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- posterDecember 2024
Poster: Whether We Are Good Enough to Detect Server-Side Request Forgeries in PHP-native Applications?
CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications SecurityPages 4928–4930https://doi.org/10.1145/3658644.3691419Server-side request forgeries (SSRFs) are inevitable in PHP web applications. Existing static taint analysis tools for PHP suffer from both high rates of false positives and false negatives in detecting SSRF because they do not incorporate application-...
- ArticleAugust 2024
SAFE: Sampling-Assisted Fast Learned Cardinality Estimation for Dynamic Spatial Data
AbstractCardinality estimation for spatial queries plays an important role in query scheduling and optimization. Spatial datasets are fully dynamic, and this setting necessitates an update-friendly, low-latency, and accurate cardinality estimator. However,...
- research-articleApril 2024
Enabling collaborative assembly between humans and robots using a digital twin system
Robotics and Computer-Integrated Manufacturing (RCIM), Volume 86, Issue Chttps://doi.org/10.1016/j.rcim.2023.102691Highlights- Proposed a digital twin-driven HRC framework that integrates humans into the work loop for full-element perception and collaboration.
- Proposed an occlusion-robust human mesh recovery algorithm to build a robust human digital twin model ...
Human–robot collaboration (HRC) systems are intelligent systems that guide robots to collaborate with humans based on a cognitive understanding of human intention, ensuring safe, flexible, and efficient collaboration between humans and robots in ...
- research-articleJune 2023
Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From ShanghaiTech University
- Guancheng Li,
- Songhui Cao,
- Chuyi Zhao,
- Siyuan Zhang,
- Yuchen Ji,
- Haotian Jing,
- Zecheng Li,
- Jiajun Cheng,
- Yiwei Yang,
- Shu Yin
IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 34, Issue 6Pages 1716–1719https://doi.org/10.1109/TPDS.2022.3205479In SC20, (Srivastava et al. 2020) proposed a Parallel F<bold>ram</bold>ework for <bold>B</bold>ayesian <bold>Le</bold>arning, or ramBLe, for short, which is a highly parallel and efficient framework for learning the structure of Bayesian Networks (BNs) ...