Search Results (346)

Human-database interaction is inevitable in intelligent system applications, and accurately converting user-entered natural language into database query language is a critical step. To improve the accuracy, generalization, and robustness of text-to-SQL, we propose Nabil (a model for natural language conversion query language based on brain-inspired computing technology and a large language model). This model first leverages the spatiotemporal encoding capabilities of spiking neural networks to capture semantic features of natural language, then fuses these features with those generated by a large language model. Finally, a champion model is designed to select the optimal query from multiple candidate SQLs. Experiments were conducted on three database engines, DuckDB, MySQL, and PostgreSQL, and the model’s effectiveness was verified on benchmark datasets such as BIRD. The results show that Nabil outperforms existing baseline methods in both execution accuracy and effective efficiency scores. Furthermore, our proposed normalization and syntax tree abstraction algorithms further enhance the champion model’s discriminative capabilities, providing new insights for text-to-SQL research. Full article

This study examines refugee integration in South Korea’s emerging asylum context by analyzing how education policies and practices shape inclusion, drawing on interviews with 23 key informants from government and civil society. Despite legal frameworks guaranteeing access, findings reveal how institutional, sociocultural, and political factors create significant policy–practice gaps, hindering both structural integration (enrollment, curriculum, language of instruction, certification) and relational integration (sense of belonging) in schools. Barriers include bureaucratic obstacles, language barriers, discrimination, and limited post-secondary pathways. While civil society actors create opportunities, broader systemic changes are needed to promote the meaningful inclusion of students with refugee backgrounds in South Korean society. Full article

This contribution provides new data on Italian first language acquisition and phonological development in preschool children. In total, 104 3- to 6;4-year-old typically developing Italian children were tested with two novel nonword tasks tackling the Italian consonantal system: one for repetition (NWR) and one for discrimination (NWD). NWR data were analyzed in terms of repetition accuracy, featural characteristics, and phonological processes, while NWD was analyzed according to signal detection theory (i.e., A-prime and d-prime) and in terms of discrimination accuracy. The results show the significant role of age on children’s repetition and discrimination abilities: as the children grow older, all the scores improve and the number of errors declines. No complete overlap is found between what children can produce and what they can discriminate, which is in line with what has already been documented in other languages. The findings contribute to the state of the art on the Italian language and provide new perspectives on some methodological issues specific to this language. Full article

Language difficulties have been highlighted as a cornerstone of the developmental profile in Down Syndrome (DS), but very few studies have examined early language abilities in children with DS to determine the initial strengths and weaknesses that might inform early language interventions to support language development in this population. This study focused on the early perception of intonation and examined whether it differed between infants with DS and typically developing (TD) peers. Using a visual habituation paradigm from a previous study on TD infants’ ability to perceive the intonation of statements and questions, infants with DS were able to successfully discriminate statement and question intonation, similarly to TD infants. However, unlike for TD infants, an age group effect was found, with older infants with DS being unable to discriminate the intonation contrast. Our findings highlight the importance of prosody in early development also in infants with DS. Moreover, the unexpected decrease in early sensitivity to intonation in older infants with DS pinpoints a crucial developmental window—the first semester of life—for early interventions using intonation to support language learning in these infants. Full article

Given the exposure to stressors in their home countries, during migration and after arrival, refugees are vulnerable to mental health problems. Their access to adequate health care and other social infrastructures, however, is hampered. This reduces, in addition to other factors, their ability to take part in social and economic activities. We examine the prevalence of mental disorders among the refugee population that arrived in Austria mainly between 2013 and 2018, drawing on data from a refugee survey. We found a high share of refugees (32%) to have moderate or severe mental health problems. When investigating the effects of stressors on the mental health situation, we found a positive association with experienced discrimination in Austria and the fear for partners and children left behind, and a negative correlation with proficiency in the German language, being employed (including volunteer work), having more supportive relationships, and satisfaction with the housing situation. Full article

The automated detection of deceptive language is a crucial challenge in computational linguistics. This study provides a rigorous comparative analysis of three tiers of machine learning models for detecting instructed deception: traditional machine learning (SVM), fine-tuned discriminative models (BERT), and in-context learning with generalist Large Language Models (LLMs). Using the “cross-cultural deception detection” dataset, our findings reveal a clear performance hierarchy. While SVM performance is inconsistent, fine-tuned BERT models achieve substantially superior accuracy. Notably, a multilingual BERT model improves cross-topic accuracy on Spanish text to 90.14%, a gain of over 22 percentage points from its monolingual counterpart (67.20%). In contrast, modern LLMs perform poorly in zero-shot settings and fail to surpass the SVM baseline even with few-shot prompting, underscoring the effectiveness of task-specific fine-tuning. By transparently addressing the limitations of the solicited, low-stakes deception dataset, we establish a robust methodological baseline that clarifies the strengths of different modeling paradigms and informs future research into more complex, real-world deception phenomena. Full article

Background/Objectives: The Seeds of Literacy project has followed infants at family risk for dyslexia (FR group) and infants not at family risk (NFR group) since the age of 5 months, exploring whether infant measures of auditory sensitivity and phonological skills are related to later reading achievement. Here, we retrospectively assessed relations between infant performance on a rise time discrimination task with new pre-reading behavioural measures administered at 60 months. In addition, we re-classified dyslexia risk at 60 months and again assessed relations to rise time sensitivity. Participants were re-grouped using the pre-reading behavioural measures as either dyslexia risk at 60 months (60mDR) or no dyslexia risk (60mNDR). Methods: FR and NFR children (44 English-learning children) completed assessments of rise time discrimination at 10 and/or 60 months, phonological awareness, phonological memory, rapid automatised naming (RAN), letter knowledge, and language skills (receptive vocabulary and grammatical awareness). Results: Longitudinal analyses showed significant time-lagged correlations between rise time sensitivity at 10 months and both RAN and letter knowledge at 60 months. Rise time sensitivity at 60 months was significantly poorer in those children re-grouped as 60mDR, and rise time sensitivity was significantly related to concurrent phonological awareness, RAN, letter knowledge, and receptive vocabulary, but not to tests of grammatical awareness. Conclusions: The data support the view that children’s rise time sensitivity is significantly related to their pre-reading phonological abilities. These findings are discussed in terms of Temporal Sampling theory. Full article

Background: Children diagnosed with Speech Sound Disorders (SSDs) encounter difficulties in speech perception, especially when listening in the presence of background noise. Recommended protocols for auditory processing evaluation include behavioral linguistic and speech processing tests, as well as objective electrophysiological measures. The present study compared the auditory processing profiles of children with SSD and typically developing (TD) children using a battery of behavioral language and auditory tests combined with auditory evoked responses. Methods: Forty (40) parents of 7–10 years old Greek Cypriot children completed parent questionnaires related to their children’s listening; their children completed an assessment comprising language, phonology, auditory processing, and auditory evoked responses. The experimental group included 24 children with a history of SSDs; the control group consisted of 16 TD children. Results: Three factors significantly differentiated SSD from TD children: Factor 1 (auditory processing screening), Factor 5 (phonological awareness), and Factor 13 (Auditory Brainstem Response—ABR wave V latency). Among these, Factor 1 consistently predicted SSD classification both independently and in combined models, indicating strong ecological and diagnostic relevance. This predictive power suggests real-world listening behaviors are central to SSD differentiation. The significant correlation between Factor 5 and Factor 13 may suggest an interaction between auditory processing at the brainstem level and higher-order phonological manipulation. Conclusions: This research underscores the diagnostic significance of integrating behavioral and physiological metrics through dimensional and predictive methodologies. Factor 1, which focuses on authentic listening environments, was identified as the strongest predictor. These results advocate for the inclusion of ecologically valid listening items in the screening for APD. Poor discrimination of speech in noise imposes discrepancies between incoming auditory information and retained phonological representations, which disrupts the implicit processing mechanisms that align auditory input with phonological representations stored in memory. Speech and language pathologists can incorporate pertinent auditory processing assessment findings to identify potential language-processing challenges and formulate more effective therapeutic intervention strategies. Full article

Optical remote sensing images play a pivotal role in numerous applications, notably feature recognition and scene semantic segmentation. Nevertheless, their efficacy is frequently compromised by various noise types, which detrimentally impact practical usage. We have meticulously crafted a novel attention module amalgamating Adaptive Rectangular Convolution (ARConv) with Top-k Sparse Attention. This design dynamically modifies feature receptive fields, effectively mitigating superfluous interference and enhancing multi-scale feature extraction. Concurrently, we introduce a Semantic-Aware Discriminator, leveraging visual-language prior knowledge derived from the Contrastive Language–Image Pretraining (CLIP) model, steering the generator towards a more realistic texture reconstruction. This research introduces an innovative image denoising model termed the Semantic-Aware ARConv Fused Top-k Generative Adversarial Network (SARFT-GAN). Addressing shortcomings in traditional convolution operations, attention mechanisms, and discriminator design, our approach facilitates a synergistic optimization between noise suppression and feature preservation. Extensive experiments on RRSSRD, SECOND, a private Jilin-1 set, and real-world NWPU-RESISC45 images demonstrate consistent gains. Across three noise levels and four scenarios, SARFT-GAN attains state-of-the-art perceptual quality—achieving the best FID in all 12 settings and strong LPIPS—while remaining competitive on PSNR/SSIM. Full article

In Italy, the debate on migrants often focuses on issues such as social integration, economic conditions, and access to services. However, a little-investigated aspect concerns the double stigmatization of LGBTQ+ migrants, a reality made invisible by both the lack of research and the strict anti-LGBTQ+ laws present in many countries of origin. This study aimed to compare homonegativity levels between first- and second-generation migrants. A quantitative approach was used, with 127 participants (age 18–55, M = 30.63, SD = 11.58) completing an anonymous online questionnaire in three different languages. The instrument included a sociodemographic form and the Italian Scale for Measuring Homonegativity. A multivariate General Linear Model (GLM) analysis revealed significant effects of generation (p < 0.001, η² = 0.688) and gender (p < 0.01, η² = 0.144), with second-generation youth and women reporting lower levels of homonegativity. Religious affiliation had a minimal impact, influencing only the perception of deviance towards gay men (p < 0.05). Interactions between factors were generally non-significant, except for gender and religion. These findings underline the importance of generational and gender differences in the formation of homonegativity and highlight the need for further research to explore the cultural and social dynamics influencing these attitudes. In addition, there is a need to further explore how experiences of discrimination influence the well-being of LGBTQ+ migrants and what resilience strategies are adopted to address the challenges of homonegativity and marginalization. Full article

Unmanned aerial vehicles (UAVs) have become indispensable tools for surveillance, enabled by their ability to capture multi-perspective imagery in dynamic environments. Among critical UAV-based tasks, cross-platform person search—detecting and identifying individuals across distributed camera networks—presents unique challenges. Severe viewpoint variations, occlusions, and cluttered backgrounds in UAV-captured data degrade the performance of conventional discriminative models, which struggle to maintain robustness under such geometric and semantic disparities. To address this, we propose view-invariant person search (VIPS), a novel two-stage framework combining Faster R-CNN with a view-invariant re-Identification (VIReID) module. Unlike conventional discriminative models, VIPS leverages the semantic flexibility of large vision–language models (VLMs) and adopts a two-stage training strategy to decouple and align text-based ID descriptors and visual features, enabling robust cross-view matching through shared semantic embeddings. To mitigate noise from occlusions and cluttered UAV-captured backgrounds, we introduce a learnable mask generator for feature purification. Furthermore, drawing from vision–language models, we design view prompts to explicitly encode perspective shifts into feature representations, enhancing adaptability to UAV-induced viewpoint changes. Extensive experiments on benchmark datasets demonstrate state-of-the-art performance, with ablation studies validating the efficacy of each component. Beyond technical advancements, this work highlights the potential of VLM-derived semantic alignment for UAV applications, offering insights for future research in real-time UAV-based surveillance systems. Full article

Panoptic scene graph generation (PSG) aims to simultaneously segment both foreground objects and background regions while predicting object relations for fine-grained scene modeling. Despite significant progress in panoptic scene understanding, current PSG methods face challenging problems: relation prediction often only relies on visual representations and is hindered by imbalanced relation category distributions. Accordingly, we propose IntegraPSG, a single-stage framework that integrates large language model (LLM) guidance with multimodal feature fusion. IntegraPSG introduces a multimodal sparse relation prediction network that efficiently integrates visual, linguistic, and depth cues to identify subject–object pairs most likely to form relations, enhancing the screening of subject–object pairs and filtering dense candidates into sparse, effective pairs. To alleviate the long-tail distribution problem of relations, we design a language-guided multimodal relation decoder where LLM is utilized to generate language descriptions for relation triplets, which are cross-modally attended with vision pair features. This design enables more accurate relation predictions for sparse subject–object pairs and effectively improves discriminative capability for rare relations. Experimental results show that IntegraPSG achieves steady and strong performance on the PSG dataset, especially with the R@100, mR@100, and mean reaching 38.7%, 28.6%, and 30.0%, respectively, indicating strong overall results and supporting the validity of the proposed method. Full article

Background/Objectives: Children with Autism Spectrum Disorder (ASD) often display heightened sensitivity to simple auditory stimuli, but have difficulty discriminating and integrating multiple phonological features (segmental: consonants and vowels; suprasegmental: lexical tones) at the syllable level, which negatively impacts their communication. This study aims to investigate the neural basis of segmental, suprasegmental and combinatorial speech processing challenges in Mandarin-speaking children with ASD compared with typically developing (TD) peers. Methods: Thirty children with ASD and thirty TD peers will complete a multi-feature oddball paradigm to elicit auditory ERP during passive listening. Stimuli include syllables with single (e.g., vowel only), dual (e.g., vowel + tone), and triple (consonant + vowel + tone) phonological deviations. Neural responses will be analyzed using temporal principal component analysis (t-PCA) to isolate overlapping ERP components (early/late MMN), and representational similarity analysis (RSA) to assess group differences in neural representational structure across feature conditions. Expected Outcomes: We adopt a dual-framework approach to hypothesis generation. First, from a theory-driven perspective, we integrate three complementary models, Enhanced Perceptual Functioning (EPF), Weak Central Coherence (WCC), and the Neural Complexity Hypothesis (NCH), to account for auditory processing in ASD. Specifically, we hypothesize that ASD children will show enhanced or intact neural discriminatory responses to isolated segmental deviations (e.g., vowel), but attenuated or delayed responses to suprasegmental (e.g., tone) and multi-feature deviants, with the most severe disruptions occurring in complex, multi-feature conditions. Second, from an empirically grounded, data-driven perspective, we derive our central hypothesis directly from the mismatch negativity (MMN) literature, which suggests reduced MMN amplitudes (with the exception of vowel deviants) and prolonged latencies accompanied by a diminished left-hemisphere advantage across all speech feature types in ASD, with the most pronounced effects in complex, multi-feature conditions. Significance: By testing alternative hypotheses and predictions, this exploratory study will clarify the extent to which speech processing differences in ASD reflect cognitive biases (local vs. global, per EPF/WCC/NCH) versus speech-specific neurophysiological disruptions. Findings will advance our understanding of the sensory and integrative mechanisms underlying communication difficulties in ASD, particularly in tonal language contexts, and may inform the development of linguistically tailored interventions. Full article

Sign language is a visual language articulated through body movements. Existing approaches predominantly leverage RGB inputs, incurring substantial computational overhead and remaining susceptible to interference from foreground and background noise. A second fundamental challenge lies in accurately modeling the nonlinear temporal dynamics and inherent asynchrony across body parts that characterize sign language sequences. To address these challenges, we propose a novel part-wise graph Fourier learning method for skeleton-based continuous sign language recognition (PGF-SLR), which uniformly models the spatiotemporal relations of multiple body parts in a globally ordered yet locally unordered manner. Specifically, different parts within different time steps are treated as nodes, while the frequency domain attention between parts is treated as edges to construct a part-level Fourier fully connected graph. This enables the graph Fourier learning module to jointly capture spatiotemporal dependencies in the frequency domain, while our adaptive frequency enhancement method further amplifies discriminative action features in a lightweight and robust fashion. Finally, a dual-branch action learning module featuring an auxiliary action prediction branch to assist the recognition branch is designed to enhance the understanding of sign language. Our experimental results show that the proposed PGF-SLR achieved relative improvements of 3.31%/3.70% and 2.81%/7.33% compared to SOTA methods on the dev/test sets of the PHOENIX14 and PHOENIX14-T datasets. It also demonstrated highly competitive recognition performance on the CSL-Daily dataset, showcasing strong generalization while reducing computational costs in both offline and online settings. Full article

Migrant children’s discrimination perceptions directly affect their cultural adaptation in the city of influx. In response to migrant children, cities in China have issued relevant urban education policies such as the different-location college entrance examination policy. This study aims to investigate the impact of China’s urban educational policies on the relationship between perceptions of discrimination and acculturation among migrant children. The research sample for this paper was drawn from nine cities that pioneered the policy reform, and a total of 1436 questionnaires were collected. This study analyzed the data using multiple regression analysis and mediation effect tests. This study reveals the following: (a) Migrant children’s educational policy identity has a significant positive impact on their acculturation, whereas their perception of discrimination has a significant negative effect on their acculturation. (b) As the influence of urban educational policies increases, the negative effects of discrimination perceptions on migrant children’s school cultural adaptation, community cultural adaptation, and customs and language adaptation will all diminish. To further explore the facilitating effect of urban educational policies on the cultural adaptation of migrant children, this study proposes recommendations for the household registration system, college entrance examination admission system, and child protection system. This paper not only puts forward policy recommendations for cities of inflow but also provides a Chinese research horizon for the urban cultural adaptation of migrant children in cities of inflow. Full article