Personality, Technology Belief Contexts and Acceptance: Framework and Empirical Testing

This paper describes a new framework for technology acceptance research. Next to an information-system specific belief context, an individual’s personality context and an overall technology-related context are introduced. It is primarily introduced to explore antecedents of technology acceptance’s independent constructs. The framework is proposed so that each of its tiers can host a model best describing the population under examination. The three tiered framework is then operationalized whereby personality is measured through the Five Factor Model, overall technology beliefs are reflected by the Technology Readiness Index and information system –specific beliefs are covered by the Unified Theory of Acceptance and Use of Technology. This research model is tested in a large university hospital setting, where the technology under scrutiny is an Electronic Patient Record

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

Deep learning for paint loss detection with a multiscale, translation invariant network

We explore the potential of deep learning in digital painting analysis to facilitate condition reporting and to support restoration treatments. We address the problem of paint loss detection and develop a multiscale deep learning system with dilated convolutions that enables a large receptive field with limited training parameters to avoid overtraining. Our model handles efficiently multimodal data that are typically acquired in art investigation. As a case study we use multimodal data of the Ghent Altarpiece. Our results indicate huge potential of the proposed approach in terms of accuracy and also its fast execution, which allows interactivity and continuous learning

Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment

In this article, we show progress in certain image processing techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign

Assessing hospital physicians' acceptance of clinical information systems : a review of the relevant literature

In view, of the tremendous potential benefits of clinical information systems (CIS) for the quality of patient care; it is hard to understand why not every CIS is embraced by its targeted users, the physicians. The aim of this study is to propose a framework for assessing hospital physicians' CIS-acceptance that can serve as a guidance for future research into this area. Hereto, a review of the relevant literature was performed in the ISI Web-of-Science database. Eleven studies were withheld from an initial dataset of 797 articles. Results show that just as in business settings, there are four core groups of variables that influence physicians' acceptance of a CIS: its usefulness and ease of use social norms, and factors in the working environment that facilitate use of the CIS (such as providing computers/workstations, compatibility between the new and existing system...). We also identified some additional variables as predictors of CIS-acceptance