CAAI Transactions on Intelligence Technology最新文献

As human-machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human-machine cooperation more harmonious, the authors need to build good human-machine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions. Linear self-organising map (SOM) introduces lateral interaction in a general form in which signals of any modality can be used. Some approaches directly incorporate SOM learning rules into neural networks, but incur complex operations and poor extendibility. The efficient way to implement lateral interaction in deep neural networks is not well established. The use of Laplacian Matrix-based Smoothing (LS) regularisation is proposed for implementing lateral interaction in a concise form. The authors’ derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS-regulated k-means, and they both show the topology-preserving capability. The authors also verify that LS-regularisation can be used in conjunction with the end-to-end training paradigm in deep auto-encoders. Additionally, the benefits of LS-regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated. Furthermore, the topologically ordered structure introduced by LS-regularisation in feature extractor can improve the generalisation performance on classification tasks. Overall, LS-regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

Generating a realistic person's image from one source pose conditioned on another different target pose is a promising computer vision task. The previous mainstream methods mainly focus on exploring the transformation relationship between the keypoint-based source pose and the target pose, but rarely investigate the region-based human semantic information. Some current methods that adopt the parsing map neither consider the precise local pose-semantic matching issues nor the correspondence between two different poses. In this study, a Region Semantics-Assisted Generative Adversarial Network (RSA-GAN) is proposed for the pose-guided person image generation task. In particular, a regional pose-guided semantic fusion module is first developed to solve the imprecise match issue between the semantic parsing map from a certain source image and the corresponding keypoints in the source pose. To well align the style of the human in the source image with the target pose, a pose correspondence guided style injection module is designed to learn the correspondence between the source pose and the target pose. In addition, one gated depth-wise convolutional cross-attention based style integration module is proposed to distribute the well-aligned coarse style information together with the precisely matched pose-guided semantic information towards the target pose. The experimental results indicate that the proposed RSA-GAN achieves a 23% reduction in LPIPS compared to the method without using the semantic maps and a 6.9% reduction in FID for the method with semantic maps, respectively, and also shows higher realistic qualitative results.

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in clutter. To effectively perform grasping and pushing manipulations, robots need to perceive the position information of objects, including the coordinates and spatial relationship between objects (e.g., proximity, adjacency). The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in clutter. Specifically, a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping operations. In addition, the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial relationships between objects in cluttered environments. To further enhance the perception capacity of position information of the objects, the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function. A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency, task completion rate, grasping success rate and action efficiency compared to state-of-the-art end-to-end methods. Noted that the authors’ system can be robustly applied to real-world use and extended to novel objects. Supplementary material is available at https://youtu.be/NhG_k5v3NnM}{https://youtu.be/NhG_k5v3NnM.

Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures. The seizures are defined as the unexpected electrical changes in brain neural activity, which leads to unconsciousness. Existing researches made an intense effort for predicting the epileptic seizures using brain signal data. However, they faced difficulty in obtaining the patients' characteristics because the model's distribution turned to fake predictions, affecting the model's reliability. In addition, the existing prediction models have severe issues, such as overfitting and false positive rates. To overcome these existing issues, we propose a deep learning approach known as Deep dual-patch attention mechanism (D²PAM) for classifying the pre-ictal signals of people with Epilepsy based on the brain signals. Deep neural network is integrated with D²PAM, and it lowers the effect of differences between patients to predict ES. The multi-network design enhances the trained model's generalisability and stability efficiently. Also, the proposed model for processing the brain signal is designed to transform the signals into data blocks, which is appropriate for pre-ictal classification. The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis. The data of real patients for the experiments provides the improved accuracy by D²PAM approximation compared to the existing techniques. To be more distinctive, the authors have analysed the performance of their work with five patients, and the accuracy comes out to be 95%, 97%, 99%, 99%, and 99% respectively. Overall, the numerical results unveil that the proposed work outperforms the existing models.

In the event of a fire breaking out or in other complicated situations, a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures. Thus, it is crucial to increase the positioning technology's accuracy. The sequential Monte Carlo (SMC) approach is used in various applications such as target tracking and intelligent surveillance, which rely on smartphone-based inertial data sequences. However, the SMC method has intrinsic flaws, such as sample impoverishment and particle degeneracy. A novel SMC approach is presented, which is built on the weighted differential evolution (WDE) algorithm. Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction, like in a high-dimensional space, such as a multistory structure. Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set, prevent the usage of an inadequate number of valid samples, and preserve smartphone user position accuracy. The values of the smartphone-based sensors and BLE-beacons are set as input to the SMC, which aids in fast approximating the posterior distributions, to speed up the particle congregation process in the proposed SMC-based WDE approach. Lastly, the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users. According to simulation findings, the suggested approach provides improved location estimation with reduced localization error and quick convergence. The results confirm that the proposed optimal fusion-based SMC-WDE scheme performs 9.92% better in terms of MAPE, 15.24% for the case of MAE, and 0.031% when evaluating based on the R2 Score.

Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super-resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance. Previous approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted priors. Recently, researchers pay more attention to deep learning methods with direct supervised or unsupervised learning, which exploit deep prior only from training dataset or testing data. In this article, an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image guidance. Specifically, a progressive HS image super-resolution network is proposed, which progressively super-resolve the LR HS image with pixel shuffled HR RGB image guidance. Then, the super-resolution network is progressively trained with supervised pre-training and unsupervised adaption, where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes. The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial constraint. It has a good generalisation capability, especially for blind HS image super-resolution. Comprehensive experimental results show that the proposed deep progressive learning method outperforms the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

As deep learning evolves, neural network structures become increasingly sophisticated, bringing a series of new optimisation challenges. For example, deep neural networks (DNNs) are vulnerable to a variety of attacks. Training neural networks under privacy constraints is a method to alleviate privacy leakage, and one way to do this is to add noise to the gradient. However, the existing optimisers suffer from weak convergence in the presence of increased noise during training, which leads to a low robustness of the optimiser. To stabilise and improve the convergence of DNNs, the authors propose a neural dynamics (ND) optimiser, which is inspired by the zeroing neural dynamics originated from zeroing neural networks. The authors first analyse the relationship between DNNs and control systems. Then, the authors construct the ND optimiser to update network parameters. Moreover, the proposed ND optimiser alleviates the non-convergence problem that may be suffered by adding noise to the gradient from different scenarios. Furthermore, experiments are conducted on different neural network structures, including ResNet18, ResNet34, Inception-v3, MobileNet, and long and short-term memory network. Comparative results using CIFAR, YouTube Faces, and R8 datasets demonstrate that the ND optimiser improves the accuracy and stability of DNNs under noise-free and noise-polluted conditions. The source code is publicly available at https://github.com/LongJin-lab/ND.