Machine Learning for Cultural Heritage: A Survey

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML

Dynamic shading systems: A review of design parameters, platforms and evaluation strategies

The advancements in software and hardware technologies provide opportunities for solar shading systems to function dynamically within their context. This development has helped dynamic shading systems respond to variable environmental parameters such as sun angles and solar insolation. However, the technical understanding of system design, mechanism and controlling methods presents a challenge for architects and designers. Therefore, this study aims to review the current applications and trends of dynamic shading systems to clarify the potentials and limitations in enhancing system performance based on integrated design objectives. This study assessed several systems on the basis of a critical review to identify different models, applications and methodologies. This study is divided into two main sections: (i) design elements and platforms that engage with specific methods in creating a dynamic shading system and (ii) evaluation strategies to examine system performance. The systems were investigated based on the multiplicity and integration of the parameters involved through various components, such as architectural, mechanical, operational and automation components. The review analysed various studies on the following two bases: (1) geometric-based analysis, which distinguishes between simple and complex shading models, and (2) performance-based analysis, which assesses the shading systems based on two groups of methodologies, namely, theoretical and experimental. The outcome of the review reflects a clear classification of shading models and a comprehensive analysis of their performance. This study generally provides a systematic framework for architects based on thorough research and investigation. Finally, the study introduced several findings and recommendations to improve the performance of dynamic shading systems

A non-additive multiple criteria approach for the evaluation of international climate think tanks

This paper outlines the application of non-additive measures and Choquet integral in the construction of a composite indicator to assess the performance of international climate think tanks and evaluate their influence in shaping climate policies and raising awareness among the general public. The composite index consists of 15 carefully selected indicators according to the feedback provided by Experts within the field and structured into three main pillars: Activities, Publications and Dissemination. In order compare Think Tanks of different size and hence to measure their efficiency, the standardized ranking is also computed dividing the Think Tank outcome in each criterion by the number of its researchers. The application of fuzzy measures and Choquet integral, allowing to take into account potential interactions existing among criteria, increases substantially the model capability both in eliciting effectively Experts’ preferences and in aggregating indicators. Moreover, we present a novel technique for the aggregation of Experts’ preferences where Decision Makers’ weights have been set proportionally to their consistency in evaluating the specific questionnaire

Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders

This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or ruined frescoes. In this particular condition, the puzzle’s pieces do not align perfectly due to the borders’ gaps; in this situation, the patches’ direct match is unfeasible due to the lack of color and line continuations. JiGAN, is a two-steps procedure that tackles this issue: first, we repair the eroded borders with a GAN-based image extension model and measure the alignment affinity between pieces; then, we solve the puzzle with the relaxation labeling algorithm to enforce consistency in pieces positioning, hence, reconstructing the puzzle. We test the method on a large dataset of small puzzles and on three commonly used benchmark datasets to demonstrate the feasibility of the proposed approach

Machine Learning for Cultural Heritage: A Survey

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML

Design optimisation of solar shading systems for tropical office buildings: Challenges and future trends

© 2018 Elsevier Ltd Most high-rise office buildings in the tropics, particularly in Malaysia and Singapore, exceed the required level of the energy efficiency index. The implementation of conventional shading systems in the tropics has been proven to have limitations in terms of controlling the quantity and quality of received solar light throughout the year, especially at different solar angles with varying sky conditions. Therefore, the main objective of this work is to investigate the challenges and future trends of solar shading systems by examining their mechanisms, functions and materials for application in tropical regions. This study used evidence review to evaluate various types and models of shading systems based on a systematic method to identify patterns and trends through classification and comparison. Three main categories of shading systems were identified based on the energy involvement and the design approach: (i) passive systems with zero energy use, (ii) active systems that use mechanical devices and (iii) hybrid systems integrated with a biomimetic approach. Specific conclusions were drawn to emphasise the efficiency of developed shading systems in the tropics