The rise of the state in Ancient Italy went hand in hand with an increase in infrastructural power i.e. settlement centralization and urbanization. The paper discusses theoretical challenges and introduces a modeling approach to a case study, one of the earliest cities in Southern Italy, Pontecagnano, with the aim of understanding the community dynamics at the time of the earliest urbanization (ca. 900-600 BC). The model is a two-mode model that derives from social network analysis, an approach that has been fruitfully adapted to archaeological research. The model is applied to detect trends in burial contexts from the community involved. Burial was, at that time, in the region, a key instrument in the creation of memory and display of status and thus for building and consolidating state power. The analytical network model is able to detect the dynamics in the community over time very well: network Cohesion is expanding and contracting, and points to the existence of tension and a tight control of funerary behavior. The study of Centrality of selected nodes provides a good understanding of the strategies in terms of the circulation of key resources. The latter is particularly significant for studying urbanization because the appropriation of resources was not possible without centralization and the development of infrastructure, as well as an ideology. Based on the study of selected resources it is suggested that an increase in crop storage has played a particular role in the development of state power and the urbanization process at Pontecagnano. In due course, the paper also addresses methodological challenges of working with fragmented datasets when applying models to study the past.

Immersive technology, such as virtual reality, provides us with novel opportunities to create and explore affective experiences with a transformative potential mediated through awe. The profound emotion of awe, that is experienced in response to witnessing vastness and creates the need for accommodation that can lead to restructuring of one's worldview and an increased feeling of connectedness. An iconic example of the powers of awe is observed in astronauts who develop instant social consciousness and strong pro-environmental values in response to the overwhelming beauty of Earth observed from space. Here on Earth, awe can also be experienced in response to observing vast natural phenomenon or even sometimes in response to some forms of art, presenting vast beauty to its audience. Can virtual reality provide a new powerful tool for reliably inducing such experiences? What are some unique potentials of this emerging medium? This paper describes the evaluation of an immersive installation "AWE" – Awe-inspiring Wellness Environment. The results indicate that the experience of being in "AWE" can elicit some components of awe emotion and induce minor cognitive shifts in participant's worldview similar to the Overview Effect, while this experience also has its own attributes that might be unique to this specific medium. Comparing the results of this study to other virtual environments designed to elicit Overview Effect provides insights on the relationship between design features and participant's experience. The qualitative results highlight the importance of perceived safety, personal background and familiarity with the environment, and the induction of a small visceral fear reaction as a part of the emotional arc of the virtual journey – as some of the key contributers to the affective experience of the immersive installation. Even though the observed components of awe and a few indications of cognitive shift support the potential of Virtual Reality as a transformative medium, many more iterations of the design and research tools are required before we can achieve and fully explore a profound awe-inspiring transformative experience mediated through immersive technologies.

Metaphors are commonly used in interface design within Human-Computer Interaction (HCI). Interface metaphors provide users with a way to interact with the computer that resembles a known activity, giving instantaneous knowledge or intuition about how the interaction works. A widely used one in Digital Musical Instruments (DMIs) is the conductor-orchestra metaphor, where the orchestra is considered as an instrument controlled by the movements of the conductor. We propose a DMI based on the conductor metaphor that allows to control tempo and dynamics and adapts its mapping specifically for each user by observing spontaneous conducting movements (i.e., movements performed on top of fixed music without any instructions). We refer to this as mapping by observation given that, even though the systemis trained specifically for each user, this training is not done explicitly and consciously by the user. More specifically, the system adapts its mapping based on the tendency of the user to anticipate or fall behind the beat and observing the Motion Capture descriptors that best correlate to loudness during spontaneous conducting. We evaluate the proposed system in an experiment with twenty four (24) participants where we compare it with a baseline that does not perform this user-specific adaptation. The comparison is done in a context where the user does not receive instructions and, instead, is allowed to discover by playing. We evaluate objective and subjective measures from tasks where participants have to make the orchestra play at different loudness levels or in synchrony with a metronome. Results of the experiment prove that the usability of the system that automatically learns its mapping from spontaneous movements is better both in terms of providing a more intuitive control over loudness and a more precise control over beat timing. Interestingly, the results also show a strong correlation betweenmeasures taken fromthe data used for training and the improvement introduced by the adapting system. This indicates that it is possible to estimate in advance how useful the observation of spontaneous movements is to build user-specific adaptations. This opens interesting directions for creating more intuitive and expressive DMIs, particularly in public installations.

The Everyday Sexism Project documents everyday examples of sexism reported by volunteer contributors from all around the world. It collected 100,000 entries in 13+ languages within the first 3 years of its existence. The content of reports in various languages submitted to Everyday Sexism is a valuable source of crowdsourced information with great potential for feminist and gender studies. In this paper, we take a computational approach to analyze the content of reports. We use topic-modelling techniques to extract emerging topics and concepts from the reports, and to map the semantic relations between those topics. The resulting picture closely resembles and adds to that arrived at through qualitative analysis, showing that this form of topic modeling could be useful for sifting through datasets that had not previously been subject to any analysis. More precisely, we come up with a map of topics for two different resolutions of our topic model and discuss the connection between the identified topics. In the low resolution picture, for instance, we found Public space/Street, Online, Work related/Office, Transport, School, Media harassment, and Domestic abuse. Among these, the strongest connection is between Public space/Street harassment and Domestic abuse and sexism in personal relationships.The strength of the relationships between topics illustrates the fluid and ubiquitous nature of sexism, with no single experience being unrelated to another.

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two main purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning); a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance.

We conduct the first experiment in the literature in which a novel is translated automatically and then post-edited by professional literary translators. Our case study is Warbreaker, a popular fantasy novel originally written in English, which we translate into Catalan. We translated one chapter of the novel (over 3,700 words, 330 sentences) with two data-driven approaches to Machine Translation (MT): phrase-based statistical MT (PBMT) and neural MT (NMT). Both systems are tailored to novels; they are trained on over 100 million words of fiction. In the post-editing experiment, six professional translators with previous experience in literary translation translate subsets of this chapter under three alternating conditions: from scratch (the norm in the novel translation industry), post-editing PBMT, and post-editing NMT. We record all the keystrokes, the time taken to translate each sentence, as well as the number of pauses and their duration. Based on these measurements, and using mixed-effects models, we study post-editing effort across its three commonly studied dimensions: temporal, technical and cognitive. We observe that both MT approaches result in increases in translation productivity: PBMT by 18%, and NMT by 36%. Post-editing also leads to reductions in the number of keystrokes: by 9% with PBMT, and by 23% with NMT. Finally, regarding cognitive effort, post-editing results in fewer (29 and 42% less with PBMT and NMT, respectively) but longer pauses (14 and 25%).

In this work, we describe a methodology to interpret large persons' networks extracted from text by classifying cliques using the DBpedia ontology. The approach relies on a combination of NLP, Semantic web technologies and network analysis. The classification methodology that first starts from single nodes and then generalises to cliques is effective in terms of performance and is able to deal also with nodes that are not linked to Wikipedia. The gold standard manually developed for evaluation shows that groups of co-occurring entities share in most of the cases a category that can be automatically assigned. This holds for both languages considered in this study. The outcome of this work may be of interest to enhance the readability of large networks and to provide an additional semantic layer on top of cliques. This would greatly help humanities scholars when dealing with large amounts of textual data that need to be interpreted or categorised. Furthermore, it represents an unsupervised approach to automatically extend DBpedia starting from a corpus.

This paper provides insights into aspects of tempo and rhythmic elaboration in Hindustani music, based on a study of a large corpus of recorded performances. Typical tempo developments and stress patterns within a metrical cycle are computed, which we refer to as tempo and rhythm patterns, respectively. Rhythm patterns are obtained by aggregating spectral features over metrical cycles. They reflect percussion patterns that are frequent in the corpus, and enable a discussion of the relation between such patterns and the underlying metrical framework, the taal. Tempo patterns, on the other hand, are computed using reference beat annotations. They document the dynamic development of tempo throughout a metrical cycle, and reveal insights into the flexibility of time in Hindustani music for the first time using quantitative methods on a large set of performances. Focusing on aspects of tempo and rhythm, we demonstrate the value of a computational methodology for the analysis of large music corpora by revealing the range of tempi used in performances, intra-cycle tempo dynamics and percussion accents at different positions of the taal cycle.

The concept of digital scholarship is much discussed. Most writing on scholarship takes its cue from the work of Boyer (1990). Boyer was a senior figure in higher education, at one point Chancellor of the State University of New York who developed an account of what it means to be a scholar. He developed a view on teaching and research activities in the modern university and came up with a conceptualization of scholarship encompassing four-dimensional functions....

Guanxi, roughly translated as "social connection", is a term commonly used in the Chinese language. In this research, we employed a linguistic approach to explore popular discourses on Guanxi. Although sharing the same Confucian roots, Chinese communities inside and outside Mainland China have undergone different historical trajectories. Hence, we took a comparative approach to examine guanxi in Mainland China and in Taiwan, Hong Kong, and Macau (TW-HK-M). Comparing guanxi discourses in two Chinese societies aims at revealing the divergence of guanxi culture. The data for this research were collected on Twitter over a three-week period by searching tweets containing guanxi written in Simplified Chinese characters and in Traditional Chinese characters. After building, visualising, and conducting community detection on both semantic networks, two guanxi discourses were then compared in terms of their major concept sub-communities. This research aims at addressing two questions: Has the meaning of guanxi transformed in contemporary Chinese societies? And how do different socio-economic configurations affect the practice of guanxi? Results suggest that guanxi in interpersonal relationships has adapted to a new family structure in both Chinese societies. In addition, the practice of guanxi in business varies in Mainland China and in TW-HK-M. Furthermore, an extended domain was identified where guanxi is used in a macro-level discussion of state relations. Network representations of the guanxi discourses enabled reification of the concept and shed lights on the understanding of social connections and social orders in contemporary China.