Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges re...
详细信息
Grammar serves as a cornerstone in programming languages and softwareengineering, providing frameworks to define the syntactic space and program structure. Existing research demonstrates the effectiveness of grammar-...
详细信息
In the field of object detection, deep learning has been used extensively, especially in algorithms like Yolov7, which have achieved significant accuracy improvements. However, traditional convolutional neural network...
详细信息
ISBN:
(数字)9781510687622
ISBN:
(纸本)9781510687615
In the field of object detection, deep learning has been used extensively, especially in algorithms like Yolov7, which have achieved significant accuracy improvements. However, traditional convolutional neural networks are computationally intensive and require powerful GPU support, making its deployment on embedded devices difficult. This presents a problem for researchers as the high device requirements hinder their related research work. Consequently, more people opt to use lightweight networks, such as Yolov7-tiny. However, when using Yolov7-tiny for underwater garbage detection, it has been observed that while achieving good accuracy in mAP (mean Average Precision) at IOU (Intersection over Union) of 0.5, the performance is not satisfactory in the mAP range of 0.5 to 0.95. This limitation may be attributed to the trade-off in network performance during the process of model lightweighting. To address these issues, An enhanced Yolov7-tiny method for the detection of underwater trash objects is proposed in this paper. First, the algorithm employs an enhanced Ghost convolutional feature extraction module, which starts with conventional convolutions using a smaller number of channels, then performs grouped convolutions to obtain partial output maps with features. Finally, the maps with features obtained from the first convolutional step are added to the channels obtained from the second grouped convolution step. This design effectively reduces model complexity while extracting richer feature information. Secondly, the algorithm utilizes the CA (Channel Attention) mechanism to weight channels based on their positional information, thereby efficiently extracting features. The network can concentrate more on important feature regions by learning position weights in the feature maps. Lastly, the algorithm combines the Repeated Weighted Bi-directional Feature Pyramid Network (BIFPN) for feature fusion. BIFPN employs multiple down-sampling steps for short skip connections,
Vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, 3D modeling from a single view is an ill-posed problem, limited by the field o...
详细信息
Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensi...
详细信息
LoRA (Low-Rank Adaptation) has achieved remarkable success in the parameter-efficient fine-tuning of large models. The trained LoRA matrix can be integrated with the base model through addition or negation operation t...
详细信息
Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving soft...
详细信息
Automatic Music Transcription (AMT) entails creating an algorithm that converts an acoustic signal from an audio file into the corresponding sheet music representation. This paper uses deep learning met...
详细信息
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying t...
ISBN:
(纸本)9798350323481
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic '1' as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.
This paper proposes a novel ETC-MTCTR, which is designed to enable more accurate, versatile and efficient traffic classification in the context of multi-scenario, low-resource encrypted traffic. Through three modules ...
详细信息
ISBN:
(数字)9798350368369
ISBN:
(纸本)9798350368376
This paper proposes a novel ETC-MTCTR, which is designed to enable more accurate, versatile and efficient traffic classification in the context of multi-scenario, low-resource encrypted traffic. Through three modules of Datagram Token conversion, pretraining and fine-tuning, the method uses large-scale unlabeled encrypted traffic for pretraining, mining and learning the traffic context and transmission relationship of encrypted traffic classification tasks, so that a small number of labeled data samples can be effectively used in the fine-tuning stage. Significantly improve the performance of the model on specific downstream classification tasks, enhance the accuracy, adaptability and robustness of the model in diverse environments, limited resources and new encryption security protocols, and realize efficient encryption traffic classification in multi-scenario and low-resource background. The results show that ETC-MTCTR achieves the best performance on three tasks: encryption malware classification, VPN encrypted traffic classification, and TLS 1.3 encryption application classification. Its F1 score is improved by 0.22% in the classification task of encrypted malware, 1.4% in the classification task of VPN encrypted traffic App, 4.56% in the classification task of VPN encrypted traffic Service, and 9.89% in the classification task of TLS 1.3 encrypted application, which is significantly better than other comparison methods.
暂无评论