We investigated the fusion of the Intelligent Internet of Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in depression identification, its correlation with stress and anxiety, the impact of machine learning (ML) and deep learning (DL) on depressive disorders, and the challenges and potential prospects of integrating depression management with IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) and Internet of Things (IoT) paradigms to expand depression studies, highlighting data science modeling’s practical application for intelligent service delivery in real‐world settings, emphasizing the benefits of data science within IoT. Furthermore, it outlines an IIoMT architecture for gathering, analyzing, and preempting depressive disorders, employing advanced analytics to enhance application intelligence. The study also identifies current challenges, future research trajectories, and potential solutions within this domain, contributing to the scientific understanding and application of IIoMT in depression management. It evaluates 168 closely related articles from various databases, including Web of Science (WoS) and Google Scholar, after the rejection of repeated articles and books. The research shows that there is 48% growth in research articles, mainly focusing on symptoms, detection, and classification. Similarly, most research is being conducted in the United States of America, and the trend is increasing in other countries around the globe. These results suggest the essence of automated detection, monitoring, and suggestions for handling depression.

Intelligent techniques, including artificial intelligence and deep learning, normally perform on complete data without missing data. Multiple imputation is indispensable for addressing missing data resulting in unbiased estimates and dealing with uncertainty by providing more valid results. Most state-of-the-art techniques focus on high-missing rates (around 50%–60%) and short missing gaps, while imputation for extreme missing gaps and missing rates is an important challenge for multivariate time-series data generated through the Internet of Things (IoT). Hence, we propose an lightweight-window-portion-based multiple imputation (LWPMI) based on multivariate variables, correlation, data fusion, regression, and multiple imputations. We conduct extensive experiments by generating extreme missing gaps and high-missing rates ranging from 10% to 90% on data generated by sensors. We also investigate different sets of feature to examine how LWPMI works when features have high, weak, or a mixture of high and weak correlation. All the obtained results prove LWPMI outperforms baseline techniques in preserving pattern, structure, and trend in both 90% extreme missing gap and missing rates.

With the increasing deployment in mission-critical domains, it is of foremost importance to improve dependability of distributed real-time applications for cyber–physical systems (CPSs) with safety & security-critical threats. Different from existing works addressing the security or safety design separately, this article makes efforts to achieve the safety and security co-design from system-level perspective, especially considering the interplay between fault tolerance and security harden techniques. To guarantee the safety of real-time applications, fault-tolerant techniques, e.g., task re-execution and active replica, are leveraged to tolerate faults in task executions. To improve the security of distributed applications, cryptography is deployed to resist confidentiality attacks on messages delivered over the communication media. We analyze the impact of task’s fault tolerance on secure message communication, and then formulate the design problem as a multiobjective optimization problem, i.e., to minimize the failure probability and security vulnerability of the application while subject to given fault-tolerant constraints, execution constraints and deadline constraints. Since the optimization problem is NP-hard, we then propose an improved multiobjective optimization algorithm, called decomposition-based dependability co-optimization (DeDeCo) algorithm, to search for the optimal Pareto solutions of security and reliability harden assignments for messages and tasks, respectively. Extensive experiments and an industrial case evaluate the efficiency of DeDeCo, indicating that our design and optimization algorithm are suitable for improving the dependability of real-time applications running on security & safety-critical CPSs.

Semi-supervised video learning is an increasingly popular approach for improving video understanding tasks by utilizing large-scale unlabeled videos along with a few labels. Recent studies have shown that multimodal contrastive learning and consistency regularization are effective techniques for generating high-quality pseudo-labels for semi-supervised action recognition. However, existing pseudo-labeling approaches are solely based on the model's class predictions and can suffer from confirmation biases due to the accumulation of false predictions. To address this issue, we propose exploiting audio-visual feature correlations to achieve high-quality pseudo-labels instead of relying on model confidence. To achieve this goal, we introduce Audio-visual Contrastive and Consistency Learning (AvCLR) for semi-supervised action recognition. AvCLR generates reliable pseudo-labels from audio-visual feature correlations using deep embedded clustering to mitigate confirmation biases. Additionally, AvCLR introduces two contrastive modules: intra-modal contrastive learning (ImCL) and cross-modal contrastive learning (XmCL) to discover complementary information from audio-visual alignments. The ImCL module learns informative representations within audio and video independently, while the XmCL module aims to leverage global high-level features of audio-visual information. Furthermore, the XmCL is constrained by introducing intra-instance negatives from one modality to the other. We jointly optimize the model with ImCL, XmCL, and consistency regularization in an end-to-end semi-supervised manner. Experimental results have demonstrated that the proposed AvCLR framework is effective in reducing confirmation biases and outperforms existing confidence-based semi-supervised action recognition methods.

Confidentiality-Specific Faults (CSFs) will put cyber physical systems in threat, since they can result in corrupted information or even retrieve the cryptographic key of security-critical applications. In this paper, we will look into fault-tolerant co-design optimization for security-critical cyber physical systems with resource constraints, such that the encryption/decryption of confidential messages are protected against transient CSF faults. We consider imperfect fault detection mechanisms to identify transient CSF faults happened on confidentiality protection, and utilize duplication code to recovery from such faults. We utilize FPGA to accelerate the executions of security tasks, reducing the overheads of fault-tolerant implementations. The system-level design problem is formulated as a two-objective optimization problem, i.e., to minimize the average reliability degradation of the fault tolerant assignments and to minimize the balanced degree of the reliability degradation, subject to available FPGA budget, deadline, and application execution constraints. Since finding Pareto-optimal solutions is NP-hard, we propose an improved multi-objective optimization algorithm, called DEcomposition-based Security Co-design Optimization (DESCO), to search for Pareto-optimal solutions of fault-tolerant assignments. Experimental results demonstrate that DESCO is effective and can outperform other candidates, proving that our approach is promising in dealing with system-level optimization problem for security-critical applications on cyber physical systems.

Self-supervised contrastive learning has shown a significant improvement in performance for action recognition tasks by discovering useful signals from unlabeled videos. Nevertheless, the unique features of existing video benchmark datasets have led the learned video representations to be contextually biased toward dominant backgrounds and scene correlations. Thus, ultimately leading to poor generalizations on scene-invariant action recognition. Therefore, we propose Actor-aware Self-supervised Learning for Semi-supervised Video Representation Learning (ActorSL). We aligned localized actors and their corresponding scene information to encourage the model to learn discriminative regions and mitigate the model’s dependency on the video background during contrastive training. Furthermore, we present an inter-video Background Mixing (iBM) augmentation strategy to introduce scene consistency into the model. We patch inter-video crops of four randomly selected frames for iBM to create a unique frame for each video. The patched frame is blended with the target video frames to generate a spatially augmented sample. Then, the actor-scene aligned features and features of iBM-augmented videos are utilized to optimize contrastive loss and consistency regularization jointly in a semi-supervised way. Moreover, iBM combines the one-hot-encoded labels of patches with the label of the target video as a label smoothing regularizer to soften the decision boundaries of the semi-supervised model. Our experimental results reveal that, ActorSL notably improved current state-of-the-art semi-supervised methods on the Kinetics-400, UCF101, and HMDB51 datasets under a low-label regime. Code released athttps://github.com/Endarzboy/ActorSL.

Due to the inherent vulnerability of deep neural networks (DNNs), the adversarial example (AE) attack has become a serious threat to intelligent systems, e.g., the failure cause of an image classification system. Different to existing works, in this article we are interested in the generation of AEs for DNNs with defensive mechanisms. To make the attack more practical, we exploit a query-based method to generate image AEs in a black-box attack setting. Considering that the generation of AEs is inherently a constrained optimization problem, this article first formulates three objectives regarding defensive DNNs, i.e., attack effectiveness, attack evasiveness and attack coverage. Then, this article proposes a query-efficient AE attack based on the genetic algorithm (GA) and particle swarm optimization (PSO) to address the perturbation optimization problem. To improve the efficiency of search and query, AE-specific operators including block-level and pixel-level crossovers, discrete perturbation mutation and direction-driven reproduction are designed within the GA-based search framework. In addition, predication-based adaptation of reproduction-related parameters is implemented to speed up the search convergence. PSO-based jumping process is further devised to avoid stuck in local optimum. Benchmark-based experiments evaluated the efficiency of our method, which can achieve an attack success rate of 100% with averagely 52.95% reduced queries in contrast to existing black-box attacks on nondefensive models. For defensive DNN models, our method can obtain top attack performance with the query reduction up to 70.92% comparing with the candidates.

Great progress has been made in deep learning over the past few years, which drives the deployment of deep learning–based applications into cyber-physical systems. But the lack of interpretability for deep learning models has led to potential security holes. Recent research has found that deep neural networks are vulnerable to well-designed input examples, called adversarial examples . Such examples are often too small to detect, but they completely fool deep learning models. In practice, adversarial attacks pose a serious threat to the success of deep learning. With the continuous development of deep learning applications, adversarial examples for different fields have also received attention. In this article, we summarize the methods of generating adversarial examples in computer vision, speech recognition, and natural language processing and study the applications of adversarial examples. We also explore emerging research and open problems.