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A
Alim, Adil, Zhao, Xujiang, Cho, Jin-Hee, Chen, Feng.  2019.  Uncertainty-Aware Opinion Inference Under Adversarial Attacks. 2019 IEEE International Conference on Big Data (Big Data). :6—15.

Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.

L
Liu, Jinghua, Chen, Pingping, Chen, Feng.  2021.  Performance of Deep Learning for Multiple Antennas Physical Layer Network Coding. 2021 15th International Symposium on Medical Information and Communication Technology (ISMICT). :179–183.
In this paper, we propose a deep learning based detection for multiple input multiple output (MIMO) physical-layer network coding (DeepPNC) over two way relay channels (TWRC). In MIMO-PNC, the relay node receives the signals superimposed from the two end nodes. The relay node aims to obtain the network-coded (NC) form of the two end nodes' signals. By training suitable deep neural networks (DNNs) with a limited set of training samples. DeepPNC can extract the NC symbols from the superimposed signals received while the output of each layer in DNNs converges. Compared with the traditional detection algorithms, DeepPNC has higher mapping accuracy and does not require channel information. The simulation results show that the DNNs based DeepPNC can achieve significant gain over the DeepNC scheme and the other traditional schemes, especially when the channel matrix changes unexpectedly.