Search Results (134)

The origin of a product, consumer familiarity, and purchasing identity are factors that affect the perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, as well as drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes, were studied using three methods: (a) flash profile to gain insight into the sensory positioning of products and description of samples; (b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria; and (c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criteria for purchase were intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products. Full article

Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks. These abilities of LLMs are promising in robotics. In general, traditional supervised learning-based robot intelligence systems have a significant lack of adaptability to dynamically changing environments. However, LLMs help a robot intelligence system to improve its generalization ability in dynamic and complex real-world environments. Indeed, findings from ongoing robotics studies indicate that LLMs can significantly improve robots’ behavior planning and execution capabilities. Additionally, vision-language models (VLMs), trained on extensive visual and linguistic data for the vision question answering (VQA) problem, excel at integrating computer vision with natural language processing. VLMs can comprehend visual contexts and execute actions through natural language. They also provide descriptions of scenes in natural language. Several studies have explored the enhancement of robot intelligence using multimodal data, including object recognition and description by VLMs, along with the execution of language-driven commands integrated with visual information. This review paper thoroughly investigates how foundation models such as LLMs and VLMs have been employed to boost robot intelligence. For clarity, the research areas are categorized into five topics: reward design in reinforcement learning, low-level control, high-level planning, manipulation, and scene understanding. This review also summarizes studies that show how foundation models, such as the Eureka model for automating reward function design in reinforcement learning, RT-2 for integrating visual data, language, and robot actions in vision-language-action models, and AutoRT for generating feasible tasks and executing robot behavior policies via LLMs, have improved robot intelligence. Full article

This study investigates the impact of language contact on three generations of bilingual Spanish and Uruguayan Portuguese speakers in Rivera City, Uruguay, located on the Uruguayan–Brazilian border. Focusing on the confirmed presence of the Portuguese-like/b/and/v/phonemic distinction, and the lower frequency of the Montevideo Spanish-like approximantized stops in Riverense Spanish (RS), the research examines the production of <v> and <b> in 29 female Rivera Spanish bilinguals belonging to different age groups. More specifically, the aim was to see if the previously observed differential use of language-specific phonological variants could be accounted for by using precise measurements of relative intensity, duration, and voicing coupled with a distributional analysis of realizations derived from auditory coding. At the same time, their production is compared to that of 30 monolingual Montevideo Spanish (MS) speakers, who served as the control group, offering a first description of the production of <v> and <b> within this distinct Rioplatense Spanish variety. Riverense’s higher overall relative intensity, duration, and voicing values support auditory coding results, providing evidence of the expected phonological differences between both Uruguayan Spanish varieties. In particular, an exclusive presence of fricative/v/and less approximantization of/b/in RS speech exposed the influence of Portuguese in Rivera bilinguals and their divergence from MS. In addition, as predicted, the findings reveal a higher presence of Portuguese-like productions of [v] and [b] in older bilinguals when compared to younger generations. This illustrates a continuum from Portuguese-like forms to Spanish-like forms, which is confirmed by both acoustic and distributional analyses. Finally, evidence of the existence of innovative forms resulting from mixing Portuguese and Spanish phonological systems in RS are presented. This study’s findings contribute to sociolinguistics and bilingualism by exposing cross-linguistic influence in a border setting with rigorous analytical methods that offer reliable results and go beyond a basic analysis based on auditory identification. Full article

An important group of decision-making problems is decision-making under uncertainty, including with incomplete or linguistically described data. Command and control systems, fitting into the multi-sensor paradigm of Industry 4.0/5.0, are becoming increasingly multifactorial. This trend will intensify, requiring uncertainty and incompleteness to be considered and mathematical description and data-processing systems better adapted to them. Aggregators are a group of tools used in solving the aforementioned decision problems, including within fuzzy systems. Aggregating functions are a useful tool mainly in those artificial intelligence systems with problems arising from incomplete data. The aim of this article is to review and describe existing aggregators used in fuzzy control in terms of their usefulness and limitations of their use. Particular attention is paid to the criteria for matching a suitable aggregator to a particular computational problem. This represents an important step towards the further use of this group of technologies in electronic devices and IT systems. Full article

Migraines are one of the most common and expensive neurological diseases worldwide. Non-pharmacological and digitally delivered treatment options have long been used in the treatment of migraines. For instance, migraine management tools, online migraine diagnosis or digitally networked patients have been used. Recently, applications of ChatGPT are used in fields of healthcare ranging from identifying potential research topics to assisting professionals in clinical diagnosis and helping patients in managing their health. Despite advances in migraine management, only a minority of patients are adequately informed and treated. It is important to provide these patients with information to help them manage the symptoms and their daily activities. The primary aim of this case study was to examine the appropriateness of ChatGPT to handle symptom descriptions responsibly, suggest supplementary assistance from credible sources, provide valuable perspectives on treatment options, and exhibit potential influences on daily life for patients with migraines. Using a deductive, qualitative study, ten interactions with ChatGPT on different migraine types were analyzed through semi-structured interviews. ChatGPT provided relevant information aligned with common scientific patient resources. Responses were generally intelligible and situationally appropriate, providing personalized insights despite occasional discrepancies in interaction. ChatGPT’s empathetic tone and linguistic clarity encouraged user engagement. However, source citations were found to be inconsistent and, in some cases, not comprehensible, which affected the overall comprehensibility of the information. ChatGPT might be promising for patients seeking information on migraine conditions. Its user-specific responses demonstrate potential benefits over static web-based sources. However, reproducibility and accuracy issues highlight the need for digital health literacy. The findings underscore the necessity for continuously evaluating AI systems and their broader societal implications in health communication. Full article

In a world where physical activity and social participation are fundamental pillars of a full and healthy life, confidence in walking has emerged as a fundamental aspect to assess, especially for older adults. Therefore, the purpose of this study was to develop a Portuguese (European) version of the Ambulatory Self-Confidence Questionnaire (ASCQ) that was both linguistically and psychometrically adapted. To do so, a translation method was used, followed by an assessment of its validity and reliability. The Portuguese version was completed by 173 older adults. To assess reliability, Cronbach’s alpha and intraclass correlation coefficients (ICCs) were used. For sociodemographic and clinical characterization, as well as questionnaire scoring, descriptive statistical analysis was used. Pearson’s correlation (r), Student’s t-test, and one-way ANOVA were used to analyze criterion and construction validity. The Portuguese interactions with ASCQ were effectively translated and adjusted, revealing exceptional internal consistency and test–retest reliability, as reflected in Cronbach’s alpha and ICC values of 0.95. No floor effect was observed; however, a ceiling effect was identified (3.5%). The criterion and construct validity were verified as all the correlations established were statistically significant. The adaptation of the ASCQ to Portuguese culture is adequate, making it valid for use within the Portuguese population. Full article

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as “brush teeth” and “brush hair” are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results. Full article

Teachers in Germany are not adequately prepared to teach in a linguistically responsive way. To change that, multiple development and research projects in this area have been established over the past decade. Recent studies show that pre-service teachers still have few opportunities to learn (OTLs) in the field of linguistically responsive teaching (LRT). This study aimed to transfer the theoretical model and the DaZKom test into pre-service teacher training and evaluate LRT-relevant OTLs at nine different universities across Germany with 1649 pre-service teachers. We focused on how LRT-relevant OTLs were perceived by pre-service teachers, how LRT-related OTLs and pre-service teachers’ academic backgrounds (course of studies and experience) were related, and how OTLs impacted LRT competence. This study was conducted during the COVID-19 pandemic. Therefore, the teacher training and evaluations were conducted digitally. We found that pre-service teachers report a relatively low number of LRT-relevant OTLs in their studies. In particular, LRT-relevant activities have so far been taught very rarely at universities. Also, different emphases still prevail at universities regarding the qualitative and quantitative offer of LRT-relevant OTLs, because of differences among the nine participating universities. Based on these findings, we recommend that universities offer LRT-relevant learning opportunities in the curriculum. Full article

Fraud exists on both legitimate e-commerce platforms and illicit dark web marketplaces, impacting both environments. Detecting fraudulent vendors proves challenging, despite clients’ reporting scams to platform administrators and specialised forums. This study introduces a method to differentiate sellers reported as scammers from others by analysing linguistic patterns in their textual traces collected from three distinct cryptomarkets (White House Market, DarkMarket, and Empire Market). It distinguished between potential scammers and reputable sellers based on claims made by Dread forum users. Vendor profiles and product descriptions were then subjected to textometric analysis for raw text and N-gram analysis for pre-processed text. Textual statistics showed no significant differences between profile descriptions and ads, which suggests the need to combine language traces with transactional traces. Textometric indicators, however, were useful in identifying unique ads in which potential scammers used longer, detailed descriptions, including purchase rules and refund policies, to build trust. These indicators aided in choosing relevant documents for qualitative analysis. A pronounced, albeit modest, emphasis on language related to ‘Quality and Price’, ‘Problem Resolution, Communicationand Trust’, and ‘Shipping’ was observed. This supports the hypothesis that scammers may more frequently provide details about transactions and delivery issues. Selective scamming and exit scams may explain the results. Consequently, an analysis of the temporal trajectory of vendors that sheds light on the developmental patterns of their profiles up until their recognition as scammers can be envisaged. Full article

Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI’s discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short, written picture description task. Natural Language Processing (NLP) and a BERT pre-trained language model were utilized to analyze the writing samples. We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performed better than cognitive measures. Written discourses analyzed by AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation. Full article

This article conducts a corpus linguistics analysis of the dative–genitive subconstruction within the broader context of Old English double object complementation. The ditransitive construction in Old English has traditionally been perceived as a network of alternating subconstructions, including dat-acc, acc-dat, acc-gen, dat-gen, and acc-acc, as the most productive variants. Recent literature has primarily focused on dat-accs and acc-dats because they are the most productive patterns across the history of English, giving also rise to the current ditransitive construction. However, the less productive case frames have received considerably less recent attention. This work, part of an ongoing investigation aimed at creating an OE dat-gen database, builds upon Visser’s list, verified and implemented by findings obtained from a search conducted in the Dictionary of Old English Web Corpus. We obtain 88 verb types and 443 tokens, incorporating 19 new verb types and 260 tokens into the database. More significantly, we offer a detailed description of the conceptual domains and verb classes associated with OE dat-gens, which display a semantics characterized by the presence or absence of actual transfer, as well as transitions from literal to metaphorical transfer, with speech verbs playing a significant role. Full article

Traditional animal pose estimation techniques based on images face significant hurdles, including scarce training data, costly data annotation, and challenges posed by non-rigid deformation. Addressing these issues, we proposed dynamic conditional prompts for the prior knowledge of animal poses in language modalities. Then, we utilized a multimodal (language–image) collaborative training and contrastive learning model to estimate animal poses. Our method leverages text prompt templates and image feature conditional tokens to construct dynamic conditional prompts that integrate rich linguistic prior knowledge in depth. The text prompts highlight key points and relevant descriptions of animal poses, enhancing their representation in the learning process. Meanwhile, transformed via a fully connected non-linear network, image feature conditional tokens efficiently embed the image features into these prompts. The resultant context vector, derived from the fusion of the text prompt template and the image feature conditional token, generates a dynamic conditional prompt for each input sample. By utilizing a contrastive language–image pre-training model, our approach effectively synchronizes and strengthens the training interactions between image and text features, resulting in an improvement to the precision of key-point localization and overall animal pose estimation accuracy. The experimental results show that language–image contrastive learning based on dynamic conditional prompts enhances the average accuracy of animal pose estimation on the AP-10K and Animal Pose datasets. Full article

Childhood apraxia of speech (CAS) represents a significant diagnostic and therapeutic challenge within the field of clinical neuropsychology, characterized by its nuanced presentation and multifactorial nature. The aim of this study was to distil and synthesize the broad spectrum of research into a coherent model for the assessment and diagnosis of CAS. Through a mixed-method design, the quantitative phase analyzed 290 studies, unveiling 10 clusters: developmental apraxia, tabby talk, intellectual disabilities, underlying speech processes, breakpoint localization, speech characteristics, functional characteristics, clinical practice, and treatment outcome. The qualitative phase conducted a thematic analysis on the most cited and recent literature, identifying 10 categories: neurobiological markers, speech motor control, perceptual speech features, auditory processing, prosody and stress patterns, parent- and self-report measures, intervention response, motor learning and generalization, comorbidity analysis, and cultural and linguistic considerations. Integrating these findings, a descriptive and prescriptive model was developed, encapsulating the complexities of CAS and providing a structured approach for clinicians. This model advances the understanding of CAS and supports the development of targeted interventions. This study concludes with a call for evidence-based personalized treatment plans that account for the diverse neurobiological and cultural backgrounds of children with CAS. Its implications for practice include the integration of cutting-edge assessment tools that embrace the heterogeneity of CAS presentations, ensuring that interventions are as unique as the children they aim to support. Full article

Background: The integration of Artificial Intelligence (AI) into various aspects of daily life has sparked growing interest in understanding public attitudes toward this technology. Despite advancements in tools to assess these perceptions, there remains a need for culturally adapted instruments, particularly in specific contexts like that of Peruvian nurses. Objective: To evaluate the psychometric properties of the AIAS-4 in a sample of Peruvian nurses. Methods: An instrumental design was employed, recruiting 200 Peruvian nurses. The Attitude toward Artificial Intelligence in Spanish (AIAS-S), a cultural and linguistic adaptation of the AIAS-4, involved data analysis using descriptive statistics, confirmatory factor analysis (CFA), and invariance tests. Results: The Confirmatory Factor Analysis (CFA) confirmed a unidimensional factor structure with an excellent model fit (χ² = 0.410, df = 1, p = 0.522, CFI = 1.00, TLI = 1.00, RMSEA = 0.00, SRMR = 0.00). The scale demonstrated high internal consistency (α = 0.94, ω = 0.91). Tests of invariance from configural to strict confirmed that the scale is stable across different demographic subgroups. Conclusions: The AIAS-S proved to be a psychometrically solid tool for assessing attitudes toward AI in the context of Peruvian nurses, providing evidence of validity, reliability, and gender invariance. This study highlights the importance of having culturally adapted instruments to explore attitudes toward emerging technologies in specific groups. Full article

In the African cultural context and beyond, personal names are not just unique forms of identifying and individuating their bearers; they also provide relevant windows that resonate with the people’s worldviews, values, and cosmology. From a socio-onomastic perspective, this article examines the representation of people and their description in the Ibibio cultural namescape, which is a source of their traditional epistemology. Personal names are symbolic linguistic resources that contain information about the Ibibio universe of meaning, where people are placed at the centre of every social relationship. Drawing on ethnographic data sourced through participant observation and semi-structured interviews with 30 participants who were name-givers, name-bearers and name-users, this study reveals that the Ibibio naming tradition provides a medium for the dissemination of its traditional cultural scripts, which capture community solidarity, support, security and a sense of belonging. This article concludes that the Ibibio anthroponymic culture reflects people as sources of empowerment. People provide the foundation for understanding the past and a path for reaching one’s life goals. This study offers significant entry points into the way the Ibibio act and react to the strength of its community and reinforces the belief that for the Ibibio, people-related regime of names is an important resource used to foster a positive sense of community and well-being. Full article