In affective neuroscience and emotion-aware AI, understanding how complex auditory stimuli drive emotion arousal dynamics remains unresolved. This study introduces a computational framework to model the brain's encoding of naturalistic auditory inputs into dynamic behavioral/neural responses across three datasets (SEED, LIRIS, self-collected BAVE). Guided by neurobiological principles of parallel auditory hierarchy, we decompose audio into multilevel auditory features (through classical algorithms and wav2vec 2.0/Hubert) from the original and isolated human voice/background soundtrack elements, mapping them to emotion-related responses via cross-dataset analyses. Our analysis reveals that high-level semantic representations (derived from the final layer of wav2vec 2.0/Hubert) exert a dominant role in emotion encoding, outperforming low-level acoustic features with significantly stronger mappings to behavioral annotations and dynamic neural synchrony across most brain regions ($p < 0.05$). Notably, middle layers of wav2vec 2.0/hubert (balancing acoustic-semantic information) surpass the final layers in emotion induction across datasets. Moreover, human voices and soundtracks show dataset-dependent emotion-evoking biases aligned with stimulus energy distribution (e.g., LIRIS favors soundtracks due to higher background energy), with neural analyses indicating voices dominate prefrontal/temporal activity while soundtracks excel in limbic regions. By integrating affective computing and neuroscience, this work uncovers hierarchical mechanisms of auditory-emotion encoding, providing a foundation for adaptive emotion-aware systems and cross-disciplinary explorations of audio-affective interactions.

In the construction industry, safety assessment is vital to ensure both the reliability of assets and the safety of workers. Scaffolding, a key structural support asset requires regular inspection to detect and identify alterations from the design rules that may compromise the integrity and stability. At present, inspections are primarily visual and are conducted by site manager or accredited personnel to identify deviations. However, visual inspection is time-intensive and can be susceptible to human errors, which can lead to unsafe conditions. This paper explores the use of Artificial Intelligence (AI) and digitization to enhance the accuracy of scaffolding inspection and contribute to the safety improvement. A cloud-based AI platform is developed to process and analyse the point cloud data of scaffolding structure. The proposed system detects structural modifications through comparison and evaluation of certified reference data with the recent point cloud data. This approach may enable automated monitoring of scaffolding, reducing the time and effort required for manual inspections while enhancing the safety on a construction site.

As smart tourism evolves, AI-powered chatbots have become indispensable for delivering personalized, real-time assistance to travelers while promoting sustainability and efficiency. However, these systems are increasingly vulnerable to prompt injection attacks, where adversaries manipulate inputs to elicit unintended behaviors such as leaking sensitive information or generating harmful content. This paper presents a case study on the design and implementation of a secure retrieval-augmented generation (RAG) chatbot for Hsinchu smart tourism services. The system integrates RAG with API function calls, multi-layered linguistic analysis, and guardrails against injections, achieving high contextual awareness and security. Key features include a tiered response strategy, RAG-driven knowledge grounding, and intent decomposition across lexical, semantic, and pragmatic levels. Defense mechanisms include system norms, gatekeepers for intent judgment, and reverse RAG text to prioritize verified data. We also benchmark a GPT-5 variant (released 2025-08-07) to assess inherent robustness. Evaluations with 674 adversarial prompts and 223 benign queries show over 95% accuracy on benign tasks and substantial detection of injection attacks. GPT-5 blocked about 85% of attacks, showing progress yet highlighting the need for layered defenses. Findings emphasize contributions to sustainable tourism, multilingual accessibility, and ethical AI deployment. This work offers a practical framework for deploying secure chatbots in smart tourism and contributes to resilient, trustworthy AI applications.

Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations. To alleviate these issues, we propose to train saliency detection networks by exploiting the supervision from not only salient object detection, but also foreground contour detection and edge detection. First, we leverage salient object detection and foreground contour detection tasks in an intertwined manner to generate saliency maps with uniform highlight. Second, the foreground contour and edge detection tasks guide each other simultaneously, thereby leading to precise foreground contour prediction and reducing the local noises for edge prediction. In addition, we develop a novel mutual learning module (MLM) which serves as the building block of our method. Each MLM consists of multiple network branches trained in a mutual learning manner, which improves the performance by a large margin. Extensive experiments on seven challenging datasets demonstrate that the proposed method has delivered state-of-the-art results in both salient object detection and edge detection.

Large language model (LLM) providers boast big numbers for maximum context window sizes. To test the real world use of context windows, we 1) define a concept of maximum effective context window, 2) formulate a testing method of a context window's effectiveness over various sizes and problem types, and 3) create a standardized way to compare model efficacy for increasingly larger context window sizes to find the point of failure. We collected hundreds of thousands of data points across several models and found significant differences between reported Maximum Context Window (MCW) size and Maximum Effective Context Window (MECW) size. Our findings show that the MECW is, not only, drastically different from the MCW but also shifts based on the problem type. A few top of the line models in our test group failed with as little as 100 tokens in context; most had severe degradation in accuracy by 1000 tokens in context. All models fell far short of their Maximum Context Window by as much as 99 percent. Our data reveals the Maximum Effective Context Window shifts based on the type of problem provided, offering clear and actionable insights into how to improve model accuracy and decrease model hallucination rates.

Text-to-image (T2I) models have been widely applied in generating high-fidelity images across various domains. However, these models may also be abused to produce Not-Safe-for-Work (NSFW) content via jailbreak attacks. Existing jailbreak methods primarily manipulate the textual prompt, leaving potential vulnerabilities in image-based inputs largely unexplored. Moreover, text-based methods face challenges in bypassing the model's safety filters. In response to these limitations, we propose the Multimodal Prompt Decoupling Attack (MPDA), which utilizes image modality to separate the harmful semantic components of the original unsafe prompt. MPDA follows three core steps: firstly, a large language model (LLM) decouples unsafe prompts into pseudo-safe prompts and harmful prompts. The former are seemingly harmless sub-prompts that can bypass filters, while the latter are sub-prompts with unsafe semantics that trigger filters. Subsequently, the LLM rewrites the harmful prompts into natural adversarial prompts to bypass safety filters, which guide the T2I model to modify the base image into an NSFW output. Finally, to ensure semantic consistency between the generated NSFW images and the original unsafe prompts, the visual language model generates image captions, providing a new pathway to guide the LLM in iterative rewriting and refining the generated content.

This study aimed to enhance disease classification accuracy from retinal fundus images by integrating fine-grained image features and global textual context using a novel multimodal deep learning architecture. Existing multimodal large language models (MLLMs) often struggle to capture low-level spatial details critical for diagnosing retinal diseases such as glaucoma, diabetic retinopathy, and retinitis pigmentosa. This model development and validation study was conducted on 1,305 fundus image-text pairs compiled from three public datasets (FIVES, HRF, and StoneRounds), covering acquired and inherited retinal diseases, and evaluated using classification accuracy and F1-score. The MDF-MLLM integrates skip features from four U-Net encoder layers into cross-attention blocks within a LLaMA 3.2 11B MLLM. Vision features are patch-wise projected and fused using scaled cross-attention and FiLM-based U-Net modulation. Baseline MLLM achieved 60% accuracy on the dual-type disease classification task. MDF-MLLM, with both U-Net and MLLM components fully fine-tuned during training, achieved a significantly higher accuracy of 94%, representing a 56% improvement. Recall and F1-scores improved by as much as 67% and 35% over baseline, respectively. Ablation studies confirmed that the multi-depth fusion approach contributed to substantial gains in spatial reasoning and classification, particularly for inherited diseases with rich clinical text. MDF-MLLM presents a generalizable, interpretable, and modular framework for fundus image classification, outperforming traditional MLLM baselines through multi-scale feature fusion. The architecture holds promise for real-world deployment in clinical decision support systems. Future work will explore synchronized training techniques, a larger pool of diseases for more generalizability, and extending the model for segmentation tasks.

With the emergence of large-scale vision language models (VLM), it is now possible to produce realistic-looking radiology reports for chest X-ray images. However, their clinical translation has been hampered by the factual errors and hallucinations in the produced descriptions during inference. In this paper, we present a novel phrase-grounded fact-checking model (FC model) that detects errors in findings and their indicated locations in automatically generated chest radiology reports. Specifically, we simulate the errors in reports through a large synthetic dataset derived by perturbing findings and their locations in ground truth reports to form real and fake findings-location pairs with images. A new multi-label cross-modal contrastive regression network is then trained on this dataset. We present results demonstrating the robustness of our method in terms of accuracy of finding veracity prediction and localization on multiple X-ray datasets. We also show its effectiveness for error detection in reports of SOTA report generators on multiple datasets achieving a concordance correlation coefficient of 0.997 with ground truth-based verification, thus pointing to its utility during clinical inference in radiology workflows.

Due to the complex and resource-intensive nature of diagnosing Autism Spectrum Condition (ASC), several computer-aided diagnostic support methods have been proposed to detect autism by analyzing behavioral cues in patient video data. While these models show promising results on some datasets, they struggle with poor gaze feature performance and lack of real-world generalizability. To tackle these challenges, we analyze a standardized video dataset comprising 168 participants with ASC (46% female) and 157 non-autistic participants (46% female), making it, to our knowledge, the largest and most balanced dataset available. We conduct a multimodal analysis of facial expressions, voice prosody, head motion, heart rate variability (HRV), and gaze behavior. To address the limitations of prior gaze models, we introduce novel statistical descriptors that quantify variability in eye gaze angles, improving gaze-based classification accuracy from 64% to 69% and aligning computational findings with clinical research on gaze aversion in ASC. Using late fusion, we achieve a classification accuracy of 74%, demonstrating the effectiveness of integrating behavioral markers across multiple modalities. Our findings highlight the potential for scalable, video-based screening tools to support autism assessment.

Radiography Report Generation (RRG) has gained significant attention in medical image analysis as a promising tool for alleviating the growing workload of radiologists. However, despite numerous advancements, existing methods have yet to achieve the quality required for deployment in real-world clinical settings. Meanwhile, large Visual Language Models (VLMs) have demonstrated remarkable progress in the general domain by adopting training strategies originally designed for Large Language Models (LLMs), such as alignment techniques. In this paper, we introduce a model-agnostic framework to enhance RRG accuracy using Direct Preference Optimization (DPO). Our approach leverages random contrastive sampling to construct training pairs, eliminating the need for reward models or human preference annotations. Experiments on supplementing three state-of-the-art models with our Random DPO show that our method improves clinical performance metrics by up to 5%, without requiring any additional training data.