Despite their proficiency with typical environmental datasets, deep learning-based object detection algorithms struggle when faced with diverse adverse weather conditions. Moreover, existing methods often address single adverse weather scenarios, neglecting situations involving multiple concurrent adverse conditions. To tackle these challenges, we propose an enhanced approach to object detection in power construction sites under various adverse weather conditions, dubbed IDP-YOLOV9. This model leverages a parallel architecture comprising the Image Dehazing and Enhancement Processing (IDP) module and an improved YOLOV9 object detection module. Specifically, for images captured in adverse weather, our approach employs a parallel architecture that includes the Three-Weather Removal Algorithm (TRA) module and the Deep Learning-based Image Enhancement (DLIE) module, which, together, filter multiple weather factors to enhance image quality. Subsequently, we introduce an improved YOLOV9 detection network module that incorporates a three-layer routing attention mechanism for object detection. Experiments demonstrate that the IDP module significantly improves image quality by mitigating the impact of various adverse weather conditions. Compared to traditional single-processing models, our method improves recognition accuracy on complex weather datasets by 6.8% in terms of mean average precision (mAP50).

The large-scale application of blockchain technology is an expected to be an inevitable trend. This study revolves around published papers and articles related to blockchain technology, relevance analysis and sorting through the retrieved documents with six core layers of blockchain: Application Layer, Contract Layer, Actuator Layer, Consensus Layer, Network Layer and Data Layer. Based on the analysis results, this study found that China’s research is more towards the preference and application of landing and industry and smart cities with blockchain as the underlying technology. International research is more focused on the research of finance as the underlying technology of blockchain and tries to combine crypto assets with real industries, such as crypted assets and payment systems for traditional industries. This paper studies the impact of monetary entropy on cryptocurrencies in smart cities and uses the monetary entropy formula to measure the crypto-economic entropy. We use Kolmogorov entropy to describe the degree of chaos in the cryptocurrency market in a smart city. The study illustrates the current status of blockchain technology and applications from the perspective of cryptocurrency in a smart city. We find that smart cities and cryptocurrencies have a mutually reinforcing effect.

Change addressmymargin2 identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross-Innovation-Lab/BitcoinAddressClustering.

To tackle the intricate challenges associated with the low detection accuracy of images taken by unmanned aerial vehicles (UAVs), arising from the diverse sizes and types of objects coupled with limited feature information, we present the SRE-YOLOv8 as an advanced method. Our method enhances the YOLOv8 object detection algorithm by leveraging the Swin Transformer and a lightweight residual feature pyramid network (RE-FPN) structure. Firstly, we introduce an optimized Swin Transformer module into the backbone network to preserve ample global contextual information during feature extraction and to extract a broader spectrum of features using self-attention mechanisms. Subsequently, we integrate a Residual Feature Augmentation (RFA) module and a lightweight attention mechanism named ECA, thereby transforming the original FPN structure to RE-FPN, intensifying the network’s emphasis on critical features. Additionally, an SOD (small object detection) layer is incorporated to enhance the network’s ability to recognize the spatial information of the model, thus augmenting accuracy in detecting small objects. Finally, we employ a Dynamic Head equipped with multiple attention mechanisms in the object detection head to enhance its performance in identifying low-resolution targets amidst complex backgrounds. Experimental evaluation conducted on the VisDrone2021 dataset reveals a significant advancement, showcasing an impressive 9.2% enhancement over the original YOLOv8 algorithm.

The fusion technique is the key to the multimodal emotion recognition task. Recently, cross-modal attention-based fusion methods have demonstrated high performance and strong robustness. However, cross-modal attention suffers from redundant features and does not capture complementary features well. We find that it is not necessary to use the entire information of one modality to reinforce the other during cross-modal interaction, and the features that can reinforce a modality may contain only a part of it. To this end, we design an innovative Transformer-based Adaptive Cross-modal Fusion Network (TACFN). Specifically, for the redundant features, we make one modality perform intra-modal feature selection through a self-attention mechanism, so that the selected features can adaptively and efficiently interact with another modality. To better capture the complementary information between the modalities, we obtain the fused weight vector by splicing and use the weight vector to achieve feature reinforcement of the modalities. We apply TCAFN to the RAVDESS and IEMOCAP datasets. For fair comparison, we use the same unimodal representations to validate the effectiveness of the proposed fusion method. The experimental results show that TACFN brings a significant performance improvement compared to other methods and reaches the state-of-the-art performance. All code and models could be accessed from https://github.com/shuzihuaiyu/TACFN.

In recent years, it is a difficult issue to integrate the deep cross-fertilization and interpretable cognitive modeling methods from the basic theory of emotional psychology with deep learning and other algorithms. To address this problem, a cognitive model that integrates the VGG-facial action coding system (FACS)-OCC model based on fer2013 expression features and the OCC-pleasure-arousal-dominance (PAD)-openness, conscientiousness, extraversion, agreeableness, and neuroticism (OCEAN) fusion of the basic theory of emotional psychology, namely, a computational affection-based OCC-PAD-OCEAN federation cognitive modeling (OPO-FCM), is constructed. By constructing this model and performing formal proof algorithms, it is shown that the OPO-FCM can acquire expression features in video streams, complete the acquisition of expression features in videos by training a deep neural network, map expressions to the PAD emotion space through the established expression–basic emotions–emotion space mapping relationship, and finally complete the mapping of the average emotion over a period time. The information of personality space is obtained through it. Finally, the experimental simulation of the model is conducted, and the results show that the average accuracy of the valid tested personalities is 79.56%. This article takes the knowledge-driven approach of emotional psychology as a starting point and combines deep learning techniques to construct interpretable cognitive models, thus providing new ideas for future cross-innovation between computer technology and psychology theory.