CAAI Transactions on Intelligence Technology最新文献

The epidemic characters of Omicron (e.g. large-scale transmission) are significantly different from the initial variants of COVID-19. The data generated by large-scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β-SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β-Recovered persons, to intelligently predict the epidemic characters of COVID-19. Note that β-Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters.

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge-based learning systems. Smart healthcare systems leverage knowledge-based learning to become more context-aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X-rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge-based learning systems to foster structured decision-making and enhance the learning abilities of AI. Moreover, in knowledge-driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit-plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high-quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.

Deep learning has recently become a viable approach for classifying Alzheimer's disease (AD) in medical imaging. However, existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness classification. To address these issues, a deep three-dimensional convolutional neural network incorporating multi-task learning and attention mechanisms is proposed. An upgraded primary C3D network is utilised to create rougher low-level feature maps. It introduces a new convolution block that focuses on the structural aspects of the magnetic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map output. Then, several fully connected layers are used to achieve multi-task learning, generating three outputs, including the primary classification task. The other two outputs employ backpropagation during training to improve the primary classification job. Experimental findings show that the authors’ proposed method outperforms current approaches for classifying AD, achieving enhanced classification accuracy and other indicators on the Alzheimer's disease Neuroimaging Initiative dataset. The authors demonstrate promise for future disease classification studies.

By automatically learning the priors embedded in images with powerful modelling capabilities, deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral (HR-HS) image. With previously collected large-amount of external data, these methods are intuitively realised under the full supervision of the ground-truth data. Thus, the database construction in merging the low-resolution (LR) HS (LR-HS) and HR multispectral (MS) or RGB image research paradigm, commonly named as HSI SR, requires collecting corresponding training triplets: HR-MS (RGB), LR-HS and HR-HS image simultaneously, and often faces difficulties in reality. The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved performance to the real images captured under diverse environments. To handle the above-mentioned limitations, the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem. The authors advocate that it is possible to train a specific CNN model at test time, called as deep internal learning (DIL), by on-line preparing the training triplet samples from the observed LR-HS/HR-MS (or RGB) images and the down-sampled LR-HS version. However, the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors, which would result in limited reconstruction performance. To solve this problem, the authors further exploit deep self-supervised learning (DSL) by considering the observations as the unlabelled training samples. Specifically, the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation, and then the reconstruction errors of the observations were formulated for measuring the network modelling performance. By consolidating the DIL and DSL into a unified deep framework, the authors construct a more robust HSI SR method without any prior training and have great potential of flexible adaptation to different settings per observation. To verify the effectiveness of the proposed approach, extensive experiments have been conducted on two benchmark HS datasets, including the CAVE and Harvard datasets, and demonstrate the great performance gain of the proposed method over the state-of-the-art methods.

How to represent a human face pattern? While it is presented in a continuous way in human visual system, computers often store and process it in a discrete manner with 2D arrays of pixels. The authors attempt to learn a continuous surface representation for face image with explicit function. First, an explicit model (EmFace) for human face representation is proposed in the form of a finite sum of mathematical terms, where each term is an analytic function element. Further, to estimate the unknown parameters of EmFace, a novel neural network, EmNet, is designed with an encoder-decoder structure and trained from massive face images, where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace. The authors demonstrate that our EmFace represents face image more accurate than the comparison method, with an average mean square error of 0.000888, 0.000936, 0.000953 on LFW, IARPA Janus Benchmark-B, and IJB-C datasets. Visualisation results show that, EmFace has a higher representation performance on faces with various expressions, postures, and other factors. Furthermore, EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation.

In the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping. Subsequently, a 3-stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks.

Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

In recent times, an image enhancement approach, which learns the global transformation function using deep neural networks, has gained attention. However, many existing methods based on this approach have a limitation: their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images. In order to address this limitation, a simple yet effective approach for image enhancement is proposed. The proposed algorithm based on the channel-wise intensity transformation is designed. However, this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours. To this end, the authors define the continuous intensity transformation (CIT) to describe the mapping between input and output intensities on the embedding space. Then, the enhancement network is developed, which produces multi-scale feature maps from input images, derives the set of transformation functions, and performs the CIT to obtain enhanced images. Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’ approach improves the performance of conventional intensity transforms on colour space metrics. Specifically, the authors achieved a 3.8% improvement in peak signal-to-noise ratio, a 1.8% improvement in structual similarity index measure, and a 27.5% improvement in learned perceptual image patch similarity. Also, the authors’ algorithm outperforms state-of-the-art alternatives on three image enhancement datasets: MIT-Adobe 5K, Low-Light, and Google HDR+.

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalised IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralised learning scenarios. A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed (HDUS) is designed, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on-device models, representing stronger scalability in real-world applications. Extensive experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.