Murals in North America

de‐Miguel‐Molina, María; Campos, Virginia Santamarina; Martínez-Carazo, Eva-María

doi:10.1007/978-3-030-53106-5_11

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…America has a rich mural heritage, and this is especially true of Mexico, Canada, and the United States. These murals show many features of their history, culture, politics, and even their education or advertising [ 36 ]. In Mexico, the murals in Teotihuacan , which is considered a major cultural and political center of ancient Mesoamerica, were painted embedded since the early stages (1–200 CE) and until the collapse of the great city (650 CE) [ 37 , 38 ], and these beautiful and complex murals are considered the important source of understanding Teotihuacan culture and society [ 39 ].…”

Section: Major Distribution Areas Of Muralsmentioning

confidence: 99%

Current progress on murals: distribution, conservation and utilization

Wang

2023

Herit Sci

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As non-renewable cultural heritages, murals have important implications in historical customs, religions, and philosophy as well as their aesthetic values. Recently, many murals are threatened by natural factors and human activities. During the past decades, there are increasing interest in the investigation of murals. Here we review the current status of murals and provide an up-to-date summary of achievements related to murals. The murals that draw the most attention are distributed in Mexico, Ireland, China, and Spain. The aesthetics, history, cultural, educational, and economic values of murals are comprehensively analyzed. The main research technologies used to detect the chemical compositions and physical structures of murals are also summarized. The restoration of murals includes several procedures such as stabilization, repair, surface cleaning, and pigment reconversion. Emerging technologies such as computer science benefit the research and conservation of murals. We also propose that tourism management and climate change should be incorporated into the conservation of murals in the future.

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Section: Major Distribution Areas Of Muralsmentioning

confidence: 99%

Current progress on murals: distribution, conservation and utilization

Wang

2023

Herit Sci

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Efficient Privacy-Preserving Scheme for Distributed Machine Learning Against Collusion Attacks (EPPS-DMLCA)

Hosen,

Billah,

Uddin

2024

ejtas

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We propose EPPS-DMLCA, a novel method integrating homomorphic encryption and DP to protect data from attacks. EPPS-DMLCA improves techniques to protect data from attacks in several ways. One, it protects data while being more computationally efficient compared to extant methods. Two, it is more robust to collusion attacks than current methods are. We evaluated EPPS-DXMCA using MNIST. We tested our method on Amazon’s cloud using 40 cloud servers. We found that our method performed as well as a method that did not protect privacy. Additionally, our method uses a firm privacy budget, which provides robust privacy protection. Last, we found that because of the low computational overhead, our method is more resistant to collusion attacks than state-of-the-art methods are. Our results show that EPPS-DMLCA is an important contribution to the literature. We propose EPPS-DMLCA, which integrates homomorphic encryption and DP to protect data from attacks. We used MNIST in a distributed cloud environment with 40 cloud servers. Our results show that EPPS-DMLCA is up to 3 times more efficient than FHE methods. Moreover, the model performs 97.8%, the same as unprotected methods. Last, DP’s noise was well-tuned, as evidenced by the model and the sufficient parameters to provide robust protection from privacy. Moreover, EPPS-DMLCA is more resistant to collusion than the state-of-the-art. Machine learning models are essential for creating information from big data in a constantly changing world. However, access to such big data raises privacy issues. Fortunately, various ways exist to protect data from attacks. One such method is DP, which adds noise to the data. The goal of DP is to generate robust protection from privacy. DP is a valuable tool, but the method has one drawback. The method uses many noise probes to be effective. This means that FHE methods do not perform as well as unprotected methods. Because of this drawback, I propose EPPS-DMLCA to users.

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Murals in North America

Cited by 2 publications

References 20 publications

Current progress on murals: distribution, conservation and utilization

Current progress on murals: distribution, conservation and utilization

Efficient Privacy-Preserving Scheme for Distributed Machine Learning Against Collusion Attacks (EPPS-DMLCA)

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