Abstract: In some implementations, a system may receive a submission associated with an innovation associated with an entity. The system may analyze, using a first machine learning model, the submission to identify a classification associated with the innovation. The system may analyze, using a second machine learning model in association with the classification, content of the submission to identify a characteristic of the innovation. The system may determine, using a third machine learning model and based on the characteristic, a ranking of the innovation relative to individual innovations in the subset of innovations. The system may determine, using a fourth machine learning model, an impact score associated with the innovation. The system may perform, based on the impact score satisfying a threshold, an action associated with a project involving the innovation.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for quantized machine learning. A quantized input matrix is accessed at a layer of a neural network, and a first interim value is generated in an accumulator by performing matrix multiplication, using the accumulator, of the quantized input matrix and a quantized weight matrix associated with the layer of the neural network. The first interim value is normalized based at least in part on one or more leading sign bits of the first interim value, and the normalized first interim value is dequantized. A second interim value is generated by applying a rounded right-shift operation to the dequantized normalized first interim value, and activation data is generated by applying an activation function to the second interim value.

Abstract: Systems for dynamically transforming data are provided. Database data may be received and ingested into a system. Ingesting the data may include executing one or more first data governance functions, such as data quality evaluation functions, data controls, and the like. The ingested data may then be output for further processing as first processed data and first data governance information may be captured and stored. The first processed data may be processed to execute one or more data transformations. Data transformations may include calculations, formatting, derivations, and the like. In some arrangements, second data governance functions may be executed on the transformed data. The transformed data may then be output as second processed data. The system may capture second data governance information as the data is transformed. The second processed data may then be published to one or more downstream databases for use in one or more applications executed by an entity.

Abstract: Systems for dynamically transforming data are provided. Database data may be received and ingested into a system. Ingesting the data may include executing one or more first data governance functions, such as data quality evaluation functions, data controls, and the like. The ingested data may then be output for further processing as first processed data and first data governance information may be captured and stored. The first processed data may be processed to execute one or more data transformations. Data transformations may include calculations, formatting, derivations, and the like. In some arrangements, second data governance functions may be executed on the transformed data. The transformed data may then be output as second processed data. The system may capture second data governance information as the data is transformed. The second processed data may then be published to one or more downstream databases for use in one or more applications executed by an entity.

Abstract: To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (108). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

Abstract: A recruiting person handling profiles of candidates need thorough knowledge about various technologies related to requirements posted with respect to a job opening, so as to correctly interpret and identify skill level of each candidate. Lack of knowledge of the recruiting person may result in skilled candidates not getting shortlisted and candidates having no or less relevant skills getting selected, which would affect work force of an organization the candidates are being recruited for. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for determining skill similarity by using the data processing. The system automatically identifies skills that match each other, and the recruiting person may use this information to identify and shortlist right candidates for the job. The system generates skill vectors for each skill, and by comparing skill vectors of different skills, identifies skills that are similar to each other.

Abstract: A noise-cancellation system includes a noise-cancellation filter configured to generate a noise-cancellation signal based on a noise signal received from a noise sensor; an actuator disposed at a first location within a predefined volume and configured to receive the noise-cancellation signal and to transduce a noise-cancellation audio signal within the predefined volume; a reference sensor disposed at a second location within the predefined volume and to output a reference sensor signal, the reference sensor signal being representative of an undesired noise at the second location; a filter configured to filter the noise-cancellation signal and the reference sensor signal to output a filter output signal, the filter output signal representing an estimate of the undesired nose at a third location remote from the first location and the second location; and an adjustment module configured to adjust the noise-cancellation filter, based on the filter output signal, such that the noise-cancellation audio signal des

Abstract: A noise-cancellation system includes a noise-cancellation filter configured to generate a noise-cancellation signal based on a noise signal received from a noise sensor; an actuator disposed at a first location within a predefined volume and configured to receive the noise-cancellation signal and to transduce a noise-cancellation audio signal within the predefined volume; a reference sensor disposed at a second location within the predefined volume and to output a reference sensor signal, the reference sensor signal being representative of an undesired noise at the second location; a filter configured to filter the noise-cancellation signal and the reference sensor signal to output a filter output signal, the filter output signal representing an estimate of the undesired nose at a third location remote from the first location and the second location; and an adjustment module configured to adjust the noise-cancellation filter, based on the filter output signal, such that the noise-cancellation audio signal des

Abstract: Systems and methods for replacement planning and management are provided. The method includes processing data specific to one or more users, identifying replacement for a predecessor in a particular position in an entity based on the data, identifying, one or more replacement patterns from replacement pattern datasets across platforms based on one or more attributes associated with the particular position. In an embodiment, one or more attributes may include at least one of records of the one or more users, one or more predecessor successor relationships specific to the particular position for a specified period. The method further includes generating one or more rules based on the identified replacement patterns and domain knowledge pertaining to the particular position, identifying successors across platforms from the one or more users based on the generated rules, and generate a list of plausible successors across platforms from the successors for replacement of the predecessor.

Abstract: To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (108). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

Abstract: This disclosure relates generally to employee assessment, and more particularly to identification of high potential employees. A method and system is provided for identifying employees with high potential. The present method and system for selecting a set of employees for testing and then selecting attributes of the employees using various feature extraction techniques. Thereafter, the method includes combining attributes selected from the feature extraction techniques. After combining the attributes, the method may include utilizing a classification or ranking model to label employees as high and non-high potential employees. Further, the method includes receiving attributes and a reference set of employees from a user. Thereafter, a distance based approach is utilized to identify high potential employees.

Abstract: System(s) and method(s) for generating campus recruitment plan for facilitating selection of a candidate for an organization is disclosed. A ranking score is assigned to each college from a list of colleges. Number of candidates to be selected from each college is determined. A predefined number of panels from a large pool of panels by using demographic details of each employee of the organization are selected. A utility score for each panel of the predefined number of panels by using the demographic details and recruitment capability of each employee is calculated. Based on skill set of each interviewer and each candidate, a panel-candidate matching index is computed. An optimal number of interview panels from the predefined number of panels for interviewing the number of candidates are selected based on the panel-candidate matching index and the utility score to generate the campus recruitment plan by using an optimization technique.