Abstract: A method for detecting bias in an evaluation process is provided. The method includes operations of receiving evaluation data from a candidate evaluation system. The evaluation data is provided by a set of evaluators based on digital interview data collected from evaluation candidates. The operations of the method further include extracting indicators of characteristics of the evaluation candidates from the digital interview data, classifying the evaluation candidates based on the indicators extracted from the digital interview data, and determining whether the evaluation data indicates a bias of one or more evaluators with respect to a classification of the evaluation candidates.

Abstract: Described herein are methods and systems for interview question or prompt recommendation and analysis to improve the quality and efficacy of subsequent evaluation campaigns by combining data sets are described herein. In one method, processing logic selects a first prompt from a first data set of a first candidate evaluation campaign and a second prompt from a second data set of a second candidate evaluation campaign. The processing logic determines whether a degree of similarity between the first prompt and the second prompt exceeds a threshold and combines data from the first data set with data from the second data set to create a combined data set associated with the first prompt and with the second prompt based on the determination.

Abstract: Methods and systems for model-driven candidate sorting for evaluating digital evaluations are described. In one embodiment, a sorting tool selects a data set of digital evaluation data for sorting. The data set includes candidate for evaluation candidates. The sorting tool analyzes the candidate data for the respective evaluation candidate to identify digital evaluation cues and applies the digital evaluation cues to a prediction model to predict an achievement index for the respective evaluation candidate. The list of evaluation candidates is sorted according the predicted achievement indices and the sorted list is presented to the reviewer in a user interface.

Abstract: A method for detecting bias in an evaluation process is provided. The method includes operations of receiving evaluation data from a candidate evaluation system. The evaluation data is provided by a set of evaluators based on digital interview data collected from evaluation candidates. The operations of the method further include extracting indicators of characteristics of the evaluation candidates from the digital interview data, classifying the evaluation candidates based on the indicators extracted from the digital interview data, and determining whether the evaluation data indicates a bias of one or more evaluators with respect to a classification of the evaluation candidates.

Abstract: Described herein are methods and systems for interview question or prompt recommendation and analysis to improve the quality and efficacy of subsequent evaluation campaigns by combining data sets are described herein. In one method, processing logic selects a first prompt from a first data set of a first candidate evaluation campaign and a second prompt from a second data set of a second candidate evaluation campaign. The processing logic determines whether a degree of similarity between the first prompt and the second prompt exceeds a threshold and combines data from the first data set with data from the second data set to create a combined data set associated with the first prompt and with the second prompt based on the determination.

Abstract: Methods and systems for model-driven candidate sorting for evaluating digital evaluations are described. In one embodiment, a sorting tool selects a data set of digital evaluation data for sorting. The data set includes candidate for evaluation candidates. The sorting tool analyzes the candidate data for the respective evaluation candidate to identify digital evaluation cues and applies the digital evaluation cues to a prediction model to predict an achievement index for the respective evaluation candidate. The list of evaluation candidates is sorted according the predicted achievement indices and the sorted list is presented to the reviewer in a user interface.

Abstract: A method for facilitating the evaluation of candidates in an evaluation process is provided. The method includes operations of selecting a first data set including ratings of recorded responses of a set of evaluated persons and of selecting a second data set including additional recorded responses of a person under evaluation. The recorded responses are responsive to multiple prompts presented to each of the set evaluated persons in a recording sequence. The first data set further includes an evaluation result of each evaluated person in the evaluation. The additional recorded responses are responsive to the plurality of prompts and ordered in a first order corresponding to a recording sequence. The method includes analyzing the first data set to calculate a reviewing sequence and presenting the additional recorded responses in a second order corresponding to the reviewing sequence. Associated systems and other methods are also provided.

Abstract: A method for facilitating the evaluation of candidates in an evaluation process is provided. The method includes operations of receiving a set of evaluation data from a candidate evaluation system, the set of evaluation data including at least evaluation ratings data and evaluation recommendation data for a set of candidate. The method further includes providing a user interface to a decision maker to display a performance of each of a set of candidates in a two-dimensional graphic representation. The method further includes receiving a first weighting for a first subset of the evaluation data and updating the graphic representation to show a weighted performance of each of the candidates in the evaluation. The updating is based on the first weighting received from the decision maker.

Abstract: Methods and systems for model-driven candidate sorting for evaluating digital interviews are described. In one embodiment, a model-driven candidate-sorting tool selects a data set of digital interview data for sorting. The data set includes candidate for interviewing candidates (also referred to herein as interviewees). The model-driven candidate-sorting tool analyzes the candidate data for the respective interviewing candidate to identify digital interviewing cues and applies the digital interview cues to a prediction model to predict an achievement index for the respective interviewing candidate. This is performed without reviewer input at the model-driven candidate-sorting tool. The list of interview candidates is sorted according the predicted achievement indices and the sorted list is presented to the reviewer in a user interface.

Abstract: Methods and systems for model-driven candidate sorting based on audio cues for evaluating digital interviews are described. In one embodiment, a processing device converts audio data of a candidate in a digital interview into voice-to-text data. An audio cue generator executing on the processing device, generates audio cues of the digital interview based on the voice-to-text data. The audio cues are applied to a prediction model to predict an achievement index for the candidate based on the audio cues. The processing device displays the candidate in a list of candidates based on the achievement index. The list of candidates is sorted according to the candidates' achievement index.

Abstract: Methods and systems for model-driven candidate sorting based on audio cues for evaluating digital interviews are described. In one embodiment, an audio cue generator identifies utterances in audio data of a digital interview. The utterances each include a group of one or more words spoken by a candidate in the digital interview. The audio cue generator generates audio cues of the digital interview based on the identified utterances. The audio cues are applies to a prediction model to predict an achievement index for the candidate based on the audio cues. The candidate is displayed in a list of candidates based on the achievement index. The list of candidates is sorted according to the candidates' achievement index.

Abstract: A system and method are provided of minimizing network bandwidth used from an external network by client peers in a local network. The method can include the operation of organizing a plurality of clients each having media streamlets and a client parent in a structure within the local network. Mapping information propagates through the structure. The mapping information represents local streamlet locations as stored by the plurality of clients. Another operation is retrieving a local streamlet from a client identified in the mapping information as having the local streamlet for the requesting client.

Abstract: This document describes tools that enable a switch to temporarily alter its forwarding behavior when statistical data characterizing the switch satisfies a user-specified condition. To do so, the tools may monitor chronological sets of statistical data associated with the switch over a period of time. If the tools determine at one point during the period of time that one set of statistical data satisfies the user-specified condition, the tools alter the forwarding behavior of the switch for the remainder of the period of time. At the conclusion of the period of time, the tools restore the original forwarding behavior to the switch.

Abstract: A system and method are provided of minimizing network bandwidth used from an external network by client peers in a local network. The method can include the operation of organizing a plurality of clients each having media streamlets and a client parent in a structure within the local network. Mapping information propagates through the structure. The mapping information represents local streamlet locations as stored by the plurality of clients. Another operation is retrieving a local streamlet from a client identified in the mapping information as having the local streamlet for the requesting client.

Abstract: An apparatus and method to provide a portable networking interface for distributed switching systems. Two Application Program Interfaces (APIs) are defined for communication to a Forwarding Database Distribution Library (FDDL). The FDDL sits between network client applications and the switch device driver in order to provide a uniform interface to the switch device driver. Towers may be added to the FDDL to provide additional functionality specific to certain client applications.

Abstract: An apparatus and method to provide a portable networking interface for distributed switching systems. Two Application Program Interfaces (APIs) are defined for communication to a Forwarding Database Distribution Library (FDDL). The FDDL sits between network client applications and the switch device driver in order to provide a uniform interface to the switch device driver. Towers may be added to the FDDL to provide additional functionality specific to certain client applications.