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   https://doi.org/10.1145/3388440.3412475  

   Chen, Junjie ; Mowlaei, Mohammad Erfan ; Shi, Xinghua ( January 2020 , Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)
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   https://doi.org/10.1145/3593013.3594105  

   Bashardoust, Ashkan ; Friedler, Sorelle ; Scheidegger, Carlos ; Sullivan, Blair D. ; Venkatasubramanian, Suresh ( June 2023 , Conference on Fairness, Accountability, and Transparency)

   In social networks, a node’s position is, in and of itself, a form of social capital. Better-positioned members not only benefit from (faster) access to diverse information, but innately have more potential influence on information spread. Structural biases often arise from network formation, and can lead to significant disparities in information access based on position. Further, processes such as link recommendation can exacerbate this inequality by relying on network structure to augment connectivity. In this paper, we argue that one can understand and quantify this social capital through the lens of information flow in the network. In contrast to prior work, we consider the setting where all nodes may be sources of distinct information, and a node’s (dis)advantage takes into account its ability to access all information available on the network, not just that from a single source. We introduce three new measures of advantage (broadcast, influence, and control), which are quantified in terms of position in the network using access signatures – vectors that represent a node’s ability to share information with each other node in the network. We then consider the problem of improving equity by making interventions to increase the access of the least-advantaged nodes. Since all nodes are already sources of information in our model, we argue that edge augmentation is most appropriate for mitigating bias in the network structure, and frame a budgeted intervention problem for maximizing broadcast (minimum pairwise access) over the network. Finally, we propose heuristic strategies for selecting edge augmentations and empirically evaluate their performance on a corpus of real-world social networks. We demonstrate that a small number of interventions can not only significantly increase the broadcast measure of access for the least-advantaged nodes (over 5 times more than random), but also simultaneously improve the minimum influence. Additional analysis shows that edge augmentations targeted at improving minimum pairwise access can also dramatically shrink the gap in advantage between nodes (over ) and reduce disparities between their access signatures. 

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3. Data augmentation with conditional GAN for automatic modulation classification

   https://doi.org/10.1145/3395352.3402622  

   Patel, Mansi ; Wang, Xuyu ; Mao, Shiwen ( July 2020 , Proc. ACM Workshop on Wireless Security and Machine Learning (WiseML 2020))
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   Ramirez Rochac, Juan F. ; Zhang, Nian ; Thompson, Lara ; Oladunni, Timothy ( August 2019 , 2019 9th International Conference on Information Science and Technology (ICIST))
5. A Knowledge-Driven Vowel-Based Approach of Depression Classification from Speech Using Data Augmentation

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   Feng, Kexin ; Chaspari, Theodora ( June 2023 , 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))