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Monitoring and managing litter on the roadways is important not only for preserving the cleanliness and esthetic appeal of our cities but also for safeguarding the overall health and sanitary environment of citizens. With the development of artificial intelligence, it is now possible to design algorithms capable of autonomously assessing the sanitation status of roadways. In this paper, we propose a method for detecting garbage on roads as our solution to tackle the issue. The proposed model is a deep learning model with an attention mechanism introduced to detect garbage in real-time scenarios. Furthermore, the object recognition capability of the model is enhanced through transfer learning to mitigate the influence of irrelevant content during training. The refined network can detect rubbish on roads with higher efficiency than the existing models. Also, compared with other methods, it consumes less amount of data for training. The experimental results demonstrate the efficiency of our proposed model, with a performance boost of 7.62% over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind power is crucial for the efficient operation and risk management of wind farms. This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN–GRU–Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model’s performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil’s U, TIC and SPL) and the Diebold–Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model’s superior accuracy and efficiency in predicting wind power. [ABSTRACT FROM AUTHOR]

This paper suggests a text emotion analysis approach built on the BMCBMA model for use in an online education environment aiming at the issues of poor text utilization, challenge in effective information extraction, and a failure to effectively recognize word polysemy in existing text emotion analysis task research. Initially, a language model is constructed using Bidirectional Encoder Representations from Transformers (BERT) pre-training. The word vector trained by BERT pre-training is selected to supplant the word vector trained in the conventional manner, and the semantic vector is generated dynamically using the context of the word. Second, the Bi-directional Long Short-Term Memory (BiLSTM) model is employed to record semantic information by simultaneously integrating both positive and negative orientations to assess the emotional polarity information for every text in its entirety. The Multi-channel Convolutional Neural Networks (CNN) model is then utilized to derive textual local feature information of text. Lastly, the attention mechanism is used to cause all types of features to interact and to derive a deeper semantic association within the context. Experiments indicate that the suggested method has accuracy, F1, recall, and precision values of 0.915, 0.911, 0.915 and 0.908, correspondingly. The suggested approach performs significantly better in text emotion analysis than the comparison method. [ABSTRACT FROM AUTHOR]

To address the issues of blurred details and distortion of color in the images recovered by the original AOD-Net dehazing method, this paper proposes a CPAD-Net dehazing network model based on attention mechanism and dense residual blocks. The network is improved on the basis of AOD-Net, which can reduce the errors arising from the separately determined transmittance and atmospheric light values. A new dense residual block structure is designed to replace the traditional convolution method, which effectively improves the detail processing capability and the representation ability of the network model for image feature information. On this basis, the attention module determines how to learn the weights according to the feature importance of distinct channels and distinct pixels, and then obtain the recovery of images in terms of color and texture. The experiments showed that the dehazing efficiency of our method are richer in texture detail information and more natural in color recovery. Compared with other algorithms, the PSNR and SSIM indexes of our method are considerably superior to those listed algorithms, which definitively demonstrates that the dehazing effect of our method is more effective, and the recovered images are more realistic and natural. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel transformer-based deep neural network model to learn semantic bug patterns from a corpus of buggy/fixed codes, then generate correct ones automatically. Transformer is a deep learning model relying entirely on attention mechanism to model global dependencies between input and output. Although there are a few endeavors to repair programs by learning neural language models (NLM), many special program properties, such as structure and semantics of an identifier, are not considered in embedding input sequence and designing model effectively, which results in undesired performance. In the proposed Bug-Transformer, we design a novel context abstraction mechanism to better support neural language models. Specifically, it is capable of 1) compressing code information but preserving the key structure and semantics, which provides more thorough information for NLM models, 2) renaming identifiers and literals based on their lexical scopes, structural and semantic information, to reduce code vocabulary size and 3) reserving keywords and selected idioms (domain- or developer-specific vocabularies) for better understanding code structure and semantics. Hence, Bug-Transformer adequately embeds code structural and semantic information into input data and optimize attention-based transformer neural network to well handle code features in order to improve learning tasks for bug repair. We evaluate the performance of the proposed work comprehensively on three datasets (Java code corpora) and generate patches to buggy code using a beam search decoder. The experimental results show that our proposed work outperforms the-state-of-art techniques: Bug-Transformer can successfully predict 54.81%, 34.45%, and 42.40% of the fixed code in these three datasets, respectively, which outperform the baseline models. These success rates steadily increase along with the increase of beam size. Besides, the overall syntactic correctness of all patches remains above 97%, 96%, and 50% on the three benchmarks, respectively, regardless of the beam size. [ABSTRACT FROM AUTHOR]