The body beyond movement: (Missed) opportunities to engage with contemporary dance in HCI

This paper argues that a significant paradigm change in contemporary dance can offer further opportunities for HCI researchers interested in embodied interaction and interactive system design. Based on the analysis of 42 HCI papers in our data set, resulting from searches in two computing research libraries, we suggest seven thematic categories that reflect how HCI researchers have been engaging with contemporary dance. Moreover, we propose a standardized usage of contemporary dance terminology in HCI literature, and discuss the current state of engagement with publications from the field of performance theory. We identify three opportunities for HCI, which can arise through further engagement with the knowledge produced in contemporary dance and performance: to engage with the field of embodied interaction from the perspective of performance research and theory; to employ contemporary dance methods and practices in HCI research; and to integrate contemporary dance choreographers and performers as researchers in interdisciplinary projects

The Ornaments of the Arma Veirana Early Mesolithic Infant Burial

Personal ornaments are widely viewed as indicators of social identity and personhood. Ornaments are ubiquitous from the Late Pleistocene to the Holocene, but they are most often found as isolated objects within archaeological assemblages without direct evidence on how they were displayed. This article presents a detailed record of the ornaments found in direct association with an Early Mesolithic buried female infant discovered in 2017 at the site of Arma Veirana (Liguria, Italy). It uses microscopic, 3D, and positional analyses of the ornaments as well as a preliminary perforation experiment to document how they were perforated, used, and what led to their deposit as part of the infant’s grave goods. This study provides important information on the use of beads in the Early Mesolithic, in general, as well as the relationship between beads and young subadults, in particular. The results of the study suggest that the beads were worn by members of the infant’s community for a considerable period before they were sewn onto a sling, possibly used to keep the infant close to the parents while allowing their mobility, as seen in some modern forager groups. The baby was then likely buried in this sling to avoid reusing the beads that had failed to protect her or simply to create a lasting connection between the deceased infant and her community.publishedVersio

The Ornaments of the Arma Veirana Early Mesolithic Infant Burial

Personal ornaments are widely viewed as indicators of social identity and personhood. Ornaments are ubiquitous from the Late Pleistocene to the Holocene, but they are most often found as isolated objects within archaeological assemblages without direct evidence on how they were displayed. This article presents a detailed record of the ornaments found in direct association with an Early Mesolithic buried female infant discovered in 2017 at the site of Arma Veirana (Liguria, Italy). It uses microscopic, 3D, and positional analyses of the ornaments as well as a preliminary perforation experiment to document how they were perforated, used, and what led to their deposit as part of the infant’s grave goods. This study provides important information on the use of beads in the Early Mesolithic, in general, as well as the relationship between beads and young subadults, in particular. The results of the study suggest that the beads were worn by members of the infant’s community for a considerable period before they were sewn onto a sling, possibly used to keep the infant close to the parents while allowing their mobility, as seen in some modern forager groups. The baby was then likely buried in this sling to avoid reusing the beads that had failed to protect her or simply to create a lasting connection between the deceased infant and her community.Funding was provided by the Wenner-Gren Foundation (#9412), L.S.B. Leakey Foundation, National Geographic Society Waitt Program (#W391-15), Hyde Family Foundation [via the Human Origins Migrations and Evolutionary Research (HOMER) consortium], Social Sciences and Humanities Research Council (SSHRC) Insight Development Grant (#430–2018-00846), University of Colorado Denver, Washington University in St. Louis, Université de Montréal, and ERC n. 724046 – SUCCESS (to S.B.; http://www.erc-success.eu/). Part of the ornament analysis was supported by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement no. 639286 HIDDEN FOODS to E.C; http://www.hidden-foods.eu) to E.C. CHEI (University of California San Diego) supported 3D imaging. S. Talamo has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 803147 RESOLUTION, https://site.unibo.it/resolution-erc/en). The micro-CT scans have been co-funded by EuroBioimaging, Italian Multi-sited Multi-modal Molecular Imaging (MMMI) Node, application n.EuBI_FANE130

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization

Translation Alignment Applied to Historical Languages: methods, evaluation, applications, and visualization

Translation alignment is an essential task in Digital Humanities and Natural Language Processing, and it aims to link words/phrases in the source text with their translation equivalents in the translation. In addition to its importance in teaching and learning historical languages, translation alignment builds bridges between ancient and modern languages through which various linguistics annotations can be transferred. This thesis focuses on word-level translation alignment applied to historical languages in general and Ancient Greek and Latin in particular. As the title indicates, the thesis addresses four interdisciplinary aspects of translation alignment. The starting point was developing Ugarit, an interactive annotation tool to perform manual alignment aiming to gather training data to train an automatic alignment model. This effort resulted in more than 190k accurate translation pairs that I used for supervised training later. Ugarit has been used by many researchers and scholars also in the classroom at several institutions for teaching and learning ancient languages, which resulted in a large, diverse crowd-sourced aligned parallel corpus allowing us to conduct experiments and qualitative analysis to detect recurring patterns in annotators’ alignment practice and the generated translation pairs. Further, I employed the recent advances in NLP and language modeling to develop an automatic alignment model for historical low-resourced languages, experimenting with various training objectives and proposing a training strategy for historical languages that combines supervised and unsupervised training with mono- and multilingual texts. Then, I integrated this alignment model into other development workflows to project cross-lingual annotations and induce bilingual dictionaries from parallel corpora. Evaluation is essential to assess the quality of any model. To ensure employing the best practice, I reviewed the current evaluation procedure, defined its limitations, and proposed two new evaluation metrics. Moreover, I introduced a visual analytics framework to explore and inspect alignment gold standard datasets and support quantitative and qualitative evaluation of translation alignment models. Besides, I designed and implemented visual analytics tools and reading environments for parallel texts and proposed various visualization approaches to support different alignment-related tasks employing the latest advances in information visualization and best practice. Overall, this thesis presents a comprehensive study that includes manual and automatic alignment techniques, evaluation methods and visual analytics tools that aim to advance the field of translation alignment for historical languages

Expressive movement generation with machine learning

Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation