Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Social touch is crucial for human well-being, as a lack of tactile interactions increases anxiety, loneliness and need for social support. To address the detrimental effects of social isolation, we build on cutting-edge research on social touch and movement sonification to investigate whether social tactile gestures could be perceived through sounds, a sensory channel giving access to remote information. Four experiments investigated participants’ perception of auditory stimuli that were recorded with our ‘audio-touch’ sonification technique, which captures the sounds of touch. In the first experiment, participants correctly categorized sonified skin-on-skin tactile gestures (i.e., stroking, rubbing, tapping, hitting). In the second experiment, the audio-touch sample consisted of the sonification of six socio-emotional intentions conveyed through touch (i.e., anger, attention, fear, joy, love, sympathy). Participants’ valence ratings of the underlying touch were coherent with the intended emotions, while their categorization presented more variability. In two additional experiments investigating the characteristics of the surface involved in the tactile interactions (i.e skin or object), skin proved to be a critical factor in the auditory categorization of both gestural and emotional audio-touch stimuli. This result reveals that human skin-on-skin interactions convey information which sets them apart, perceptually, from object-on-object interactions. Our research thus unveils that social touch can be perceived through sounds, when they are obtained with our specific sonifying methodology, tailored for skin-on-skin interactions. This bears great promise for giving remote access, through the auditory channel, to meaningful social touch interactions.
This corpus-based study aimed to investigate the presence of context-dependent linguistic errors in a corpus of clinical reports. The data were taken from a corpus comprising more than 2 million words and made up of clinical reports from emergency medicine, intensive care unit, general surgery, and psychiatry. Quantitative and qualitative analyses were carried out. A language model based on n-grams was developed for the detection of errors, parameters for the selection of cases were defined, and a classification tool was implemented. The findings indicated that emergency medicine was the medical specialty with the highest number of context-dependent errors and that the most frequent type of error was omission of written accent. Furthermore, the analysis revealed the presence of errors of competence due to the incorrect application of the linguistic norm of Spanish, phenomena of phonetic similarity, and composition of words; it is also worth noting that performance errors occurred due to rapid typing on the keyboard. This study constituted the first analysis and creation of a typology of context-dependent errors for the medical domain in Spanish. It contributed to the design of a module based on linguistic knowledge that can be used for the development and improvement of automatic correction systems that, in turn, are used for data processing in medicine.
The article is devoted to the study of the texts of the museum exhibition, which play the mediator role between visitors of a museum and its artifacts. The purpose of the study is to consider the text of the museum exhibition as an effective tool for intercultural communication. The review of foreign and domestic works shows that multilingual museum exposition helps to develop a dialogue and understanding between different cultures. The author studied the museum labels of the State Hermitage Museum, their grammatical and stylistic peculiarities, which should be taken into account while translating museum texts from Russian into English. It is determined that the grammatical peculiarities of the museum texts are verbless constructions, incomplete sentences, where linguistic norms are violated. The style of the texts of museum expositions is, as a rule, publicistic, some texts also have features of scientific, spoken and literary styles. While translating, the translator should aim to save the meaning and functional style.
BACKGROUND AND OBJECTIVES: Previous research identified cognitive reappraisal as an adaptive emotion regulation strategy. However, theories on emotion regulation flexibility suggest that reappraisal effectiveness (RE) may depend on an individual's familiarity with stressors. In this study, we expect high reappraisal inventiveness (RI), i.e., the generation of many and categorically different reappraisals, to increase RE for individuals with low situational familiarity. Individuals with high situational familiarity, however, would be more effective with low RI. DESIGN: A total of 148 participants completed the Script-based Reappraisal Task, in which they were presented with fear- and anger-eliciting scripts. Depending on trial type, participants were instructed to reappraise (reappraisal-trial) or react naturally (control-trial) to the scripts. After each trial, participants indicated affective states and reappraisals. We assessed RI and calculated RE-scores as difference between affect ratings in reappraisal- and control-trials for valence and arousal. Finally, participants rated the familiarity with each situation. RESULTS: The results indicated a significant moderating effect of situational familiarity on the relationship between RI and RE-valence (not RE-arousal). The moderation was mainly driven by a detrimental effect of RI for individuals with high situational familiarity. CONCLUSIONS: Our results hint at the importance of individual experience with emotional content in the research of cognitive reappraisal.
Genre classification for text documents is useful in media monitoring and detection of misinformation. Recent work in text classification for genre has shown that advanced algorithms such as neural networks and transformers are well-suited for the purpose. However, for shorter text documents, such as those obtained from social media or news articles, training of deep learning models becomes challenging since they require a large amount of input. Furthermore, genre classification of text summaries, such as headlines of news, is an important direction which has not been explored at large. In this work, the effect of Extractive and Abstractive Summarization on classification for genre of text documents was evaluated. Gensim summarizer was used to obtain extractive summaries and the Pegasus summarizer to obtain abstractive summaries. For classification, two classes of genres, Fiction and Non-fiction, were considered while the gold standard Brown Corpus was used for experimentation. The features used for genre classification were frequencies of various Part-of-Speech (PoS) tags derived from five Penn TreeBank annotated tags. Logistic Regression (LR) and Support Vector Machines (SVM) were used for classification purposes. The results of classification were better for summaries obtained using the extractive technique, indicating that the features of extractive summaries remain in agreement with the documents from which the summary is constructed as compared to abstractive summaries. Further, the SVM classifier performed better than the LR classifier. For exhaustive coverage of the research goal, further experimentation with the number of words of the output summaries of the extractive technique was performed to arrive at a threshold value of the length of summaries. The value indicated that summaries as short as 80 words can be successfully classified using this method.
<h3>BACKGROUND AND PURPOSE:</h3> Brain atrophy is an important surrogate for brain reserve, the capacity of the brain to cope with acquired injuries such as acute stroke. It is unclear how well atrophy measurements on MR imaging can be reproduced using NCCT imaging. We aimed to compare pragmatic atrophy measures on NCCT with MR imaging in patients with acute ischemic stroke. <h3>MATERIALS AND METHODS:</h3> This is a post hoc analysis, including baseline NCCT and 24-hour follow-up MR imaging data from the Safety and Efficacy of Nerinetide (NA-1) in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial. Cortical atrophy was measured using the global cortical atrophy scale, and subcortical atrophy was measured using the intercaudate distance-to-inner-table width (CC/IT) ratio. Agreement and correlation between these measures on NCCT and MR imaging were calculated using the Gwet agreement coefficient 1 and Pearson correlation coefficients, respectively. <h3>RESULTS:</h3> Among 1105 participants in the ESCAPE-NA1 trial, interpretable NCCT and 24-hour MR imaging were available in 558 (50.5%) patients (mean age, 67.2 [SD, 13.7] years; 282 women). Cortical atrophy assessments performed on NCCT underestimated atrophy severity compared with MR imaging (eg, patients with global cortical atrophy of ≥1 assessed on NCCT = 133/558 [23.8%] and on MR imaging = 247/558 [44.3%]; a 20.5% difference). Overall, cortical (ie, global cortical atrophy) atrophy assessments on NCCT had substantial or better agreement with MR imaging (Gwet agreement coefficient 1 of > 0.784; <i>P </i><.001). Subcortical atrophy measures (CC/IT ratio) showed strong correlations between NCCT and MR imaging (Pearson correlation = 0.746, <i>P </i><.001). <h3>CONCLUSIONS:</h3> Brain atrophy can be evaluated using simple measures in emergently acquired NCCT. Subcortical atrophy assessments on NCCT show strong correlations with MR imaging. Although cortical atrophy assessments on NCCT are strongly correlated with MR imaging ratings, there is a general underestimation of atrophy severity on NCCT.
Abstract The present study analyzes the transformation of the vowel system and especially the process of vowel mergers based on the Latin inscriptions of the Gallic and Germanic provinces. With the help of the Computerized Historical Linguistic Database of the Latin Inscriptions of the Imperial Age ( http://lldb.elte.hu/ ), it tries to draw and then compare the phonological profiles of the selected provinces and to describe the dialectal position of Gaul and the Germanic provinces regarding vocalism in three periods (AD 1–300, 301–500 and 501–700). The analysis, which also covers comparisons with certain provinces of Italy, Spain and Dalmatia, is carried out considering four aspects: the ratio of vocalic versus consonantal changes, the ratio of vowel mergers compared to vocalic changes, the ratio of e-i and o-u mergers compared to each other, and the ratio of vowel mergers by stressed and unstressed syllable. As a result of the present study, it was revealed that Gallic provinces cannot be treated as a unit or as clearly separate from the other areas studied according to either aspect of the study, especially not in the early, pre-Christian period. Gallic provinces appear to behave in the same or a levelled manner at most in the later and/or latest periods. The Germanic provinces, especially Germania Superior, have, albeit with some delay, adapted to the Gallic provinces in their late development. The present study, which continued József Herman's research, managed to explore the hitherto little-known linguistic and dialectological features of Latin in the Gallic and Germanic provinces.
In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks. Our approach uses a custom dictionary of action words, designed to store together words that share meaning, allowing for easy expansion of the vocabulary by adding more action words from a lexical database. We validate our Natural Language Robot Programming (NLRP) framework through simulation and real-world experimentation, using a Franka Panda robotic arm equipped with a calibrated camera-in-hand and a microphone. Participants were asked to complete a pick-and-place task using verbal commands, which were converted into text using Google's Speech-to-Text API and processed through the NLRP framework to obtain joint space trajectories for the robot. Our results indicate that our approach has a high system usability score. The framework's dictionary can be easily extended without relying on transfer learning or large data sets. In the future, we plan to compare the presented framework with different approaches of human-assisted pick-and-place tasks via a comprehensive user study.
Hoarding disorder is characterised by the acquisition of, and failure to discard large numbers of items regardless of their actual value, a perceived need to save the items and distress associated with discarding them, significant clutter in living spaces that render the activities associated with those spaces very difficult causing significant distress or impairment in functioning. To aid development of an intervention for hoarding disorder we aimed to identify current practice by investigating key stakeholders existing practice regarding identification, assessment and intervention associated with people with hoarding disorder. Two focus groups with a purposive sample of 17 (eight male, nine female) stakeholders representing a range of services from housing, health, and social care were audio recorded, transcribed verbatim and analysed thematically. There was a lack of consensus regarding how hoarding disorder was understood and of the number of cases of hoarding disorder however all stakeholders agreed hoarding disorder appeared to be increasing. The clutter image rating scale was most used to identify people who needed help for hoarding disorder, in addition to other assessments relevant to the stakeholder. People with hoarding disorder were commonly identified in social housing where regular access to property was required. Stakeholders reported that symptoms of hoarding disorder were often tackled by enforced cleaning, eviction, or other legal action however these approaches were extremely traumatic for the person with hoarding disorder and failed to address the root cause of the disorder. While stakeholders reported there was no established services or treatment pathways specifically for people with hoarding disorder, stakeholders were unanimous in their support for a multi-agency approach. The absence of an established multiagency service that would offer an appropriate and effective pathway when working with a hoarding disorder presentation led stakeholders to work together to suggest a psychology led multiagency model for people who present with hoarding disorder. There is currently a need to examine the acceptability of such a model.
Vision-Language Pre-training (VLP) has advanced the performance of many visionlanguage tasks, such as image-text retrieval, visual entailment, and visual reasoning. The pre-training mostly utilizes lexical databases and image queries in English. Previous work has demonstrated that the pre-training in English does not transfer well to other languages in a zero-shot setting. However, multilingual pre-trained language models (MPLM) have excelled at a variety of single-modal language tasks. In this paper, we propose a simple yet efficient approach to adapt VLP to unseen languages using MPLM. We utilize a cross-lingual contextualized token embeddings alignment approach to train text encoders for non-English languages. Our approach does not require image input and primarily uses machine translation, eliminating the need for target language data. Our evaluation across three distinct tasks (image-text retrieval, visual entailment, and natural language visual reasoning) demonstrates that this approach outperforms the state-of-the-art multilingual vision-language models without requiring large parallel corpora. Our code is available at https://github.com/Yasminekaroui/CliCoTea.
The Japanese CCGBank serves as training and evaluation data for developing Japanese CCG parsers. However, since it is automatically generated from the Kyoto Corpus, a dependency treebank, its linguistic validity still needs to be sufficiently verified. In this paper, we focus on the analysis of passive/causative constructions in the Japanese CCGBank and show that, together with the compositional semantics of ccg2lambda, a semantic parsing system, it yields empirically wrong predictions for the nested construction of passives and causatives.
Tastes affect the body and our emotions. We used tasteless, sweet, and bitter stimuli to induce participants' moods, and we examined the effect of mood on an emotional evaluation of pleasant, neutral, and unpleasant images using event-related potentials, N2, N400, and late positive potential (LPP), which reflect emotional evaluation in the brain. The results indicated that mood valence was most positive for sweetness and most negative for bitterness. Moreover, there was no significant mood effect on subjective valence ratings of emotional images. Furthermore, the N2 amplitude, which is related to the early semantic processing of preceding stimuli, was unaffected by the taste induced mood. In contrast, we found that the N400 amplitude, which is related to the mismatch of emotional valence between stimuli, increased significantly for unpleasant images when participants were in a positive rather than negative mood state. Also, the LPP amplitude, which is related to the emotional valence of images, showed only the main effect of the images' emotional valence. The N2's results suggest that the early semantic processing of taste stimuli might have had a negligible impact on emotional evaluation because taste stimuli minimize semantic processing that accompanies mood induction. In contrast, the N400 reflected the effects of the induced mood, and the LPP reflected the impact of the valence of emotional images. The use of taste stimuli to induce mood revealed different brain processing of taste-induced mood effects on emotional evaluation, including N2's involvement in semantic processing, N400's involvement in matching emotions between mood and stimuli, and LPP's involvement in subjective evaluations of stimuli.
Aesthetic evaluations, including beauty and attractiveness, have an important role in our lives. Despite its importance in our every-day life, enough attention has not been devoted to the assessment of place attractiveness in previous studies. We assume that changes in elements of square attractiveness are associated with changes in brain functional connectivity patterns. In this study, we have tried to explore the relationship between elements of square attractiveness and individuals' emotional perception as well as the brain mechanism involved in the process of cognitive development. There has been a focus on using objective measures of physiological rather than using self-reported data of an individual's emotions because people cannot understand their emotions properly and it is needed to compare self-report emotions with physiological processes. Classification of the five main elements of attractiveness was performed using the Delphi technique. Subsequently, twenty-four healthy young adults were exposed to the visual stimuli consists of five elements. A 32-channel EEG system was used to record the brain activities of participants while watching the stimuli. The subjects' feelings about valence and arousal levels of the elements were evaluated using the Self-Assessment Manikin (SAM) technique. The findings showed that “visual openness” is the most important element to increase the square attractiveness of everyday landscape in residential areas. The analysis revealed a significant difference (p = 0.048) in arousal ratings between more attractive (more openness) (M = 4.77) and less attractive (less openness) (M = 4.52). Attractiveness elements of the stimuli have a region-specific association with brain functional connectivity networks. This pattern is mainly found in the functional connections between central parts of the brain.
Real-world applications of language models entail data privacy constraints when learning from diverse data domains. Federated learning with pretrained language models for language tasks has been gaining attention lately but there are definite confounders that warrants a careful study. Specifically, understanding the limits of federated NLP applications through varying the effects of different aspects (such as data heterogeneity, the trade-off between training time and performance, the effect of different data, and client distributions and sensitivity of the shared model to learning local distributions) is necessary to evaluate whether language models indeed learn to generalize by adapting to the different domains. Towards that, we elaborate different hypotheses over the components in federated NLP architectures and study them in detail with relevant experiments over three tasks: Stanford Sentiment Treebank-2, OntoNotes-5.0 and GigaWord. The experiments with different Transformer inductive biases on the variety of tasks provide a glimpse at the understanding of federated learning at NLP tasks. Specifically, the analysis suggests that regularization due to the ensembling effect may be masquerading as domain adaptation of federated learning in NLP with pre-trained language models.
Genres such as indie (Beal 2009) and hip-hop (Eberhardt & Freeman 2015) feature dialectal traits in English, but whether genres form targets distinct from speech remains unclear. We examine genre effects on phonetic variation in Quebec French music by probing the role of genres (pop, country, alternative, and indie) on laxing and diphthongization, processes characteristic of Quebec French (Walker 1984). Stigma facing formal varieties of Quebec French has vanished within dialect (Kircher 2012), yet remains for processes that vary regionally or socioeconomically (Côté 2012; Côté & Lancien, 2019). Whereas laxing is categorical and non-stigmatized (Côté 2012; Paradis & Dolbec, 1998), diphthongization is variable and stigmatized (Côté 2012). We use a novel corpus of ten Québécois singers who released multiple albums from 2011-2021 (29 albums; 326 songs). We find the emergence of genre-specific linguistic norms distinct from speech and argue that genres in music parallel sociolects.
Research into cultural tastes has commonly sought to analyze and understand preferences in terms of notions of familiarity. Such approaches are inadequate, however, when it comes to examining our engagement with unfamiliar cultural content. This paper responds to this gap by examining how people respond to algorithmic recommendations of culture through a case study of unfamiliar Australian art music. It firstly identifies three different “techniques’ by which audiences engage with and value music: functional, emotional, and intellectual. The analysis then examines how these techniques, together with measures of familiarity and the acoustic “materiality” of the music itself, combine to predict the affective ratings given to music recommendations. The findings show that audiences display a surprising capacity to engage with the unfamiliar. The paper argues for the need to develop more nuanced understandings of the relationship between familiarity and preferences which are capable of accommodating a taste for the unfamiliar.
This study aims to evaluate and provide recommendations by testing the usability level of the Odoo Operation & Maintenance ERP Module application. Using the SUS Questionnaire method, the results of usability testing were analyzed into each parameter of the SUS Questionnaire. The SUS questionnaire has an assessment criteria of “Unacceptable category” with a value range of 0–50 class F and has an adjective rating of “Worst Imaginable to Poor”, the category “Low Marginal” with a score range of 51-62.5 class F and has a word rating OK trait, Marginal High Category with a range of 62.6–70 grade D and adjective rating OK, and Acceptable Category with a score range of 70–100 grade C to A and adjective rating good, very good to best. The test results get a value of 53.25 including the OK Marginal Low category, meaning that it needs improvement so that the system can be well received by users. Further interviews were also conducted with ten informants to obtain a specific description of the complaints of system users. From the results of the interview, an analysis was carried out to provide evaluations and recommendations for improvement of the Odoo ERP Operation & Maintenance Module.
This article proposes to look at the theory of translanguaging from the angle of its universality/non-universality, bringing into discussion a new empirical case - speech strategies and practices of highly educated multilingual migrants from the former USSR and multilingual residents of modern Russia. The authors conclude that people whose socialization took place in this region are characterized by rigid ideas about the language norm and authenticity, about the “purity” of languages and high standards of speaking them. These concepts apply to all languages, both native and foreign. Traditional and conservative language ideologies are widespread in the Soviet and post-Soviet area, which is the reason why translanguaging is often perceived as careless speech, incomplete language competence. In this sense, this regional case demonstrates the non-universality of translanguaging theory and the importance of contexts in which attitudes towards multilingualism, linguistic norm and related linguistic phenomena are formed. It is primarily the western democratic, postcolonial context of translanguaging practice and theory that explains why the language attitudes and speech behavior of residents of (post)Soviet region do not fit into it.
This paper offers the basic guidelines of a formalized version of the Lexical Constructional Model (LCM; Ruiz de Mendoza & Mairal Usón, 2008, 2011; Ruiz de Mendoza & Galera, 2014), the Formalized Lexical-Constructional Grammar (FL_CxG), which will pave the way for future computational developments, such as parsers or lexical databases. The FL_CxG deploys (i) the typologically oriented syntactic apparatus of Role and Reference Grammar (Van Valin, 2005; Van Valin & LaPolla, 1997), (ii) the catalogue of constructional units arranged in a 4-layer typology, as proposed by the LCM, and (iii) some insights for semantic representations from the Generative Lexicon Theory (Pustejovsky, 1995; Pustejovsky & Batiukova, 2019), and Minimal English (Goddard, 2018). All the components of the FL_CxG (lexical units and construct(ion)s) are formally encoded as Typed Feature Structures in the format of Attribute Value Matrixes. These units are to be understood as constraints operating in the unification processes which underlie the generation/decoding of a given fragment of language.
This work investigates the effect of grid-connected converter topology on equivalent converter output impedance with a specific focus on the diagonal dominance of the impedance matrix across a frequency range. When considering multiple-input multiple-output systems most traditional stability techniques are reliant on the diagonal dominance of the studied system. Therefore, a rating of diagonal dominance is proposed based upon the correlation coefficient between row and column in the impedance matrix. This provides a scale that ranges from off-diagonally dominant (-1) through uniformly distributed (0) and up to diagonally dominant (1) across a range of frequencies. The scale is used to specify which control structures can be considered as diagonally dominant at certain frequencies and which control components have the greatest effect on the rating. A direct relation is found between system exhibiting a diagonal dominance rating of 0.7 and above and the efficacy of traditional stability margins. Traditionally strong systems with low network impedance where controllers can be tuned conservatively exhibit high degrees of diagonal dominance and can be analysed quickly with traditional margins with minimal error. For systems exhibiting a lower rating, disk margins are explored as an alternative which offer greater accuracy. Additionally, more realistic perturbations of gain and phase occurring simultaneously in multiple channels can be considered which is more applicable for the modern electricity network with a high penetration of grid-connected converters.
This work delves into the semantics of Old English lexical paradigms based on strong verbs. Its aim is to describe the patterns of semantic inheritance that hold in these paradigms, which present morphologically related words sharing the form and meaning of the base of derivation. The analysis carried out permits to circumscribe semantic derivation in these Old English lexical paradigms into the lexical entailment relations of troponymy, -troponymy, backward presupposition and cause, and the semantic relations of synonymy and opposition. The data of research has been retrieved from the lexical database Nerthus (Martín Arista et. al 2016). On the theoretical side, this examination follows the English lexical database WordNet (Princeton, 2010). This research unfolds a systematic methodology that is thoroughly described and illustrated by means of the paradigm (ge)berstan. It has not only been possible to circumscribe semantic derivation into the six semantic relations mentioned, but also to determine their frequency of occurrence in the paradigms under analysis, which indicates that synonymy and troponymy are the most recurrent semantic relations.
Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.
The specificities of Arabic parsing, such as agglutination, vocalization, and the relatively order-free words in Arabic sentences, remain major issues to consider. To promote its robustness, such parseing should define different types of constraints. Property Grammar (PG) formalism verifies the satisfiability of the constraints directly on the units of the structure, thanks to its properties (or relations). In this context, we propose to build a probabilistic parser with syntactic properties, using a PG, and we measure the production rules in terms of different implicit information and in particular the syntactic properties. We experimented with our parser on the treebank ATB, using the parsing algorithm CYK, and we obtained encouraging results. Our method is also automatic for implementation of most property types. Its generalization for other languages or corpus domains (using treebanks) could be a good perspective. Its combination with pre-trained models of BERT may also make our parser faster.
One of the most crucial Natural Language Processing (NLP) tasks is associated with the universality-driven development of language resources for different languages (e.g. Universal Dependencies (UD), UniMorph, PARSEME, etc.). This article describes the possibility of creating a Syntactic TreeBank for Georgian, and consists of four sections. The first section briefly describes different linguistic resources that exist concerning Georgian and mentions the importance of syntactic annotation. The second section focuses on the tools used for mapping the existing targets for Georgian to the UD format. The third section includes a description of the principles of syntactic annotation and language-specific documentation files, while the fourth section summarizes the work done and describes the future stages of the development of the syntactic TreeBank.
Linguistic heterogeneity and fluidity – prominently captured in the notion of ‘translanguaging’– are starting to be seen as normative and natural. In turn, homogeneity and fixity, instantiated for example in standard languages, are becoming the ‘odd-ones-out.’ I challenge the dichotomy between linguistic fluidity (languaging) and fixity (named languages), in a situated conceptual account of translingual writing practices in English classrooms in a Khayelitshan primary school. These spaces fold the linguistic fluidity typical of South African townships, and the fixity of two standard languages, into one complex spatial repertoire. Operationalizing this spatial perspective, I suggest that students are constantly engaged in relanguaging, recursively sorting out the classroom repertoire according to the various linguistic norms enfolded in the space, and of bringing together linguistic resources in various combinations. Relanguaging systematically unsettles the dichotomy between fluid languaging and fix institutional language norms retained in dominant conceptualizations of translanguaging. This way it opens up new conceptual and analytical perspectives with possible pedagogical implication for writing instruction and testing. Standard English could, for example, be assessed beyond its own confines, using writing tasks that can make visible increasingly sophisticated linguistic sorting skills as students.
This article delves into the literary canon, a concept shaped by social biases and influenced by successive receptions. The canonization process is a multifaceted phenomenon, emerging from the intricate interplay of sociological, economic, and political factors. Our objective is to detect the underlying textual dynamics that grant certain works exceptional longevity while jeopardizing the transmission of the majority. Drawing on various criteria, we present an operational framework for defining the French literary canon, centered on its contemporary reception and emphasizing the role of institutions, particularly schools, in its formation. Leveraging natural language processing and machine learning techniques, we unveil an intrinsic norm inherent to the literary canon. Through statistical modeling, we achieve predictive outcomes with accuracy ranging from 70% to 74%, contingent on the chosen scale of canonicity. We believe that these findings detect what Charles Altieri calls a “cultural grammar”, referring to the idea that canonical works in literature serve as foundational texts that shape the norms, values, and conventions of a particular cultural tradition. We posit that this linguistic norm arises from biased latent selection mechanisms linked to the role of the educational system in the canon-formation process.
Humanlike androids can function as social agents in social situations and in experimental research. While some androids can imitate facial emotion expressions, it is unclear whether their expressions tap the same processing mechanisms utilized in human expression processing, for example configural processing. In this study, the effects of global inversion and asynchrony between facial features as configuration manipulations were compared in android and human dynamic emotion expressions. Seventy-five participants rated (1) angry and happy emotion recognition and (2) arousal and valence ratings of upright or inverted, synchronous or asynchronous, android or human agent dynamic emotion expressions. Asynchrony in dynamic expressions significantly decreased all ratings (except valence in angry expressions) in all human expressions, but did not affect android expressions. Inversion did not affect any measures regardless of agent type. These results suggest that dynamic facial expressions are processed in a synchrony-based configural manner for humans, but not for androids.
Abstract Proposals such as continuity and causality-by-default relate the level of expectedness of a relation to its linguistic marking as an explicit or implicit relation. We investigate these two proposals with regard to the English transcripts of six TED Talks and their Lithuanian, Portuguese and Turkish translations in the TED-Multilingual Discourse Bank (TED-MDB), annotated for discourse relations, following the Penn Discourse Treebank style of annotation. Our data shows that the discontinuous relations contrast and concession are indeed frequently explicit in all languages. But continuous relations show differences per relation and language. For instance, cause is frequently conveyed implicitly in English and Portuguese, but not in Lithuanian and Turkish. We explore temporal continuity by analysing whether the forward-order sense result is more frequently implicit than the backward-order reason. The hypothesis is confirmed by English and Portuguese, but not Lithuanian and Turkish. However, in Turkish, the arguments of the backward-order relation reason are frequently presented by the reversed order of arguments, retaining the linear order of events even in the presence of the connective. The causality-by-default hypothesis is not confirmed, as cause is not the most frequent implicit relation in the four languages.
This study explores the nuanced effects of social media on society, emphasising how it has both positive and negative aspects. Positively, social media has revolutionised global connectivity by democratising journalism, encouraging participation across great distances, and giving companies access to low-cost advertising channels. The report does admit many drawbacks, too, such as privacy issues, cyberbullying, and the quick dissemination of false information. The study promotes digital literacy, user education, and proactive actions from social media companies to address these problems. It highlights how critical thinking abilities are necessary to successfully traverse the internet environment. Furthermore, the study draws attention to the linguistic influence of social media by presenting acronyms, abbreviations, and colloquial language, prompting concerns about possible negative effects on written language proficiency and the significance of maintaining linguistic norms. Overall, the study highlights how social media has a significant impact on a variety of fields, including activism, politics, marketing, and education. It also highlights the need for a balanced strategy to maximise social media’s advantages while minimising its drawbacks
The Covid-19 pandemic in the last 3 years has strengthened E-commerce growth, making online shopping the new norm due to restricted offline activities. To aid buyers and sellers in conducting transactions in E-commerce, E-commerce platforms have introduced features like product descriptions, product photos, ratings, and reviews. These features have created a competitive landscape, benefiting sellers who can use them effectively. Nevertheless, many sellers still struggle to optimize these features and market their products effectively to buyers. Failure to optimize these features correctly restricts the marketing strategy's effectiveness and puts sellers at risk for unanticipated difficulties that may be prevented by determining the various effects of these features on customers’ purchase intentions. Therefore, this research aims to analyze the impact of product descriptions, product photos, and ratings & reviews on customers' purchase intention in E-commerce. A quantitative approach is used in this study, where the data is analyzed through descriptive statistics and PLS-SEM. The result of this study suggested that all three features of product description, product photo, and rating & review significantly and positively influence purchase intention in E-commerce. In addition, the author also found that moderation of perceived trust significantly affects product description and rating & review on purchase intention, while the moderation of perceived risk only significantly affects rating & review on purchase intention. The finding of this research is expected to give insights to E-commerce sellers on optimizing the features in E-commerce to increase the customers’ purchase intention.
Few constructs in language teaching are as important as communicative competence: The term has for many decades informed how teachers design lessons and address student needs. Its central role in informing language teaching has resulted in communicative competence being defined and operationalised in multiple ways in the literature. The different iterations of the term have, however, led to some ambiguity with regard to how the existing notions of communicative competence reflect the current needs of English-speaking professionals. To this end, the current paper examines how different interpretations of communicative competence present opportunities to address the needs of workplace English learners. Specifically, the paper investigates the extent to which two offshoots of communicative competence – that is, pragmatic competence and interactional competence – can be used to teach workplace English. This chapter demonstrates that awareness and understanding of both terms are needed to adequately address contemporary workplace issues that require workers to increasingly communicate in global contexts where English is spoken as a medium language. In such contexts, interpersonal skills are needed to adjust communicative practices according to diverse cultural and linguistic norms and expectations.
The rapid brain maturation in childhood and adolescence accompanies the development of socio-emotional functioning. However, it is unclear how the maturation of the neural activity drives the development of socio-emotional functioning and individual differences. This study aimed to reflect the age dependence of inter-individual differences in brain responses to socio-emotional scenarios and to develop naturalistic imaging indicators to assess the maturity of socio-emotional ability at the individual level. Using three independent naturalistic imaging datasets containing healthy participants (n = 111, 21 and 122), we found and validated that age-modulated inter-individual concordance of brain responses to socio-emotional movies in specific brain regions. The similarity of an individual's brain response to the average response of older participants was defined as response typicality, which predicted an individual's emotion regulation strategies in adolescence and theory of mind (ToM) in childhood. Its predictive power was not superseded by age, sex, cognitive performance or executive function. We further showed that the movie's valence and arousal ratings grounded the response typicality. The findings highlight that forming typical brain response patterns may be a neural phenotype underlying the maturation of socio-emotional ability. The proposed response typicality represents a neuroimaging approach to measure individuals' maturity of cognitive reappraisal and ToM.
Recently, Shirai and Watanabe Royal Society Open Science, 9(1), 211128 (2022) developed OBNIS (Open Biological Negative Image Set), a comprehensive database containing images (primarily animals but also fruits, mushrooms, and vegetables) that visually elicit disgust, fear, or neither. OBNIS was initially validated for a Japanese population. In this article, we validated the color version of OBNIS for a Portuguese population. In study 1, the methodology of the original article was used. This allowed direct comparisons between the Portuguese and Japanese populations. Aside from a few emotional classification mismatches between disgust, fear, or neither-related images, we found that arousal and valence relate distinctively in both populations. In contrast to the Japanese sample, the Portuguese reported increased arousal for more positive valenced stimuli, suggesting that OBNIS images elicit positive emotions in the Portuguese population. These results showed important cross-cultural differences regarding OBNIS. In study 2, a methodological change was introduced: instead of the three classification options used originally (fear, disgust, or neither), six basic emotions were used (fear, disgust, sadness, surprise, anger, happiness), and a "neither" option, to confirm whether some of the originally "neither-related" images are associated with positive emotions (happiness). Additionally, the low-order visual properties of images (luminosity, contrast, chromatic complexity, and spatial frequency distribution) were explored due to their important role in emotion-related research. A fourth image group associated with happiness was found in the Portuguese sample. Moreover, image groups present differences regarding the low-order visual characteristics, which are correlated with arousal and valence ratings, highlighting the importance of controlling such characteristics in emotion-related research.
The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology.
Dataset and analysis related to the paper Järveläinen, H., and Larrieux, E. Vibrotactile feedback enhances perceived arousal and listening experience in music. Proc. Sound and Music Computing Conference (SMC) 2023, Stockholm, Sweden. The dataset “Cello.reg.df” contains registered continuous measurements of perceived arousal in solo cello performance under varying types and intensities of vibrotactile feedback. Vibrotactile feedback was provided in the Table or in the Chair. See details in the conference paper. Variables: time: time stamp (s) Arousal: Arousal rating, numeric [0,1] Amplitude: factor, levels = High, Low, 0 Vibration: factor, levels = Signal, Noise, No vibration Location (of vibrotactile feedback): factor, levels = Table, Chair sID = subject number (1-30) trial = trial number (redundant, 1-10)
Background: Music therapy is a promising complementary intervention for addressing various mental health conditions. Despite evidence of the beneficial effects of music, the acoustic features that make music effective in therapeutic contexts remain elusive. Aims: This study aimed to identify and validate distinctive acoustic features of healing music. Methods: We constructed a healing music dataset (HMD) based on nominations from related professionals and extracted 370 acoustic features. Healing-distinctive acoustic features were identified as those that were (1) independent from genre within the HMD, (2) significantly different from music pieces in a classical music dataset (CMD) and (3) similar to pieces in a five-element music dataset (FEMD). We validated the identified features by comparing jazz pieces in the HMD with a jazz music dataset (JMD). We also examined the emotional properties of the features in a Chinese affective music system (CAMS). Results: The HMD comprised 165 pieces. Among all the acoustic features, 74.59% shared commonalities across genres, and 26.22% significantly differed between the HMD classical pieces and the CMD. The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46% of the features. The potential healing-distinctive acoustic features were identified as the standard deviation of the roughness, mean and period entropy of the third coefficient of the mel-frequency cepstral coefficients. In a three-dimensional space defined by these features, HMD's jazz pieces could be distinguished from those of the JMD. These three features could significantly predict both subjective valence and arousal ratings in the CAMS. Conclusions: The distinctive acoustic features of healing music that have been identified and validated in this study have implications for the development of artificial intelligence models for identifying therapeutic music, particularly in contexts where access to professional expertise may be limited. This study contributes to the growing body of research exploring the potential of digital technologies for healthcare interventions.
This study aims to investigate how musical expressions of emotion and individuals’ psychological distress impact subjective ratings of emotional response and subjective appraisals, including familiarity, complexity, and preference. A sample of 123 healthy adults participated in an online survey experiment. After listening to four music excerpts with distinct musical expressions of emotional valence and arousal in a randomized sequence. Participants rated subjective emotions of energy, tension, and valence, as well as subjective appraisals, on a visual analogue scale ranging from 0 to 100. The results of repeated measures ANOVA demonstrated significant differences in emotional responses and appraisals across the ratings for different music excerpts (p > 0.01, respectively). The generalized linear mixed model results further revealed a significant main effect of musical valence on all emotional response dimensions of energy (β = −4.73 **), tension (β = 14.31 ***), valence level (β = −18.81 ***), and subjective appraisal in terms of familiarity (β = −23.06 ***), complexity (β = −6.67 ***), and preference (β = −19.54 ***). Musical arousal showed comparable results except for effects on emotional valence ratings. However, significant effects of psychological distress regarding depression, anxiety, and stress scores were only partially observed. Findings suggest that the expression of emotions through music primarily influences emotional responses and subjective appraisals, while the influence of an individual’s psychological distress level may be relatively subtle.
The majority of projects fail to achieve their intended objectives, according to research.This could arise for a number of reasons, such as ensuring requirements are managed, excessive documentation of the code, or the difficulty in delivering software that includes all the requested features on time.An effort could be made to overcome such failure rates by establishing a proper management of requirements and concept of reusability.The correct requirements can be identified by checking similarity between the requirements received from the various stakeholders.A reusable software component can result in substantial savings in both time and money.It can be challenging to make a choice regarding the reuse of certain software components.A comparison of the requirements of a new project with those of previous projects prior to starting a new project or even at a later stage during development is useful for identifying reusable components.This paper proposes a framework (ReSim) for identifying software requirements' similarities, in an attempt to improve reusability and identify the correct requirements.A crucial component of ReSim is to measure similarity between software requirements.Different well-known similarity measurement techniques used by the researchers to evaluate the similarity between the software requirements.Some of the methods used to measure this include dice, jaccard, and cosine coefficients, but in this paper, we have used recently developed hybrid method which considers not only semantic information including lexical databases, word embeddings, and corpus statistics, but also implied word order information and produced significant improvements in the results related to the measurement of semantic similarity between words and sentences.As part of the experiments, the study used PURE dataset -in order to demonstrate the efficacy of the proposed framework.As a result, recently developed hybrid method of measuring the requirements similarity is more accurate than Dice, Jaccard, and Cosine, while Cosine is a better choice than Dice, and Jaccard is more accurate than Dice.Thus, ReSim outperforms existing approaches when tested on the PURE dataset, providing the most accurate results for both functional and non-functional requirements.
Constituency parsing plays a fundamental role in advancing natural language processing (NLP) tasks. However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the inherent challenges in building treebanks for such languages. It demands extensive linguistic expertise, leading to a scarcity of available resources. To overcome this hurdle, cross-lingual transfer techniques which require minimal or even no annotated data for low-resource target languages offer a promising solution. In this study, we focus on building a constituency parser for $\mathbf{M}$iddle $\mathbf{H}$igh $\mathbf{G}$erman ($\mathbf{MHG}$) under realistic conditions, where no annotated MHG treebank is available for training. In our approach, we leverage the linguistic continuity and structural similarity between MHG and $\mathbf{M}$odern $\mathbf{G}$erman ($\mathbf{MG}$), along with the abundance of MG treebank resources. Specifically, by employing the $\mathit{delexicalization}$ method, we train a constituency parser on MG parse datasets and perform cross-lingual transfer to MHG parsing. Our delexicalized constituency parser demonstrates remarkable performance on the MHG test set, achieving an F1-score of 67.3%. It outperforms the best zero-shot cross-lingual baseline by a margin of 28.6% points. These encouraging results underscore the practicality and potential for automatic syntactic analysis in other ancient languages that face similar challenges as MHG.
Automatic syntactic analysis of a sentence is an important computational linguistics task. At present, there are no syntactic structure parsers for Russian that are publicly available and suitable for practical applications. Ground-up creation of such parsers requires building of a treebank annotated according to a given formal grammar, which is quite a cumbersome task. However, since there are several syntactic dependency parsers for Russian, it seems reasonable to employ dependency parsing results for syntactic structure analysis. The article introduces an algorithm that allows to construct the constituency tree of a Russian sentence by a syntactic dependency tree. The formal grammar used by the algorithm is based on the D.E. Rosenthal’s classic reference. The algorithm was evaluated on 300 Russian-language sentences. 200 of them were selected from the aforementioned reference, and 100 from OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. During the evaluation, the sentences were passed to syntactic dependency parsers from Stanza, SpaCy, and Natasha packages, then the resulted dependency trees were processed by the proposed algorithm. The obtained constituency trees were compared with the trees manually annotated by experts in linguistics. The best performance was achieved using the Stanza parser: the constituency parsing F1–score was 0.85, and the sentence parts tagging accuracy was 0.93, that would be sufficient for many practical applications, such as event extraction, information retrieval and sentiment analysis.
Cannabis Use Disorder (CUD) is increasingly prevalent in the United States, while perceived addiction risk and treatment-seeking are declining. Emotional salience of cannabis-use-related problems and benefits likely contribute to motivation to change, but measurement of this process has been limited. The present study sought to validate a novel assessment of emotional appraisal of self-referential cannabis-use-related information across subjective and neurophysiological units of analysis. Non-treatment-seeking individuals with DSM-5 severe CUD (N = 42) completed a task that presented auditory self-referential, personalized cannabis-use-related problem and benefit statements, as well as neutral self-referential statements, during electroencephalography recording. The late positive potential (LPP) was used as a neurophysiological measure of emotional salience. Valence/arousal ratings of each statement, along with their motivational importance in sustaining vs. reducing cannabis use, were also obtained. As predicted, valence and arousal ratings significantly differentiated cannabis-use-related problems and benefits from neutral statements. Partially consistent with predictions, the LPP to cannabis-use-related benefits was significantly larger than LPPs to cannabis-use-related problems and neutral statements, which did not differ from each other. Bonferroni-adjusted exploratory correlations revealed that the LPP to cannabis-use-related problems was sensitive to recent cannabis use frequency. These results provide some support for the validity of this novel multi-method assessment of emotional reactivity to personalized cannabis-use-related self-referential information in non-treatment-seeking individuals with severe CUD. The dissociation between subjective and neurophysiological reactivity to self-referential cannabis-related problem statements should be further explored.
Bullying has moved online as a result of the technological revolution, which was previously limited to physical boundaries. One type of cyber bullying is ridicule or insult. According to the report the cyber bullying on social media is getting worse. Insulting words change over time, and the same word can mean different things depending on the situation. A comment cannot be considered bullying simply because it contains such a word. Therefore, simple keyword spotting methods are insufficient for labelling comments. Lexical databases like Word Net, which provide synonyms and homonyms for words, have been utilized in other languages to address this issue. It is difficult to identify a word as bullying because there is no English-language lexical database. As a result, the proposed work solved the problem by following the rules. Outliers were removed from the collection of tweets containing profane language, and the remaining tweets were pre-processed. Five feature extraction rules were used to find insults in the text. The Support Vector Machine (SVM), K-nearest neighbor (KNN), and Naive Bayes algorithms were then utilized. With F1-score of 91 percent, the findings demonstrate that SVM along with an RBF kernel performs better. The fact that this research focuses on English-language cyberbully detection is novel and has not been done before.
Purpose The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis. Design/methodology/approach The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model. Findings On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets. Originality/value The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.
This study examined the relationship between the age-related positivity effect (ARPE) of odor-evoked involuntary autobiographical memories and subjective well-being. In this study, a diary method was used to measure memory characteristics of autobiographical memories recalled by odor stimuli in daily life in 200 young and 200 older adults. They were also asked to complete the subjective well-being scale. The results showed that older adults recalled more positive memories than younger adults, suggesting the ARPE does occur. A weak but significant correlation was found between valence rating of autobiographical memory and subjective well-being in older people.