Co-located interaction in interactive art takes place among two or more co-located audience members and the technical system of an artwork. In this paper, we aim to assess the descriptive and... Show moreCo-located interaction in interactive art takes place among two or more co-located audience members and the technical system of an artwork. In this paper, we aim to assess the descriptive and comparative qualities of our previously developed relational model for describing and analysing such forms of interaction. The model focuses on specifying the actions of the interacting elements, such as the audience and art system, and the various forms of communication between them. To assess its significance, we first develop selection criteria and classification dimensions to select eight artworks that are representative of diverse forms of co-located interaction. The relational model is shown to be suitable for describing the selected artworks and comparing their similarities and differences. As outcome, it reveals different types of relationships between the actions of interacting elements that would otherwise not be highlighted. As such, it provides a context for analysing and discussing strategies for co-located interaction and points to opportunities for research and creation in this field. Show less
The recent surge in deployment and use of generative machine learning models has sparked an interest in the relationships between AI and creativity, or more specifically into the question and... Show moreThe recent surge in deployment and use of generative machine learning models has sparked an interest in the relationships between AI and creativity, or more specifically into the question and debate of whether machines can exhibit human-level creativity. This is by no means a new discussion, going back in time decades if not centuries. The debate was approached from multiple angles, and a general consensus was not yet reached. In this position paper, we present the long-standing debate as it formed across various fields such as cognitive science, philosophy, and computing, approaching it mainly from a historical perspective. Along the way we identify how the various views relate to recent developments in machine learning models and argue our own position regarding the question of whether machines can exhibit human-level creativity. As such we aim to involve computer scientists and AI practitioners into the ongoing debate. Show less
The eighteenth-century passion to order and systematize as well as to measure and calculate has been explained as a result of both the Scientific Revolution and the emergence of centralized states.... Show moreThe eighteenth-century passion to order and systematize as well as to measure and calculate has been explained as a result of both the Scientific Revolution and the emergence of centralized states. The first, enabled the new experimental philosophy that quantified the 'sciences', while the latter created the need for statistics (e.g., demographic data). This paper explores the diffusion of the ‘quantifying spirit’ among the wider public in the eighteenth century and offers alternative explanation for the interest of the population at large in structured quantitative data. Using a corpus of 188 handwritten chronicles, produced by a heterogenous group of middle-class authors from the Low Countries, between 1500-1800, it analyses how early modern chroniclers used Western/Hindu-Arabic numerals in their writings, and under which circumstances this changed in the eighteenth century. From the analysis it appears that chroniclers used meteorological measurement and demographic data for different purposes than natural philosophers and (centralized) governments. Moreover, it transpires that the collection of quantitative data was initially stimulated by local governments, subsequently made public by various media, and picked up by the society at large and higher authorities. Show less
The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator. This paper...Show moreThe problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator. This paper establishes the analytical expression of the Hessian matrix of the mapping from a (fixed size) collection of n points in the d-dimensional decision space (or m dimensional objective space) to the scalar hypervolume indicator value. To define the Hessian matrix, the input set is vectorized, and the matrix is derived by analytical differentiation of the mapping from a vectorized set to the hypervolume indicator. The Hessian matrix plays a crucial role in second-order methods, such as the Newton-Raphson optimization method, and it can be used for the verification of local optimal sets. So far, the full analytical expression was only established and analyzed for the relatively simple bi-objective case. This paper will derive the full expression for arbitrary dimensions (m ≥ 2 objective functions). For the practically important three-dimensional case, we also provide an asymptotically efficient algorithm with time complexity in O(n log n) for the exact computation of the Hessian Matrix’ non-zero entries. We establish a sharp bound of 12m−6 for the number of non-zero entries. Also, for the general m-dimensional case, a compact recursive analytical expression is established, and its algorithmic implementation is discussed. Also, for the general case, some sparsity results can be established; these results are implied by the recursive expression. To validate and illustrate the analytically derived algorithms and results, we provide a few numerical examples using Python and Mathematica implementations. Open-source implementations of the algorithms and testing data are made available as a supplement to this paper.Show less
Stathis, G.; Trantas, A.; Biagioni, G.; Herik, H.J. van den; Custers, B.H.M.; Daniele, L.; Katsigiannis, T. 2023
This paper reflects on the justifications and impacts of militarism in contemporary global narcotic governance, focusing on the interrelated questions on how state leaders and elites justify state... Show moreThis paper reflects on the justifications and impacts of militarism in contemporary global narcotic governance, focusing on the interrelated questions on how state leaders and elites justify state-perpetrated violence by invoking seemingly anti-violence concepts such as peace, security, human rights, justice, democracy, and development, and how drug war perpetrators justify their actions within and outside the state apparatus. The paper demonstrates that the war-on-drugs approach institutionalizes death and militarism as the default state policy, which represses marginalized groups based on material endowments, race, and gender, while highlighting the mechanisms of justification and implementation of a war on drugs policy approach. The paper maintains that state leaders actualize a war-on-drugs approach through intensified state violence and the perpetration of an impunity culture that protects state agents from any sort of legal prosecution for their human rights abuses. Show less
The algorithm selection problem is of paramount importance in achieving high-quality results while minimizing computational effort, especially when dealing with expensive black-box optimization... Show moreThe algorithm selection problem is of paramount importance in achieving high-quality results while minimizing computational effort, especially when dealing with expensive black-box optimization problems. In this paper, we address this challenge by using randomly generated artificial functions that mimic the landscape characteristics of the original problem while being inexpensive to evaluate. The similarity between the artificial function and the original problem is quantified using Exploratory Landscape Analysis. We demonstrate a significant performance improvement on five real-world vehicle dynamics problems by transferring the parameters of the Covariance Matrix Adaptation Evolution Strategy tuned to these artificial functions.We provide a complete set of simulated values of braking distance for fully enumerated 2D design spaces of all five real-world optimization problems. So, replication of our results and benchmarking directly on the real-world problems is possible. Beyond the scope of this paper, this data can be used as a benchmarking set for multi-objective optimization with up to five objectives. Show less
The performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore,... Show moreThe performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore, optimal CMA-ES parameter configurations vary across different problem landscapes, making the task of tuning CMA-ES to a specific optimization problem a challenging mixed-integer optimization problem. In recent years, several advanced algorithms have been developed to address this problem, including the Sequential Model-based Algorithm Configuration (SMAC) and the Tree-structured Parzen Estimator (TPE).In this study, we propose a novel approach for tuning CMA-ES by leveraging CMA-ES itself. Therefore, we combine the modular CMA-ES implementation with the margin extension to handle mixed-integer optimization problems. We show that CMA-ES can not only compete with SMAC and TPE but also outperform them in terms of wall clock time. Show less
