Search Results (43)

Driven by the development of next-generation wireless networks and the widespread adoption of sensing, communication is shifting from traditional bit-level transmission to intelligent, rich interactions within our digital social system. However, existing speech semantic communication frameworks predominantly focus on textual accuracy, neglecting the critical affective information (e.g., tone and emotion) that is essential for natural human-centric interactions in the real world. To address this limitation, we propose the Heterogeneous Affective Speech Semantic Communication (HASCom) framework, designed for the robust transmission of highly expressive speech over complex wireless channels. Specifically, we design a heterogeneous dual-stream transmission architecture that decouples discrete phoneme-level linguistic content from continuous emotional embeddings. For discrete semantic information, we use reliable digital coding protected by Low-Density Parity-Check (LDPC) to guarantee strict recoverability. Conversely, for emotional features, we employ Deep Joint Source-Channel Coding (JSCC) analog transmission to prevent irreversible quantization errors and the cliff effect. Additionally, we develop a prior-guided diffusion reconstruction module at the receiving end. This module leverages a structural prior network to align the decoded semantics, which then steers the reverse diffusion process conditioned on the recovered affective features. Extensive experiments under both AWGN and Rayleigh fading channels demonstrate that HASCom significantly outperforms state-of-the-art baselines. Specifically, it achieves superior objective semantic similarity and subjective Mean Opinion Score (MOS) at low Signal-to-Noise Ratios (SNRs), while the JSCC transmission modules maintain an ultra-low inference latency of less than 0.1 ms, validating its high efficiency and robustness for practical deployments. Full article

This study reconstructs the geography of meaning of the German perception verb schmecken on the basis of 30 major dialect dictionaries, treating them as a distributed semantic corpus and coding attestations as binary variables reflecting the presence or absence of semantic options. Combining a construal-based framework with spatial modeling, the analysis shows that the polysemy of schmecken is structured by three mutually reinforcing forces: embodied sensory organization, construal-based perspectivization, and regionally patterned areal dynamics. The gustatory–olfactory axis forms the semantic core of the verb, from which tactile, visual, affective, and epistemic extensions emerge. These extensions align with systematic pathways constrained by agentive, experiential, emissive, and evaluative construals, demonstrating that semantic extension is channeled through specific construal modes—notably emissive and agentive—rather than determined by sensory modality alone. A detailed areal analysis reveals a pronounced north–south divide. While Low German dialects conform to the cross-linguistically more common tendency to avoid colexifying taste and smekk—itself the outcome of historical change rather than uninterrupted differentiation—Upper German varieties preserve a typologically rare gustatory–olfactory cluster and exhibit the richest range of cross-modal and abstract extensions. The resulting semantic graph formalizes how regional varieties activate different subsets of a lexeme’s semantic potential and demonstrates that semantic networks themselves display spatial organization. The study thus provides an empirically grounded reconstruction of a German geography of meaning and illustrates how dialect data illuminate the interplay between embodied cognition, construal-based lexical architecture, and areal dynamics. Full article

Terms are specialized words and expressions used in particular disciplines, cultures, or fields. They usually carry precise meanings and aim to describe referents accurately and clearly. Due to differences in culture, history, and other factors across countries, the development of indigenous Chinese linguistic terms plays a vital role in bridging cultural gaps and promoting the dissemination of Chinese culture. These terms not only explain specific words in Chinese and describe unique linguistic phenomena, but also embody the core concepts and academic traditions of Chinese linguistics, thereby contributing to the global spread and development of Chinese civilization. In order to achieve cross-linguistic dissemination of indigenous terms, we construct a linguistically informed bilingual corpus encompassing a broad spectrum of linguistic subfields, together with novel methods for the automatic extraction and cross-linguistic alignment of terminologies. The resulting corpus contains over 22,000 aligned sentence pairs across nine linguistic domains, providing a robust foundation for bilingual term mining. Building upon this resource, we further propose a multi-channel graph neural network (MCGNN) that jointly models semantic, syntactic, sequential, and co-occurrence relations, thereby enabling multi-perspective reasoning and achieving more accurate bilingual term extraction and alignment. Experimental results demonstrate that our approach substantially improves the accuracy and consistency of bilingual term extraction, alleviates the resource scarcity in the linguistic domain, and provides a solid foundation for future research and applications in cross-linguistic knowledge sharing and academic communication. Full article

Background: This equity audit assessed enrolment and completion of a state-funded cardiometabolic risk-reduction programme for women with prior gestational diabetes in Victoria, Australia. The analyses compared completion rates between the standard prevention programme Life! with one specifically adapted for women with prior gestational diabetes (Life! GDM) using the PROGRESS equity framework. Methods: Women with a history of GDM in the Life! GDM or the mainstream Life! programme in 2022–2025 were included. Multinomial logistic regression was used to impute categorical variables, logistic regression for binary variables, and linear regression for continuous variables. Estimates were combined across imputed datasets using Rubin’s rules. Results: A total of 2261 women were included: 370 in Life! GDM, and 1891 in Life! from 2022 to 2025, with completion rates of 36.7% and 52.2%, respectively. Compared with women in Life!, women in Life! GDM were more likely to come from non-English-speaking backgrounds, particularly South and Central Asian (30.5% vs. 17.0%) and South-East Asian backgrounds (13.0% vs. 4.3%). After multiple imputation, multivariable logistic regression showed that none of the examined participant characteristics were significantly associated with programme completion in Life! GDM. In the Life! cohort, completion was significantly associated with marital status, with single participants having lower odds of completion (OR = 0.59, 95% CI: 0.41–0.85), and with referral channel, with self-referral associated with higher odds of completion (OR = 1.71, 95% CI: 1.39–2.12). Conclusions: The adapted programme appeared to have reached more culturally and linguistically diverse women; however, lower completion among those experiencing disadvantage highlights the need for enhanced support and retention strategies to ensure equitable postpartum diabetes prevention. Full article

Understanding how moral and emotional language operates in paid social advertising is essential for evaluating persuasion and its ethical contours. We provide a descriptive map of Moral Foundations Theory (MFT) language in Meta ad copy (Facebook/Instagram) drawn from seven global beverage brands across eight English-speaking markets. Using the moralstrength toolkit, we implement a two-channel pipeline that combines an unsupervised semantic estimator (SIMON) with supervised classifiers, enforces a strict cross-channel consensus rule, and adds a non-overriding purity diagnostic to reduce attribute-based false positives. The corpus comprises 758 text units, of which only 25 ads (3.3%) exhibit strong consensus, indicating that much of the copy is either non-moral or linguistically ambiguous. Within this high-consensus subset, the distribution of moral cues varies systematically by brand and category, with loyalty, fairness, and purity emerging as the most prominent frames. A valence pass (VADER) indicates that moralized copy tends toward negative valence, yet it may still yield a constructive overall tone when advertisers follow a crisis–resolution structure in which high-intensity moral cues set the stakes while surrounding copy positions the brand as the solution. We caution that text-only models undercapture multimodal signaling and that platform policies and algorithmic recombination shape which moral cues appear in copy. Overall, the study demonstrates both the promise and the limits of current text-based MFT estimators for advertising: they support transparent, reproducible mapping of moral rhetoric, but future progress requires multimodal, domain-sensitive pipelines, policy-aware sampling, and (where available) impression/spend weighting to contextualize descriptive labels. Full article

Accurate scene understanding is crucial for the safe and stable operation of underground utility tunnel inspections. Addressing the characteristics of low-light environments, this paper proposes an object recognition method based on low-light enhanced image semantic segmentation. Secondly, by analyzing image data from real underground utility tunnel environments, the visual language model undergoes scene image fine-tuning to generate scene description text. Thirdly, integrating these functionalities into the system enables real-time processing of captured images and generation of scene understanding results. In practical applications, the average accuracy of the improved recognition model increased by nearly 1% compared to the original model, while the accuracy and recall of the fine-tuned visual-language model surpassed the untuned model by over 70%. Full article

The aim of the present paper is to further develop a theory on the flow of linguistic variables making a sentence, namely, the transformation of (a) characters into words; (b) words into word intervals; and (c) word intervals into sentences. The relationship between two linguistic variables is studied as a communication channel whose performance is determined by the slope of their regression line and by their correlation coefficient. The mathematical theory is applicable to any field/specialty in which a linear relationship holds between two variables. The signal-to-noise ratio $\Gamma$ is a figure of merit of a channel being “deterministic”, i.e., a channel in which the scattering of the data around the regression line is negligible. The larger $\Gamma$ is, the more the channel is “deterministic”. In conclusion, humans have invented codes whose sequences of symbols that make words cannot vary very much when indicating single physical or mental objects of their experience (larger $\Gamma )$. On the contrary, large variability (smaller $\Gamma )$ is achieved by introducing interpunctions to make word intervals, and word intervals make sentences that communicate concepts. This theory can inspire new research lines in cognitive science research. Full article

This study explores how older Chinese migrants in London engage with climate change discourse using participatory co-design workshops. Although already practising sustainability behaviours such as recycling, this group faces significant barriers—particularly language difficulties and cultural differences—that limit their active participation in broader climate initiatives. The research addresses three key aspects: (1) identifying opportunities for sustainable practices within migrants’ daily routines; (2) understanding their influential roles within families and communities; and (3) examining their trusted sources and preferred channels for climate communication. Results highlight that family and community networks, combined with digital platforms (e.g., WeChat) and visually engaging materials, play essential roles in disseminating climate information. Participants expressed strong motivations rooted in intergenerational responsibility and economic benefits. The findings emphasise the necessity of inclusive and peer-led communication strategies that are attuned to older migrants’ linguistic preferences, media habits, and cultural values—underscoring their significant but often overlooked potential to meaningfully contribute to climate action. Full article

Humor is a pragmatic and interdisciplinary phenomenon whose sociocultural relevance has been increasingly recognized by the Academia. Surprisingly, although the anthropo-philosophical theory of homo risu emerged in the 7th century, linguists became interested in the study of the linguistic mechanisms of humor only a few years ago. One of those mechanisms is the use of Anglicisms, because of their pragmatic potential to provide some added value, a halo of prestige and modernity, which creates playful effects of complicity. This paper examines the way Anglicisms crucially contribute to the humorous discourse of the satirical news show El Intermedio, the longest-running program on a Spanish private TV channel. Monitoring of 300 episodes broadcast between April 2022 and December 2024 proves how, in addition to puns and irony, scriptwriters tend to resort to a number of strategies involving the creative use of Anglicisms, which perform different pragmatic functions, while showing sociolinguistic awareness. They also offer an up-to-date sample of the great vitality of Anglicisms in contemporary Spain. Full article

Visual grounding for remote sensing (RSVG) focuses on localizing specific objects in remote sensing (RS) imagery based on linguistic expressions. Existing methods typically employ pre-trained models to locate the referenced objects. However, due to the insufficient capability of cross-modal interaction and alignment, the extracted visual features may suffer from semantic drift, limiting the performance of RSVG. To address this, the article introduces a novel RSVG framework named the text–visual interaction multilevel feature alignment network (TVI-MFAN), which leverages a text–visual interaction attention (TVIA) module to dynamically generate adaptive weights and biases at both spatial and channel dimensions, enabling the visual feature to focus on relevant linguistic expressions. Additionally, a multilevel feature alignment network (MFAN) aggregates contextual information by using cross-modal alignment to enhance features and suppress irrelevant regions. Experiments demonstrate that the proposed method achieves 75.65% and 80.24% (2.42% and 3.1% absolute improvement) accuracy on the OPT-RSVG and DIOR-RSVG dataset, validating its effectiveness. Full article

Linguistic steganography can be utilized to establish covert communication channels on social media platforms, thus facilitating the dissemination of illegal messages, seriously compromising cyberspace security. Synonym substitution-based linguistic steganography methods have garnered considerable attention due to their simplicity and strong imperceptibility. Existing linguistic steganalysis methods have not achieved excellent detection performance for the aforementioned type of linguistic steganography. In this paper, based on the idea of focusing on accumulated differences, we propose a two-stage synonym substitution-based linguistic steganalysis method that does not require a synonym database and can effectively detect texts with very low embedding rates. Experimental results demonstrate that this method achieves an average detection accuracy 2.4% higher than the comparative method. Full article

The detection of abusive language in Roman Urdu is important for secure digital interaction. This work investigates machine learning (ML), deep learning (DL), and transformer-based methods for detecting offensive language in Roman Urdu comments collected from YouTube news channels. Extracted features use TF-IDF and Count Vectorizer for unigrams, bigrams, and trigrams. Of all the ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB)—the best performance was achieved by the same SVM. DL models involved evaluating Bi-LSTM and CNN models, where the CNN model outperformed the others. Moreover, transformer variants such as LLaMA 2 and ModernBERT (MBERT) were instantiated and fine-tuned with LoRA (Low-Rank Adaptation) for better efficiency. LoRA has been tuned for large language models (LLMs), a family of advanced machine learning frameworks, based on the principle of making the process efficient with extremely low computational cost with better enhancement. According to the experimental results, LLaMA 2 with LoRA attained the highest F1-score of 96.58%, greatly exceeding the performance of other approaches. To elaborate, LoRA-optimized transformers perform well in capturing detailed subtleties of linguistic nuances, lending themselves well to Roman Urdu offensive language detection. The study compares the performance of conventional and contemporary NLP methods, highlighting the relevance of effective fine-tuning methods. Our findings pave the way for scalable and accurate automated moderation systems for online platforms supporting multiple languages. Full article

Ironic remarks are often pronounced with a distinctive intonation. It is not clear whether children rely on acoustic cues to attribute an ironic intent. This question has been only indirectly tackled, with studies that manipulated the intonation with which the final remark is pronounced within an irony comprehension task. We propose a new task that is meant to assess whether children rely on prosody to infer speakers’ sincere or ironic communicative intentions, without requiring meta-linguistic judgments (since pragmatic awareness is challenging for young children). Children listen to evaluative remarks (e.g., “That house is really beautiful”), pronounced with sincere or ironic intonation, and they are asked to identify what the speaker is referring to by selecting one of two pictures depicting an image corresponding to a literal interpretation (a luxury house) and one to its reverse interpretation (a hovel). We tested eighty children aged 3 to 11 years and found a clear developmental trend, with children consistently responding above the chance level from age seven, and there was no correlation with the recognition of emotions transmitted through the vocal channel. Full article

This research examined the domain of intelligent transportation systems (ITS) by analyzing the impact of scholarly work and thematic prevalence, as well as focusing attention on vehicles, their technologies, cybersecurity, and related scholarly technologies. This was performed by examining the scientific literature indexed in the Scopus database. This study analysed 2919 documents published between 2018 and 2025. The findings indicated that the highest and most significant journal was derived from IEEE Transactions on Vehicular Technology, with significant standing to the growth of communication and computing on vehicles with edge computing and AI optimization of vehicular systems. In addition, important PST research conferences highlighted the growing interest in academic research in cybersecurity for vehicle networks. Sensor networks, pose forensics, and privacy-preserving communication frameworks were some of the significant contributing fields marking the significance of the interdisciplinary nature of this research. Employing bibliometric analysis, the literature illustrated the multiple channels integrating knowledge creation and innovation in ITS through citation analysis. The outcome suggested an increasingly sophisticated research area, weighing technical progress and increasing concern about security and privacy measures. Further studies must investigate edge computing integrated with AI, advanced privacy-preserving linguistic protocols, and new vehicular network intrusion detection systems. Full article

This study aims to analyze the spatial effects triggered by dollar liquidity by constructing a multidimensional spatial matrix that modifies the traditional monetary spatial framework. We utilized a three-level spatial econometric model (Spatial Lag, Durbin, and Generalized Nested Space) to measure Gross Domestic Product (GDP), Consumer Price Index (CPI), and Asset Price Bubbles (BBL) through five spillover channels (geography, linguistics, politics, war, and economy). Our aim is to establish a systematic relationship between the conduction mechanism, means, economic indicators, and dollar externalities to examine liquidity spillover effects at varying distances in the global monetary space. We find that the spatial effects induced by the global circulation of the US dollar behave significantly differently in a single matrix space compared to in a multidimensional space. While the model verifies the existence of a positive correlation between the complexity of a single space and the spillover effect from a conduction mechanism perspective, the measure of the multidimensional matrix shows that the significance of the spillover effect weakens with an increase in abstraction level from a conduction means perspective. It suggests that spatial matrices of different dimensions reflect different economic realities. The former shows hierarchical multivariate details in independent matrices, while the variation in the level of abstraction of matrices of different dimensions in the latter enhances their interactivity and complexity. Full article