InsightNet: Structured Insight Mining from Customer Feedback

We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11% F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.

Overview of Guidance, Navigation and Control System of the TeamIndus lunar lander

TeamIndus' lunar logistics vision includes multiple lunar missions to meet requirements of science, commercial and efforts towards global exploration. The first mission is slated for launch in 2020. The prime objective is to demonstrate autonomous precision lunar landing, and Surface Exploration Rover to collect data on the vicinity of the landing site. TeamIndus has developed various technologies towards lowering the access barrier to the lunar surface. This paper shall provide an overview of design of lander GNC system. The design of the GNC system has been described after concluding studies on sensor and actuator configurations. Frugal design approach is followed in the selection of GNC hardware. The paper describes the constraints for the orbital maneuvers and the lunar descent strategy. Various aspects of the GNC design of autonomous lunar descent maneuver: timeline of events, guidance, inertial and optical terrain-relative navigation schemes are described. The GNC software description focuses on system architecture, modes of operation, and core elements of the GNC software. The GNC algorithms have been tested using Monte-Carlo simulations and Processor-in-Loop runs. The paper concludes with a summary of key risk-mitigation studies for soft landing.