Zobrazeno 1 - 10
of 247
pro vyhledávání: '"Chang-Tien Lu"'
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the “dynamics on graphs” (e.g., changing node attribute values) are visible, and they may be connected t
Externí odkaz:
https://doaj.org/article/b8f8f8a2190c44aaad162ae91a52db58
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 8, Pp 4729-4742 (2022)
Anomaly detection over traffic data is crucial for transportation management and abnormal behavior identification. An anomaly in real-world scenarios usually causes abnormal observations for multiple detectors in an extended period. However, existing
Externí odkaz:
https://doaj.org/article/82fd8813076d4bf3ad11dc4f22bf052c
Publikováno v:
Frontiers in Environmental Science, Vol 10 (2022)
Environmental sensors are essential for tracking weather conditions and changing trends, thus preventing adverse effects on species and environment. Missing values are inevitable in sensor recordings due to equipment malfunctions and measurement erro
Externí odkaz:
https://doaj.org/article/6614c701c82a4ff28ef04b54e9356631
Publikováno v:
Frontiers in Big Data, Vol 4 (2021)
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of
Externí odkaz:
https://doaj.org/article/40a299de02404c709d8b65adaea5aaa7
Autor:
Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao
Publikováno v:
Frontiers in Big Data, Vol 4 (2021)
Circuit obfuscation is a recently proposed defense mechanism to protect the intellectual property (IP) of digital integrated circuits (ICs) from reverse engineering. There have been effective schemes, such as satisfiability (SAT)-checking based attac
Externí odkaz:
https://doaj.org/article/62b4404a03924c0f80439ce35087eda3
Publikováno v:
Journal of Big Data, Vol 5, Iss 1, Pp 1-29 (2018)
Abstract Drones are increasingly being used to perform risky and labor intensive aerial tasks cheaply and safely. To ensure operating costs are low and flights autonomous, their flight plans must be pre-built. In existing techniques drone flight path
Externí odkaz:
https://doaj.org/article/ad32c2e6a80b4fbab2b814b0d64b01b0
Publikováno v:
Journal of Big Data, Vol 4, Iss 1, Pp 1-23 (2017)
Abstract Determining user’s perception of a brand in short periods of time has become crucial for business. Distilling brand perception directly from people’s comments in social media has promise. Current techniques for determining brand percepti
Externí odkaz:
https://doaj.org/article/1e125123d2924a619bfd4f3bd994c598
Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling.
Publikováno v:
PLoS ONE, Vol 9, Iss 10, p e110206 (2014)
Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to
Externí odkaz:
https://doaj.org/article/be4eaef3e5824bd784aaf001258a26dc
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 5, Iss 10, p 193 (2016)
In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan stati
Externí odkaz:
https://doaj.org/article/83102bfa31814b28ac55b2f1a1bf5c50
Publikováno v:
IEEE Transactions on Computational Social Systems. 10:435-447
For decades, the spread of disinformation in online social networks (OSNs) has been a serious social issue. Disinformation via social media can easily mislead people's beliefs toward or against an event that may mislead their behaviors based on the m