Candidate skills may be determined based on the skill terms and a unified skill dictionary such that the skill terms may be mapped to the candidate skills based on the unified skill dictionary. A summary of the course information and the candidate skills may be displayed to a subject matter expert. if the subject matter expert confirms that a candidate skill may be taught by the course, the candidate skill may be associated with a course profile for the course that includes the confirmed candidate skills and proficiency scores for the confirmed skills.

Abstract: Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.

Abstract: Provided are methods of forming a carbon-carbon bond between a first compound and a second compound through a hydroarylation chemical reaction. The methods include contacting the first compound and the second compound in the presence of an acid catalyst. The methods include forming a carbon-carbon bond wherein the first compound includes a first aryl group that is electron-deficient. Provided is a method of generating a quaternary carbon through a hydroarylation chemical reaction.

Abstract: Disclosed are an organic compound based on triazine and benzoxazole and an application thereof in an OLED device. The compound of the present application has a relatively high glass transition temperature and molecular thermal stability, is low in absorption and high in refractive index in the field of visible light, and is capable of effectively improving the light extraction efficiency of an OLED device when applied to a capping layer of the OLED device; with a deep HOMO energy level and high electronic mobility, the compound of the present application can be used as the hole blocking layer or the electron transport layer material, so that the recombination degree of the hole and the electron in the light-emitting layer can be improved, and thus the light-emitting efficiency of the OLED device can be enhanced and the service life of the OLED device can be prolonged.

Abstract: Embodiments are directed to managing data for a predictive learner recommendation platform. A platform that includes applications hosted in an application layer may be provided. The applications may be employed to provide a request to determine a pathway prediction for a learner such that the pathway prediction may be associated with a role offered by employers. Prediction engines associated with the request may be determined based on the service layer interface and the request such that the request may be provided to the determined prediction engines via the service layer interface. The prediction engines may be employed to generate the pathway prediction based on a learner profile that corresponds with the learner, a role success profile that corresponds to the employers, and models that are trained to predict matches between the learner profile and the role success profile.

Abstract: Embodiments are directed to managing data correlation over a network. Student information may be provided. Position information based on potential employers may be provided. Student profiles may be generated based on translation models and the student information. The student information may be translated into unified facts included in the student profiles. Position profiles may be generated based on the translation models and the position information. The position information may be translated into other unified facts in the position profiles. The student profiles may be correlated with the position profiles based on recommendation models, the unified facts, and the other unified facts. Each student profile and position profile pair may be associated with a score based on a strength of the correlation. Reports may be provided that include each pair of the student profile. A plurality of pairs may be ordered based on the score associated with each pair.