CAAI Transactions on Intelligence Technology最新文献

With the introduction of more recent deep learning models such as encoder-decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword-to-text framework. A novel Topic-Controllable Key-to-Text (TC-K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject-controlled information from previous research is presented. TC-K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC-K2T can produce more informative and controllable senescence, outperforming state-of-the-art models, according to empirical research on automatic evaluation metrics and human annotations.

A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification. Capitalising on the unique geometrical relationship between the two biometric modalities, the cross-modality intersecting points provides a stable set of features for identity verification. To facilitate flexibility in template changes, a template transformation is proposed. While maintaining non-invertibility, the template transformation allows transformation sizes beyond that offered by the conventional means. Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Few-shot image classification is the task of classifying novel classes using extremely limited labelled samples. To perform classification using the limited samples, one solution is to learn the feature alignment (FA) information between the labelled and unlabelled sample features. Most FA methods use the feature mean as the class prototype and calculate the correlation between prototype and unlabelled features to learn an alignment strategy. However, mean prototypes tend to degenerate informative features because spatial features at the same position may not be equally important for the final classification, leading to inaccurate correlation calculations. Therefore, the authors propose an effective intraclass FA strategy that aggregates semantically similar spatial features from an adaptive reference prototype in low-dimensional feature space to obtain an informative prototype feature map for precise correlation computation. Moreover, a dual correlation module to learn the hard and soft correlations was developed by the authors. This module combines the correlation information between the prototype and unlabelled features in both the original and learnable feature spaces, aiming to produce a comprehensive cross-correlation between the prototypes and unlabelled features. Using both FA and cross-attention modules, our model can maintain informative class features and capture important shared features for classification. Experimental results on three few-shot classification benchmarks show that the proposed method outperformed related methods and resulted in a 3% performance boost in the 1-shot setting by inserting the proposed module into the related methods.

Green Infrastructure (GI) has garnered increasing attention from various regions due to its potential to mitigate urban heat island (UHI), which has been exacerbated by global climate change. This study focuses on the central area of Fuzhou city, one of the “furnace” cities, and aims to explore the correlation between the GI pattern and land surface temperature (LST) in the spring and autumn seasons. The research adopts a multiscale approach, starting from the urban scale and using urban geographic spatial characteristics, multispectral remote sensing data, and morphological spatial pattern analysis (MSPA). Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression (GWR) experiment. The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of “high temperature in the middle and low temperature around,” which is coupled with a “central scattered and peripheral concentrated” distribution of GI. This suggests that remote sensing data can effectively be utilised for UHI inversion. Additionally, the study finds that the complexity of GI, whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area, has an impact on the alleviation of UHI in both seasons. In conclusion, this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.

In traditional secret image sharing schemes, a secret image is shared among shareholders who have the same position. But if the shareholders have two different positions, essential and non-essential, it is necessary to use essential secret image sharing schemes. In this article, a verifiable essential secret image sharing scheme based on HLRs is proposed. Shareholder's share consists of two parts. The first part is produced by the shareholders, which prevents the fraud of dealers. The second part is a shadow image that is produced by using HLRs and the first part of share. The verification of the first part of the shares is done for the first time by using multilinear and bilinear maps. Also, for verifying shadow images, Bloom Filters are used for the first time. The proposed scheme is more efficient than similar schemes, and for the first part of the shares, has formal security.

Magnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1-weighted (T1-w) to T2-weighted (T2-w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation-based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation, it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.