3,547 research outputs found
Atlas.txt : Linking Geo-referenced Data to Text for NLG
Peer reviewedPreprin
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
A General Purpose Neural Architecture for Geospatial Systems
Geospatial Information Systems are used by researchers and Humanitarian
Assistance and Disaster Response (HADR) practitioners to support a wide variety
of important applications. However, collaboration between these actors is
difficult due to the heterogeneous nature of geospatial data modalities (e.g.,
multi-spectral images of various resolutions, timeseries, weather data) and
diversity of tasks (e.g., regression of human activity indicators or detecting
forest fires). In this work, we present a roadmap towards the construction of a
general-purpose neural architecture (GPNA) with a geospatial inductive bias,
pre-trained on large amounts of unlabelled earth observation data in a
self-supervised manner. We envision how such a model may facilitate cooperation
between members of the community. We show preliminary results on the first step
of the roadmap, where we instantiate an architecture that can process a wide
variety of geospatial data modalities and demonstrate that it can achieve
competitive performance with domain-specific architectures on tasks relating to
the U.N.'s Sustainable Development Goals.Comment: Presented at AI + HADR Workshop at NeurIPS 202
A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality
The Internet of Medical Things (IoMT) is a platform that combines Internet of
Things (IoT) technology with medical applications, enabling the realization of
precision medicine, intelligent healthcare, and telemedicine in the era of
digitalization and intelligence. However, the IoMT faces various challenges,
including sustainable power supply, human adaptability of sensors and the
intelligence of sensors. In this study, we designed a robust and intelligent
IoMT system through the synergistic integration of flexible wearable
triboelectric sensors and deep learning-assisted data analytics. We embedded
four triboelectric sensors into a wristband to detect and analyze limb
movements in patients suffering from Parkinson's Disease (PD). By further
integrating deep learning-assisted data analytics, we actualized an intelligent
healthcare monitoring system for the surveillance and interaction of PD
patients, which includes location/trajectory tracking, heart monitoring and
identity recognition. This innovative approach enabled us to accurately capture
and scrutinize the subtle movements and fine motor of PD patients, thus
providing insightful feedback and comprehensive assessment of the patients
conditions. This monitoring system is cost-effective, easily fabricated, highly
sensitive, and intelligent, consequently underscores the immense potential of
human body sensing technology in a Health 4.0 society
Data Science, Data Visualization, and Digital Twins
Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development
- …