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In order to remain alive and relevant, cultural heritage sites have to react and adapt to changing context in a coherent manner, i.e., in a way that is in line with the memory and identity of the place. The incoherent changes, i.e., the transformations that according to the local community do not agree with a character of a place, can be destructive for the long-term vitality of urban cultural heritage. In this study, we test which factors influence social acceptance of different alternations within the context of urban historical gardens that might, in turn, ensure the resilience of the place. Our study focuses on the intangible qualities of the place measured by intrinsic value, perceived essentialism and anti-essentialism as important predictors shaping the response to change. The correlational study was conducted using an online questionnaire designed to empirically grasp intangible qualities of cultural heritage sites. Five hundred twenty-nine responses were included in the analysis. The study shows that perceived historic value, inherent value (uniqueness and importance of the place) and (anti-)essentialist character of a place capture the differences between parks well and enables the finding of interventions that are coherent with a site’s genius loci. Measuring intangible qualities of urban gardens can help to design changes that find higher approval among local community members and users of the site. We discuss how the analysis of an intrinsic value and essentialism allows for planning better spatial interventions that align with the human-centered approach to urban development.

Part 4: Communications: Types and Implementations; International audience; There are two approaches to defining subtyping relations: the syntactic and the semantic approach. In semantic subtyping, one defines a model of the language and an interpretation of types as subsets of this model. Subtyping is defined as inclusion of subsets denoting types.An orthogonal subtyping question, typical of object-oriented languages, is the nominal versus the structural subtyping. Dardha et al. [11, 12] defined boolean types and semantic subtyping for Featherweight Java (FJ) and integrated both nominal and structural subtyping, thus exploiting the benefits of both approaches. However, these benefits were illustrated only at a theoretical level, but not exploited practically.We present SFJ—Semantic Featherweight Java, an implementation of FJ which features boolean types, semantic subtyping and integrates nominal as well as structural subtyping. The benefits of SFJ, illustrated in the paper and the accompanying video (with audio/subtitles) [27], show how static type-checking of boolean types and semantic subtyping gives higher guarantees of program correctness, more flexibility and compactness of program writing.

Studies have documented that traditional motor skills (i.e. motor habits) are part of the cultural way of life that characterises each society. Yet, it is still unclear to what extent motor skills are inherited through culture. Drawing on ethnology and motor behaviour, we addressed this issue through a detailed description of traditional pottery skills. Our goal was to quantify the influence of three kinds of constraints: the transcultural constraints of wheelthrowing, the cultural constraints induced via cultural transmission, and the potters’ individual constraints. Five expert Nepalese potters were invited to produce three familiar pottery types, each in five specimens. A total of 31 different fashioning hand positions were identified. Most of them (14) were cross-cultural, ten positions were cultural, five positions were individual, and two positions were unique. Statistical tests indicated that the subset of positions used by the participants in this study were distinct from those of other cultural groups. Behaviours described in terms of fashioning duration, number of gestures, and hand position repertoires size highlighted both individual and cross-cultural traits. We also analysed the time series of the successive hand positions used throughout the fashioning of each vessel. Results showed, for each pottery type, strong reproducible sequences at the individual level and a clearly higher level of variability between potters. Overall, our findings confirm the existence of a cultural transmission in craft skills but also demonstrated that the skill is not fully determined by a cultural marking. We conclude that the influence of culture on craft skills should not be overstated, even if its role is significant given the fact that it reflects the socially transmitted part of the skill. Such research offers insights into archaeological problems in providing a representative view of how cultural constraints influence the motor skills implied in artefact manufacturing.

Neologism detection is a key task in the constructing of lexical resources and has wider implications for NLP, however the identification of multiword neologisms has received little attention. In this paper, we show that we can effectively identify the distinction between compositional and non-compositional adjective-noun pairs by using pretrained language models and comparing this with individual word embeddings. Our results show that the use of these models significantly improves over baseline linguistic features, however the combination with linguistic features still further improves the results, suggesting the strength of a hybrid approach.

In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.

Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. The research has been mostly focused on English language, though. While massively multilingual models exist, studies have shown that monolingual models produce much better results. We train two trilingual BERT-like models, one for Finnish, Estonian, and English, the other for Croatian, Slovenian, and English. We evaluate their performance on several downstream tasks, NER, POS-tagging, and dependency parsing, using the multilingual BERT and XLM-R as baselines. The newly created FinEst BERT and CroSloEngual BERT improve the results on all tasks in most monolingual and cross-lingual situations Comment: 10 pages, accepted at TSD 2020 conference

Reusing terms results in a Network of Linked vOcabularies (NeLO), where the nodes are the vocabularies that use at least one term from some other vocabulary and thus depend on each other. These dependencies become a problem when vocabularies in the network change, e. g., when terms are deprecated or deleted. In these cases, all dependent vocabularies in the network need to be updated. So far, there has been no study that analyzes vocabulary changes in NeLO over time. To address this shortcoming, we compute the state of NeLO from the available versions of the vocabularies over 17 years. We analyze static parameters of NeLO such as its size, density, average degree, and the most important vocabularies at certain points in time. We further investigate how NeLO changes over time. Specifically, we measure the impact of a change in one vocabulary to others, how the reuse of terms changes, and the importance of vocabularies changes. Our analyses provide for the first time in-depth insights into the structure and evolution of NeLO. This study helps ontology engineers to identify shortcomings of the data modeling and to assess the dependencies implied with reusing a specific vocabulary.

One of the keys for supervised learning techniques to succeed resides in the access to vast amounts of labelled training data. The process of data collection, however, is expensive, time- consuming, and application dependent. In the current digital era, data can be collected continuously. This continuity renders data annotation into an endless task, which potentially, in problems such as emotion recognition, requires annotators with different cultural backgrounds. Herein, we study the impact of utilising data from different cultures in a semi-supervised learning ap- proach to label training material for the automatic recognition of arousal and valence. Specifically, we compare the performance of culture-specific affect recognition models trained with man- ual or cross-cultural automatic annotations. The experiments performed in this work use the dataset released for the Cross- cultural Emotion Sub-challenge of the Audio/Visual Emotion Challenge (AVEC) 2019. The results obtained convey that the cultures used for training impact on the system performance. Furthermore, in most of the scenarios assessed, affect recogni- tion models trained with hybrid solutions, combining manual and automatic annotations, surpass the baseline model, which was exclusively trained with manual annotations.

This paper analyzes the application of usage labels in three representative lexicographic works, namely the Portuguese, Spanish, and French Academy Dictionaries as a starting point for creating a consistent classification of usage labels and their encoding in accordance with TEI Lex-0. The use of labels is not always entirely consistent within individual dictionaries and even less so across different lexicographic projects. This makes the tasks of accurately classifying and encoding them quite difficult. This difficulty is compounded by the differences and partial incompatibilities found in the lexicographic literature on the treatment of diasystemic information. We address the existing literature and the initial classification of TEI Lex-0, and argue for the need to introduce some changes to TEI Lex-0, most notably in terms of diatextual labels. Finally, we argue that the existing classifications based on examples rather than on clear and explicit definitions of classification categories will always lack in precision and lead to mutually incompatible encodings of different dictionaries. We propose a set of definitions for usage label categories that can be adopted by TEI Lex-0 and used in other similar attempts to create interoperable lexical resources. An agreement on usage label categories is a first and necessary step before proceeding in the direction of harmonizing and standardizing the actual values of usage labels across various dictionaries and across different languages.

Digital technologies can help with preservation of cultural heritage and virtual reality can be used for that purpose. In this paper, a virtual reality application that has the potential of assisting the learning process of folk dances is introduced. This application includes three different assisting approaches that are presented and evaluated with 30 healthy participants. An animated avatar of the professional dancer is shown in immersive virtual reality and participants were asked to imitate the movements in order to learn the dance. Movements were recorded using a passive optical motion capture system and afterwards compared to the recordings from the professional dancers. Questionnaire data were also collected and initial results indicate that participants that had feedback provided achieved better performance.