Search Results (1,281)

Background: The COVID-19 pandemic exposed major deficiencies in global health governance, including fragmented authority, inequitable resource distribution, and weak compliance mechanisms. Although the WHO Pandemic Agreement (2025) addresses several of these gaps, significant operational and institutional challenges remain. This study aims to develop and empirically validate a Global Governance for Health (GGFH) Framework that strengthens leadership, financing, equity, and legal accountability across global, regional, and national levels. Methods: A three-round Delphi study was conducted. Thirty-one experts from diverse sectors, including public health, international law, economics, environment, and diplomacy, evaluated 32 structured governance statements across seven domains. Experts rated all statements using a 7-point Likert scale. Consensus was determined using a strict threshold median ≥ 6; SD ≤ 1.35; ≥75% agreement. Open-text comments were systematically reviewed through thematic analysis. All statements were systematically mapped to the WHO Pandemic Agreement articles to identify areas lacking operational clarity or enforceability. Results: All seven governance domains achieved consensus by Round 3. High agreement emerged on strengthening WHO leadership, implementing sustainable and equitable financing mechanisms, embedding LMIC representation, establishing legal preparedness and capacity-building, and integrating independent accountability tools. Correlation and interdependence analyses demonstrated that governance goals form an integrated, mutually reinforcing system, with financing, equity, and legal frameworks identified as core enablers of effective treaty implementation. Conclusions: The Delphi process validated a comprehensive and operational Global Governance for Health Framework. The GGFH complements the WHO Pandemic Agreement by addressing its unresolved governance, financing, and equity limitations and offers a structured roadmap to guide global pandemic preparedness and treaty implementation. Full article

Roses held profound cultural and economic significance in ancient Greece and Rome, yet comprehensive documentation of their species diversity, cultivation practices, and horticultural innovations remains fragmented across archaeological, iconographic, and textual sources. This multidisciplinary study synthesizes evidence from classical texts, archaeological remains including recently identified rose stem fragments from Oplontis, and iconographic materials—including frescoes, coins, and mosaics—to reconstruct the horticultural systems and cultural landscape of roses in classical antiquity. Analysis of literary sources, particularly Theophrastus’s fourth-century BCE taxonomic descriptions, reveals systematic cultivation of diverse rose varieties with flowers ranging from white to deep crimson, including yellow variants, characterized by morphologies from simple to double forms and valued for fragrance intensity and re-blooming capacity. Archaeological evidence from sites such as Paestum, Pompeii, and Oplontis, including pollen samples, preserved wood fragments with diagnostic prickle patterns, and fresco representations, documents commercial rose production and specialized cultivation techniques that demonstrate significantly greater morphological diversity than textual sources alone indicate. Field research and collection documentation establish the origins of Mediterranean rose cultivation, while iconographic analysis identifies roses in religious ceremonies, festivals, and daily life contexts. Textual sources provide detailed propagation methods, seasonal management practices, and evidence of Mediterranean hybridization events, alongside extensive documentation of medicinal and cosmetic applications. Economic analysis reveals specialized trade networks, commercial production centers, and diverse applications in perfumery, garland making, and pharmaceutical industries. This research establishes that Greek and Roman civilizations developed sophisticated rose cultivation systems integrating botanical selection, horticultural innovation, and cultural symbolism that directly influenced medieval and Renaissance practices and informed modern trait categorization systems. These findings demonstrate the foundational role of classical antiquity in European rose heritage, revealing how ancient horticultural knowledge, species diversification through hybridization, and cultivation techniques created an unbroken transmission that shaped contemporary rose industries and established conservation priorities for this horticultural heritage. Full article

Metaphor is ubiquitous in daily communication and makes language expression more vivid. Identifying metaphorical words, known as metaphor detection, is crucial for capturing the real meaning of a sentence. As an important step of metaphorical understanding, the correct interpretation of metaphorical words directly affects metaphor detection. This article investigates how to use metaphor interpretation to enhance metaphor detection. Since previous approaches for metaphor interpretation are coarse-grained or constrained by ambiguous meanings of substitute words, we propose a different interpretation mechanism that explains metaphorical words by means of gloss-based interpretations. To comprehensively explore the optimal joint strategy, we go beyond previous work by designing diverse model architectures. We investigate both classification and sequence labeling paradigms, incorporating distinct component designs based on MIP and SPV theories. Furthermore, we integrate Part-of-Speech tags and external knowledge to further refine the feature representation. All methods utilize pre-trained language models to encode text and capture semantic information of the text. Since this mechanism involves both metaphor detection and metaphor interpretation but there is a lack of datasets annotated for both tasks, we have enhanced three datasets with glosses for metaphor detection: one Chinese dataset (PSUCMC) and two English datasets (TroFi and VUA). Experimental results demonstrate that the proposed joint methods are superior to or at least comparable to state-of-the-art methods on the three enhanced datasets. Results confirm that joint learning of metaphor detection and gloss-based interpretation makes metaphor detection more accurate. Full article

In recent years, Advanced Persistent Threat (APT) malware, with its high stealth, has made it difficult for unimodal detection methods to accurately identify its disguised malicious behaviors. To address this challenge, this paper proposes an APT Malware Detection Model based on Heterogeneous Multimodal Semantic Fusion (HMSF-ADM). By integrating the API call sequence features of APT malware in the operating system and the RGB image features of PE files, the model constructs multimodal representations with stronger discriminability, thus achieving efficient and accurate identification of APT malicious behaviors. First, the model employs two encoders, namely a Transformer encoder equipped with the DPCFTE module and a CAS-ViT encoder, to encode sequence features and image features, respectively, completing local–global collaborative context modeling. Then, the sequence encoding results and image encoding results are interactively fused via two cross-attention mechanisms to generate fused representations. Finally, a TextCNN-based classifier is utilized to perform classification prediction on the fused representations. Experimental results on two APT malware datasets demonstrate that the proposed HMSF-ADM model outperforms various mainstream multimodal comparison models in core metrics such as accuracy, precision, and F1-score. Notably, the F1-score of the model exceeds 0.95 for the vast majority of APT malware families, and its accuracy and F1-score both remain above 0.986 in the task of distinguishing between ordinary malware and APT malware. Full article

Manual pill identification is often inefficient and error-prone due to the large variety of medications and frequent visual similarity among pills, leading to misuse or dispensing errors. These challenges are exacerbated when pill imprints are engraved, curved, or irregularly arranged, conditions under which conventional optical character recognition (OCR)-based methods degrade significantly. To address this problem, we propose GO-PILL, a geometry-aware OCR pipeline for robust pill imprint recognition. The framework extracts text centerlines and imprint regions using the TextSnake algorithm. During imprint refinement, background noise is suppressed and contrast is enhanced to improve the visibility of embossed and debossed imprints. The imprint localization and alignment stage then rectifies curved or obliquely oriented text into a linear representation, producing geometrically normalized inputs suitable for OCR decoding. The refined imprints are processed by a multimodal OCR module that integrates a non-autoregressive language–vision fusion architecture for accurate character-level recognition. Experiments on a pill image dataset from the U.S. National Library of Medicine show that GO-PILL achieves an F1-score of 81.83% under set-based evaluation and a Top-10 pill identification accuracy of 76.52% in a simulated clinical scenario. GO-PILL consistently outperforms existing methods under challenging imprint conditions, demonstrating strong robustness and practical feasibility. Full article

This entry introduces an integrated model of reading that situates the Lexical Quality Hypothesis (LQH) within the Reading Systems Framework (RSF). The LQH posits that skilled reading depends on high-quality lexical representations—precise and flexible mappings of orthographic, phonological, morpho-syntactic, and semantic features—stored in the mental lexicon. These representations facilitate automatic word identification, accurate meaning retrieval, and efficient word-to-text integration (WTI), forming the foundation of text comprehension. Extending this micro-level perspective, the RSF positions lexical quality (LQ) within a macro-level cognitive architecture where the lexicon bridges word identification and reading comprehension systems. The RSF integrates multiple knowledge systems (linguistic, orthographic, and general world knowledge) with higher-order processes (sentence parsing, inference generation, comprehension monitoring, and situation model construction), emphasizing the bidirectional interactions between lower-level lexical knowledge and higher-order text comprehension. Central to this model is WTI, a dynamic mechanism through which lexical representations are incrementally incorporated into a coherent mental model of the text. This integrated model carries important implications for theory refinement, empirical investigation, and evidence-based instructional practices. Full article

Cross-site scripting (XSS) attacks are among the threats facing web security, resulting from the diversity and complexity of HTML formats. Research has shown that some text processing-based methods are limited in their ability to detect this type of attack. This article proposes an approach aimed at improving the detection of this type of attack, taking into account the limitations of certain techniques. It combines the effectiveness of deep learning represented by convolutional neural networks (CNN) and the accuracy of classification methods represented by support vector machines (SVM). It takes advantage of the ability of CNNs to effectively detect complex visual patterns in the face of injection variations and the SVM’s powerful classification capability, as XSS attacks often use obfuscation or encryption techniques that are difficult to be detected with textual methods alone. This work relies on a dataset that focuses specifically on XSS attacks, which is available on Kaggle and contains 13,686 sentences in script form, including benign and malicious cases associated with these attacks. Benign data represents 6313 cases, while malicious data represents 7373 cases. The model was trained on 80% of this data, while the remaining 20% was allocated for test. Computer vision techniques were used to analyze the visual patterns in the images and extract distinctive features, moving from a textual representation to a visual one where each character is converted into its ASCII encoding, then into grayscale pixels. In order to visually distinguish the characteristics of normal and malicious code strings and the differences in their visual representation, a CNN model was used in the analysis. The convolution and subsampling (pooling) layers extract significant patterns at different levels of abstraction, while the final output is converted into a feature vector that can be exploited by a classification algorithm such as an Optimized SVM. The experimental results showed excellent performance for the model, with an accuracy of (99.7%), and this model is capable of generalizing effectively without the risk of overfitting or loss of performance. This significantly enhances the security of web applications by providing robust protection against complex XSS threats. Full article

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

The inherent semantic symmetry and cross-modal alignment between textual prompts and generated images have fueled the success of text-to-image (T2I) generation. However, this strong correlation also introduces security vulnerabilities, specifically prompt stealing attacks, where valuable prompts are reverse-engineered from images. In this paper, we address the challenge of information asymmetry in black-box attack scenarios and propose PromptTrace, a fine-grained prompt stealing framework via Contrastive Language-Image Pre-training (CLIP)-guidedbeam search. Unlike existing methods that rely on single-stage generation, PromptTrace structurally decomposes prompt reconstruction into subject generation, modifier extraction, and iterative search optimization to effectively restore the visual–textual correspondence. By leveraging a CLIP-guided beam search strategy, our method progressively optimizes candidate prompts based on image–text similarity feedback, ensuring the stolen prompt achieves high fidelity in both semantic intent and stylistic representation. Extensive evaluations across multiple datasets and T2I models demonstrate that PromptTrace outperforms existing methods, highlighting the feasibility of exploiting cross-modal symmetry for attacks and underscoring the urgent need for defense mechanisms in the T2I ecosystem. Full article

Supervised fine-tuning is key for model alignment, but its mechanisms are debated, with conflicting evidence supporting either a superficial alignment hypothesis or significant task improvements. This paper examines supervised fine-tuning’s impact from the perspective of verbatim memorization. Using the open-source OLMo-2 model series and test datasets (instruction format, safety-sensitive, and factual knowledge) constructed from its pre-training corpus, we analyzed changes across memorization, linguistic styles, and task performance. We found that supervised fine-tuning significantly weakens the model’s verbatim memorization of pre-training data. Simultaneously, it improves generated text in terms of alignment objectives, such as polite expression and structured organization. However, this process also leads to performance degradation on knowledge-intensive downstream tasks. Further representation analysis reveals that these changes are mainly concentrated in the later layers of the model. We conclude that supervised fine-tuning acts as a continuation of the learning process on new data. By adjusting model representations, supervised fine-tuning induces a learning tilt toward the styles and content of the instruction-tuning dataset. This inclination successfully instills alignment objectives while consequently reducing the effective accessibility of previously learned knowledge, which indicates the observed degradation in both pre-training data memorization and factual task performance. The source code is publicly available. Full article

Vision-language models can leverage natural language descriptions to encode stable farmland characteristics, providing a new paradigm for farmland extraction, yet existing methods face challenges in ambiguous text-visual alignment and loss of high-frequency boundary details during fusion. To address this, this article utilizes the semantic prior knowledge provided by textual descriptions in vision–language models to enhance the model’s ability to recognize polymorphic features, and proposes an Uncertainty-Guided and Frequency-Fused Vision-Language Model (UGFF-VLM) for remote sensing farmland extraction. The UGFF-VLM combines the semantic representation ability of vision-language models, further integrates an Uncertainty-Guided Adaptive Alignment (UGAA) module to dynamically adjust cross-modal fusion based on alignment confidence, and a Frequency-Enhanced Cross-Modal Fusion (FECF) mechanism to preserve high-frequency boundary details in the frequency domain. Experimental results on the FarmSeg-VL dataset demonstrate that the proposed method delivers excellent and stable performance, achieving the highest mIoU across diverse geographical environments while showing significant improvements in boundary precision and robustness against false positives. Therefore, the proposed UGFF-VLM not only mitigates the issues of recognition confusion and poor generalization in purely vision-based models caused by farmland feature polymorphism but also effectively enhances boundary segmentation accuracy, providing a reliable method for the precise delineation of agricultural parcels in diverse landscapes. Full article

The integration of emojis into Early Childhood Education (ECE) presents a promising avenue for enhancing student engagement, emotional expression, and comprehension. While prior studies suggest the benefit of visual aids in learning, systematic frameworks for pedagogically aligned emoji recommendation remain underdeveloped. This paper presents EduEmoji-ECE, a pedagogically annotated dataset of early-childhood learning text segments. Specifically, the proposed model incorporates Bidirectional Encoder Representations from Transformers (BERTs) for contextual embedding extraction, Gated Recurrent Units (GRUs) for sequential pattern recognition, Deep Neural Networks (DNNs) for classification and emoji recommendation, and DECOC for improving emoji class prediction robustness. This hybrid BERT-GRU-DNN-DECOC architecture effectively captures textual semantics, emotional tone, and pedagogical intent, ensuring the alignment of emoji class recommendation with learning objectives. The experimental results show that the system is effective, with an accuracy of 95.3%, a precision of 93%, a recall of 91.8%, and an F1-score of 92.3%, outperforming baseline models in terms of contextual understanding and overall accuracy. This work helps fill a gap in AI-based education by combining learning with visual support for young children. The results suggest an association between emoji-enhanced materials and improved engagement/comprehension indicators in our exploratory classroom setting; however, causal attribution to the AI placement mechanism is not supported by the current study design. Full article

This essay offers a comparative reading of Antonio Di Benedetto’s Los suicidas and Leila Guerriero’s Los suicidas del fin del mundo through the lens of the “suicidal archive.” Drawing on literary criticism, trauma studies, and biopolitical theory, it explores how both works transform suicide into a problem of representation, where writing functions as an aesthetic mediation against the chaos of reality. In dialogue with the ideas of Mbembe, De Martelaere, and Caruth, I argue that Di Benedetto and Guerriero move beyond the rational frameworks of scientific or journalistic discourse to probe the ethical and affective dimensions of suicidal acts. While Di Benedetto’s text renders repetition as a metaphysical and introspective structure, Guerriero’s transforms it into a collective, polyphonic archive of trauma. In both cases, literature emerges as a symbolic space of containment that, rather than closing off meaning, keeps the wound open. Ultimately, the essay concludes that the suicidal archive does not seek to explain or domesticate death but to inhabit its enigma—affirming writing as an act of resistance against silence and disappearance. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

Retrieval-Augmented Generation (RAG) is widely used for long-text summarization due to its efficiency and scalability. However, standard RAG methods flatten documents into independent chunks, disrupting sequential flow and thematic structure, resulting in significant loss of contextual information. This paper presents MOEGAT, a novel graph-enhanced retrieval framework that addresses this limitation by explicitly modeling document structure. MOEGAT constructs an Orthogonal Context Graph to capture sequential discourse and global semantic relationships—long-range dependencies between non-adjacent text spans that reflect topical similarity and logical associations beyond local context. It then employs a query-aware Mixture-of-Experts Graph Attention Network to dynamically activate specialized reasoning pathways. Experiments conducted on three public long-text summarization datasets demonstrate that MOEGAT achieves state-of-the-art performance. Notably, on the WCEP dataset, it outperforms the previous state-of-the-art Graph of Records (GOR) baseline by 14.9%, 18.1%, and 18.4% on ROUGE-L, ROUGE-1, and ROUGE-2, respectively. These substantial gains, especially the 14.9% improvement in ROUGE-L, reflect significantly better capture of long-range coherence and thematic integrity in summaries. Ablation studies confirm the effectiveness of the orthogonal graph and Mixture-of-Experts components. Overall, this work introduces a novel structure-aware approach to RAG that explicitly models and leverages document structure through an orthogonal graph representation and query-aware Mixture-of-Experts reasoning. Full article