dataesr/scientific-paragraphs-categorization huggingface dataset:
UKC is the only resource introducing a hierarchy of shared meanings across languages for each semantic eld, such as kinship, colors, or food. Furthermore, UKC uses a hybrid linguistic-conceptual approach in modeling each domain. is approach adopts actual domain ontology and linguistic data from typological literature. For example, a fragment of the brotherhood hierarchy in the top layer of the UKC is shown in Figure  1. A native speaker can compare each examined concept from the spreadsheet with the hierarchy of its domain to extract additional knowledge Methodology macro-steps and data sources. about its meaning based on a concept's position in the hierarchy, which helps to provide a concrete answer in terms of a gap or a lexical unit.
e process of providing such contributions is depicted in two owcharts; for instance, Figure  3  shows the owchart of the candidate gap (on the le-hand side of the gure) and candidate equivalent word meaning (on the right-hand side of the gure) exploration; it starts identifying a standard language and a local language (or dialect) and providing a native speaker with a spreadsheet including a list of subdomain concepts (inputs). en, a native speaker is asked to nd a linguistic resource in the local language and use it to search for concepts (concept-by-concept) to con rm lexicalizations and gaps. He/she can use a linguistic resource in the search process as the following steps: searching in a well-known dictionary, then in Wiktionary-a large multilingual online lexicon aer that in a typology dataset (if it is available), and nally, using Google search (based on the count of search hits).
More details about these steps are described in Section 5. e native speaker can rely on search results and the count of Google hits to give a more concrete answer on whether the concept in the standard language has a lexicalization or is a gap in the local language; such candidates are passed to the next phase-lexicon-level validation. A new concept collection is a third contribution in this phase, where the steps of a candidate new concept exploration in a local language can be seen in Figure  4. A native speaker can examine the list of subdomain concepts and provide his/her (own) concepts with their de nitions that he/she believes have not existed in the list. e same search steps in gap identi cation can be followed in this task. As shown in Figure  4 , All candidate new concepts are passed to the two subsequent validation phases: lexicon-and conceptual level.
Lexicon-level validation Our lexicon-level validation method formally and explicitly addresses individual gap identi cations and their quality, as well as equivalent word meanings and new concepts. It allows a qualitative evaluation of the entire list of provided contributions through wordby-word and gap-by-gap in a loop between a native speaker and a validator. A word, a gap, or a new concept does not pass this validation until the native speaker provides the correct answer for each of them, as shown in the owcharts in Figures  3, 4. A language expert who is also a native speaker of the determined language (or dialect) will carry out this validation on a spreadsheet containing the data and results gathered in the previous step with two additional empty columns: the evaluation and lexicon-level validator's comment, producing the following information: 1.
e selected dialects are Egyptian, Moroccan, Tunisian, Algerian, Gulf, and South Levantine (two examples: Palestinian and Syrian). Let us take the example of the Gulf word ْ ‫العود‬ ‫ال‬ َ ‫خ‬ ‫ال‬ meaning "mother's elder brother, " which has no equivalent in South Levantine or Moroccan; instead, they use the more general word ‫ال‬ َ ‫خ‬ ‫ال‬ meaning "mother's brother, " which can be used for both meanings "mother's younger brother" or "mother's elder brother". In this paper, we perform an experiment on the Arabic dialects to capture their diversity in the kinship domain. e resulting dataset with dialect-speci c kinship terms will be integrated with an instance of the Universal Knowledge Core for Arabic (Arabic UKC) 6 ongoing project, which is the rst diversity-aware lexical resource for Arabic dialects so far.
Experiment setup As mentioned in Section 3, the UKC resource is our data source in building the input dataset of kinship-independent language concepts and formalizing such concepts and new word meanings (not existing in the inputs) explored in this experiment. For example, the brotherhood hierarchy is shown in the top layer of the UKC in Figure  1. In this study, contributions are provided by seven native speakers (one per Arabic dialect). Regarding the contributors' sociolinguistic background, each has at least a master's degree and was born and educated, at least up to high school level, within the native speaker community. e participants' linguistic backgrounds are presented below: Seven experiments (one for each dialect) are performed to explore lexical units and gaps using our method.
In each experiment, a spreadsheet of kinship concepts is imported from the UKC (as the source, they were computed from the KinDiv database), which serves as an input dataset to the contribution (diversity-aspects) collection step. ese kinship domain concepts are languageindependent units representing lexical meaning shared across 699  Structural elements in the UKC database after merging new concepts. languages and spanning 184 distinct concepts. UKC categorizes kinship concepts into six groups; each one contains a distinct subset of concepts sharing a common kinship type meaning called a subdomain, for example, sibling and cousin subdomains. e spreadsheet (the dataset) consists of six sheets, and each one represents a kinship subdomain. See Table  2 , which shows FIGURE 6 Exploring the concept of ّ ‫َد‬ ‫ج‬ as lexicalized in the Arabic language (left), in the world (middle), and as part of the shared concept hierarchy (right).
Evidence for validation was obtained from the dictionary Dictionnaire arabe algérien 9  and from usage attested in Algerian TV lms. Upon discussion between the validator and the participants, the mistakes made by the latter can be explained by misunderstandings of the meanings of certain concepts provided in MSA and English. e validator made sure to exclude or x the mistakes, bringing the correctness of the nal dataset closer to 100%. In this study, we use the UKC for creating the input dataset and the domain hierarchy and for storing and visualizing diversity The number of words in the intersection of Indonesian languages according to shared meaning. data. us, the 19 new concepts were merged with the UKC by reconstructing a domain hierarchy at the supra-lingual concept layer. For example, the hierarchy of siblings was redesigned to contain ve new brotherhood concepts and ve new sisterhood concepts.
For instance, in the Arabic-Egyptian lexicon, as shown in Figure  5 , ‫الرضاعة‬ ‫في‬ ٌ ‫خ‬ ‫أَ‬ meaning "breastfeeding brother, " is set up as a sub-node for a newly created concept of the brother, "a male person who has the same father, mother, or both parents as another person or has the same breastfeeding woman. ", also, from the gure, can be seen ‫لأب‬ ‫ٌت‬ ‫خ‬ ‫أَ‬ meaning "paternal brother" and ّ ‫لأم‬ ‫ٌت‬ ‫خ‬ ‫أَ‬ meaning "maternal brother" are inserted and connected the halfbrother concept. New concepts and lexicalization are marked with white nodes and connected with blue lines. Additionally, resulting lexical units and gaps were added into UKC lexicons. e website of the UKC provides several services for system users, such as browsable online access to database contents, source materials, and data visualization tools.
e interactive exploration of linguistic diversity data in lexicons is the central feature of the website. e user can browse: (1) all meanings within a language of a word typed in by the user; and (2) lexicalizations and gaps of a concept in all languages contained in the database. Figure  6  shows a screenshot of the concept exploration functionality describing the concept ّ ‫َد‬ ‫ج‬ meaning "parent's father". On the le-hand side of the screenshot, details are provided on the lexicalization of the concept in Arabic, such as synonymous words, a de nition, and a part of speech. e middle part of the screenshot shows an interactive clickable map of all lexicons that either contain the concept or, on the contrary, lack it due to their languages being known not to lexicalize it. e color-coded dots indicate the language family, while the black circled dot represents a lexical gap.
is map presents an instant global typological overview of the concept selected; for instance, from Figure  6 , one can see that most languages in Europe lexicalize the concept ّ ‫َد‬ ‫ج‬ while several languages in the American United States do not lexicalize it. Finally, the right-hand side shows the concept ّ ‫َد‬ ‫ج‬ in the context of concept hierarchy, depicted as an interactive graph: the concept, its parent and child concepts, and other lexical-semantic relations (as metonymy and meronymy) are also presented when they exist. Note that the graph only shows a part of the complete hierarchy for usability reasons. Nevertheless, it is navigable and allows the exploration of the whole concept graph in the selected language. As mentioned at the beginning of this section, the resulting Arabic dataset will be imported into the Arabic UKC, which is an instance of the UKC system; the top layer contains independent language concepts, and the bottom layer contains twenty lexicons as the number of Arabic dialects.
For a given domain d and languages l a ,. , l n , the formula below calculates the similarity of the two languages in terms of the overlap of lexicalized concepts from that domain, where LexConcepts(d, l) stands for the set of domain concepts that are lexicalized by the language l. overlap(d, l a ,. , l n ) = |LexConcepts(d, l a ) ∩. ∩ LexConcepts(d, l n )| max(|LexConcepts(d, l a )|,. , |LexConcepts(d, l n )|) (1) Figure  8  shows the overlaps between pairs of Arabic dialects over the kinship domain. For example, the intersection of Egyptian and Gulf dialects gives a shared coverage of 74. 5%, while all dialects are 47. 1% the same. In the former case, the number of lexicalizations in Egyptian is 51, and in Gulf is 42. Also, 38 of these lexical units are included in both dialects; see the dataset uploaded to GitHub.
10  For example, Formula 1 calculates the overlap between Egyptian and Gulf in the Kinship domain (K) as follows: overlap(K, Egyptian, Gulf) = |LexConcepts(K, Egyptian) ∩ LexConcepts(K, Gulf)| max(|LexConcepts(K, Egyptian)|, |LexConcepts(K, Gulf)|) FIGURE 12 The number of words in the intersection of Indonesian and Arabic languages according to shared meaning. More detail about the analysis of shared coverage between the rest of the Arabic dialects can be found in the same dataset uploaded to GitHub. We nd these overlaps-e. g. , an overlap of 59. 5% between Gulf and Tunisian, or the overall overlap of 47. 1% among all seven dialects-lower than our initial expectations on dialectal variations. Arab dialectologists justify such differences with two major factors: linguistic and religious in uence  (Zaidan and Callison-Burch, 2014).
Experiment setup As in the Arabic experiment, we use the UKC lexicon to create the input dataset of kinship terms, which are independent language and formalizing such terms and also new concepts (not existing in the input dataset) identi ed in this experiment, as shown in the top layer of the UKC in Figure  1  for the brotherhood categorization. In this study, three native speakers (one per language), born and educated (high school level) within the speaker community, were recruited to contribute. e participants' linguistic backgrounds are listed below: 1. Participant 1: a native Indonesian speaker with good command of English, Javanese, and Banjarese. 2. Participant 2: a native Banjarese speaker with good command of Indonesian and English. 3. Participant 3: a native Javanese speaker with good command of Indonesian and English.
For each language, an experiment was carried out to identify words and gaps associated with the same 184 kinship concepts as in the Arabic study (see Table  2 ). For example, in Banjarese, the dictionary Kamus Bahasa Banjar Dialek Hulu-Indonesia  (Balai Bahasa Banjarmasin, 2008)  and Google Search hits were used in subsequent steps to provide a precise answer on each concept from the given list of inputs. Such search steps were also followed by the Banjarese native speaker for the task of judgment on new concepts identi ed in the uncle/aunt subdomain. For instance, the Banjarese term gulu, expressing an uncle/aunt relationship with the meaning of a parent's second eldest sibling and attested by the dictionary above, did not previously exist in the UKC or in the KinDiv dataset, nor in  Murdock (1970).
In this step, the new concepts were veri ed and approved to be added to the concept layer of the UKC. Table  7  provides correctness results over native speaker contributions, provided by the validator. Upon discussion between the validator and the contributors, the mistakes made by the latter can be explained by misunderstandings of the meanings of certain concepts, provided in English. e validator made sure to exclude or x the mistakes, bringing the correctness of the nal dataset closer to 100%. e produced kinship datasets from this experiment will be merged with the under-construction Indonesian UKC 11  , a diversityaware lexicon for languages spoken in Indonesia, also imported into the main UKC database. Figure  9  shows how UKC explores information about a speci c Indonesian word. However, the screenshot provides information about the Indonesian word saudara, which means "sibling" in English.
Discussion More than 90% of our 184 initial kinship concepts were found to be gaps in the three Indonesian languages, as shown in Table  6. Using Formula 1, we calculated the overlaps between the Indonesian languages in terms of kinship lexicalizations, shown in Figure  11. For more details, see the dataset uploaded to the GitHub repository 12. 35. 3% of the concepts are lexicalized by the three Indonesian languages studied. e Javanese-Banjarese overlap is 52. 9%, Javanese-Indonesian is 60%, and nally Banjarese-Indonesian is 41. 2%. Even though all three languages are included in the Malayo-Polynesian branch of the Austronesian language family, Indonesian and Banjarese are considered Malayic languages, while Javanese is not, which is the rst reason for this result. Furthermore, these languages exist on different islands in Indonesia; Javanese exists on Java Island, Banjarese is located on the southern part of Borneo Island, and the Indonesian language is based on Malay, which is spoken on Sumatra Island  (Sneddon, 2003) , so this geographical barrier restricts interactions between speakers, and each language has developed within its own speech community.
Related work Ethnologists and linguists have for a long time studied how family structures map to kinship terminology across languages and social groups. e most famous and comprehensive ethnographic study on kin term patterns is that of  Murdock (1970) , upon which our work also indirectly relied: our cross-lingual formalization of kin terms is based on the one provided by the KinDiv resource, itself in part derived from Murdock's data. KinDiv covers 699 languages and is a computer-processable database that can also be exploited for applications in computational linguistics. Our results provide linguistic evidence in seven Arabic dialects and three Indonesian languages that do not gure in these resources. e exploration of kin terminology and the building of largescale databases on the topic has also been the subject of more recent efforts-we only cite two examples here.
e AustKin project 13  has produced a large-scale database on kin terms in hundreds of indigenous Australian languages. e recent Kinbank database  (Passmore et al. , 2023 ) is a comprehensive resource on kinship terminology, covering over 1,173 languages, with a broad coverage of kinship subdomains. As Kinbank was released aer the initial submission of our paper, we did not rely on it for our work. We consider our research as complementary to Kinbank: concentrating on a relatively low number of dialects and languages, our results could, in principle, be integrated into Kinbank in order to extend its coverage. And vice versa, we see potential in using Kinbank data in order to cross-validate and possibly to extend the Indonesian terms we collected (as the three Indonesian languages of our study are also covered by Kinbank).
For example, in Bahasa Wordnet, the English word sister, which means "a female person who has the same parents as another person, " was mapped to the Indonesian word kakak which means "elder sibling. " Finally, we mention MultiWordNet as an early effort at improving the representation of linguistic diversity in multilingual lexical databases  (Pianta et al. , 2002). It is a multilingual lexicon that was built using the merge method that, contrary to the translationbased expand approach presented above, maps together existing high-quality bilingual dictionaries. MultiWordNet explicitly represents lexical gaps in its Italian and Hebrew wordnets: about 1,000 in Italian and about 300 in Hebrew  (Bentivogli and Pianta, 2000; Ordan and Wintner, 2007). MultiWordNet, however, is a discontinued effort that does not cover the kinship domain and is thus was not suitable for our purposes.
e experiments show that our method outperforms the existing methods in terms of the quantity of explored gaps and words and the quality of results. Overall efforts resulted in 1619 gaps, and 223 words were identi ed in 10 languages and dialects. Moreover, 22 new word meanings with respect to the imported list of independent-language concepts from the UKC are explored in this research. In future work, we plan to automate the method presented in this paper and apply it to new languages, such as the rest of the Arabic dialects and Indonesian language, as well as to new domains that are known to be diverse, such as body parts, food, color, or visual objects  (Giunchiglia and Bagchi, 2021; Giunchiglia et al. , 2023). Finally, diversity-aware lexicons such as the UKC (which includes our produced datasets) provide essential information to cross-lingual applications, such as multilingual NLP tasks or crosslingual language models.
Data analysis To remove oscillations due to stepping activity, data were filtered using a 10 Hz butterworth low pass filter. Shoulder and aperture widths were calculated using the horizontal position of both GH and door markers respectively. Separate kinematic measures were taken for the approach phase, corresponding to the time between the 2 nd and 4 th second after the movement starts (approach), and the crossing time which refers to the moment when the participant walks through the aperture (tcross) (Fig. 2. ; NB: a similar method was adopted by Wilmut et al. [18, 19] ). We then computed the following kinematic variables: Walking speed Participant's horizontal position of Centre Of Mass (COM) was approximated as the middle of the right and left GH and C7. Instantaneous walking speed was calculated as the first time derivative of COM anteroposterior position.
Statistical analysis J o u r n a l P r e -p r o o f Data analysis was performed using RStudio (Version 4. 1. 3; RStudio, Inc) and MATLAB (Version R2021b, MathWorks, Natick, USA). Statistical significance was set at p < 0. 05. Normality and homogeneity of variance were assessed using Shapiro-Wilk and Levene tests. Differences between groups for participants' characteristics (age, sex, shoulder width, MMSE, HADS, EQ-5D) and critical points were assessed using student's or welch's t-test according to the conditions of homogeneity of the variance or Wilcoxon rank-sum test and Chi-Squared test. Mixed Anovas followed by pairwise comparisons post-Hoc tests with Bonferroni correction were used to assess the group effect on the SA ratio for walking speed, shoulder rotation angle, phase shift between the scapular and pelvic angles and trunk stability during approach and tcross.
Data analysis The proportion of responses perceiving the second displacement as larger was calculated for each condition, according to the different comparison values. This was used to fit psychometric curves for each participant and condition with a normal cumulative function via the quickpsy package of R (version 4. 0. 0). We used this package since it has been specifically developed for this type of analysis  (Linares and López-Moliner, 2016)  and recently used in a similar task  (Paraskevoudi and SanMiguel, 2021). The lower asymptote of the psychometric function that corresponds to the gamma parameter of the fitting model was set to 0. The upper asymptote (i. e. , lambda) which corresponds to the lapse rate was set to 0. 001. These fitting parameters have previously been used in other 2-AFC discrimination tasks  (Paraskevoudi and SanMiguel, 2021)  and enabled us to generate fitting models with the most satisfactory Akaike information criterion for our data.
The Akaike Information Criterion (AIC) is a mathematical method for evaluating how well a model fits the data from which it was generated. An AIC score is assigned based on the relative amount of information lost by a given model. Thus, the less information a model loses, the lower the score, and the higher the quality of the model. Two variables were extracted from the psychometric curves for each participant in each condition. First, the Point of Subjective Equality (PSE) corresponding to the value at which the comparison displacement is judged statistically equal to the standard displacement, which is used to express a potential perceptual bias across conditions. Indeed, a shift in PSE relative to the Point of Objective Equality (i. e. , the point of physical equality between the two displacements, here 0 m.
Study Design and Procedure The present study employed an observational, cross-sectional design. Surveys were applied using a paper-and-pencil self-administered method, and all design elements adhered to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines  [28]. Healthcare students were recruited using a stratified random sampling method according to matriculation year and major from Hangzhou, China, during the fall semester of 2020. Data were collected during a low-risk COVID-19 infection period in the region of the school. Data collection was conducted during breaks between classes, and all students who reported to class on data collection days were included. The exclusion criteria were as follows: (1) international students who could not fully understand and write Chinese; (2) students who were on suspension or long-term medical leave.
Statistical Analysis The database was built in EpiData (version 3. 1) software. Statistical analysis was conducted with R (version 4. 1. 2) and JASP (version 0. 16. 1). For missing values (0. 229%), continuous variables were replaced by mean (i. e. , age) and categorical variables were replaced by median (e. g. , gender, home location, and academic year)  [36]. A one-sample t-test and one-way analysis of variance (ANOVA) were used to compare differences in the SQQ scores between subgroups (e. g. , academic year and part-time employment). Spearman's correlation was used to evaluate the association among the SQQ, PHQ-4, and SRHQ. A structural equation model (SEM) was used to model the direct, indirect, and total effects of sleep quality on self-rated health, and whether depressive and anxiety symptoms explained any observed associations.