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339 results on '"Wu, Shijun"'

7. Comprehensive Integration of API Usage Patterns

Author

Shen, Qi, Wu, Shijun, Zou, Yanzhen, and Xie, Bing

Subjects
Computer Science - Software Engineering
Abstract

Nowadays, developers often reuse existing APIs to implement their programming tasks. A lot of API usage patterns are mined to help developers learn API usage rules. However, there are still many missing variables to be synthesized when developers integrate the patterns into their programming context. To deal with this issue, we propose a comprehensive approach to integrate API usage patterns in this paper. We first perform an empirical study by analyzing how API usage patterns are integrated in real-world projects. We find the expressions for variable synthesis is often non-trivial and can be divided into 5 syntax types. Based on the observation, we promote an approach to help developers interactively complete API usage patterns. Compared to the existing code completion techniques, our approach can recommend infrequent expressions accompanied with their real-world usage examples according to the user intent. The evaluation shows that our approach could assist users to integrate APIs more efficiently and complete the programming tasks faster than existing works., Comment: 11 pages, Accepted to the 29th IEEE/ACM International Conference on Program Comprehension

Published
2021

8. Design of an Autonomous Underwater Vehicle for Targeted Water Sampling

Author

Wu, Shijun, Zhang, Wenbo, Wang, Xun, Fu, Xiaoyang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor

Published
2023
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9. From API to NLI: A New Interface for Library Reuse

Author

Shen, Qi, Wu, Shijun, Zou, Yanzhen, Zhu, Zixiao, and Xie, Bing

Subjects
Computer Science - Software Engineering
Abstract

Developers frequently reuse APIs from existing libraries to implement certain functionality. However, learning APIs is difficult due to their large scale and complexity. In this paper, we design an abstract framework NLI2Code to ease the reuse process. Under the framework, users can reuse library functionalities with a high-level, automatically-generated NLI (Natural Language Interface) instead of the detailed API elements. The framework consists of three components: a functional feature extractor to summarize the frequently-used library functions in natural language form, a code pattern miner to give a code template for each functional feature, and a synthesizer to complete code patterns into well-typed snippets. From the perspective of a user, a reuse task under NLI2Code starts from choosing a functional feature and our framework will guide the user to synthesize the desired solution. We instantiated the framework as a tool to reuse Java libraries. The evaluation shows our tool can generate a high-quality natural language interface and save half of the coding time for newcomers to solve real-world programming tasks., Comment: To be published in the Journal of Systems and Software (JSS); 19 pages, 17 figures

Published
2020

27. NLI2Code: Reusing Libraries with Natural Language Interface

Author

Shen, Qi, Xie, Bing, Zou, Yanzhen, Zhu, Zixiao, Wu, Shijun, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Peng, Xin, editor, Ampatzoglou, Apostolos, editor, and Bhowmik, Tanmay, editor

Published
2019
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39. Real-time detection of deep-sea hydrothermal plume based on machine vision and deep learning

Author

Wang, Xun, Cao, Yanpeng, Wu, Shijun, and Yang, Canjun

Subjects
Global and Planetary Change, Ocean Engineering, Aquatic Science, Oceanography, Water Science and Technology
Abstract

Recent years have witnessed an increase in applications of artificial intelligence (AI) in the detection of oceanic features with the tremendous success of deep learning. Given the unique biological ecosystems and mineral-rich deposits, the exploration of hydrothermal fields is both scientifically and commercially important. To achieve autonomous and intelligent sampling of the hydrothermal plume by using AUV, this paper proposes an innovative method for real-time plume detection based on the YOLOv5n deep learning algorithm designed with a light-weight neural network architecture to meet the requirements of embedded platforms. Ground truth labeler app LabelImg was used to generate the ground truth data from the plume dataset created by ourselves. To accurately and efficiently detect hydrothermal plumes using an embedded system, we improved the original structure of YOLOv5n in two aspects. First, SiLU activation functions in the model were replaced by ReLU activations at shallow layers and Hard-SiLU activations at deep layers to reduce the number of calculations. Second, an attention module termed Coordinate Attention (CA) was integrated into the model to improve its sensitivity to both channel and spatial features. In addition, a transfer learning training method was adopted to further improve the model’s accuracy and generalizability. Finally, we successfully deployed the proposed model in a low-cost embedded device (NVIDIA Jetson TX2 NX) by using the TensorRT inference engine. We then installed the Jetson TX2 NX into a hovering-type AUV as its vision processing unit and conducted a plume detection test in the water tank. The water tank experimental results demonstrated that the proposed method can achieve real-time onboard hydrothermal plume detection.

Published
2023

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