An empirical study on deployment faults of deep learning based mobile applications
Deep learning (DL) is moving its step into a growing number of mobile software applications.
These software applications, named as DL based mobile applications (abbreviated as …
These software applications, named as DL based mobile applications (abbreviated as …
Cross-Modality Graph-Based Language and Sensor Data Co-Learning of Human-Mobility Interaction
Learning the human--mobility interaction (HMI) on interactive scenes (eg, how a vehicle
turns at an intersection in response to traffic lights and other oncoming vehicles) can …
turns at an intersection in response to traffic lights and other oncoming vehicles) can …
Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations
M Tabatabaie, S He - Proceedings of the ACM on Interactive, Mobile …, 2024 - dl.acm.org
Driver maneuver interaction learning (DMIL) refers to the classification task with the goal of
identifying different driver-vehicle maneuver interactions (eg, left/right turns). Existing …
identifying different driver-vehicle maneuver interactions (eg, left/right turns). Existing …
Crowdsourced Geospatial Intelligence: Constructing 3D Urban Maps with Satellitic Radiance Fields
In urban planning and research, 3D city maps are crucial for activities such as cellular
network design, urban development, and climate research. Traditionally, creating these …
network design, urban development, and climate research. Traditionally, creating these …
SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery Behavior
Accurate road networks play a crucial role in modern mobile applications such as navigation
and last-mile delivery. Most existing studies primarily focus on generating road networks in …
and last-mile delivery. Most existing studies primarily focus on generating road networks in …
Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical Guidelines
R Kraft, M Blasi, M Schickler, M Reichert… - International Conference …, 2024 - Springer
Smartphones have found their way into many domains because they can be used to
measure phenomena of common interest. The Global Overview Report Digital 2022 states …
measure phenomena of common interest. The Global Overview Report Digital 2022 states …
FedmPT: Federated learning for multiple personalized tasks over mobile computing
X Zhang, Z Ou, Z Yang - IEEE Transactions on Network …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is a privacy-preserving collaborative learning framework that can be
used in mobile computing where multiple user devices jointly train a deep learning model …
used in mobile computing where multiple user devices jointly train a deep learning model …
[图书][B] Multi-dimensional Urban Sensing Using Crowdsensing Data
In smart cities, the indispensable devices of people's daily life, such as smartphones,
smartwatches, vehicles, and smart buildings, are equipped with more and more sensors. For …
smartwatches, vehicles, and smart buildings, are equipped with more and more sensors. For …
Evaluating Sensor Data in the Context of Mobile Crowdsensing
M Blasi - 2022 - dbis.eprints.uni-ulm.de
With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life
experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that …
experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that …