Browsing by Subject "Performance evaluation"

Now showing 1 - 4 of 4
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    Diversity-Valuing Behaviors as a Performance Asset instead of a Liability: The Role of DEI Accountability Mechanisms
    (2023-06) Washington, Darius M.; Stockdale, Margaret S.; Derricks, Veronica; Johnson, India R.
    Women and racial minorities are perceived negatively when they engage in diversity-valuing behaviors (i.e., behaviors that promote demographic balance), which increases negative perceptions of their competence and performance effectiveness in modern organizations. Although organizational attention to the topics of workplace equity and inclusion has increased, Black women continue to be excluded from leadership positions motivated by race and sex-based judgments of intellectual inferiority and leadership incongruity. Diversity management continues to be an important research domain to ensure the effective implementation of diversity, equity, and inclusion (DEI) relevant strategies to reduce bias and discrimination. This work infuses accountability for DEI into a performance management system to address the backlash Black women receive for engaging in DEI-relevant behaviors. I used accountability for DEI as a relevant structure to test whether holding employees accountable for diversity-valuing behavior (i.e., promoting DEI goals) through competence mitigates negative performance evaluation and promotion rating of a Black woman. In the current study, MTurk participants (N = 280) with employment experience were surveyed about their evaluation of performance and promotion ratings. Participants were randomly assigned to receive information about dimensions of an employee’s annual evaluation, including “Diversity and Inclusion” (DEI accountability condition) or “Corporate Social Environmental Responsibility” (CSR; control condition). Dependent on participant condition, participants received more information about extra-role behaviors (i.e., diversity-valuing behavior vs. organizational citizenship behaviors) demonstrated by a Black woman. Results were not statistically significant but showed that participants reported more favorable performance evaluation and promotion ratings toward the fictitious employee in the DEI accountability condition who engaged in diversity-valuing behavior compared to the fictitious employee in the CSR (control condition) who engaged in diversity-valuing behavior. These results suggest that organizations that embed a framework for measuring and evaluating DEI efforts among all employees may reduce negative competence perceptions, which in turn, can help mitigate negative performance evaluations and increase promotion ratings among Black women.
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    Implementation and Performance Evaluation of In-vehicle Highway Back-of-Queue Alerting System Using the Driving Simulator
    (IEEE Xplore, 2021-09) Zhang, Zhengming; Shen, Dan; Tian, Renran; Li, Lingxi; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
    This paper proposes a prototype in-vehicle highway back-of-queue alerting system that is based on an Android-based smartphone app, which is capable of delivering warning information to on-road drivers approaching traffic queues. To evaluate the effectiveness of this alerting system, subjects were recruited to participate in the designed test scenarios on a driving simulator. The test scenarios include three warning types (no alerts, roadside alerts, and in-vehicle auditory alerts), three driver states (normal, distracted, and drowsy), and two weather conditions (sunny and foggy). Driver responses related to vehicle dynamics data were collected and analyzed. The results indicate that on average, the drowsy state decreases the minimum time-to-collision by 1.6 seconds compared to the normal state. In-vehicle auditory alerts can effectively increase the driving safety across different combinations of situations (driver states and weather conditions), while roadside alerts perform better than no alerts.
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    mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave
    (IEEE, 2022) Xie, Yucheng; Jiang, Ruizhe; Guo, Xiaonan; Wang, Yan; Cheng, Jerry; Chen, Yingying; Engineering Technology, School of Engineering and Technology
    There is a growing trend for people to perform work-outs at home due to the global pandemic of COVID-19 and the stay-at-home policy of many countries. Since a self-designed fitness plan often lacks professional guidance to achieve ideal outcomes, it is important to have an in-home fitness monitoring system that can track the exercise process of users. Traditional camera-based fitness monitoring may raise serious privacy concerns, while sensor-based methods require users to wear dedicated devices. Recently, researchers propose to utilize RF signals to enable non-intrusive fitness monitoring, but these approaches all require huge training efforts from users to achieve a satisfactory performance, especially when the system is used by multiple users (e.g., family members). In this work, we design and implement a fitness monitoring system using a single COTS mm Wave device. The proposed system integrates workout recognition, user identification, multi-user monitoring, and training effort reduction modules and makes them work together in a single system. In particular, we develop a domain adaptation framework to reduce the amount of training data collected from different domains via mitigating impacts caused by domain characteristics embedded in mm Wave signals. We also develop a GAN-assisted method to achieve better user identification and workout recognition when only limited training data from the same domain is available. We propose a unique spatialtemporal heatmap feature to achieve personalized workout recognition and develop a clustering-based method for concurrent workout monitoring. Extensive experiments with 14 typical workouts involving 11 participants demonstrate that our system can achieve 97% average workout recognition accuracy and 91% user identification accuracy.
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    Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
    (IEEE Xplore, 2021-10) Hu, Qin; Wang, Shengling; Xiong, Zehui; Cheng, Xiuzhen; Computer and Information Science, School of Science
    The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose a collective extortion (CE) strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.