Search Results (115)

Video temporal grounding (VTG) aims to localize a semantically relevant temporal segment within an untrimmed video based on a natural language query. The task continues to face challenges arising from cross-modal semantic misalignment, which is largely attributed to redundant visual content in sensor-acquired video streams, linguistic ambiguity, and discrepancies in modality-specific representations. Most existing approaches rely on intra-modal feature modeling, processing video and text independently throughout the representation learning stage. However, this isolation undermines semantic alignment by neglecting the potential of cross-modal interactions. In practice, a natural language query typically corresponds to spatiotemporal content in video signals collected through camera-based sensing systems, encompassing a particular sequence of frames and its associated salient subregions. We propose a text-guided visual representation optimization framework tailored to enhance semantic interpretation over video signals captured by visual sensors. This framework leverages textual information to focus on spatiotemporal video content, thereby narrowing the cross-modal gap. Built upon the unified cross-modal embedding space provided by CLIP, our model leverages video data from sensing devices to structure representations and introduces two dedicated modules to semantically refine visual representations across spatial and temporal dimensions. First, we design a Spatial Visual Representation Optimization (SVRO) module to learn spatial information within intra-frames. It selects salient patches related to the text, capturing more fine-grained visual details. Second, we introduce a Temporal Visual Representation Optimization (TVRO) module to learn temporal relations from inter-frames. Temporal triplet loss is employed in TVRO to enhance attention on text-relevant frames and capture clip semantics. Additionally, a self-supervised contrastive loss is introduced at the clip–text level to improve inter-clip discrimination by maximizing semantic variance during training. Experiments on Charades-STA, ActivityNet Captions, and TACoS, widely used benchmark datasets, demonstrate that our method outperforms state-of-the-art methods across multiple metrics. Full article

Dense video captioning involves identifying, localizing, and describing multiple events within a video. Capturing temporal and contextual dependencies between events is essential for generating coherent and accurate captions. To effectively capture temporal and contextual dependencies between events, we propose Dense Video Captioning with Dual Contextual, Systematic, and Linguistic Modules (DVC-DCSL), a novel dense video captioning model that integrates contextual, semantic, and linguistic modules. The proposed approach employs two uni-directional LSTMs (forward and backward) to generate distinct captions for each event. A caption selection mechanism then processes these outputs to determine the final caption. In addition, contextual alignment is improved by incorporating visual and textual features from previous video segments into the captioning module, ensuring smoother narrative transitions. Comprehensive experiments conducted using the ActivityNet dataset demonstrate that DVC-DCSL increases the Meteor score from 11.28 to 12.71, representing a 12% improvement over state-of-the-art models in the field of dense video captioning. These results highlight the effectiveness of the proposed approach in improving dense video captioning quality through contextual and linguistic integration. Full article

This study provides a comprehensive philological analysis of ten Old Uyghur manuscript fragments of the Saddharmapuṇḍarīka-sūtra (Lotus Sutra) in the Berlin Turfan Collection, while systematically examining all extant Old Uyghur Lotus Sutra manuscripts to establish a complete corpus for comparative analysis. By collating this complete corpus with Kumārajīva’s Chinese translation, this research demonstrates a typology of Old Uyghur Lotus Sutra fragments. It identifies at least two distinct translation lineages: (1) early translations (pre-10th century) exhibiting lexical and structural divergences indicative of Sogdian mediation or hybrid source traditions, and (2) late translations (11th–14th centuries) directly derived from the Chinese version, characterized by syntactic fidelity and a standardized terminology. Through comparative textual analysis, orthographic scrutiny, and terminological cross-referencing, this paper aims to reconstruct the historical trajectory of the Lotus Sutra’s transmission. In addition, it discusses some facts indicating linguistic and cultural contact between the Sogdians and the progressive alignment of Uyghur Buddhist texts with Chinese Buddhist traditions. Full article

This study analyzed the effects of various educational materials used in Tool Box Meetings conducted prior to work at construction sites on educational effectiveness. Specifically, the study examined the impact of changes in information quantity, linguistic explanation, and the number of educational materials on the cognitive load of construction workers. The study involved 345 construction workers. Group A utilized visual materials with higher information quantity compared to Group B. Group B, in turn, used visual materials that simplified the information to match linguistic explanations provided for Group A’s materials. Group C conducted the education meeting by reducing the number of educational materials from 13 to 8 after using Group B’s materials. Cognitive load, based on recall counts and recall rates, was then analyzed. In Group A, the use of visual educational materials with high information quantity was associated with reduced learning effectiveness, likely due to increased cognitive load. Meanwhile, in Group B, using educational materials that simplified information to match linguistic explanations resulted in an increase in recall counts and recall rates. In Group C, reducing the number of educational materials resulted in no difference in recall counts compared to Group B; however, there was an increase in the overall recall rate. Based on these research findings, it was concluded that utilizing visually simplified materials aligned with linguistic explanations and considering the cognitive load of workers to establish an appropriate number of educational materials are effective approaches in Tool Box Meeting education. Full article

Text-based person search (TPS), a critical technology for security and surveillance, aims to retrieve target individuals from image galleries using textual descriptions. The existing methods face two challenges: (1) ambiguous attribute–noun association (AANA), where syntactic ambiguities lead to incorrect associations between attributes and the intended nouns; and (2) textual noise and relevance imbalance (TNRI), where irrelevant or non-discriminative tokens (e.g., ‘wearing’) reduce the saliency of critical visual attributes in the textual description. To address these aspects, we propose the dependency-aware entity–attribute alignment network (DEAAN), a novel framework that explicitly tackles AANA through dependency-guided attention and TNRI via adaptive token filtering. The DEAAN introduces two modules: (1) dependency-assisted implicit reasoning (DAIR) to resolve AANA through syntactic parsing, and (2) relevance-adaptive token selection (RATS) to suppress TNRI by learning token saliency. Experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate state-of-the-art performance, with the DEAAN achieving a Rank-1 accuracy of 76.71% and an mAP of 69.07% on CUHK-PEDES, surpassing RDE by 0.77% in Rank-1 and 1.51% in mAP. Ablation studies reveal that DAIR and RATS individually improve Rank-1 by 2.54% and 3.42%, while their combination elevates the performance by 6.35%, validating their synergy. This work bridges structured linguistic analysis with adaptive feature selection, demonstrating practical robustness in surveillance-oriented TPS scenarios. Full article

This study explores how Speech and Elocution Training (SET) activates language potential and fosters career-oriented development among higher vocational students through self-efficacy mechanisms. Through qualitative interviews with four vocational graduates who participated in SET 5 to 10 years ago, the research identifies three key findings. First, SET comprises curriculum content (e.g., workplace communication modules such as hosting, storytelling, and sales pitching) and classroom training using multimodal TED resources and Toastmasters International-simulated practices, which spark language potential through skill-focused, realistic exercises. Second, these pedagogies facilitate a progression where initial language potential evolves from nascent career interests into concrete job-seeking intentions and long-term career plans: completing workplace-related speech tasks boosts confidence in career choices, planning, and job competencies, enabling adaptability to professional challenges. Third, SET aligns with Bandura’s four self-efficacy determinants; these are successful experiences (including personalized and virtual skill acquisition and certified affirmation), vicarious experiences (via observation platforms and constructive peer modeling), verbal persuasion (direct instructional feedback and indirect emotional support), and the arousal of optimistic emotions (the cognitive reframing of challenges and direct desensitization to anxieties). These mechanisms collectively create a positive cycle that enhances self-efficacy, amplifies language potential, and clarifies career intentions. While highlighting SET’s efficacy, this study notes a small sample size limitation, urging future mixed-methods studies with diverse samples to validate these mechanisms across broader vocational contexts and refine understanding of language training’s role in fostering linguistic competence and career readiness. Full article

The cross-lingual aspect-based sentiment analysis (ABSA) task continues to pose a significant challenge, as it involves training a classifier on high-resource source languages and then applying it to classify texts in low-resource target languages, thereby bridging linguistic gaps while preserving accuracy. Most existing methods achieve exceptional performance by relying on multilingual pre-trained language models (mPLM) and translation systems to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which ultimately hinders classification performance in target languages. To address this challenge, we propose CLQT, a novel framework that combines contrastive learning pre-training with quantum theory to address the cross-lingual ABSA task. Firstly, we develop a contrastive learning strategy to align data between the source and target languages. Subsequently, we incorporate a quantum network that employs quantum projection and quantum entanglement to facilitate effective knowledge transfer across languages. Extensive experiments reveal that the novel CLQT framework both achieves strong results and has a beneficial overall influence on the cross-lingual ABSA task. Full article

Readability formulas are mathematical functions that assess the ‘difficulty’ level of a given text. They play a crucial role in aligning educational texts with student reading abilities; however, existing models are often not tailored to specific linguistic or regional contexts. This study aims to develop and evaluate two novel readability formulas specifically designed for the Mexican Spanish language, targeting elementary education levels. The formulas were trained on a corpus of 540 texts drawn from official elementary-level textbooks issued by the Mexican public education system. The first formula was constructed using multiple linear regression, emulating the structure of traditional readability models. The second was derived through genetic programming (GP), a machine learning technique that evolves symbolic expressions based on training data. Both approaches prioritize interpretability and use standard textual features, such as sentence length, word length, and lexical and syntactic complexity. Experimental results show that the proposed formulas outperform several well-established Spanish and non-Spanish readability formulas in distinguishing between grade levels, particularly for early and intermediate stages of elementary education. The GP-based formula achieved the highest alignment with target grade levels while maintaining a clear analytical form. These findings underscore the potential of combining machine learning with interpretable modeling techniques and highlight the importance of linguistic and curricular adaptation in readability assessment tools. Full article

Although Singing Voice Synthesis (SVS) has revolutionized audio content creation, global linguistic diversity remains challenging. Current SVS research shows scant exploration of cross-lingual generalization, as fragmented, language-specific phoneme encodings (e.g., Pinyin, ARPA) hinder unified phonetic modeling. To address this challenge, we built a four-language dataset based on GTSinger’s speech data, using the International Phonetic Alphabet (IPA) for consistent phonetic representation and applying precise segmentation and calibration for improved quality. In particular, we propose a novel method of decomposing IPA phonemes into letters and diacritics, enabling the model to deeply learn the underlying rules of pronunciation and achieve better generalization. A dynamic IPA adaptation strategy further enables the application of learned phonetic representations to unseen languages. Based on VISinger2, we introduce Transinger, an innovative cross-lingual synthesis framework. Transinger achieves breakthroughs in phoneme representation learning by precisely modeling pronunciation, which effectively enables compositional generalization to unseen languages. It also integrates Conformer and RVQ techniques to optimize information extraction and generation, achieving outstanding cross-lingual synthesis performance. Objective and subjective experiments have confirmed that Transinger significantly outperforms state-of-the-art singing synthesis methods in terms of cross-lingual generalization. These results demonstrate that multilingual aligned representations can markedly enhance model learning efficacy and robustness, even for languages not seen during training. Moreover, the integration of a strategy that splits IPA phonemes into letters and diacritics allows the model to learn pronunciation more effectively, resulting in a qualitative improvement in generalization. Full article

This paper investigates transgenerational change in the use of formal address forms among Polish heritage speakers in Germany by analyzing their language attitudes and usage preferences. The survey-based study involved 100 bilingual Polish speakers with a migration background, including both late and early immigrants vs. representatives of the first and second generations, respectively. The survey included two parts: (1) a questionnaire assessing language attitudes toward formal address systems in Polish and German, respectively, and (2) an Acceptability Judgment Task evaluating respondents’ preferences for different address variants, including contact-induced hybrid forms, in simulated communicative situations. By comparing language attitudes and usage preferences among heritage speakers, the study seeks to identify mechanisms of transgenerational change in pragmatics of their heritage language. The findings reveal a discrepancy between language attitudes and actual language use by heritage speakers. While respondents recognize asymmetries between Polish and German formal address systems, their usage preferences align predominantly with the Polish monolingual norm, particularly in perceptually oriented tasks. However, the emergence of hybrid forms of formal address suggests a gradual shift toward increased tolerance and acceptance of contact-induced variations. This finding supports the hypothesis that pragmatics, like other linguistic levels, undergoes a transgenerational shift in migration settings, with language attitudes serving as earlier indicators of change. Full article

This study validates a Spanish-language instrument designed to assess self-efficacy, digital competence, and anxiety in data-driven decision-making (DDDM) among pre-service teachers. Based on the 3D-MEA and the Beliefs about Basic ICT Competencies scale, the instrument was culturally adapted for Chile and Spain. A sample of 512 participants underwent exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Given the ordinal nature of the data and the assumption of non-normality, appropriate estimation methods were utilized. Results supported a well-defined four-factor structure: Interpretation and Application, Technology, Identification, and Anxiety. Factor loadings ranged from 0.678 to 0.869, and internal consistency was strong (α = 0.802–0.888). The CFA confirmed good model fit (χ² (129) = 189.25, p < 0.001; CFI = 0.985; TLI = 0.981; RMSEA = 0.041; SRMR = 0.061). Measurement invariance was established across gender and nationality, reinforcing the validity of cross-group comparisons. The study is framed within an educational context aligned with socioformative principles and sustainable education goals, which support reflective and ethical data use. This validated tool addresses the lack of culturally adapted and psychometrically validated instruments for assessing DDDM competencies in Spanish-speaking contexts, offering a culturally and linguistically relevant instrument with strong internal consistency and a well-supported factor structure. It supports the design of formative strategies in teacher education, enabling the identification of training needs and promoting evidence-based pedagogical decision-making in diverse Hispanic contexts. Future studies should test factorial invariance across additional contexts and explore longitudinal applications. Full article

Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena. Full article

The authors conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers’ profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet, Llama, Gemini Pro 1.5, and ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, and physics) to accommodate four different profiles varying in knowledge, reading skills, and learning goals. Natural Language Processing (NLP) was leveraged to evaluate the GenAI-adapted texts using an array of linguistic and semantic features empirically associated with text readability. NLP analyses revealed variations in the degree to which the LLMs successfully adjusted linguistic features to suit reader profiles. Most notably, NLP highlighted inconsistent alignment between potential reader abilities and text complexity. The results pointed toward the need to augment the AI prompts using personification, chain-of-thought, and documents regarding text comprehension, text readability, and individual differences (i.e., leveraging RAG). The resulting text modifications in Experiment 2 were better aligned with readers’ profiles. Augmented prompts resulted in LLM modifications with more appropriate cohesion features tailored to high- and low-knowledge readers for optimal comprehension. This study demonstrates how LLMs can be prompted to modify text and uniquely demonstrates the application of NLP to evaluate theory-driven content personalization using GenAI. NLP offers an efficient, real-time solution to validate personalized content across multiple domains and contexts. Full article

The cognitive core of human beings is closely connected to the concept of animacy, which significantly influences their memory, vision, and complex language comprehension. While animacy is reflected in language through subtle constraints on verbs and adjectives, it is also acquired and honed through non-linguistic experiences. In the same vein, we suggest that the limited capacity of LLMs to grasp natural language, particularly in relation to animacy, stems from the fact that these models are trained solely on textual data. Hence, the question this paper aims to answer arises: Can LLMs, in their digital wisdom, process animacy in a similar way to what humans would do? We then propose a systematic analysis via prompting approaches. In particular, we probe different LLMs using controlled lexical contrasts (animate vs. inanimate nouns) and narrative contexts in which typically inanimate entities behave as animate. Results reveal that, although LLMs have been trained predominantly on textual data, they exhibit human-like behavior when faced with typical animate and inanimate entities in alignment with earlier studies, specifically on seven LLMs selected from three major families—OpenAI (GPT-3.5, GPT-4), Meta (Llama2 7B, 13B, 70B), and Mistral (Mistral-7B, Mixtral). GPT models generally achieve the most consistent and human-like performance, and in some tasks, such as sentence plausibility and acceptability judgments, even surpass human baselines. Moreover, although to a lesser degree, the other models also assume comparable results. Hence, LLMs can adapt to understand unconventional situations by recognising oddities as animated without needing to interface with unspoken cognitive triggers humans rely on to break down animations. Full article

Background: Multicultural education and second-language acquisition engaged neural networks, supporting executive function, memory, and social cognition in adulthood, represent powerful forms of brain-inspired multisensory learning. The neuroeducational framework integrates neuroscience with pedagogical practice to understand how linguistically and culturally rich environments drive neuroplasticity and cognitive adaptation in adult learners. Objective: This systematic review synthesizes findings from 80 studies examining neuroplasticity and cognitive outcomes in adults undergoing multicultural and second-language acquisition, focusing on underlying neural mechanisms and educational effectiveness. Methods: The analysis included randomized controlled trials and longitudinal studies employing diverse neuroimaging techniques (fMRI, MEG, DTI) to assess structural and functional brain network changes. Interventions varied in terms of immersion intensity (ranging from limited classroom contact to complete environmental immersion), multimodal approaches (integrating visual, auditory, and kinesthetic elements), feedback mechanisms (immediate vs. delayed, social vs. automated), and learning contexts (formal instruction, naturalistic acquisition, and technology-enhanced environments). Outcomes encompassed cognitive domains (executive function, working memory, attention) and socio-emotional processes (empathy, cultural adaptation). Results: Strong evidence demonstrates that multicultural and second-language acquisition induce specific neuroplastic adaptations, including enhanced connectivity between language and executive networks, increased cortical thickness in frontal–temporal regions, and white matter reorganization supporting processing efficiency. These neural changes are correlated with significant improvements in working memory, attentional control, and cognitive flexibility. Immersion intensity, multimodal design features, learning context, and individual differences, including age and sociocultural background, moderate the effectiveness of interventions across adult populations. Conclusions: Adult multicultural and second-language acquisition represents a biologically aligned educational approach that leverages natural neuroplastic mechanisms to enhance cognitive resilience. Findings support the design of interventions that engage integrated neural networks through rich, culturally relevant environments, with significant implications for cognitive health across the adult lifespan and for evidence-based educational practice. Full article