Direct teleoperation of robots in unstructured environments by non-experts often leads to low efficiency and increased risk. To this end, this paper proposes a shared control architecture where the robot can generalize demonstrations based on variable environments (start, obstacles, and goal positions) and infer user intention online to assist tasks safely and efficiently. First, the complex task is decomposed into unit actions, where the cubic-quintic-cubic Bezier curve is utilized to resolve the limitation of the classical dynamic movement primitives (DMP) algorithm in generalizing via-points. Gaussian process regression (GPR) was implemented to generalize expert demonstrations to different environments, ensuring full generalizable trajectory in the subtask space. GPR further extrapolate simple demonstrations from a subspace to the entire task space, avoiding the demand for numerous original demonstrations. Then, the online evolution of DMP is optimized: 1) an adaptive temporal scaling system is developed to synchronize evolution with human operations; 2) an intention prediction and expected evolution selection method based on operational input is proposed, achieving humanled operation guidance. Experiments validate the effectiveness of the generalization, obstacle avoidance, and operation assistance. Tests with non-experts reported the designed architecture enhances safety and efficiency, and reduces collisions.

The global trend of population aging poses significant challenges to society and healthcare systems, particularly because of neurocognitive disorders (NCDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD). In this context, artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of NCDs. Multimodal contactless screening technologies, such as speech-language processing, computer vision, and virtual reality, offer efficient and convenient methods for disease diagnosis and progression tracking. This paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms, feature extraction, and modeling approaches. Additionally, the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are explored. By providing a comprehensive summary and refinement of the extant theories, methodologies, and applications, this study aims to facilitate an in-depth understanding of these technologies for researchers, both within and outside the field. To the best of our knowledge, this is the first survey to cover the use of speech-language processing, computer vision, and virtual reality technologies for the detection of NSDs.

Abstract With the global aging problem becoming more and more serious, the initial screening for Alzheimer's disease (AD) will become increasingly important. We understand that facial expressions are related to the severity of dementia, but there is no face‐related data in the existing Alzheimer's dataset. This article attempts to establish a facial video‐based AD recognition dataset through a human‐computer interaction method. This interactive task was designed for AD in attention, execution, visual space ability, facial apraxia, and facial changes in task success and failure. Using this task as the collection method, the final dataset includes 102 faces video data, specific task scores, and emotional self‐evaluation. For baseline evaluation, the improved local binary pattern on three orthogonal planes and RF were employed respectively for feature extraction and classification with the 5‐fold cross‐validation method. The best performance was 76.00% for 3‐class classification. In addition, a frame attention network based on fine‐grained local region localization was proposed, which improved the accuracy of cognitive classification to 84.45%. Finally, the analysis was conducted for the association of expressions with cognition and emotion in the AD dataset. This study aims to solve the current lack of standards for AD in the field of facial recognition and contribute to future research and clinical applications.

Regression modeling is a significant aspect of multi-modal continuous dimensional emotion recognition. Despite the developments of this domain, one of the limitations that severely impede the application of emotion recognition is that most methods utilized for regression modeling merely capture the temporal information. Motivated by this, we propose a new branch feature fusion framework BF-Net, whose core idea is to deeply combine local features captured by convolutional neural networks and temporal dependencies captured by long-short-term memory recurrent neural networks. The framework consists of two main components: two-branch structure and fusion of branches. The former captures local features and temporal dependencies respectively in an effective way. Besides, the latter utilizes an attention mechanism to fuse the features on a deep level. In this way, the framework achieves full utilization of the local features and temporal dependencies. The experiments on ULM-TSST dataset show that the proposed method is competitive or superior to the state-of-the-art works.

Abstract Facial micro‐expression is a superposition of micro‐expression features and identity information of a subject. For the problem of identity information interference in micro‐expression recognition, this study proposes a new method for facial micro‐expression recognition by de‐identity information, called two‐stream difference network (TSDN). First, a two‐stream encoder‐decoder network is trained by a convolutional neural network, where the input of the micro‐expression stream is a micro‐expression image, and the identity stream is a facial identity image. The micro‐expression image is the apex image, and the identity image is the onset image in the micro‐expression sequence. The identity information and micro‐expression features are recorded in the intermediate layer of the micro‐expression stream, while the intermediate layer of the identity stream contains only the identity information of a subject. Then, the identity information is removed by the difference network, but micro‐expression features are stored in the intermediate layer of the micro‐expression stream. Given the sequence of the micro‐expressions, the TSDN model of de‐identity information learns the difference that stores in the expression stream. Two public spontaneous facial micro‐expression data sets (SMIC and CASME II) are employed in our experiments. The experiment results show that our model can achieve a superior performance in micro‐expression recognition.

Facial micro-expressions are short and imperceptible expressions that involuntarily reveal the true emotions that a person may be attempting to suppress, hide, disguise, or conceal. Such expressions can reflect a person's real emotions and have a wide range of application in public safety and clinical diagnosis. The analysis of facial micro-expressions in video sequences through computer vision is still relatively recent. In this research, a comprehensive review on the topic of spotting and recognition used in microexpression analysis databases and methods, is conducted, and advanced technologies in this area are summarized. In addition, we discuss challenges that remain unresolved alongside future work to be completed in the field of micro-expression analysis.

To reduce the impact of low intensity on the spot micro-expressions in long video sequences when facial microexpressions occur, this paper presents a method based on Local Bilinear Convolutional Neural Network (LBCNN) for spotting macro- and micro-expressions in long videos sequences. Considering the low intensity of facial micro-expressions, and the occurrence of micro-expressions is only related to the local area of the facial, we turn the micro-expression spot in long videos sequences into a fine-grained image recognition. Bilinear Convolutional Neural Network (BCNN) has proven to be effective in fine-grained image recognition. Therefore, we use the BCNN structure to extract the global and local features of the face area of each frame of the image in the long video sequence and obtains the final classification result by fusing the global and local features. The metric F1-scores of our proposed methods were evaluated on the CAS(ME)2and SAMM Long Videos dataset of the Third Facial Micro-Expression Grand Challenge (MEGC 2020). For the CAS(ME)2, the overall F1-scores are 0.0595 for macro- and micro-expressions; for SAMM Long Videos, the overall F1-scores are 0.0813 for macro- and microexpressions. The experiments show that our method achieved superiorly higher results than the baseline method (MDMD) provided. https://github.com/panhang1023/MEGC2020.

Active compliance control is one of the necessary prerequisites for the fine manipulation of hydraulic heavy duty manipulators (HHDMs). The establishment of a rigid-flexible coupling machine-hydraulic multibody dynamics model and an active compliance control algorithm for HHDM are the key problems to be solved urgently in the compliance control of heavy duty manipulators. In this paper, a multibody dynamics model of a machine-hydraulic system with seven degrees of freedom is developed with consideration of HHDM characteristics, such as multi-input multioutput, nonlinearity, and rigid-flexible coupling. Meanwhile, a position/force same loop control algorithm for the compliance control of HHDM is proposed based on genetic neural network. Specifically, the force control system is decomposed into subsystems, while the feedback position and force are output through the processing of the joint position controller, torque controller, and multibody dynamics model. Cosimulation results present the feasibility and effectiveness of the proposed dynamics model and control algorithm. Moreover, the experimental environment and the HHDM control system are also developed, while the operation experiment of the control system for HHDM is accordingly conducted. Experiment results show that the control system can realize precise control of position and force, and can successfully complete the function of active compliance control operation.

In the field of covert data integrity attacks, considerable attention has focused on two important issues. One is the issue of how to change the state of a plant, and the other is how to avoid being detected by anomaly detectors. A two-loop covert attack is presented to provide an integrated solution for these two issues. As an exploratory attempt to establish the feasibility of machine learning-based covert attacks, it applies the least squares support vector machine to constructing covert attacks. The proposed attack consists of an attack loop and a covert loop, which are based on an attack agent and a covert agent, respectively. The attack agent can move the steady state of a target plant to a desired state, and the covert agent can closely imitate the normal steady state of the plant to cover up the attack agent. In particular, the attack is directed to proportional-integral-derivative algorithms. Experiments are carried out to demonstrate the feasibility of the proposed attack and show the applicability of machine learning methods in constructing covert attacks.

A cyber security incident in SCADA systems can cause the disruption of physical process, and may result in significant economic loss, environmental disasters or even human casualties. To exploit the feature of the physical process and find the potential attacks, this paper presents and analyzes a new class of cyber-physical attacks, named false logic attacks, against the logic of control process in SCADA systems. In addition, it proposes a model for false logic attacks, which is useful for analyzing how attacks can affect the physical system. An experiment is performed to illustrate the concepts, and the effect of false logic attacks are also discussed.