Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness

Physiological records-based situation awareness evaluation under aviation context: A comparative analysis

Situational Awareness (SA) assessment is of paramount importance in various domains, with particular significance in the military for safe aviation decision-making. It involves encompassing perception, comprehension, and projection levels in human beings. Accurate evaluation of SA statuses across these three levels is crucial for mitigating human false-positive and false-negative rates in monitoring complex scenarios in the aviation context. This study proposes a comprehensive comparative analysis by involving two types of physiological records: electroencephalogram (EEG) signals and brain electrical activity mapping (BEAM) images. These two modalities are leveraged to automate precise SA evaluation using both conventional machine learning and advanced deep learning techniques. Benchmarking experiments reveal that the BEAM-based deep learning models attain state-of-the-art performance scores of 0.955 for both SA perception and comprehension levels, respectively. Conversely, the EEG signals-based manual feature extraction, selection, and classification approach achieved a superior accuracy of 0.929 for the projection level of SA. These findings collectively highlight the potential of deploying diverse physiological records as valuable computational tools for enhancing SA evaluation throughout aviation decision-making safety.

Exploring Descriptions of Movement Through Geovisual Analytics

Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable.