The rapid advancement of large language models (LLMs) has resulted in increasingly sophisticated AI-generated content, posing significant challenges in distinguishing LLM-generated text from human-written language. Existing detection methods, primarily based on lexical heuristics or fine-tuned classifiers, often suffer from limited generalizability and are vulnerable to paraphrasing, adversarial perturbations, and cross-domain shifts. In this work, we propose SentiDetect, a model-agnostic framework for detecting LLM-generated text by analyzing the divergence in sentiment distribution stability. Our method is motivated by the empirical observation that LLM outputs tend to exhibit emotionally consistent patterns, whereas human-written texts display greater emotional variability. To capture this phenomenon, we define two complementary metrics: sentiment distribution consistency and sentiment distribution preservation, which quantify stability under sentiment-altering and semantic-preserving transformations. We evaluate SentiDetect on five diverse domains and a range of advanced LLMs, including Gemini-1.5-Pro, Claude-3, GPT-4-0613, and LLaMa-3.3. Experimental results demonstrate its superiority over state-of-the-art baselines, with over 16% and 11% F1 score improvements on Gemini-1.5-Pro and GPT-4-0613, respectively. Moreover, SentiDetect also shows greater robustness to paraphrasing, adversarial attacks, and text length variations, outperforming existing detectors in challenging scenarios.

Edge computing-enabled virtual reality (VR) is increasingly explored in smart healthcare systems due to its potential to deliver immersive, real-time medical services. However, ensuring ultra-low latency and interpretable decision-making in such systems remains a significant challenge. In this paper, we propose an explainable deep reinforcement learning (XDRL)-enhanced VR video streaming framework for immersive healthcare systems to provide smooth and reliable VR services. Specifically, we first model the VR content analysis process at the edge sides and formulate a joint caching, communication, and computing (3C) resource optimization problem to maximize the VR quality of service (QoS) and minimize service latency. To address this complex 3C resource scheduling decision problem, we propose HXRL, a novel implementation of explainable deep reinforcement learning (XDRL), specifically designed to optimize immersive healthcare VR services. Comprehensive experiments show that our HXRL method improves average tile bitrate by 18.3%, cache hit ratio by 24.7%, and reduces system latency by 32.5% compared to baselines on real-world VR healthcare datasets. Moreover, a class activation map (CAM)-based visual analysis is integrated to interpret the learned policies of our model, highlighting the spatial attention of decision-making and enhancing trustworthiness in medical contexts.

Generative AI has garnered substantial attention due to the limited defect samples in the industrial Internet of Things (IIoT). However, addressing the challenge of few-shot defect detection in industrial edge networks remains a key issue. In this paper, we propose ABEL, a novel AI-generated content (AIGC)-based edge learning framework for fast and efficient few-shot defect detection. This framework facilitates fast few-shot defect detection by harnessing the capabilities of realistic sample synthesis and edge-based AIGC task execution. Specifically, we propose an energy-based model (EBM)-guided Langevin Markov chain Monte Carlo (L-MCMC) image generation algorithm, synthesizing high-resolution industrial defect samples for efficient few-shot defect detection. Then, we formulate a large-scale mixed cooperative-competitive AIGC computation offloading problem and propose an attention and memory-based multi-agent reinforcement learning (AMMARL) algorithm to ensure fast edge execution of heterogeneous defect samples generative tasks. Particularly, the challenges of partial observability and high-dimensional state space are addressed by introducing multi-head attention mechanisms and long-term memory modules. Comprehensive synthesis experiments are conducted utilizing real-world industrial datasets NEU-CLS and DeepPCB. The experimental results demonstrate the effectiveness of our framework and algorithm's effectiveness in efficiently synthesizing realistic industrial defect images and optimizing edge-based AIGC task execution.

The existing Artificial Intelligence (AI)-based industrial defect detection methods have received extensive attention in the industrial Internet of Things (IoT). However, due to the limited defect samples, it is difficult for discriminative models to achieve better performance in imbalanced learning. In addition, the privacy concerns surrounding sensitive information hinder the sharing of synthetic industrial data. In this paper, we propose a novel framework called the Differentially Private Generative AI-empowered Digital Twin (DPG-DT) framework, aiming to synthesize realistic samples while satisfying differential privacy and empowering the construction of digital space and its connection with physical space. Specifically, the core of the DPG-DT framework is the proposed Private Synthetic Industry Energy-guided model (PSIE), in which we privatize the energybased model-empowered Langevin Markov Chain Monte Carlo (MCMC) sampling method with Gaussian noise and random response. Our method could replace the conventional generator while guaranteeing privacy. Extensive experiments on real-world industrial datasets NEU-CLS and DeepPCB demonstrate that the proposed framework is capable of generating synthetic industrial images with both high fidelity and differential privacy. Moreover, the achieved downstream accuracy outperforms baselines by 23.9 % in industrial scenarios.