With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.

Malicious websites present a substantial threat to the security and privacy of individuals using the internet. Traditional approaches for identifying these malicious sites have struggled to keep pace with evolving attack strategies. In recent years, language models have emerged as a potential solution for effectively detecting and categorizing malicious websites. This study introduces a novel Bidirectional Encoder Representations from Transformers (BERT) model, based on the Transformer encoder architecture, designed to capture pertinent characteristics of malicious web addresses. Additionally, large-scale language models are employed for training, dataset assessment, and interpretability analysis. The evaluation results demonstrate the effectiveness of the large language model in accurately classifying malicious websites, achieving an impressive precision rate of 94.42%. This performance surpasses that of existing language models. Furthermore, the interpretability analysis sheds light on the decision-making process of the model, enhancing our understanding of its classification outcomes. In conclusion, the proposed BERT model, built on the Transformer encoder architecture, exhibits robust performance and interpretability in the identification of malicious websites. It holds promise as a solution to bolster the security of network users and mitigate the risks associated with malicious online activities.

Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are mainly due to weak safety awareness among staff and the failure to standardize the wearing of safety helmets. In order to effectively address this situation, technical means such as video surveillance technology and computer vision technology can be utilized to monitor whether staff are wearing helmets and provide timely feedback. Such measures will greatly enhance the safety level of power operation. This paper proposes an improved lightweight helmet detection algorithm named YOLO-M3C. The algorithm first replaces the YOLOv5s backbone network with MobileNetV3, successfully reducing the model size from 13.7 MB to 10.2 MB, thereby increasing the model’s detection speed from 42.0 frames per second to 55.6 frames per second. Then, the CA attention mechanism is introduced into the backbone network to enhance the feature extraction capability of the model. Finally, in order to further improve the detection recall rate and accuracy of the model, a knowledge distillation of the model was carried out. The experimental results show that, compared with the original YOLOv5s algorithm, the average accuracy of the improved YOLO-M3C algorithm is improved by 0.123, and the recall rate is the same. These results verify that the algorithm YOLO-M3C has excellent performance in target detection and recognition, which can improve accuracy and confidence, while reducing false detection and missing detection, and effectively meet the needs of helmet-wearing detection.

A smart city plays an increasing role in citizens’ daily life. A smart city has six main components, namely smart mobility, smart economy, smart governance, smart living, smart environment, and smart people. For the past several decades, government and organizations throughout the world have initiated many smart city projects. Citizens’ transportation, education, investment, and other activities have been shifted from traditional to smart models through emerging technologies and the internet. In this paper, following the pioneering work of the intelligent quotient (IQ) test, we propose a framework to measure a city's intelligence level. We also propose a definition for the intelligence of an information system. To evaluate such intelligence, 382 technology indicators have been developed. We have applied smart city IQ testing on the top 100 largest cities in the United States (according to 2017 population). In addition, the smart city IQ score is designed so that it is convertible to the age of a human being. Various text mining and data mining methods (including classification) have been applied. The results of our analysis indicate that the average smart city IQ score is like that of an eight-year-old child. We also show the breakdown of IQ scores in terms of the six main components. Although the evaluation of the smart city projects in the United States is still ongoing, the results obtained so far as reported in this paper can provide important insights for research in this particular field, as well as intelligent information systems in general.

Accurate segmentation of skin lesions is crucial for reliable clinical diagnosis and effective treatment planning. Automated techniques for skin lesion segmentation assist dermatologists in early detection and ongoing monitoring of various skin diseases, ultimately improving patient outcomes and reducing healthcare costs. To address limitations in existing approaches, we introduce a novel U-shaped segmentation architecture based on our Residual Space State Block. This efficient model, termed ‘SSR-UNet,’ leverages bidirectional scanning to capture both global and local features in image data, achieving strong performance with low computational complexity. Traditional CNNs struggle with long-range dependencies, while Transformers, though excellent at global feature extraction, are computationally intensive and require large amounts of data. Our SSR-UNet model overcomes these challenges by efficiently balancing computational load and feature extraction capabilities. Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. We rigorously evaluated SSR-UNet on the ISIC2017 and ISIC2018 skin lesion segmentation benchmarks. The results showed that the accuracy of Mean Intersection Over Union, Classification Accuracy and Specificity indexes in ISIC2017 datasets reached 80.98, 96.50 and 98.04, respectively, exceeding the best indexes of other models by 0.83, 0.99 and 0.38, respectively. The accuracy of Mean Intersection Over Union, Dice Coefficient, Classification Accuracy and Sensitivity on ISIC2018 datasets reached 82.17, 90.21, 95.34 and 88.49, respectively, exceeding the best indicators of other models by 1.71, 0.27, 0.65 and 0.04, respectively. It can be seen that SSR-UNet model has excellent performance in most aspects.