|
[1]P. Zhu, Y. Li, T. Li, W. Yang, and Y. Xu, “GUI Widget Detection and Intent Generation via Image Understanding,” IEEE Access, vol. 9, pp. 160697–160707, 2021, doi: 10.1109/ACCESS.2021.3131753. [2]B. Kang, M. Jo, and C.-S. Jeong, “Clickable Object Detection Network for a Wide Range of Mobile Screen Resolutions,” IEEE Access, vol. 10, pp. 115051–115060, 2022, doi: 10.1109/ACCESS.2022.3202222. [3]F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” 2016, doi: 10.48550/ARXIV.1602.07360. [4]K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. [5]J. Cooley and S. Smith, “Privacy-preserving screen capture: Towards closing the loop for health IT usability,” Journal of Biomedical Informatics, vol. 46, no. 4, pp. 721–733, Aug. 2013, doi: 10.1016/j.jbi.2013.05.007. [6]L. Bao, Z. Xing, X. Xia, D. Lo, M. Wu, and X. Yang, “psc2code: Denoising Code Extraction from Programming Screencasts,” ACM Trans. Softw. Eng. Methodol., vol. 29, no. 3, pp. 1–38, Jul. 2020, doi: 10.1145/3392093. [7]X. Pournaras and D. A. Koutsomitropoulos, “Deep Learning on the Web: State-of-the-art Object Detection using Web-based Client-side Frameworks,” in 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA, Piraeus, Greece: IEEE, Jul. 2020, pp. 1–8. doi: 10.1109/IISA50023.2020.9284358. [8]H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual Instruction Tuning,” 2023, doi: 10.48550/ARXIV.2304.08485. [9]B. Deka, Z. Huang, and R. Kumar, “ERICA: Interaction mining mobile apps,” in Proceedings of the 29th annual symposium on user interface software and technology, 2016, pp. 767–776. [10]B. Deka et al., “Rico: A mobile app dataset for building data-driven design applications,” in Proceedings of the 30th annual ACM symposium on user interface software and technology, 2017, pp. 845–854. [11]B. Deka et al., “An Early Rico Retrospective: Three Years of Uses for a Mobile App Dataset,” Artificial Intelligence for Human Computer Interaction: A Modern Approach, pp. 229–256, 2021. [12]L. A. Leiva, A. Hota, and A. Oulasvirta, “Enrico: A dataset for topic modeling of mobile UI designs,” in 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, 2020, pp. 1–4. [13]S. Bunian, K. Li, C. Jemmali, C. Harteveld, Y. Fu, and M. S. Seif El-Nasr, “VINS: Visual Search for Mobile User Interface Design,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama Japan: ACM, May 2021, pp. 1–14. doi: 10.1145/3411764.3445762. [14]G. Li, G. Baechler, M. Tragut, and Y. Li, “Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale,” in CHI Conference on Human Factors in Computing Systems, New Orleans LA USA: ACM, Apr. 2022, pp. 1–13. doi: 10.1145/3491102.3502042. [15]J. Wu, S. Wang, S. Shen, Y.-H. Peng, J. Nichols, and J. P. Bigham, “WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg Germany: ACM, Apr. 2023, pp. 1–14. doi: 10.1145/3544548.3581158. [16]K. Cao, Y. Liu, G. Meng, and Q. Sun, “An Overview on Edge Computing Research,” IEEE Access, vol. 8, pp. 85714–85728, 2020, doi: 10.1109/ACCESS.2020.2991734. [17]T. Parisi, WebGL: up and running. O’Reilly Media, Inc., 2012. [18]A. Dakkak, C. Pearson, and W.-M. Hwu, “WebGPU: A Scalable Online Development Platform for GPU Programming Courses,” in 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, IL, USA: IEEE, May 2016, pp. 942–949. doi: 10.1109/IPDPSW.2016.63. [19]C. Gerard, Practical machine learning in JavaScript: TensorFlow. js for web developers. Springer, 2021. [20]O. R. developers, “ONNX Runtime.” Jan. 2018. Accessed: Aug. 21, 2023. [Online]. Available: https://github.com/microsoft/onnxruntime [21]D. Smilkov et al., “TensorFlow.js: Machine Learning for the Web and Beyond,” 2019, doi: 10.48550/ARXIV.1901.05350. [22]E. N. Li Yulong Wang, Du, “ONNX Runtime Web—running your machine learning model in browser,” Microsoft Open Source Blog, Sep. 02, 2021. https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-runtime-web-running-your-machine-learning-model-in-browser/ (accessed Aug. 22, 2023). [23]J. Lee, R. Tang, and J. Lin, “JavaScript Convolutional Neural Networks for Keyword Spotting in the Browser: An Experimental Analysis,” arXiv preprint arXiv:1810.12859, 2018. [24]A. Karpathy, “ConvNetJS: Deep Learning in your browser,” Accessed: Mar, vol. 23, p. 2018, 2014. [25]“Home - Keras.js Documentation.” https://transcranial.github.io/keras-js-docs/ (accessed Sep. 04, 2023). [26]“cazala/synaptic: architecture-free neural network library for node.js and the browser.” https://github.com/cazala/synaptic/tree/master (accessed Aug. 22, 2023). [27]“WebDNN.” Machine Intelligence Laboratory (The University of Tokyo), Aug. 20, 2023. Accessed: Aug. 22, 2023. [Online]. Available: https://github.com/mil-tokyo/webdnn [28]“ml5js/ml5-library.” ml5, Aug. 21, 2023. Accessed: Aug. 22, 2023. [Online]. Available: https://github.com/ml5js/ml5-library [29]J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection.” arXiv, May 09, 2016. Accessed: Jun. 25, 2023. [Online]. Available: http://arxiv.org/abs/1506.02640 [30]Y. Amit, P. Felzenszwalb, and R. Girshick, “Object detection,” Computer Vision: A Reference Guide, pp. 1–9, 2020. [31]G. Jocher, “YOLOv5 by Ultralytics.” May 2020. doi: 10.5281/zenodo.3908559. [32]G. Jocher, A. Chaurasia, and J. Qiu, “YOLO by Ultralytics.” Jan. 2023. Accessed: Jun. 28, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics [33]Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-IoU loss: Faster and better learning for bounding box regression,” in Proceedings of the AAAI conference on artificial intelligence, 2020, pp. 12993–13000. [34]X. Li et al., “Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,” Advances in Neural Information Processing Systems, vol. 33, pp. 21002–21012, 2020. [35]廖家瑤, “應用RESTful技術於線上程式考試及監考系統之研究,” 國防大學, 桃園縣, 2017. [Online]. Available: https://hdl.handle.net/11296/y59hs7 [36]杜威德, “線上程式設計考試系統監考機制之研究,” 國防大學, 桃園縣, 2018. [Online]. Available: https://hdl.handle.net/11296/u98dvz [37]卜邦榮, “線上考試監控系統之設計,” 東吳大學, 台北市, 2010. [Online]. Available: https://hdl.handle.net/11296/9rz6xr [38]S. Prathish and K. Bijlani, “An intelligent system for online exam monitoring,” in 2016 International Conference on Information Science (ICIS), IEEE, 2016, pp. 138–143. [39]P. Beust, I. Duchatelle, and V. Cauchard, “Exams taken at the student’s home,” in Online, Open and Flexible Higher Education Conference, EADTU 2018, 2018. [40]Y. Cheung and Q. Peng, “Eye Gaze Tracking With a Web Camera in a Desktop Environment,” IEEE Trans. Human-Mach. Syst., vol. 45, no. 4, pp. 419–430, Aug. 2015, doi: 10.1109/THMS.2015.2400442. [41]“Kryterion : Home,” Kryterion. https://www.kryterion.com/ (accessed Aug. 23, 2023). [42]“The ProctorU Proctoring Platform - Advanced Exam Technology Backed by Human Validation,” ProctorU. https://www.proctoru.com/ (accessed Aug. 23, 2023). [43]S. Liu et al., “Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection,” 2023, doi: 10.48550/ARXIV.2303.05499. [44]M. Caron et al., “Emerging Properties in Self-Supervised Vision Transformers,” 2021, doi: 10.48550/ARXIV.2104.14294. [45]B. Su, H. Zhang, J. Li, and Z. Zhou, “Towards Few-Shot Open-Set Object Detection,” 2022, doi: 10.48550/ARXIV.2210.15996. [46]J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL: IEEE, Jun. 2009, pp. 248–255. doi: 10.1109/CVPR.2009.5206848. [47]“MediaTrackConstraints: displaySurface property - Web APIs | MDN,” Apr. 07, 2023. https://developer.mozilla.org/en-US/docs/Web/API/MediaTrackConstraints/displaySurface (accessed Jun. 29, 2023). [48]“MediaStreamTrack: getSettings() method - Web APIs | MDN,” Apr. 07, 2023. https://developer.mozilla.org/en-US/docs/Web/API/MediaStreamTrack/getSettings (accessed Jun. 29, 2023). [49]W. Setianto, “YOLOv8 with onnxruntime-web.” Aug. 07, 2023. Accessed: Aug. 09, 2023. [Online]. Available: https://github.com/Hyuto/yolov8-onnxruntime-web [50]W. Setianto, “Object Detection using YOLOv8 and Tensorflow.js.” Aug. 06, 2023. Accessed: Aug. 09, 2023. [Online]. Available: https://github.com/Hyuto/yolov8-tfjs [51]“Introducing the WebAssembly backend for TensorFlow.js.” https://blog.tensorflow.org/2020/03/introducing-webassembly-backend-for-tensorflow-js.html (accessed Aug. 18, 2023).
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