no code implementations • 19 Dec 2023 • Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon, Xishuang Dong
Fairness AI aims to detect and alleviate bias across the entire AI development life cycle, encompassing data curation, modeling, evaluation, and deployment-a pivotal aspect of ethical AI implementation.
no code implementations • 10 Jun 2023 • Shouvon Sarker, Lijun Qian, Xishuang Dong
In the N2C2 2022 competitions, various tasks were presented to promote the identification of key factors in electronic health records (EHRs) using the Contextualized Medication Event Dataset (CMED).
no code implementations • 11 Jan 2023 • Taoreed Akinola, Xiangfang Li, Richard Wilkins, Pamela Obiomon, Lijun Qian
Image segmentation is a very popular and important task in computer vision.
no code implementations • 30 May 2022 • Oluwaseyi Onasami, Ming Feng, Hao Xu, Mulugeta Haile, Lijun Qian
Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment.
no code implementations • 27 May 2022 • Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim, Xishuang Dong
However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated.
no code implementations • 15 May 2022 • Omobayode Fagbohungbe, Lijun Qian
The fast execution speed and energy efficiency of analog hardware has made them a strong contender for deployment of deep learning model at the edge.
no code implementations • 8 May 2022 • Omobayode Fagbohungbe, Lijun Qian
However, significant performance degradation suffered by deep learning models due to the inherent noise present in the analog computation can limit their use in mission-critical applications.
no code implementations • 7 May 2022 • Omobayode Fagbohungbe, Lijun Qian
In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model building block, in designing DNN models with excellent noise-resistant property is proposed.
no code implementations • 25 Jan 2022 • Oluwaseyi Onasami, Damilola Adesina, Lijun Qian
With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important.
no code implementations • 4 Jan 2022 • Xishuang Dong, Lijun Qian
Moreover, it cannot fully involve user feedback in the loop of learning detection models for further enhancing fake news detection.
no code implementations • 19 Apr 2021 • Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
no code implementations • 2 Mar 2021 • Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, Lijun Qian, Marco Di Renzo
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts.
no code implementations • 27 Feb 2021 • Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim, Xishuang Dong
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community.
no code implementations • 28 Jan 2021 • Joshua Bassey, Lijun Qian, Xianfang Li
Artificial neural networks (ANNs) based machine learning models and especially deep learning models have been widely applied in computer vision, signal processing, wireless communications, and many other domains, where complex numbers occur either naturally or by design.
no code implementations • 28 Dec 2020 • Damilola Adesina, Chung-Chu Hsieh, Yalin E. Sagduyu, Lijun Qian
In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented.
no code implementations • 24 Nov 2020 • Omobayode Fagbohungbe, Lijun Qian
Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices.
no code implementations • 18 Aug 2020 • Bo Yang, Xuelin Cao, Chau Yuen, Lijun Qian
This motivates us to consider offloading this type of deep learning (DL) tasks to a mobile edge computing (MEC) server due to limited computational resource and energy budget of the UAV, and further improve the inference accuracy.
no code implementations • 29 Jun 2020 • Bo Yang, Xuelin Cao, Joshua Bassey, Xiangfang Li, Timothy Kroecker, Lijun Qian
Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES).
no code implementations • 27 Jun 2020 • Bo Yang, Xuelin Cao, Xiangfang Li, Chau Yuen, Lijun Qian
This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint.
no code implementations • 10 May 2020 • Omobayode Fagbohungbe, Sheikh Rufsan Reza, Xishuang Dong, Lijun Qian
In order to extract knowledge from the large data collected by edge devices, traditional cloud based approach that requires data upload may not be feasible due to communication bandwidth limitation as well as privacy and security concerns of end users.
no code implementations • 6 May 2020 • Xishuang Dong, Shanta Chowdhury, Uboho Victor, Xiangfang Li, Lijun Qian
Then genes in these sentences are represented as gene embeddings to reduce data sparsity.
no code implementations • 26 Apr 2020 • Chandra Mouli Madhav Kotteti, Xishuang Dong, Lijun Qian
By combining the proposed data pre-processing method with the ensemble model, better performance of rumor detection has been demonstrated in the experiments using PHEME dataset.
no code implementations • 19 Apr 2020 • Joshua Bassey, Xiangfang Li, Lijun Qian
Specifically, an autoencoder is used to automatically extract features from the RF traces, and the reconstruction error is used as the DAC and this DAC is unique to the device and the particular message of interest.
no code implementations • 31 Jan 2020 • Xishuang Dong, Uboho Victor, Lijun Qian
In addition, we build a shared CNN to extract the low level features on both labeled data and unlabeled data to feed them into these two paths.
1 code implementation • 10 Jun 2019 • Xishuang Dong, Uboho Victor, Shanta Chowdhury, Lijun Qian
News in social media such as Twitter has been generated in high volume and speed.
no code implementations • 30 Mar 2018 • Xishuang Dong, Hsiang-Huang Wu, Yuzhong Yan, Lijun Qian
In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification.