Search Results (234)

To address challenges in complex group decision-making (GDM)—specifically preference fuzziness, intricate subgroup segmentation, and non-cooperative behavior—this study proposes an adaptive consensus model based on probabilistic linguistic term sets (PLTSs). By integrating fuzzy C-means (FCM) clustering with a Gaussian mixture model (GMM), a fuzzy Gaussian mixture model (FGMM) is constructed to achieve soft segmentation of expert preference distributions. On this basis, an adaptive consensus feedback mechanism is developed, which dynamically integrates interactive and automated adjustment strategies via multi-level consensus thresholds, thereby balancing decision efficiency and quality. To identify and control non-cooperative behaviors, a cooperation index and a three-tier management strategy, which incorporates intra-group negotiation, weight penalties and an exit-delegation mechanism, were introduced. In the case of strategic decision-making of new energy vehicles (NEV), after four rounds of feedback iterations, the group consensus level increased from the initial 0.316 to 0.804, reaching the preset threshold and verifying the effectiveness of the consensus mechanism. Compared with the existing literature methods, the framework in this paper achieves more comprehensive integration and innovation in four aspects: preference expression, clustering mechanism, consensus feedback and behavior management. Full article

Student evaluations of teaching (SETs) play a central role in hiring, promotion, and retention decisions in higher education; however, research indicates that they may be influenced by perceptions about instructor identity rather than teaching effectiveness. Guided by role congruity theory, which suggests that gendered expectations influence judgments when individuals occupy roles historically associated with another sex or gender, this study examines how students’ written comments reflect stereotypes, notably those related to gender. Using qualitative analysis of narrative SET responses, we identify recurring linguistic patterns that reveal how gender intersects in shaping perceptions of (Black) cisgender faculty. Results from the study show that women instructors were frequently described in relational and mentorship-oriented language, whereas men instructors were framed in terms of authority, rigor, and intellectual challenge. While both groups received overall positive evaluations, these differentiated descriptors highlight subtle mechanisms through which bias can operate and reinforce normative expectations. We also consider our positionality as cisgender scholars and reflect on the broader cultural and institutional contexts that inform evaluations of teaching, underscoring the need for equitable and reflective evaluation practices to further educational equalities in higher education, including the disruption of cisnormativity. Full article

This study examines a theoretically revealing subtype of interrogatives in Chinese that are formulated with the first person singular pronoun wo ‘I’ as subject, termed first person formulated interrogatives. Unlike most interrogatives that are conventionally answer-seeking, first person interrogatives in Chinese are found to serve a dual function, operating either as answer-seeking or as non-answer-seeking actions. This duality raises a fundamental question for action ascription: how do participants interpret such grammatically underspecified interrogatives and respond accordingly? Drawing on 116 instances from a large corpus of Chinese telephone conversations, this study identifies the crucial role of interrogative markers and recipient-addressed terms in action ascription. Further analyses show that these two sets of morphosyntactic resources function by signaling the epistemic relationship between speakers and recipients as well as the recipient’s relevance to the matter at hand. Interrogative designs that imply low epistemic stance of speaker and high relevance of recipients are commonly treated by recipients as answer-seeking, whereas those that imply high epistemic stance of speakers are commonly treated by recipients as non-answer-seeking. These findings advance our understanding of the importance of optional, redundant linguistic resources in action ascription, highlighting that social action is not structurally pre-given but interactionally achieved through cumulative turn-design practices. Full article

The recent use of large language models (LLMs) in code generation and code summarization tasks has been widely adopted by the software engineering community. New LLMs are emerging regularly with improved functionalities, efficiency, and expanding data that allow models to learn more effectively. The lack of guidelines for selecting the right LLMs for coding tasks makes the selection a subjective choice by developers rather than a choice built on code complexity, code correctness, and linguistic similarity analysis. This research investigates the use of machine learning classification and ranking methods to select the best-suited open-source LLMs for code generation and code summarization tasks. This work conducts a comparison experiment on four open-source LLMs (Mistral, CodeLlama, Gemma 2, and Phi-3) and uses the MBPP coding question dataset to analyze code-generated outputs in terms of code complexity, maintainability, cyclomatic complexity, code structure, and LLM perplexity by collecting these as a set of features. An SVM classification problem is conducted on the highest correlated feature pairs, where the models are evaluated through performance metrics, including accuracy, area under the ROC curve (AUC), precision, recall, and F1 scores. The RankNet ranking methodology is used to evaluate code summarization model capabilities by measuring ROUGE and BERTScore accuracies between LLM code-generated summaries and the coding questions used from the dataset. The study results show a maximum accuracy of 49% for the code generation experiment, with the highest AUC score reaching 86% among the top four correlated feature pairs. The highest precision score reached is 90%, and the recall score reached up to 92%. Code summarization experiment results show Gemma 2 scored a 1.93 RankNet win probability score, and represented the highest ranking reached among other models. The phi3 model was the second-highest ranking with a 1.66 score. The research highlights the potential of machine learning to select LLMs based on coding metrics and paves the way for advancements in terms of accuracy, dataset diversity, and exploring other machine learning algorithms for other researchers. Full article

Information asymmetry between complex source texts and general-audience comprehension remains a critical challenge in Artificial Intelligence. However, existing supervised simplification methods suffer from the scarcity of parallel training data, while standard text summarization methods often discard essential details to reduce length. Furthermore, zero-shot large language models frequently lack fine-grained controllability over linguistic complexity. To address these technical limitations, we present a framework to resolve information asymmetry by casting text simplification as a controllable denoising language modeling task. Unlike summarization, our approach preserves full semantic coverage while reducing difficulty. Our algorithm targets the problem of identifying and rewriting complex spans without labeled data through three mechanisms: (1) Asymmetry-Aware Masking, which uses model-based reconstruction difficulty (Negative Log-Likelihood) to isolate high-complexity terms; (2) paraphrase context prompting to enforce semantic invariance; and (3) an adaptive decoding strategy that dynamically penalizes complex tokens based on input difficulty. On ASSET (Abstractive Sentence Simplification Evaluation and Tuning dataset), our best setting reaches SARI (System output Against References and against the Input) 42.90 with FKGL (Flesch–Kincaid Grade Level) 7.10 (Sentence Similarity 0.948), and performs consistently on TurkCorpus (SARI 41.10), while requiring no parallel data or fine-tuning. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

Offensive-language detection systems that perform well at a given point in time often degrade as linguistic patterns evolve, particularly in dialectal Arabic social media, where new terms emerge and familiar expressions shift in meaning. This study investigates temporal linguistic drift in Saudi-dialect offensive-language detection through a systematic evaluation of continual-learning approaches. Building on the Saudi Offensive Dialect (SOD) dataset, we designed test scenarios incorporating newly introduced offensive terms, context-shifting expressions, and varying proportions of historical data to assess both adaptation and knowledge retention. Eight continual-learning configurations—Experience Replay (ER), Elastic Weight Consolidation (EWC), Low-Rank Adaptation (LoRA), and their combinations—were evaluated across five test scenarios. Results show that models without continual-learning experience a 13.4-percentage-point decline in F1-macro on evolved patterns. In our experiments, Experience Replay achieved a relatively favorable balance, maintaining 0.812 F1-macro on historical data and 0.976 on contemporary patterns (KR = −0.035; AG = +0.264), though with increased memory and training time. EWC showed moderate retention (KR = −0.052) with comparable adaptation (AG = +0.255). On the SimuReal test set—designed with realistic class imbalance and only 5% drift terms—ER achieved 0.842 and EWC achieved 0.833, compared to the original model’s 0.817, representing modest improvements under realistic conditions. LoRA-based methods showed lower adaptation in our experiments, likely reflecting the specific LoRA configuration used in this study. Further investigation with alternative settings is warranted. Full article

Current approaches to dynamic sign language recognition commonly rely on dense landmark representations, which impose high computational cost and hinder real-time deployment on resource-constrained devices. To address this limitation, this work proposes a computationally efficient framework for real-time dynamic Mexican Sign Language (MSL) recognition based on a multimodal minimal angular-geometry representation. Instead of processing complete landmark sets (e.g., MediaPipe Holistic with up to 468 keypoints), the proposed method encodes the relational geometry of the hands, face, and upper body into a compact set of 28 invariant internal angular descriptors. This representation substantially reduces feature dimensionality and computational complexity while preserving linguistically relevant manual and non-manual information required for grammatical and semantic discrimination in MSL. A real-time end-to-end pipeline is developed, comprising multimodal landmark extraction, angular feature computation, and temporal modeling using a Bidirectional Long Short-Term Memory (BiLSTM) network. The system is evaluated on a custom dataset of dynamic MSL gestures acquired under controlled real-time conditions. Experimental results demonstrate that the proposed approach achieves 99% accuracy and 99% macro F1-score, matching state-of-the-art performance while using fewer features dramatically. The compactness, interpretability, and efficiency of the minimal angular descriptor make the proposed system suitable for real-time deployment on low-cost devices, contributing toward more accessible and inclusive sign language recognition technologies. Full article

The assessment of Chinese text readability plays a significant role in Chinese language education. Due to the intrinsic differences between alphabetic languages and Chinese character representations, the readability assessment becomes more challenging in terms of the language’s inherent complexity in vocabulary, syntax, and semantics. The article proposed the conceptual analogy between Chinese readability assessment and music’s rhythm and tempo patterns, in which the syntactic structures of the Chinese sentences could be transformed into an image. The Chinese Knowledge and Information Processing Tagger (CkipTagger) tool developed by Sinica-Taiwan is utilized to decompose the Chinese text into a set of tokens. These tokens are then refined through a user-defined token pool to retain meaningful units. An image with part-of-speech (POS) information will be generated by using the token versus syntax alignment. A discrete cosine transform (DCT) is then applied to extract the temporal characteristics of the text. Moreover, the study integrated four categories: linguistic features–type–token ratio, average sentence length, total word, and difficulty level of vocabulary for the readability assessment. Finally, these features were fed into the Support Vector Machine (SVM) network for the classifications. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network is adopted for quantitative comparisons. In simulation, a total of 774 Chinese texts fitted with Taiwan Benchmarks for the Chinese Language were selected and graded by Chinese language experts, consisting of equal amounts of basic, intermediate, and advanced levels. The finding indicated the proposed POS with the linguistic features work well in the SVM network, and the performance matches with the more complex architectures like the Bi-LSTM network in Chinese readability assessments. Full article

The traditional multi-attribute group decision-making (MAGDM) method easily ignores the interference effect among decision-makers (DMs), while quantum theory can effectively portray the uncertainty in the decision-making process and quantify the preference interference among DMs. The asymmetry of evaluation information in social networks can have a significant impact on decision-making. In this paper, a quantum MAGDM algorithm based on probabilistic linguistic term sets (PLTSs) and a quantum-like Bayesian network (QLBN) is proposed (PL-QLBN), utilizing quantum theory and social network concepts and introducing a novel method for calculating interference effects based on belief entropy. Firstly, a complete trust network is constructed based on the probabilistic linguistic trust transfer operator and the minimum path method. A trust aggregation method, considering interference effects, is proposed for the QLBN to determine the DM weights. Next, the attribute weights are calculated based on the entropy weight method. Then, a probabilistic linguistic MAGDM considering interference effects is proposed based on the QLBN. Finally, the feasibility and validity of the provided method are verified through Hefei City’s selection of wastewater reuse alternatives. Full article

Figurative language (metaphor, idiom, irony/sarcasm) is central to pragmatic communication but is frequently challenging for children and adolescents with autism spectrum disorder (ASD). A scoping review was conducted to map pedagogical and clinical interventions that target figurative-language skills in school-age learners with ASD and to summarize reported outcomes. Following a PCC (Population–Concept–Context) framework and PRISMA-ScR reporting, systematic searches were performed in ERIC and Google Scholar (2010–2025). Eligibility required an ASD sample (ages 5–18), an intervention explicitly addressing figurative-language comprehension, and empirical outcome data from educational or related practice settings. Seven studies met inclusion criteria: five targeting metaphors, one targeting idioms, and one targeting sarcasm/irony. Interventions were predominantly delivered one-to-one or in small groups and emphasized structured, explicit instruction with visual scaffolds and stepwise prompting. Across studies, participants demonstrated clear gains on trained items. Generalization beyond trained material was most often observed for metaphor and sarcasm interventions, particularly when instruction highlighted underlying semantic relations or cue-based pragmatic signals; by contrast, the idiom program yielded item-specific learning with minimal near-term transfer. Limited follow-up data suggested short-term maintenance where assessed. Reported variability across individuals was substantial, underscoring the influence of underlying structural-language skills and social-pragmatic demands. Overall, the evidence indicates that figurative-language skills in ASD are amenable to targeted intervention; effective programs tend to combine explicit teaching, visual supports, multiple exemplars, and planned generalization opportunities. Given small samples and methodological heterogeneity, further classroom-based trials with longer follow-up and detailed learner profiles are needed. The findings support integrating figurative-language goals within individualized education and speech-language therapy plans, while aligning instructional complexity with each learner’s linguistic and pragmatic profile. Full article

It is crucial to evaluate the quality of standing timber for the rational and effective management of forest land. In practice, it is often difficult to obtain accurate data on various indicators of standing timber due to constraints such as measurement conditions, accuracy, and cost. Therefore, this study developed a multi-attribute decision-making method based on trapezium clouds and applied it to evaluate the standing timber quality of forest land. Firstly, a trapezium cloud transformation method was designed to handle multi-granularity symmetric linguistic information problems caused by different knowledge backgrounds of decision-makers, and the symmetric structure inherent in trapezium clouds helped to ensure the balanced processing of information from various asymmetric cognitive perspectives. Secondly, a trapezium cloud generalized weighted Heronian mean was proposed for the information aggregation process of trapezium clouds. Then, the concept of trapezium cloud interval similarity was defined, and an optimization model was constructed to determine the normalized interval weights of attributes. Based on the symmetric numerical feature, the calculation formula for the approximate centroid coordinates of trapezium clouds was derived, and based on this, the ranking method of trapezium clouds was obtained. Finally, taking the evaluation of standing timber quality in forest land as a numerical example, the applicability of the constructed multi-attribute decision-making method was demonstrated. In addition, the corresponding comparison analysis verified the superiority and effectiveness of the proposed method. Full article

Background/Objectives: This study examines the impact of long-term institutionalization on the linguistic and communicative abilities of people diagnosed with schizophrenia, focusing on the influence of educational background. Schizophrenia is characterized by cognitive and social deficits, including disruptions to language use and communicative engagement. Prolonged institutionalization can exacerbate these impairments by depriving individuals of essential social interactions and cognitive stimulation. Methods: A case series approach was employed with 18 participants, and validated assessment tools such as the Montreal Evaluation of Communication and the Boston Diagnostic Aphasia Test were used to measure communicative performance. Results: Participants with higher educational attainment (nine or more years of schooling) who had been institutionalized for ten years or more exhibited significantly better performance than their less-educated counterparts across various communication domains, including comprehension of linguistic prosody, lexical fluency, and auditory comprehension. This implies that completing a higher degree may mitigate the cognitive decline impact of prolonged stays in an institution. However, the study design does not allow us to ascertain whether education functions as a mitigating factor. Conclusions: The results highlight the importance of incorporating educational considerations into therapeutic strategies for individuals with schizophrenia, especially those experiencing long-term institutionalization. Providing enhanced educational opportunities within institutional settings could mitigate the adverse effects of prolonged confinement and foster improved communication and social skills. These findings are consistent with research on cognitive reserve, which suggests that education fosters adaptive strategies and the utilization of alternative neural pathways. This enables individuals to maintain communication skills despite the cognitive impairment associated with schizophrenia. Full article

Consensus in group decision-making has become a hotspot to ensure the agreement opinions of decision makers (DMs). The irrational behaviors of DMs, such as confidence, will impact the consensus results, which should be considered. In addition, the existing self-confidence level directly given by DMs rather than exacted from evaluation information may generate malicious manipulation. Furthermore, double-hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is an effective tool to express the complex evaluations of DMs. In this paper, the endo-confidence of DHHFLTS to reflect confidence of DMs from the perspective of evaluation information is defined. Then, we propose a novel consensus model with endo-confidence of DMs based on DHHFLTSs. First, some improved operators of DHHFLTSs are developed. Second, the weight is determined based on both entropy and endo-confidence. Due to the fact that the consensus threshold should decrease as the endo-confidence increases, we give a novel method to obtain the consensus threshold considering endo-confidence level. Moreover, the two-stage adjustment mechanism is presented for non-consensus DMs and the selection process is constructed. Finally, an illustrative example is carried out to demonstrate the feasibility of the proposed model, and a series of comparative analysis is used to show its stability. Full article

Rail transit dispatch speech plays a critical role in ensuring the safety of urban rail operations. To enable automated and accurate compliance assessment of dispatch speech, this study proposes an improved deep learning model to address the limitations of conventional approaches in terms of accuracy and robustness. Building upon the baseline Whisper model, two key enhancements are introduced: (1) low-rank adaptation (LoRA) fine-tuning to better adapt the model to the specific acoustic and linguistic characteristics of rail transit dispatch speech, and (2) a novel entity-aware attention mechanism that incorporates named entity recognition (NER) embeddings into the decoder. This mechanism enables attention computation between words belonging to the same entity category across different commands and recitations, which helps highlight keywords critical for compliance assessment and achieve precise inter-sentence element alignment. Experimental results on real-world test sets demonstrate that the proposed model improves recognition accuracy by 30.5% compared to the baseline model. In terms of robustness, we evaluate the relative performance retention under severe noise conditions. While Zero-shot, Full Fine-tuning, and LoRA-only models achieve robustness scores of 72.2%, 72.4%, and 72.1%, respectively, and the NER-only variant reaches 88.1%, our proposed approach further improves to 89.6%. These results validate the model’s significant robustness and its potential to provide efficient and reliable technical support for ensuring the normative use of dispatch speech in urban rail transit operations. Full article