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

Designing Promotion Incentive to Embrace Social Sharing: Evidence from Field and Lab Experiments

Despite the increasing connectivity between customers enabled by digital technologies, there is an absence of research investigating how firms should redesign the promotion incentives to engage customers as both ‘purchaser’ and ‘sharer’ in this social media era. In this study, we conduct a large-scale field experiment and two lab experiments to test the effectiveness of different incentive designs (varied by shareability and quantity of promo codes) in driving social sharing senders’ purchase and referrals. Providing senders with one non-shareable code significantly increases their purchase likelihood. In comparison, the senders who receive one shareable code are less likely to purchase themselves, but are much more likely to make successful referrals. We further conduct two lab experiments, which replicate the field experiment findings and explore the underlying mechanisms. We find that the exclusivity perception and social motive triggered by various incentive designs mediate and explain their effect on sender’s purchase and referrals. Our study extends prior IS literature on social sharing that has focused on sharing information to the domain of sharing incentives, providing implications to firms on how to design promotional incentive that accommodates the dual role of customers as purchasers and sharers and sheds light on the motives underlying social sharing

Dynamics and Impacts of Human-Algorithm Consensus in Logistics Scheduling: Evidence from A Field Experiment

Algorithms are being implemented to aid human decision-making and most studies on human-algorithm interactions focus on how to improve human-algorithm cooperation. However, excessive reliance on algorithms in decision-making may hinder the complementary value of humans and algorithms. There is a lack of empirical evidence on the impacts of human-algorithm consensus in collaborative decision-making. To address this gap, this paper reports a large-scale field experiment conducted by one of China\u27s largest logistics firms in the context of route scheduling. The experiment involved assigning routes to either a treatment group, where algorithms and human operators collaborated in decision-making, or a control group, where human operators made decisions independently. We plan to collect data to evaluate the effects of algorithm implementation and to analyze the patterns and effects of human-algorithm consensus in a long-term cooperation. Our study aims to contribute to the literature on human-algorithm interactions in operational decisions