The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath.
Deploying machine learning models to edge devices has many real-world applications, especially for the scenarios that demand low latency, low power, or data privacy. However, it requires substantial research and engineering efforts due to the limited computational resources and memory of edge devices. In this demo, we present BED, an object detection system for edge devices practiced on the MAX78000 DNN accelerator. BED integrates on-device DNN inference with a camera and a screen for image acquisition and output exhibition, respectively. Experiment results indicate BED can provide accurate detection with an only 300KB tiny DNN model.