Lightweight human pose estimation (HPE) has garnered significant attention due to its widespread applications in edge and mobile devices. However, lightweight human pose estimation methods demonstrate limited accuracy when detecting complex movements, thus restricting their practical utility. To address this issue, we propose MGDNet, a novel lightweight network featuring three innovative modules: the GA-Bottleneck module, the MAC-Block module, and the Dual-View Feature Enhancement Module (DV-FEM). The GA-Bottleneck module integrates ghost convolution with multi-dimensional adaptive frequency-aware attention to capture multi-scale frequency characteristics, enhancing robustness for complex actions. The MAC-Block combines multi-receptive field depth convolution with frequency domain analysis to achieve precise joint localization. The DV-FEM leverages complementary local-global information to enhance feature representation for complex actions. Extensive experiments conducted on the COCO and MPII datasets demonstrate that MGDNet achieves state-of-the-art performance among lightweight models, attaining an average precision (AP) of 73.0% on COCO val2017, which represents a 1.6% improvement over Greit-HRNet. On the MPII dataset, MGDNet achieves the highest PCKh score of 87.6%. The proposed MGDNet outperforms existing lightweight approaches by addressing the challenge of low detection accuracy in complex motion scenarios while maintaining comparable computational efficiency.

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in a sentence. Graph convolutional networks(GCN) have recently been widely applied for modeling the associations between aspects and opinion words. However, most studies treat the relationships between all words in the graph equally through dependency analysis, without considering their dependency types, which may lead to the inability to distinguish important relationships. On the other hand, dependency trees can only reveal relationships between words and cannot simulate complex sentence relationships, such as conditional, parallel, and contrast relationships, which are crucial for capturing more accurate sentiment relationships for different aspects. To address these challenges, this paper proposes a Collaborative computation model based on Dependency Types and Constituent trees(CDTC) for aspect-based sentiment analysis. Specifically, collaborative computation of dependency type graph convolutional networks and syntax encoders enhances task efficiency. Utilizing dependency types helps distinguish important relationships, while the phrase segmentation and hierarchical structure of constituent trees provide richer syntactic information. Extensive experiments conducted on three datasets demonstrate that our proposed CDTC model achieves state-of-the-art performance.

In the digital society era, the generation frequency of multimodal data is rapidly increasing. Blockchain, recognized as a trusted distributed database technology, provides a new solution for the trustworthy storage and efficient management of multimodal data. However, blockchain systems support only query that use transaction hash values as keywords and cannot directly leverage the content features of multimodal data, leading to generally low query efficiency. To address this issue, this paper introduces an efficient multi-layer indexing method on blockchain for multimodal data querying. It establishes an effective mapping between on-chain and off-chain data through a verifiable on-chain and off-chain collaborative storage architecture. The paper also proposes Multi-layer Bitmap Block Index (MBBI) and Cuckoo Merkel Tree (C-MT) to optimize the querying process. Experimental results demonstrate that this method not only ensures the consistency and integrity of metadata across on-chain and off-chain but also significantly enhances the efficiency of multimodal data query. This offers a feasible solution for the storage and query needs of large-scale multimodal data.

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, [email protected] has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models.

Traditional multi-channel cooperative spectrum sensing (CSS) scheme schedules a group of cognitive users (CU) to sense a particular channel in any given sensing slot, which means that the same sensing sequence pattern is shared by all CUs in the group. Although the sensing accuracy can be improved, the energy consumption will correspondingly increase. In order to reduce the energy consumption without loss of the sensing accuracy, we in this letter propose a cyclic-shifting based sequential CSS strategy for multi-channel cognitive networks (CN). Specifically, instead of employing the common shared sensing sequence pattern, our proposed strategy assigns a unique cyclic-shifting based sensing sequence for each CU, such that different channels will be sensed simultaneously in any given sensing slot. Moreover, if the decision for a particular channel can be made by current sensing information, the channel will not be sensed in the following sensing slots. Theoretical analysis shows that our proposed strategy can efficiently reduce the number of both sensing slots and reporting slots consumed for each channel and achieve the same probabilities of detection and false-alarm as the traditional CSS scheme. This implies that the energy efficiency of the system can be improved while maintaining the sensing accuracy undegraded. Simulation results are also provided to demonstrate the superiority of our proposed strategy as compared to the existing scheme.