Suchergebnisse

The study was intended to examine whether gender differences exist with regard to equity sensitivity between Korean female and male workers. Employing equity sensitivity theory to represent gender and individual differences in a collectivistic country such as Korea may allow internationally located organisations and their managers to understand and accommodate different behaviours of individuals in a different culture. Understanding individual and gender differences in a particular culture can enable organisations and managers to design equitable reward systems. A total of 400 survey packets were mailed to two regional universities in Korea. A total of 380 survey packages were returned, and 374 surveys were useable for data analysis. The results of the study illustrated that gender differences in equity sensitivity exist in Korea. Korean workers felt more entitled than did workers from other countries in earlier studies. The significant finding was that Korean female students felt more entitled than did Korean male participants and their behaviours as 'entitleds' was consistent with the dimensions of equity sensitivity theory.

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database. ; This research was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00655). This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion). This research was supported by the MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2017-0-00093).