A low-light colonoscopy video enhancement method is needed as poor illumination in colonoscopy can hinder accurate disease diagnosis and adversely affect surgical procedures. Existing low-light video enhancement methods usually apply a frame-by-frame enhancement strategy without considering the temporal correlation between them, which often causes a flickering problem. In addition, most methods are designed for endoscopic devices with fixed imaging styles and cannot be easily adapted to different devices. In this paper, we propose a Style-Guided Network (SGNet) for unpaired Low-Light Colonoscopy Video Enhancement (LLCVE). Given that collecting content-consistent paired videos is difficult, SGNet adopts a CycleGAN-based framework to convert low-light videos to normal-light videos, in which a Temporal Compensation (TC) module and a Style Guidance (SG) module are proposed to alleviate the flickering problem and achieve flexible style transfer, respectively. The TC module compensates for a low-light frame by learning the correlated feature of its adjacent frames, thereby improving the temporal smoothness of the enhanced video. The SG module encodes the text of the imaging style and adaptively explores its intrinsic relationships with video features to obtain style representations, which are then used to guide the subsequent enhancement process. Extensive experiments on a curated database show that SGNet achieves promising performance on the LLCVE task, outperforming state-of-the-art methods in both quantitative metrics and visual quality.

In recent years, deep learning has been widely applied in smart contract vulnerability detection due to its automatic feature extraction and strong generalization capabilities. However, existing methods still face challenges such as redundant information in graph structures, insufficient utilization of data flow information, and single-scale feature extraction. To address these issues, we propose a function-level smart contract graph representation, namely the Multi-relational Semantic Graph (MSG), which employs various types of data flow edges to represent data dependency information within contracts. Subsequently, we introduce a detection model, REA_DCN, which combines a Residual Multi-scale Dilated Convolutional Network with a Multi-head Attention mechanism to capture syntactic and semantic features in the MSG. The model comprises two key modules: the Residual Multi-scale Dilated Convolutional Network (RE_DCN) can extract node features from three different dimensions, while the Multi-head Attention Network (MEA) is utilized for edge feature extraction. Experimental results on real-world datasets demonstrate that the highest score of REA_DCN in terms of accuracy, precision, recall and F1 score exceeds 97%, proving its effectiveness and feasibility.

Olfactory-enhanced virtual reality (OVR) creates a complex and rich emotional experience, thus promoting a new generation of human-computer interaction experiences in real-world scenarios. However, with the rise of virtual reality (VR) as a mood induction procedure (MIP), few studies have incorporated olfactory stimuli into emotion induction in three-dimensional (3D) environments. Considering the differences in electroencephalography (EEG) dynamics between sensory stimuli, all previous two-dimensional (2D) and 3D emotional studies have been less effective in reality because they only use visual and audio senses. To overcome these limitations, we developed a novel EEG signal dataset based on OVR. We systematically analyzed the influence of olfactory stimuli on emotion induction in a VR environment from a neurophysiological perspective. Specifically, synchronous EEG signals were collected from 65 participants as they watched positive and negative videos in traditional VR and OVR. Their power spectral densities (PSDs) were then calculated to compare the differences in brain activation between their VR and OVR modes during the induction of positive and negative emotions, while their brain states were classified after feature selection. The results showed that olfactory stimuli enhanced EEG responses for positive emotions, but the opposite was true for negative emotions. Additionally, the recognition rate of brain emotional states was more than 90% under both positive and negative emotions, while the high-frequencyβandγbands could effectively distinguish VR and OVR modes. This study introduced the olfaction into the field of human-computer interaction, which could promote research on emotion induction and recognition in real-world environments.