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Wang, Y., Wen, M., Liu, Y., Wang, Y., Li, Z., Wang, C., Yu, H., Cheung, S.-C., Xu, C., Zhu, Z..  2020.  Watchman: Monitoring Dependency Conflicts for Python Library Ecosystem. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :125–135.
The PyPI ecosystem has indexed millions of Python libraries to allow developers to automatically download and install dependencies of their projects based on the specified version constraints. Despite the convenience brought by automation, version constraints in Python projects can easily conflict, resulting in build failures. We refer to such conflicts as Dependency Conflict (DC) issues. Although DC issues are common in Python projects, developers lack tool support to gain a comprehensive knowledge for diagnosing the root causes of these issues. In this paper, we conducted an empirical study on 235 real-world DC issues. We studied the manifestation patterns and fixing strategies of these issues and found several key factors that can lead to DC issues and their regressions. Based on our findings, we designed and implemented Watchman, a technique to continuously monitor dependency conflicts for the PyPI ecosystem. In our evaluation, Watchman analyzed PyPI snapshots between 11 Jul 2019 and 16 Aug 2019, and found 117 potential DC issues. We reported these issues to the developers of the corresponding projects. So far, 63 issues have been confirmed, 38 of which have been quickly fixed by applying our suggested patches.
Zhu, Z., Jiang, R., Jia, Y., Xu, J., Li, A..  2018.  Cyber Security Knowledge Graph Based Cyber Attack Attribution Framework for Space-ground Integration Information Network. 2018 IEEE 18th International Conference on Communication Technology (ICCT). :870–874.
Comparing with the traditional Internet, the space-ground integration information network has more complicated topology, wider coverage area and is more difficult to find the source of attacks. In this paper, a cyber attack attribution framework is proposed to trace the attack source in space-ground integration information network. First, we constructs a cyber security knowledge graph for space-ground integration information network. An automated attributing framework for cyber-attack is proposed. It attributes the source of the attack by querying the cyber security knowledge graph we constructed. Experiments show that the proposed framework can attribute network attacks simply, effectively, and automatically.
Liu, B., Zhu, Z., Yang, Y..  2017.  Convolutional Neural Networks Based Scale-Adaptive Kernelized Correlation Filter for Robust Visual Object Tracking. 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). :423–428.

Visual object tracking is challenging when the object appearances occur significant changes, such as scale change, background clutter, occlusion, and so on. In this paper, we crop different sizes of multiscale templates around object and input these multiscale templates into network to pretrain the network adaptive the size change of tracking object. Different from previous the tracking method based on deep convolutional neural network (CNN), we exploit deep Residual Network (ResNet) to offline train a multiscale object appearance model on the ImageNet, and then the features from pretrained network are transferred into tracking tasks. Meanwhile, the proposed method combines the multilayer convolutional features, it is robust to disturbance, scale change, and occlusion. In addition, we fuse multiscale search strategy into three kernelized correlation filter, which strengthens the ability of adaptive scale change of object. Unlike the previous methods, we directly learn object appearance change by integrating multiscale templates into the ResNet. We compared our method with other CNN-based or correlation filter tracking methods, the experimental results show that our tracking method is superior to the existing state-of-the-art tracking method on Object Tracking Benchmark (OTB-2015) and Visual Object Tracking Benchmark (VOT-2015).