Assisting classical paintings restoration : efficient paint loss detection and descriptor-based inpainting using shared pretraining

In the restoration process of classical paintings, one of the tasks is to map paint loss for documentation and analysing purposes. Because this is such a sizable and tedious job automatic techniques are highly on demand. The currently available tools allow only rough mapping of the paint loss areas while still requiring considerable manual work. We develop here a learning method for paint loss detection that makes use of multimodal image acquisitions and we apply it within the current restoration of the Ghent Altarpiece. Our neural network architecture is inspired by a multiscale convolutional neural network known as U-Net. In our proposed model, the downsampling of the pooling layers is omitted to enforce translation invariance and the convolutional layers are replaced with dilated convolutions. The dilated convolutions lead to denser computations and improved classification accuracy. Moreover, the proposed method is designed such to make use of multimodal data, which are nowadays routinely acquired during the restoration of master paintings, and which allow more accurate detection of features of interest, including paint losses. Our focus is on developing a robust approach with minimal user-interference. Adequate transfer learning is here crucial in order to extend the applicability of pre-trained models to the paintings that were not included in the training set, with only modest additional re-training. We introduce a pre-training strategy based on a multimodal, convolutional autoencoder and we fine-tune the model when applying it to other paintings. We evaluate the results by comparing the detected paint loss maps to manual expert annotations and also by running virtual inpainting based on the detected paint losses and comparing the virtually inpainted results with the actual physical restorations. The results indicate clearly the efficacy of the proposed method and its potential to assist in the art conservation and restoration processes

Animal Bodies, Renaissance Culture by Karen Raber

Chad Weidner reviews Animal Bodies, Renaissance Culture by Karen Raber

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Análisis tafonómicos de conjuntos arqueomalacológicos: concheros en la costa norte de Santa Cruz (Patagonia, Argentina)

En este trabajo se presenta una propuesta metodológica para el estudio de conjuntos arqueomalacológicos de concheros y su aplicación en el análisis de restos recuperados a partir de excavaciones sistemáticas en sitios ubicados al sur de la ría Deseado, en la costa norte de Santa Cruz, Patagonia argentina. Esta metodología se focaliza en el estudio de diferentes variables tafonómicas que afectan el registro arqueomalacológico para avanzar en la interpretación de los agentes y procesos involucrados en la formación de las estructuras de concheros y sobre las actividades humanas desarrolladas en los sitios. Además estos análisis son significativos para realizar interpretaciones paleoambientales, paleoecológicas, así como para evaluar la integridad de los conjuntos, interpretar las características estructurales y la variabilidad de los sitios.This paper proposes a method of studying archaeomalacological assemblages from shell middens, and describes an application of this method in the analysis of remains recovered from systematic excavations at sites located south of the Ría Deseado estuary (northern coast of Santa Cruz Province, Argentina). This methodology aims to isolate taphonomic variables affecting archaeomalacological records to aid identification of the agents and processes involved in shell midden formation and to improve interpretations of the human activities performed at the sites. These analyses are also relevant to paleoenvironmental and paleoecological reconstructions, and to interpretations of site variability through assessments of assemblage integrity and structure.Fil: Hammond, Heidi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Arqueología; Argentin