Robust object detection in autonomous driving is challenged by inherent limitations of conventional frame-based cameras, such as motion blur and limited dynamic range. In contrast, event-based cameras, which operate asynchronously and capture rapid changes with high temporal resolution and expansive dynamic range, offer a promising augmentation. While the previous research on event-based object detection has predominantly focused on algorithmic enhancements via advanced preprocessing and network optimizations to improve detection accuracy, the practical engineering and integration challenges of deploying these sensors in real-world systems remain underexplored. To address this gap, our study investigates the integration of event-based cameras as a complementary sensor modality in autonomous driving. We adapted a conventional frame-based detection model (YOLOv8) for event-based inputs by training it on the GEN1 dataset, achieving a mean average precision (mAP) of 70.1%, a significant improvement over previous benchmarks. Additionally, we developed a real-time object detection pipeline optimized for event-based data, integrating it into the CARLA simulation environment and ROS for system prototyping. The model was further refined using transfer learning to better adapt to simulation conditions, and the complete pipeline was validated across diverse simulated scenarios to address practical challenges. These results underscore the feasibility of incorporating event cameras into existing perception systems, paving the way for their broader deployment in autonomous vehicle applications.

In recent years, underwater image enhancement (UIE) processing technology has developed rapidly, and underwater optical imaging technology has shown great advantages in the intelligent operation of underwater robots. In underwater environments, light absorption and scattering often cause seabed images to be blurry and distorted in color. Therefore, acquiring high-definition underwater imagery with superior quality holds essential significance for advancing the exploration and development of marine resources. In order to resolve the problems associated with chromatic aberration, insufficient exposure, and blurring in underwater images, a high-dimensional attention generative adversarial network framework for underwater image enhancement (HDAGAN) is proposed. The introduced method is composed of a generator and a discriminator. The generator comprises an encoder and a decoder. In the encoder, a channel attention residual module (CARM) is designed to capture both semantic features and contextual details from visual data, incorporating multi-scale feature extraction layers and multi-scale feature fusion layers. Furthermore, in the decoder, to refine the feature representation of latent vectors for detail recovery, a strengthen–operate–subtract module (SOSM) is introduced to strengthen the model’s capability to comprehend the picture’s geometric structure and semantic information. Additionally, in the discriminator, a multi-scale feature discrimination module (MFDM) is proposed, which aids in achieving more precise discrimination. Experimental findings demonstrate that the novel approach significantly outperforms state-of-the-art UIE techniques, delivering enhanced outcomes with higher visual appeal.

Weibo sentiment analysis has gained prominence, particularly during the COVID-19 pandemic, as a means to monitor public emotions and detect emerging mental health trends. However, challenges arise from Weibo’s informal language, nuanced expressions, and stylistic features unique to social media, which complicate the accurate interpretation of sentiments. Existing models often fall short, relying on text-based methods that inadequately capture the rich emotional texture of Weibo posts, and are constrained by single loss functions that limit emotional depth. To address these limitations, we propose a novel framework incorporating a sentiment graph and self-supervised learning. Our approach introduces a “sentiment graph” that leverages both word-to-post and post-to-post relational connections, allowing the model to capture fine-grained sentiment cues and context-dependent meanings. Enhanced by a gated mechanism within the graph, our model selectively filters emotional signals based on intensity and relevance, improving its sensitivity to subtle variations such as sarcasm. Additionally, a self-supervised objective enables the model to generalize beyond labeled data, capturing latent emotional structures within the graph. Through this integration of sentiment graph and self-supervised learning, our approach advances Weibo sentiment analysis, offering a robust method for understanding the complex emotional landscape of social media.

In this paper, we aim to improve sentiment analysis on weibo, a vital platform for sentiment and opinion expression. Current sentiment analysis methods struggle with the unique challenges of Weibo posts, which often contain informal language, sarcasm, and lack of context, making it difficult to capture complex emotions. Additionally, existing text-based models fail to account for visual cues and rely heavily on predefined emotion categories, limiting their ability to detect nuanced sentiments. To address these issues, we propose a novel multimodal approach that integrates both textual and visual elements, leveraging a ppretrainedext-to-image model to generate images from posts. Integrated with a self-supervised contrastive learning objective, this approach empowers our model to extract knowledge from unlabeled data and achieve enhanced comprehension of nuanced emotional cues. Our method enhances sentiment analysis on microblogs by introducing a framework that captures both text and synthesized visual cues, enabling deeper sentiment interpretation. By incorporating self-supervised learning, we significantly improve the detection of complex emotions, particularly those related to mental health, offering a more comprehensive solution for sentiment analysis on social platforms.

Detecting semantically equivalent but syntactically diverse code clones (Type-4) remains challenging for traditional AST- or token-based approaches. We propose a clone detection framework that couples LLM-based code revision with AST graph modeling and Graph Attention Networks (GAT), trained via a joint objective that includes a contrastive loss to align embeddings of semantically equivalent fragments. Concretely, we use an LLM to generate syntactically altered but functionally identical variants, thereby augmenting training data with nuanced code clones—subtle edits such as identifier renaming, control-flow restructuring, or statement reordering that preserve program behavior while confusing purely syntactic matchers. On Google Code Jam (GCJ), the proposed method attains Precision 0.99, Recall 0.98, and F1 0.985, outperforming the strongest baseline (F1 = 0.97) by +1.5 percentage points (relative ≈ +1.6%). On BigCloneBench, it achieves Precision 0.97, Recall 0.96, and F1 0.97, improving over the best baseline (F1 = 0.93) by +4.0 pp (relative ≈ +4.3%). For the hardest WT3/T4 category, the framework reaches F1 97.4 versus 93.6 for a strong GNN baseline (+3.8 pp). Ablations indicate that (i) LLM-based augmentation and (ii) GAT-based AST encoding are both critical to performance. In addition to accuracy, we discuss computational considerations (augmentation/training cost), portability beyond Java, and potential extensions toward dynamic clone detection. The results suggest that marrying LLM-generated nuanced variants with graph-based program representations yields robust Type-4 clone detection while remaining scalable to practical codebases.

Underwater optical images are the primary carriers of underwater scene information, playing a crucial role in marine resource exploration, underwater environmental monitoring, and engineering inspection. However, wavelength-dependent absorption and scattering severely deteriorate underwater images, leading to reduced contrast, chromatic distortions, and loss of structural details. To address these issues, we propose a U-shaped underwater image enhancement framework that integrates Swin-Transformer blocks with lightweight attention and residual modules. A Dual-Window Multi-Head Self-Attention (DWMSA) in the bottleneck models long-range context while preserving fine local structure. A Global-Aware Attention Map (GAMP) adaptively re-weights channels and spatial locations to focus on severely degraded regions. A Feature-Augmentation Residual Network (FARN) stabilizes deep training and emphasizes texture and color fidelity. Trained with a combination of Charbonnier, perceptual, and edge losses, our method achieves state-of-the-art results in PSNR and SSIM, the lowest LPIPS, and improvements in UIQM and UCIQE on the UFO-120 and EUVP datasets, with average metrics of PSNR 29.5 dB, SSIM 0.94, LPIPS 0.17, UIQM 3.62, and UCIQE 0.59. Qualitative results show reduced color cast, restored contrast, and sharper details. Code, weights, and evaluation scripts will be released to support reproducibility.