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370 results on '"Sun, Zeyu"'

41. Fairness Testing of Machine Translation Systems.

Author

Sun, Zeyu, Chen, Zhenpeng, Zhang, Jie, and Hao, Dan

Subjects
MACHINE translating, FAIRNESS, INTERNATIONAL communication
Abstract

Machine translation is integral to international communication and extensively employed in diverse human-related applications. Despite remarkable progress, fairness issues persist within current machine translation systems. In this article, we propose FairMT, an automated fairness testing approach tailored for machine translation systems. FairMT operates on the assumption that translations of semantically similar sentences, containing protected attributes from distinct demographic groups, should maintain comparable meanings. It comprises three key steps: (1) test input generation, producing inputs covering various demographic groups; (2) test oracle generation, identifying potential unfair translations based on semantic similarity measurements; and (3) regression, discerning genuine fairness issues from those caused by low-quality translation. Leveraging FairMT, we conduct an empirical study on three leading machine translation systems–Google Translate, T5, and Transformer. Our investigation uncovers up to 832, 1,984, and 2,627 unfair translations across the three systems, respectively. Intriguingly, we observe that fair translations tend to exhibit superior translation performance, challenging the conventional wisdom of a fairness-performance tradeoff prevalent in the fairness literature. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Improved Information Fusion for Agricultural Machinery Navigation Based on Context-Constrained Kalman Filter and Dual-Antenna RTK.

Author

Cui, Bingbo, Zhang, Jianxin, Wei, Xinhua, Cui, Xinyu, Sun, Zeyu, Zhao, Yan, and Liu, Yufei

Abstract

Automatic navigation based on dual-antenna real-time kinematic (RTK) positioning has been widely employed for unmanned agricultural machinery, whereas GNSS inevitably suffers from signal blocking and electromagnetic interference. In order to improve the reliability of an RTK-based navigation system in a GNSS-challenged environment, an integrated navigation system is preferred for autonomous navigation, which increases the complexity and cost of the navigation system. The information fusion of integrated navigation has been dominated by Kalman filter (KF) for several decades, but the KF cannot assimilate the known knowledge of the navigation context efficiently. In this paper, the geometric characteristics of the straight path and path-tracking error were employed to formulate the constraint measurement model, which suppresses the position error in the case of RTK-degraded scenarios. The pseudo-measurements were then imported into the KF framework, and the smoothed navigation state was generated as a byproduct, which improves the reliability of the RTK positioning without external sensors. The experiment result of the mobile vehicle automatic navigation indicates that the tracking error-constrained KF (EC-KF) outperforms the trajectory-constrained KF (TC-KF) and KF when the RTK system outputs a float or single-point position (SPP) solution. In the case where the duration of the SPP solution was 20 s, the positioning errors of the EC-KF and TC-KF were reduced by 38.50% and 24.04%, respectively, compared with those of the KF. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Optimization Cooperative Coverage Algorithm with Controllable Threshold- Parameters in WSNs

Author

SUN Zeyu, LI Chuanfeng, XING Xiaofei, LAI Chunxiao

Subjects
wireless sensor networks (wsns), controllable threshold-parameters, optimization cooperative coverage, coverage rate, network lifetime, Electronic computers. Computer science, QA75.5-76.95
Abstract

The coverage of traditional wireless sensor networks (WSNs) is limited by node energy and data redundancy, forcing WSNs to be interrupted abnormally. To this end, a novel optimization cooperative coverage algorithm with controllable threshold-parameters (OCC-CT) is proposed. The algorithm first determines the location information of the focus target nodes (FTNs), and uses the genetic algorithm (GA) to give the node path planning. Second, the controllable threshold parameters and mutation parameters are used to control event domain nodes. Clustering is optimized to make the clustering of the nodes more uniform, so as to reduce the energy consumption of the nodes and improve the search ability of the global target nodes. Third, the adaptation function is used to cover the continuity of the covered target locations and the monitoring range of the nodes. Optimization is performed to achieve the goals of increasing network coverage and extending the network lifetime. Finally, simulation results show that the OCC-CT algorithm, compared with other three algorithms in this paper, improves 0.11 and 0.16 on average in terms of network coverage, network lifetime, and 0.14 in terms of network energy overhead, thereby further verifying OCC-CT algorithm has strong stability and effectiveness.

Published
2021
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