BEAR: Reinforcement Learning for Throughput Aware Borrowing in Energy Harvesting Systems
December 2021 · Author's Copy ↗ · Publication Copy ↗ · Presentation ↗
Anubhav Sachan, Dr. Deepak Mishra, Dr. Ganesh Prasad
Published in the proceedings of
IEEE Global Communications Conference (GLOBECOM 2021)
at Madrid, Spain.
Abstract:
Energy Borrowing (EB) aided Energy harvesting (EH) systems provide a greener alternative to self-sustaining electronic devices in a complex, unprecedented environment by borrowing energy from a supplementary source to regulate the data transmission flow. We propose a reinforcement learning-based algorithm for energy scheduling policy which jointly optimizes the EB and utilizes harvested energy for efficient data transfer at every time instant. As the exact pattern of harvested energy and channel conditions at any time slot is unknown, the proposed algorithm, BEAR (_Borrowing Energy with Adaptive Rewards_), based on actor-critic architecture, learns the optimal power allocation policy for the transmission node. Our designed reward function accommodates the concept of adaptive penalty to punish the transmission node for selecting unfavourable actions. Our simulations show that the BEAR algorithm providing efficient energy management with a focus on throughput maximization yields a 35.45% enhancement in sum throughput over a typical non-borrowing system. Lastly, nontrivial design insights are outlined via numerical results to quantify the practical efficacy of BEAR for EH systems.
Debunking Fake News by Leveraging Speaker Credibility and BERT Based Model
December 2020 · Github ↗ · Author's Copy ↗ · Publication Copy ↗ · Presentation ↗
Dr. Thoudam Doren Singh, Divyansha, Apoorva Vikram Singh, Anubhav Sachan, Abdullah Khilji
Published in the proceedings of
The 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology
at Melbourne, Australia.
Abstract:
The exponential growth in fake news and its role in deteriorating general public trust and democratic standards certainly calls for some counter combat approaches. The prediction of chances of news to be fake is deemed to be hard task since most of the deceptive news has its roots in true news. With a minor fabrication in legitimate news, influential fake news can be created that can be used for political, entertainment, or business-related gains. This work provides a novel intuitive approach to exploit data from multiple sources to segregate news into real and fake. To efficiently capture the contextual information present in the data, Bidirectional Encoder Representations from Transformer (BERT) have been deployed. It attempts to further enhance the performance of the deceptive news detection model by incorporating information about the speaker profile and the credibility associated with him/her. A hybrid sequence encoding model has been proposed to harvest the speaker profile and speaker credibility data which makes it useful for prediction. On evaluation over benchmark fake news dataset LIAR, our model outperformed the previous state-of-the-art works. This attests to the fact that the speaker’s profile and credibility play a crucial role in predicting the validity of news.