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
Pipeline-parallel training has emerged as a popular method to train large Deep Neural Networks (DNNs), as it allows the use of the combined compute power and memory capacity of multiple Graphics... Show morePipeline-parallel training has emerged as a popular method to train large Deep Neural Networks (DNNs), as it allows the use of the combined compute power and memory capacity of multiple Graphics Processing Units (GPUs). However, with the sustaining increase in Deep Learning (DL) model sizes, pipeline parallelism provides only a partial solution to the memory bottleneck in large-scale DNN training. Careful partitioning of the DL model over the available GPUs based on memory usage is required to further alleviate the memory bottleneck and train larger DNNs. mCAP is such a memory-oriented partitioning approach for pipeline parallel systems, but it does not scale to models with many layers and very large hardware setups, as it requires extensive profiling and fails to efficiently navigate the partitioning space to find the most memory-friendly partitioning. In this work, we propose CAPSlog, a scalable memory-centric partitioning approach that can recommend model partitionings for larger and more heterogeneous DL models and for larger hardware setups than existing approaches. CAPSlog introduces a new profiling method and a new, much more scalable algorithm for recommending memory-efficient partitionings. CAPSlog reduces the profiling time by 67% compared to existing approaches, searches the partitioning space for the optimal solution orders of magnitude faster and can train significantly larger models. Show less
Indonesia, as the world's most populous Muslim nation, finds itself at a pivotal juncture in the realm of technological advancement. Recognizing the profound impact of emerging technologies,... Show moreIndonesia, as the world's most populous Muslim nation, finds itself at a pivotal juncture in the realm of technological advancement. Recognizing the profound impact of emerging technologies, especially Artificial Intelligence (AI), on its trajectory is of utmost importance. While AI and Islamic beliefs may initially seem distinct, they share a common theme: an orientation toward envisioning future possibilities. Employing a blend of multimodal and mixed research methods, our objective is to scrutinize and draw comparisons between narratives and visual representations of AI's influence on religious prospects, particularly within the higher education landscape of Indonesia. We propose to investigate the curriculum, teaching practices and careers of young AI-professionals at Indonesia's oldest secular state university, namely Institut Teknologi Bandung (ITB), as well as Indonesia's first Islamic state university, namely Universitas Islamic Negeri Syarif Hidayatullah Jakarta (UIN-JKT). Notably, both institutions have recently introduced AI programs, making them rare in this regard. In this STEM-focused context, shedding light on the societal implications of AI education within Islam takes on heightened significance, with a focus on challenging Western-centric perspectives and contributing to a decolonization-centered research narrative. As this project is still in its early stages, this short paper will discuss related work and propose future directions to study Indonesia's AI-Islamic-educational future(s). 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 rapid advancement of Artificial Intelligence (AI) has opened up new frontiers of technological possibilities, yet the traditional education system in Indonesia has largely remained entrenched... Show moreThe rapid advancement of Artificial Intelligence (AI) has opened up new frontiers of technological possibilities, yet the traditional education system in Indonesia has largely remained entrenched in con- ventional practices. Despite Indonesia’s recognition for its vibrant AI innovation scene and its diverse student population, there is a signifi- cant gap in exploring the multifaceted implications of AI in, for, and by education within the country. This paper aims to address this gap by delving into three key areas: AI tutors, governance, and (virtue-based) ethical considerations. Drawing insights from both global and Islamic literature, we first examine the discourse surrounding AI tutors within Indonesia’s education system. Next, we discuss the potential applications of AI in governance, including the role of the government and the emer- gence of AI-related education in Indonesia. Thirdly, we contemplate an ethical framework encompassing issues of inequality, public policy, and Islamic-based principles. Throughout, this paper emphasizes the critical importance of examining these three facets of AI’s impact in education. Ultimately, this research raises the intriguing question of how education and AI will mutually shape each other in the future, urging further ex- ploration of this dynamic relationship. Show less
da Silva, C.A.; Hilpert, B.; Bhuvaneshwara, C.; Gebhard, P.; Nunnari, F.; Tsovaltzi, D. 2023
This paper proposes a new conceptual framework for the creation of an interoperable metaverse seamlessly incorporating AI and blockchain technologies. To achieve this, current issues of... Show moreThis paper proposes a new conceptual framework for the creation of an interoperable metaverse seamlessly incorporating AI and blockchain technologies. To achieve this, current issues of interoperability within the discipline’s state-of-the-art are first identified. Next, a new virtual ontology of the interoperable metaverse is proposed, inspired by recent developments in assemblage theory and in specific concepts by Gilles Deleuze, Manuel DeLanda, and Paulo de Assis. In the third and fourth parts of this paper, a new technical solution is presented: a taxonomy of Periodic Spacetime Sequences, devised for practical implementation of a new distributed version control system. This solution could offer an innovative framework for the interoperable metaverse, which could be built using the proposed taxonomy. 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
Deep Learning (DL) model sizes are increasing at a rapid pace, as larger models typically offer better statistical performance. Modern Large Language Models (LLMs) and image processing models... Show moreDeep Learning (DL) model sizes are increasing at a rapid pace, as larger models typically offer better statistical performance. Modern Large Language Models (LLMs) and image processing models contain billions of trainable parameters. Training such massive neural networks incurs significant memory requirements and financial cost. Hybrid-parallel training approaches have emerged that combine pipelining with data and tensor parallelism to facilitate the training of large DL models on distributed hardware setups. However, existing approaches to design a hybrid-parallel partitioning and parallelization plan for DL models focus on achieving high throughput and not on minimizing memory usage and financial cost. We introduce CAPTURE, a partitioning and parallelization approach for hybrid parallelism that minimizes peak memory usage. CAPTURE combines a profiling-based approach with statistical modeling to recommend a partitioning and parallelization plan that minimizes the peak memory usage across all the Graphics Processing Units (GPUs) in the hardware setup. Our results show a reduction in memory usage of up to 43.9% compared to partitioners in state-of-the-art hybridparallel training systems. The reduced memory footprint enables the training of larger DL models on the same hardware resources and training with larger batch sizes. CAPTURE can also train a given model on a smaller hardware setup than other approaches, reducing the financial cost of training massive DL models. Show less
In this work, we propose a novel joint frailty model assuming bivariate discretely- distributed non-parametric frailties, with an unknown finite number of mass points. This ap- proach allows to... Show moreIn this work, we propose a novel joint frailty model assuming bivariate discretely- distributed non-parametric frailties, with an unknown finite number of mass points. This ap- proach allows to detect a latent structure among subjects, clustering them in sub-populations where individuals are characterized by a common frailty value. Our method can be interpreted as an unsupervised classification tool and motivates further investigation into the reasons for similarities within the clustered subjects and dissimilarities across the clusters. This work is motivated by a study of patients with Heart Failure (HF) undergoing ACE inhibitors treatment in the Lombardia region of Italy. Recurrent events of interest are hos- pitalizations due to HF and terminal event is death for any cause. Show less
