ABSTRACT Deepfake detection models achieve high accuracy, yet their interpretability remains underexplored. This study presents a unified evaluation pipeline for post hoc visual explanations, grounded in the Co‐12 attributes of explanation quality and operationalised through a structured framework that assesses Coherence, Composition, Correctness, Completeness, Compactness, Covariate completeness and Continuity. Applying this pipeline to 16 representative explanation methods reveals systematic differences across methodological categories. CAM‐based approaches demonstrate strong spatial coherence and temporal continuity; gradient‐based techniques such as Guided Backprop and LRP yield compact and accurate attributions; and redistribution‐based methods including ExcitationBP and Deep Taylor maintain high consistency across evaluation conditions. In contrast, perturbation‐based approaches such as SHAP and LIME exhibit weaker localisation and reduced temporal stability. By enabling controlled, attribute‐level comparison of explanation methods, the proposed pipeline bridges conceptual interpretability frameworks and empirical analysis, offering practical guidance for the development and deployment of interpretable deepfake detectors in forensic and auditing applications. The source code is publicly available at: https://github.com/junxinchenieee/EAI‐Deepfake‐Detection .

ABSTRACT With the explosive growth in the volume of image usage, selective image encryption (SIE) has emerged as an efficient method to enhance encryption efficiency. The challenge of how to identify images containing sensitive content has long been a difficult issue. Deep learning technology, with its powerful semantic extraction capabilities, has naturally become an auxiliary tool for recognizing images containing specific content. This review primarily focuses on recent advancements in SIE integrated with semantic understanding. First, it reviews the current state of development in image ROI encryption. Subsequently, it proposes two semantic‐aware SIE schemes based on image‐to‐image and text‐to‐image search paradigms. The review also introduces evaluation metrics for assessing both the encryption algorithms and deep learning models involved in such SIE systems. Finally, it analyzes potential security issues in SIE, such as privacy protection of deep learning models and leakage of ROI edge regions, as well as possible optimization directions, including model lightweighting and encryption parallelization to enhance efficiency. In conclusion, this review indicates that selective encryption is not limited to ROI‐based approaches but also includes semantic retrieval followed by targeted encryption. Moreover, with the integration of deep learning models, considerations regarding security and efficiency have become more complex, representing key areas for further exploration in future research.

ABSTRACT As a fundamental task in restoring continuous visual signals, image inpainting plays a critical role in autonomous driving perception, medical imaging, video editing and digital heritage preservation. Driven by deep learning and large‐scale generative models, the field has transitioned from low‐level texture synthesis to high‐level semantic generation, yielding major breakthroughs in structural fidelity and visual realism. Centring on the generative paradigm as the architectural trajectory, this survey systematically categorizes the 30‐year evolution of image inpainting into three distinct technological generations: traditional prior‐driven synthesis, deep learning data‐driven reconstruction and modern foundation model‐driven generation. Despite this progress, highly competitive methods still struggle with large‐scale missing regions, global consistency in complex scenes, fine‐grained micro‐details and alignment with human visual perception. To address these gaps, we critically evaluate the technical paradigms and main bottlenecks within each of these evolutionary stages. We categorize and compare mainstream breakthroughs across high‐resolution restoration, text‐guided synthesis and complex scene generation. Furthermore, we compile standard benchmarks, evaluation metrics and quantitative performance comparisons of representative algorithms. Finally, we dissect open challenges—focusing on cross‐scene generalization and evaluation metric alignment—and outline future trajectories, particularly the integration of inpainting with text‐guided foundation models, providing a definitive reference for future theoretical and engineering advancements.

ABSTRACT Accurate medical image segmentation is essential for disease diagnosis, treatment planning and outcome monitoring. However, current segmentation methods heavily rely on large‐scale, pixel‐level annotations, which are costly and labour‐intensive to obtain. To address this challenge, we propose TriS‐Net (Triple‐Supervision Segmentation Network) , a progressive framework that integrates image‐level, bounding box‐level and pixel‐level labels into a multigranularity supervision pipeline under limited annotation settings. In the first stage, TriS‐Net uses image‐level labels to train a classification branch, enabling the network to learn discriminative features and localise potential lesion regions. In the second stage, a box‐guided mask refinement strategy (BMR) is proposed, which combines Soft‐NMS filtering and a one‐to‐one matching mechanism to obtain reliable candidate regions. CIoU is further employed to derive image‐level quality metrics that impose quality‐aware weighted constraints on segmentation learning, thereby improving spatial localisation and structural consistency. In the third stage, a small number of pixel‐level labels are used for fine‐grained supervision, further enhancing segmentation accuracy and boundary details. The proposed method is validated on the BraTS 2019 and LiTS 2017 datasets, on which it outperforms several existing methods under limited annotation settings. Additional experiments on the BUSI ultrasound dataset further demonstrate its good generalisation capability across different imaging modalities.

With the popularity of cloud storage, video data, especially videos containing personal information, faces serious security challenges. However, existing video encryption schemes typically adopt full-frame encryption, resulting in high computational overhead, low efficiency, and difficulty balancing privacy protection and lightweight encryption requirements. To this end, a lightweight video encryption scheme based on object detection and adaptive frame multiplexing is designed in this paper. First, the YOLOv8 algorithm is employed to accurately identify multiple human target regions in videos, thereby avoiding the computational resource waste associated with full-frame encryption. Second, combined with SCI-HMC hyperchaotic map, an adaptive frame multiplexing strategy is adopted to realize the dynamic adjustment of chaotic sequences by correlating the encryption process before and after. On this basis, a cube lightweight encryption algorithm based on video frame combination is designed, which is spliced layer by layer in terms of color channels, traversed in terms of 3 layers of data (one video frame), and performs the 3D confusion and 3D mod diffusion sequentially according to the target coordinates, and finally realizes the accurate encryption of the region of interest. The experimental results show that the scheme performs well in terms of practicality and resistance to attacks. The information entropy of the encrypted area is as high as 7.9982, and the encryption speed can be increased to 0.2343 seconds per frame. This deep collaboration framework tightly integrates modern object detection with dynamic encryption processes, providing a balanced security and lightweight solution for cloud video data protection.

To Date, the application of digital twin (DT) in the industrial internet of things (IIoT) has been continuously promoted and deepened, and has become the focus of the industry. IIoT serves as the foundational infrastructure that enables pervasive connectivity, real-time data acquisition, and intelligent control within industrial environments. DTs provide enterprises with an empathetic, virtual environment that enables them to manage and operate their production facilities in a more efficient and intelligent manner. However, there is not a special summary and analysis of the combinability and combination mode of the two. Therefore, this paper firstly sorted out the professional definitions, characteristics and frameworks of IIoT and DT, and deeply analyzed the semantic context of data flow. Secondly, this paper discusses the combinability and combination mode of IIoT and DT, and summarizes the enabling technologies and tools at each layers. Finally, the applications status of DT empowered IIoT in different fields was summarized, and the challenges of the combined application of the two were analyzed.

As the application of image emotion recognition technology grows increasingly widespread, emotional data faces potential privacy risks during transmission and storage. Images depicting negative emotions are particularly prone to revealing an individual is psychological state and sensitive information. Therefore, effective security protection for such images is of practical importance. This paper design a chaotic system and use CNN-based recognition to select the images requiring enhanced protection. First, a novel second-order memristor is designed and coupled with a neuron model to construct a chaotic system (SOM-Rulkov). Analysis of its phase diagram, bifurcation diagram, and Lyapunov exponent spectrum indicates that SOM-Rulkov exhibits rich dynamical characteristics, which can provide a pseudo-random key stream with good performance for encryption algorithms. Then, using a CNN to recognise emotions in images, employing the identified negative emotion images as encryption images to enhance data security during transmission and storage. During the encryption process, This paper proposes an enhanced diffusion structure with a parallel pool mechanism that enables four-directional diffusion to improve encryption efficiency. Experimental results show that the proposed scheme achieves strong security and high speed for emotional image privacy protection.

Electroencephalography (EEG) emotion recognition plays a key role in improving human-machine interactions. Advanced algorithms have been proposed for this task. However, two challenges remain, i.e., unclear decision boundary in the embedded space and noise in physiological signals from various devices. To this end, we develop a novel framework, namely, UACL-Net, for EEG emotion recognition. It is based on uncertainty-aware contrastive learning (UACL) and frequency-aware self-attention (FASA). Specifically, UACL uses a multivariate Gaussian distribution to construct the latent space for different emotions. It is able to highlight interclass differences, thereby improving the robustness of model decisions. In addition, FASA generates learnable weights by applying self-attention (SA) to the real and imaginary components in the frequency domain. This helps adaptively reduce noise and capture global dependencies in temporal sequences. Our model is trained and tested on four benchmark datasets, achieving up to 94.88%, 98.71%, 96.91%, and 99.29% accuracy on SEED, DEAP, DREAMER, and FACED, respectively. Experimental results demonstrate that it is effective and has advantages over peer state-of-the-art (SOTA) methods.

To address the high computational costs of full-frame encryption and the risk of exposing sensitive locations in partial encryption, this paper proposes a video selective encryption and steganography scheme based on object detection and image inpainting. First, YOLOv8 is employed to achieve real-time and accurate detection of human targets in video frames. Then, the LIS-HMC hyperchaotic map and a new chaotic-driven interframe chain modulation (CDICM) strategy, combined with a designed row-column interchange and Roller confusion algorithm, are applied to selectively encrypt the target regions. Next, the globally and locally consistent image completion (GLCIC) algorithm is used to restore the background panoramically, eliminating visual discontinuities. Meanwhile, based on the Walsh-Hadamard transform (WHT), a multi-round embedding (MRE) steganography strategy is developed to hide the encrypted information within the restored background. Experimental results show that the encrypted data achieve an information entropy of 7.9925, a steganographic capacity of 0.75 bpp, and a PSNR above 44.91 dB after data embedding, demonstrating that the proposed method provides a new solution for video privacy protection that balances security, real-time performance, and visual naturalness.

With the rapid development of the electronic information industry, massive private data are continuously uploaded to the Internet, posing severe challenges to data security. Thus, a high-capacity private data information protection library based on correlation generator is proposed. For private data uploaded by multiple individuals or organizations, the proposed library enables efficient bulk protection. Through the biometric images held by individuals or organizations, correlation values are generated by the correlation generator, which are combined with such initial values of the four dimensional hyperchaotic system (4DHS) to generate private keys and master keys. The chaotic sequences generated by the iteration of the system are combined with the encryption scheme to provide effective protection of private data information. Afterwards, the scheme is tested for simulation and security, which verify the feasibility and security of the proposed scheme.

Recent years have witnessed the increasing applications of artificial intelligence for tooth treatment, among which tooth instance segmentation and disease detection are two important research directions. Advanced algorithms have been proposed, however, two challenging issues remain unsolved, i.e., unclear prediction boundaries for adjacent teeth, and high parameters of the model. To this end, our work proposes a lightweight framework, namely UCL-Net, for efficient tooth instance segmentation and disease detection. Specifically, uncertainty-aware contrastive learning is first employed for tooth segmentation. It is based on a multivariate Gaussian distribution to model the boundary pixel and is able to highlight inter-class differences, thereby refining the segmentation boundary. In addition, a lightweight segmentation model which has only 34.9 M parameters is further developed. Benefiting from the cross-scale attention, it is able to efficiently fuse different scale features, and therefore yields accurate tooth disease detection with a lightweight load. Four benchmark datasets are employed for performance validation. Both the qualitative and quantitative results demonstrate that the proposed UCL-Net is lightweight, effective, and advantageous over peer state-of-the-art (SOTA) methods.

As a ubiquitous portable device, mobile phones play an important role in large-scale data collection and remote health detection. Parkinson's disease (PD), a typical movement disorder, can be detected by capturing digital biomarkers using mobile phone sensors. Nevertheless, it is difficult to obtain reliable label information in large-scale remote data collection, especially for time-series digital biomarkers. Based on this, we develop a novel multi-dimensional self-supervised contrastive learning framework for remote detection of early PD by mobile phone time-series digital biomarkers. Specifically, depending on two different augmentation views, the proposed framework considers temporal contrasting, spatial contrasting, contextual contrasting, and inter-modal contrasting to enable the model to learn more discriminative features. For temporal and spatial contrasting, certain time steps (channels) of one view are used to predict the next time steps (channels) of the other view, thereby constructing a cross-view prediction task. Meanwhile, contextual contrasting is introduced into the contrast framework to consider temporal prediction and spatial prediction context representation, respectively, further increasing similarity between positive pairs and decreasing it between negative pairs. In addition, inter-modal contrasting is used to force the model to capture the potential relationship between different modal data. Experimental results show that fine-tuning with only 10% of the labeled data can outperform supervised learning and generally surpass state-of the-art algorithms.

Network Function Virtualization (NFV) facilitates on-demand and flexible service provisioning to meet the escalating demands of Internet of Things (IoT) applications, enabled by Service Function Chain (SFC) technique. The widespread deployment of 5G has connected a massive number of devices and users to IoT networks, accelerating the expansion of IoT scales. IoT users’ service requirements exhibit heightened diversity and dynamism. Consequently, the SFC placement problem in Next Generation Multi-domain IoT (NGMIoT) networks has garnered significant attention. How to efficiently place SFCs under uncertain resource demands to adapt to evolving service request dynamics poses substantial challenges. Therefore, this paper investigates the Robust SFC Placement (RSFCP) problem in NGMIoT networks under resource demand uncertainty. Specifically, we formulate the RSFCP problem as an integer linear programming model to minimize overall SFC placement cost while ensuring service quality. We further prove the RSFCP problem is NP-hard and propose a greedy strategy based heuristic SFC placement algorithm to solve it. Finally, extensive simulation experiments are conducted to evaluate performance, demonstrating that the proposed algorithm outperforms benchmark mechanisms in terms of service acceptance rate and placement cost.

Nowadays, massive amounts of facial images have been tampered with and then widely spread through social networks. Many studies have developed algorithms for frame-level DeepFake detection. However, they have low robustness due to their focus on tamper-independent features during training. To this end, we propose a framework, namely MIF-Net, based on multi-information fusion for robust frame-level DeepFake detection. Specifically, key landmarks and the facial area are first detected in the original frame. Then, the graph convolutional network constructs biometric information from these landmarks. Meanwhile, the facial region is processed into multi-view inputs by noise and edge enhancement algorithms. Finally, these products are encoded as high-level features and classified as real or fake. Five benchmark datasets are utilized for testing our model through within-dataset and cross-dataset validations. Extensive experiment results demonstrate that our proposed MIF-Net is robust and has advantages over peer algorithms.

In the digital age, surveillance videos face severe security threats during transmission. Chaotic systems are often used for encrypted transmission due to their sensitivity to initial conditions and unpredictability. However, existing chaotic encryption schemes are at risk of core information leakage, lack adaptive detection of targets, and are inefficient. To address these issues, this article proposes a lightweight video secondary-encryption scheme integrating YOLOv11 and a Discrete Bi-Neuron Hopfield Neural Network (DBHNN). The YOLOv11 model is used to detect sensitive objects in the video, enabling the scheme to further protect sensitive information. The hyperchaotic sequences generated by DBHNN are used for lightweight secondary-encryption: the point-to-point confusion for target detection objects. Subsequently, enhanced alternating confusion and diffusion are applied to encrypt all frames. The proposed scheme can process batch frames and perform secondary encryption on sensitive objects to enhance security. The simulations and tests show that the proposed lightweight encryption scheme has an encryption speed that is more than 5% better than other schemes, and YOLOv11 is also superior to other models in terms of accuracy and efficiency.

This paper proposes an effective Gaussian management framework for high-fidelity scene reconstruction of both appearance and geometry. Unlike recent Gaussian Splatting (GS) pipelines that treat all primitives uniformly during optimization, our framework explicitly manages the attribute activation, representation and pruning of Gaussian. Specifically, our framework first introduces GauSep, a novel densification strategy that selectively activates Gaussian color or normal attributes to alleviate destructive gradient conflicts arising from dual supervision. We further propose GauRep, an adaptive Gaussian representation that dynamically adjusts spherical harmonics (SHs) orders and performs task-decoupled pruning to reduce redundancy at both the individual and global levels. To provide reliable geometric supervision for above mangement process, we additionally introduce CoRe, an regularized surface reconstruction module that distills robust normal fields from an SDF branch to the Gaussian representation through a confidence mechanism. Notably, the proposed Gaussian management is compatible with various reconstruction architectures and can be seamlessly integrated to improve performance while reducing size of the model. Extensive experiments demonstrate that our approach achieves superior or comparable performance in appearance and geometry reconstruction compared with state-of-the-art methods, while using significantly fewer parameters.

ABSTRACT With the increasing prevalence of smartphones and advancements in sensors, smartphone‐based solutions for physiological parameter monitoring appear to offer notable advantages over traditional methods, potentially enhancing safety, convenience and efficiency. This paper aims to present a systematic survey of smartphone sensor‐based physiological parameter monitoring apps, with particular discussions of gaps between their current functional capabilities and recent advances. We conducted a systematic analysis of relevant apps available on the App Store and Google Play, mainly focusing on four vital signs: heart rate (HR), blood pressure(BP), body temperature (BT) and respiratory rate (RR), as well as oxygen saturation (), blood glucose (BG) and haemoglobin (Hb). The analysis revealed that HR measurement apps were the most prevalent, while BP, , BT and RR measurement apps were comparatively fewer, and no smartphone sensor‐based BG measurement apps were identified. The contact photoplethysmography method is widely adopted by current apps, while non‐contact approach holds potential. Novel techniques require further investigation beyond laboratory settings to enhance robustness. Smartphone‐based measurement of physiological parameters shows promise, though further research and development are needed to bridge the gap between current capabilities and the demands of accurate, real‐world health monitoring.

ABSTRACT Plant disease detection is able to control disease spread and help prevent significant food production losses. However, existing detection methods are still limited to different target scales and high model parameters. To this end, we develop a novel framework, that is, FPDD‐Net, for lightweight plant disease detection. It is based on YOLOv8 with an adaptive multi‐scale model (AMSM) and relationship‐based knowledge distillation (RKD). More specifically, the original cross stage partial (CSP) bottleneck is replaced by an AMSM to effectively fuse the multi‐scale features. Next, an Alpha‐IoU loss optimization is adopted for aligning predicted boxes more precisely with ground truth, leading to fewer localization errors. Finally, RKD is introduced to assist the training and further improve the performance of target detection. To evaluate our network, the FPDD‐Net is trained and tested on two typical datasets, that is, the plant village dataset and the plant‐doc dataset. Experimental results indicated that our FPDD‐Net is lightweight and has advantages over peer methods.

ABSTRACT Rock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in‐depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in‐depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours.

Diagnosing Parkinson’s disease (PD) remains challenging due to its complex motor symptoms and the reliance on subjective clinical evaluations. To address this issue, this study proposes the multiscale frequency-sharing attention network (MSF-CANet), an end-to-end framework designed to identify PD and healthy control subjects using smartwatch-based inertial sensor data. MSF-CANet integrates a multiscale perception module to capture temporal features of tremors at different frequencies, a frequency-aware module to enhance PD-specific tremor signals within the 3–7 Hz range, and a shared channel attention mechanism to focus on key sensor channels while ensuring computational efficiency. The model was trained and evaluated on the PADS dataset using nested 5-fold cross-validation. The proposed method achieved an accuracy of 92.39% and an AUC of 0.9797, outperforming existing methods. The findings indicate that dual-hand data significantly improves detection performance compared to single-hand data, and dynamic tasks like “Drink from Glass” and “cross and extend both arms” achieved higher accuracy than static activities. These findings underscore the potential of MSF-CANet as a robust, noninvasive tool for real-time PD monitoring through wearable devices.

With the rise of the Internet of Things, wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns interlead relationships to improve multilabel ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a temporal graph construction module to convert ecg signals into temporal graphs and uses a residual pyramid graph convolution module for multilevel graph embeddings, utilizing a graph convolutional network with independently learnable adjacency matrices. Combined with a temporal context convolution module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on seven tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available athttps://github.com/rosemary333/TGLLnet.

Since its inception, blockchain technology has been characterized by its core attributes of immutability, traceability, and decentralization, which are fundamental to ensuring data security. In the contemporary digital landscape, medical data has emerged as a critical asset, and the integration of blockchain into healthcare has facilitated a range of innovative solutions for secure and efficient data sharing. Beyond its role in data security, blockchain’s smart contracts have attracted significant research interest due to their potential to automate processes and enhance efficiency in medical research and healthcare operations. In this context, this survey provides a systematic and in-depth exploration of blockchain applications in healthcare, with a focus on: (1) analyzing the technical foundations of blockchain and its suitability for healthcare applications; (2) synthesizing the eight key domains where blockchain has demonstrated impact in the healthcare sector; and (3) critically examining the challenges that hinder blockchain adoption in healthcare while identifying future research directions. By presenting a comprehensive review of blockchains transformative potential in healthcare, this survey offers valuable insights for researchers and practitioners engaged in this evolving interdisciplinary field.

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this article proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

Multi-modal emotion recognition plays a crucial role in human-computer interaction. Nowadays, many studies have developed fusion algorithms for this purpose. However, two challenges are still present, i.e., insufficient cross-modal information sharing and weak fusion feature representations. To this end, we develop a novel framework, namely CH-Net, for multi-modal emotion recognition with physiological signals. It is based on cross-modal attention and hypercomplex domain fusion. First, our learnable cross-modal attention mechanism adaptively aligns features across modalities, enhancing both complementarity and modality-specific discrepancies. Second, a hypercomplex fusion module encodes these features, yielding more robust representations while reducing parameter overhead. Two benchmark datasets, i.e., MAHNOB-HCI and DEAP, are utilized to train and test our model. Extensive experiments demonstrate that CH-Net is effective and outperforms state-of-the-art (SOTA) methods. Our code will be available athttps://github.com/xuxusky/CH-Net.

The surge in visual general big data has notably advanced data-driven deep learning-based computer vision technologies. Transformer-based methods shine in this era of big data because of their attention mechanism architecture and demand for massive data. However, the difficulty of obtaining medical images has caused the field to continue facing the limited-data challenge. In this paper, we propose a novel deep neuro-fuzzy system named Fuzzy-ViT, which synergistically integrates fuzzy logic with the Vision Transformer (ViT) for cross-domain transfer learning from large-scale general data to medical image domain. Specifically, Fuzzy-ViT utilizes a ViT backbone pre-trained on extensive general datasets such as ImageNet-21K, LAION-400M, and LAION-2B to extract rich general features. Then, a Fuzzy Attention Cross-Domain Module (FACM) is presented to transfer general features to medical features, thereby enhancing the medical image analysis. Thanks to the Fuzzy System Transitioner (FST) in FACM, fuzzy and uninterpretable general domain features can be effectively converted into those needed in the medical domain. In addition, the Attention Mechanism Smoother (AMS) in FACM smoothes the conversion outcomes, ensuring a harmonious integration of the fuzzy system with the neural network architecture. Experimental results demonstrate that the proposed Fuzzy-ViT achieves state-of-the-art and satisfactory performance on popular medical image benchmarks (BreakHis and HCRF) with 93.37% and 97.22% F1 scores. Detailed ablation analysis demonstrates that the effectiveness of our method for bridging large general visual and medical images.

Transformers have recently gained significant attention in medical image segmentation due to their ability to capture long-range dependencies. However, the presence of excessive background noise in large regions of medical images introduces distractions and increases the computational burden on the fine-grained self-attention (SA) mechanism, which is a key component of the transformer model. Meanwhile, preserving fine-grained details is essential for accurately segmenting complex, blurred medical images with diverse shapes and sizes. Thus, we propose a novel Multi-scale Dynamic Sparse Attention (MDSA) module, which flexibly reduces computational costs while maintaining multi-scale fine-grained interactions with content awareness. Specifically, multi-scale aggregation is first applied to the feature maps to enrich the diversity of interaction information. Then, for each query, irrelevant key-value pairs are filtered out at a coarse-grained level. Finally, fine-grained SA is performed on the remaining key-value pairs. In addition, we design an enhanced downsampling merging (EDM) module and an enhanced upsampling fusion (EUF) module for building pyramid architectures. Using MDSA to construct the basic blocks, combined with EDMs and EUFs, we develop a UNet-like model named MDSA-UNet. Since MDSA-UNet dynamically processes only a small subset of relevant fine-grained features, it achieves strong segmentation performance with high computational efficiency. Extensive experiments on four datasets spanning three different types demonstrate that our MDSA-UNet, without using pre-training, significantly outperforms other non-pretrained methods and even competes with pre-trained models, achieving Dice scores of 82.10% on DDTI, 80.20% on TN3K, 90.75% on ISIC2018, and 91.05% on ACDC. Meanwhile, our model maintains lower complexity, with only 6.65 M parameters and 4.54 G FLOPs at a resolution of 224 × 224, ensuring both effectiveness and efficiency. Code is available at URL.

Sleep stage classification plays a crucial role in sleep quality assessment and sleep disorder prevention. Nowadays, many studies have developed algorithms for this purpose, but they still face two challenges. The first is noise in physiological signals from various devices. The second challenge is that most studies simply concatenate multi-modal features without considering their correlations. To this end, we propose a framework, namely Diff-SleepNet, to efficiently classify sleep stages from multi-modal input. This framework begins with a diffusion model with peak signal-to-noise ratio (PNSR) loss function that adaptively filters noise. The filtered signals are then transformed into a multi-view spectrum through data pre-processing. These spectra are processed by a transformer-based backbone to extract multi-modal features. The production is fed into the following multi-scale attention module for robust feature fusion. The sleep stage category is finally determined by a fully connected layer. Our framework is trained and validated on three typical datasets, i.e., SHHS, Sleep-EDF-SC, and Sleep-EDF-X. Experimental results demonstrate that it is effective and has advantages over other peer methods.

Mobile phones have evolved from basic communication tools to feature-rich mobile devices. These ubiquitous and portable devices, equipped with inertial sensors and high-speed network access, create opportunities for remote health monitoring, especially for movement disorders such as Parkinson's disease (PD). Inertial sensors (gyroscopes and accelerometers) endow smartphones with a natural ability to monitor movement disorders. Based on this, we develop a novel vision-based time-series feature augmentation framework for remote diagnosis and severity grading of PD using mobile phone walking records. Specifically, preprocessed time-series data is encoded into RGB images for the teacher model, while the time-series data is input into the student model, with the teacher guiding the student's learning. The teacher model is based on MobileNetV2 and incorporates spatial and channel relation-aware attention mechanisms to capture important features and filter out irrelevant information. The inter-modal feature fusion module combines attention and CNN to emphasize both global and local features. The student model utilizes a simple CNN to directly extract features from time-series data and perform classification. For the three-level classification task, the teacher model achieves accuracies of 0.887, 0.886, and 0.896 across the three datasets, while the distillation student model reaches 0.779, 0.828, and 0.827, generally surpassing state-of-the-art algorithms.

Existing High Efficiency Video Coding (HEVC) selective encryption algorithms only consider the encoding characteristics of syntax elements to keep format compliance, but ignore the semantic features of video content, which may lead to unnecessary computational and bit rate costs. To tackle this problem, we present a content-aware tunable selective encryption (CATSE) scheme for HEVC. First, a deep hashing network is adopted to retrieve groups of pictures (GOPs) containing sensitive objects. Then, the retrieved sensitive GOPs and the remaining insensitive ones are encrypted with different encryption strengths. For the former, multiple syntax elements are encrypted to ensure security, whereas for the latter, only a few bypass-coded syntax elements are encrypted to improve the encryption efficiency and reduce the bit rate overhead. The keystream sequence used is extracted from the time series of a new improved logistic map with complex dynamic behavior, which is generated by our proposed sine-modular chaotification model. Finally, a reversible steganography is applied to embed the flag bits of the GOP type into the encrypted bitstream, so that the decoder can distinguish the encrypted syntax elements that need to be decrypted in different GOPs. Experimental results indicate that the proposed HEVC CATSE scheme not only provides high encryption speed and low bit rate overhead, but also has superior encryption strength than other state-of-the-art HEVC selective encryption algorithms.

Existing selective encryption schemes for High Efficiency Video Coding (HEVC) only focus on the encoding characteristics of syntax elements in entropy coding and lack an understanding of the video content. Consequently, a large amount of unnecessary encryption operations are utilized to protect insensitive video frames, resulting in low encryption efficiency. In this article, we propose a content-aware selective encryption scheme for H.265/HEVC, which encrypts only the groups of pictures (GOPs) containing sensitive content and thus offers high efficiency. In our scheme, a deep hashing network is first adopted to retrieve video frames to determine the content-sensitive GOPs. Then, multiple prediction and residual syntax elements in sensitive GOPs are encrypted using a keystream sequence generated by the hyper-chaotic Lorenz system. In addition, the direct current coefficient of each \(4\times 4\) transform block is exchanged with a pseudo-randomly selected non-zero alternating current coefficient to further offer stronger visual distortion. Finally, the sign bits used for marking each GOP-type are reversibly embedded into the encrypted syntax elements to facilitate the decoder to distinguish the GOPs that need to be decrypted. Experimental results indicate that the proposed content-aware selective encryption scheme can efficiently protect sensitive content and is robust against all common attacks. Furthermore, it outperforms other state-of-the-art HEVC selective encryption algorithms in terms of security performance.

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

Online chatting has become an essential aspect of our daily interactions, with stickers emerging as a prevalent tool for conveying emotions more vividly than plain text. While conventional image emotion recognition focuses on global features, sticker emotion recognition necessitates incorporating both global and local features, along with additional modalities like text. To address this, we introduce a topic ID-guided transformer method to facilitate a more nuanced analysis of the stickers. Since each sticker will have a topic, and the same topic will have the same object, we introduce a topic ID as a flag to group images by theme. Our approach encompasses a novel topic-guided context-aware module and a topic-guided attention mechanism, enabling the extraction of comprehensive topic context features from stickers sharing the same topic ID, significantly enhancing emotion recognition accuracy. Moreover, we integrate a frequency linear attention module to leverage frequency domain information to capture better the object information of the stickers and a locally enhanced re-attention mechanism for improved local feature extraction. Extensive experiments and ablation studies on the large-scale sticker emotion dataset SER30k validate the efficacy of our method. Experimental results show that our proposed method obtains the best accuracy on both single-modal and multi-modal sticker emotion recognition.

Abstract Heart sound signal analysis is very important for the early identification and treatment of cardiovascular illness. With rapid advancements in science and technology, artificial intelligence technologies are providing tremendous opportunities to enhance diagnosis and clinical decision‐making. Instruments can now perform clinical diagnoses that previously could only be handled by human experts more conveniently and efficiently. Despite multiple works on automatic heart sound analysis, there are few summarization and review works. This article attempts to give a thorough overview of various heart sound analysis subtasks and examine the improvements made in each subtask by both machine learning techniques and deep learning algorithms. It goals to highlight the potential of AI to revolutionize cardiovascular healthcare by enabling accurate and automated analysis of heart sounds. The findings of this review are beneficial for researchers, clinicians, and engineers in the development and application of AI‐based solutions for improved heart sound classification and diagnosis.

Benefiting from the progresses of sensing and sustainable computing technologies, recent years have witnessed the dramatic progresses of artificial intelligence of things (AIoT). As a typical AIoT application, WiFi-based human activity recognition has increasing popularities in smart homes. However, WiFibased action recognition often has unstable performance due to environmental interference. To this end, a robust deep learning framework called MSF-Net is proposed for coarse and fine activity recognition using channel state information (CSI) information. First, a dual-stream structure incorporating short-time Fourier transform and discrete wavelet transform is developed to highlight abnormal information in the CSI data. Then, a Transformer is employed as the backbone to effectively extract high-level features. In addition, an attention-based fusion branch is designed to enhance cross-model fusion. Experimental results show that MSF-Net achieves Cohens Kappa scores of 91.82%, 69.76%, 85.91%, and 75.66% on the SignFi, Widar3.0, UT-HAR, and NTU-HAR datasets, respectively. These performance records demonstrate the advantages of MSF-Net over existing methods for coarse and fine activity recognition based on WiFi data.

Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

Existing H.265/HEVC selective encryption (SE) schemes do not take into account the semantic features of input videos, nor do they adjust the encryption syntax elements according to the sensitivity of video content, which greatly limits their applicability. In this paper, we propose a chaos-based tunable H.265/HEVC SE scheme with semantic understanding. First, a deep hashing network is employed to identify content-sensitive videos by analyzing the semantic features of video sequences. Then, the non-sensitive videos and the retrieved sensitive ones are encrypted with different encryption strengths, respectively. Specifically, for non-sensitive videos, seven syntax elements with bypass-coded bins are selected for encryption at a constant bit rate. Hence, the encrypted bitstream keeps exactly the same compression ratio. To provide heavier visual distortion for content-sensitive videos, the regular-coded bins of four syntax elements and the intra prediction mode (IPM) are encrypted based on their corresponding encoding characteristics as well. Additionally, the selected syntax elements are all masked using a keystream generated by a chaotic system to ensure real-time constraints. Experimental results demonstrate that our suggested scheme offers format compatibility and is secure against all common attacks. Meanwhile, it outperforms state-of-the-art SE schemes in terms of security strength. Furthermore, the proposed scheme can be flexibly used in a wide range of applications according to the user’s requirements for encryption strength and bit rate.

Incomplete multi-view clustering (IMVC), excavating diversity and consistency from multiple incomplete views, has aroused widespread research enthusiasm. Nevertheless, most existing methods still encounter the following issues: 1) they generally concentrate on pair-wise instance correlation, which consumes at least a quadratic complexity and precludes them from applying at large scales; 2) they only concentrate on pair-wise instance relevance, whereas ignoring the discriminative correlation hidden across views. To overcome these drawbacks, we propose the Self-Completed Bipartite Graph Learning (SCBGL) method for fast IMVC, which adaptively learns a self-completed consensus bipartite graph with the guidance of global information. Specifically, SCBGL learns the consensus anchor matrix shared among diverse views and further constructs a consensus intra-view bipartite graph with missing instances to explore the diversity and complementarity underlying different views. Meanwhile, we concatenate all the multiple features with projection learning to learn global anchors that would be employed to construct an inter-view bipartite graph. Furthermore, SCBGL dexterously utilizes the abundant inter-view information to tutor the self-completion of the consensus intra-view bipartite graph. By devising an alternatively iterative strategy, we present an efficient algorithm, which enjoys a linear time complexity, to solve the proposed SCBGL model. Numerous experiments conducted on large-scale datasets substantiate the superior performance of the SCBGL beyond the state-of-the-arts.

In this article, a unified multiview subspace learning model, called partial tubal nuclear norm-regularized multiview subspace learning (PTN2MSL), was proposed for unsupervised multiview subspace clustering (MVSC), semisupervised MVSC, and multiview dimension reduction. Unlike most of the existing methods which treat the above three related tasks independently, PTN2MSL integrates the projection learning and the low-rank tensor representation to promote each other and mine their underlying correlations. Moreover, instead of minimizing the tensor nuclear norm which treats all singular values equally and neglects their differences, PTN2MSL develops the partial tubal nuclear norm (PTNN) as a better alternative solution by minimizing the partial sum of tubal singular values. The PTN2MSL method was applied to the above three multiview subspace learning tasks. It demonstrated that these tasks organically benefited from each other and PTN2MSL has achieved better performance in comparison to state-of-the-art methods.

As The cornerstone of human health, physical and mental well-being are intricately linked, influencing both an individual's physical condition and their emotional state [1]. Chronic diseases such as hypertension and diabetes can have a significant impact on mental health, leading to anxiety and depression [2]. Similarly, psychological problems such as stress, anxiety, and depression can weaken the immune system, making individuals more susceptible to physical illnesses. In recent years, the rapid development of technology has brought exciting new possibilities to the field of physical and psychological health. The Internet of Things (IoT) and artificial intelligence (AI) have shown great potential in building a comprehensive health management system that empowers individuals to take a more proactive role in their well-being.

Depression is one of the most common mental disorders, with sleep disturbances as typical symptoms. With the popularity of wearable devices increasing in recent years, more and more people wear portable devices to track sleep quality. Based on this, we believe that depression detection through wearable sleep data is more intelligent and economical. However, the majority of wearable devices face the problem of missing data during the data collection process. Otherwise, most existing studies of depression identification focus on the utilization of complex data, making it difficult to generalize and susceptible to noise interference. To address these issues, we propose a systematic ensemble classification model for depression (ECD). For the missing data problem of wearable devices, we design an improved GAIN method to further control the generation range of interpolated values, which can achieve a more reasonable treatment of missing values. Compared with the original GAIN approach, the improved method shows a 28.56% improvement when using MAE as the metric. For depression recognition, we use ensemble learning to construct a depression classification model which combines five classification models, including SVM, KNN, LR, CBR, and DT. Ensemble learning can improve the model's robustness and generalization. The voting mechanism is used in several places to improve noise immunity. The final classification model performed great on the dataset, with a precision of 92.55% and a recall of 91.89%. These results illustrate how efficient this method is in automatically detecting depression.

Lane detection is an important task in the field of automatic driving. Since lane lines usually have complex topologies and exist in various complex scenes (e.g., damaged lanes, severe occlusion, etc.), lane detection remains challenging. In this work, we propose a Polynomial Global Attention Network (PGA-Net) for lane detection, which is an end-to-end model for mining global road information and predicting lanes shape parameter formulas simultaneously. We model lane shape with cubic polynomial function and use the transformer-based DETR model to introduce the context information of lanes and roads to better regress the lane parameters. For polynomial curve modeling, we propose Mean Curvature Loss (MCL) to constrain the curvature of the predicted lanes, thereby enhancing the quality of curve lanes prediction. In addition, we design an improved supervision strategy to eliminate information bias between our parametric prediction methods and the labeling methods of lane datasets. Our method achieves state-of-the-art performance on two popular benchmarks (TuSimple and LLAMAS) and a most challenging benchmark (CULane), while exhibiting accelerated speed (>140fps on 3090 GPU, 28.9% improvement in average) and lightweight model size (https://github.com/qklee-lz/PGA-Net.

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data for performance improvement. Unfortunately, some instances may be with partially available information because of some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVC aims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneity of multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods like BSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without taking advantage of the correlation between samples and information redundancy. To overcome the above issue, we propose one novel IMVC method named data completion-guided unified graph learning (DCUGL), which could complete the data of missing views and fuse multiple learned view-specific similarity matrices into one unified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specific similarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensor with the low-rank constraint, such that sample correlation within and between views can be well explored. Finally, by dividing the original data into observed data and unobserved data, DCUGL can infer and complete the missing data according to the view-specific similarity matrices, and obtain a unified graph, which can be directly used for clustering. To solve the proposed model, we design an iterative algorithm, which is based on the alternating direction method of multipliers framework. The proposed model proves to be superior by benchmarking on six challenging datasets compared with state-of-the-art IMVC methods.

In recent years, a number of image encryption schemes based on DNA coding and nonlinear dynamics have been proposed. Generally, these DNA-based schemes first encode plaintext images into DNA sequences and then encrypt them with pseudorandom elements produced by chaotic systems or other nonlinear dynamics. Although ciphertexts can pass some security tests, many image encryption schemes are being shown to have intrinsic flaws and that they cannot guarantee a high level of security. In this article, we cryptanalyze a family of image encryption schemes for which the encryption kernel is DNA coding or its variant. The complex DNA operation can be simplified as a substitution box (S-box). The whole cryptosystem's security level is thus significantly decreased and is vulnerable to the chosen-plaintext attack. Applications of this concept to break five ciphers are theoretically presented and experimentally verified. In addition, some suggestions for resisting similar attacks are also given in this article.

Joint image compression and encryption algorithms are intensively investigated due to their powerful capability of simultaneous image data compression and sensitive information protection. Unfortunately, most of the existing algorithms suffered from either poor compression efficiency or weak encryption strength, making them vulnerable to cryptanalysis. To address these limitations, we propose a chaos-based JPEG-compatible joint image compression and encryption algorithm. We separate the luminance and chrominance coefficients to preserve file size and encrypt the discrete cosine transform (DCT) coefficients in parallel. The proposed inter-block DC encryption strategy achieves high encryption intensity based on the permutation-substitution structure. In addition, we apply both inter- and intra-block permutations to AC coefficients and strengthen the encryption using an inter-block substitution for non-zero AC coefficients. The results of security and performance analyses demonstrate that the proposed algorithm offers robust encryption of image data while maintaining compression efficiency for real-time transmission.

In recent years, 3D volumetric medical images have been widely used in clinical diagnosis, however, the popular 2D networks were reported unsuitable for segmenting them. In this direction, we propose a plug-and-play (PnP-AE) module to improve the performance of using 2D network for 3D medical image segmentation. Our method takes advantage of the intrinsic correlation between adjacent slices, by multiple encoders and fusion components to decouple plane feature extraction and depth information integration. In addition, the proposed weight sharing and feature storage strategies make PnP-AE extremely efficient. Our method is able to conveniently incorporate with mainstream 2D networks to segment 3D volumetric medical images. Experimental results demonstrate the excellent performance of our method. The source code is available at https://github.com/qklee-lz/PnP-AE.

This paper presents a Lead-Assisting Backbone Attention Network (LABANet), which is able to perform multi-pathology instance segmentation of dental panoramic X-rays. A Lead-Assisting Attention Backbone (LAAB), containing two Swin-Transformers, is first developed for feature extraction. The following Region Proposal Network (RPN) and RoIAlign modules further convert the extracted features to a fixed-size feature map. Finally, an improved attention head with a Squeeze-and-Excitation (SE) block is constructed for object classification, bounding-box regression, and mask segmentation. By taking advantage of the global attention mechanism, the LABANet can better achieve multiple pathology segmentation. Experiment results demonstrate its effectiveness and advantages over state-of-the-art methods.

The roadside video surveillance signal can help people achieve vehicle tracking and trajectory generation. Using these trajectories can learn the future motion of vehicles. Existing prediction methods can not analyze the interaction between vehicles well. To this end, we design a dynamic vehicle graph to represent the dynamic interaction between vehicles for trajectory prediction. A graph convolution module with an attention mechanism is used to extract the feature of the dynamic vehicle graph. Experiments comparing with baseline methods are conducted on a real-world dataset to demonstrate our model’s improvement in forecast accuracy.

Recent years have witnessed dramatic progress in computer vision technologies and their broad applications, where manufacturing and industrial fields are important branches that highly require computer vision to bring them intelligent updating. As the name suggests, our workshop focus on advanced multimedia computing for smart manufacturing and engineering. We want to collect advances in using computer vision in various applications of smart manufacturing and engineering, theoretical research and practical applications are both welcome. The accepted papers cover the practical applications of advanced multimedia computing in tunnel water leakage recognition and segmentation, multi-class lane detection, gaze estimation, and also the theoretical achievements on spectrum sensing, semantic segmentation, image classification, information security, etc. This workshop goals to boost the concern of the public on exploiting multimedia computing for intelligent manufacturing and engineering.

The dramatic progresses of wireless technologies and wearable devices have significantly promoted the development and popularity of smart home, while digital twin (DT) emerges as a game changer benefiting from its enhanced capabilities of visualization and interaction. The DT is able to build a realtime and continuous visual replica of a physical object or process, and to provide realtime monitoring, anomaly prediction, smart interaction, and lifecycle management. This paper presents a DT model to empower healthcare monitoring in the smart home with the goals of graphical monitoring, healthcare prediction, and intelligent control. High fidelity DT of the house and its equipments is created for visualized monitoring, and two suites of devices are deployed for continuously acquiring the users’ electrocardiograph (ECG) waves and the WiFi signals in the house. Two intelligent algorithms are then developed to perform fall detection from WiFi signals and to screen atrial fibrillation from ECG waves collected by wearable devices. Experimental results well validate the proposed model’s effectiveness for smart home monitoring, and the advantages of the developed smart algorithms for healthcare prediction over counterparts.

Accurate segmentation of ventricle and myocardium from the late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is an important tool for myocardial infarction (MI) analysis. However, the complex enhancement pattern of LGE-CMR and the lack of labeled samples make its automatic segmentation difficult to be implemented. In this paper, we propose an unsupervised LGE-CMR segmentation algorithm by using multiple style transfer networks for data augmentation. It adopts two different style transfer networks to perform style transfer of the easily available annotated balanced-Steady State Free Precession (bSSFP)-CMR images. Then, multiple sets of synthetic LGE-CMR images are generated by the style transfer networks and used as the training data for the improved U-Net. The entire implementation of the algorithm does not require the labeled LGE-CMR. Validation experiments demonstrate the effectiveness and advantages of the proposed algorithm.

Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.

With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.

Image segmentation is a challenging problem in imaging informatics, which stems from the intersection of imaging techniques, computer science and biomedicine. In particular, accurate segmentation of cardiac structures in late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of great clinical importance for cardiac function assessment and myocardial disease diagnosis. However, it is a well-known challenge due to its special imaging modality and the lack of labeled LGE samples. In this paper, we propose an unsupervised ventricular segmentation algorithm that can perform biventricular segmentation of LGE images in the absence of labeled LGE data. There are two primary modules, the data augmentation procedure and the segmentation network. The easily available annotated balanced-Steady State Free Precession (bSSFP) images are employed for cross-modal data augmentation by image translation, where a single bSSFP image is converted into multiple synthetic LGE images while preserving the original morphological structure. Then, the proposed segmentation network is trained with the synthetic LGE images and used for segmenting real LGE images. Validation experiments demonstrated the effectiveness and advantages of the proposed algorithm.

This paper presents an algorithm for segmenting late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) in the absence of labeled training data. The proposed method includes a data augmentation part and a segmentation network. Multiple style transfer networks are employed for data augmentation to increase the data diversity, and then the synthetic images are used for training an improved U-Net. Finally, the trained model is fine-tuned with a few LGE images and labels. Experiment results demonstrate the effectiveness and advantages of the proposed method.

Using some specific optimization problems with specific regularizers, multi-view subspace clustering has achieved better performance over single-view subspace clustering. However, they simply assume the noise obeys the Gaussian distribution only, and thus the dataset with non-Gaussian noise or outliers may not be accurately clustered. To address this issue, this paper proposes a novel correntropy-induced tensor learning method for multi-view subspace clustering (CTMSC). Specifically, CTMSC adopts the correntropy-induced metric to substitute the traditional mean square error (MSE) to handle non-Gaussian noise or outliers. Furthermore, the proposed objective function is optimized using an alternating direction method of multipliers with the aid of half-quadratic technology in the form of multiplication. Extensive experimental results on various real-world datasets demonstrate the effectiveness of the proposed method by comparing several state-of-the-art multi-view subspace clustering methods.

For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios.

With the popularity of the wireless body sensor network, real-time and continuous collection of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data mining from the collected single-lead ECG waves has therefore aroused extensive attention worldwide, where early detection of atrial fibrillation (AF) is a hot research topic. In this paper, a two-channel convolutional neural network combined with a data augmentation method is proposed to detect AF from single-lead short ECG recordings. It consists of three modules, the first module denoises the raw ECG signals and produces 9-s ECG signals and heart rate (HR) values. Then, the ECG signals and HR rate values are fed into the convolutional layers for feature extraction, followed by three fully connected layers to perform the classification. The data augmentation method is used to generate synthetic signals to enlarge the training set and increase the diversity of the single-lead ECG signals. Validation experiments and the comparison with state-of-the-art studies demonstrate the effectiveness and advantages of the proposed method.

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this article, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order Σ∆ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.

In recent decades, the introduction of chaos to image encryption has drawn worldwide attention. The permutation-substitution architecture has been widely applied, and chaotic systems are generally employed to produce the required encryption elements. Although many security assessment tests have been conducted, some chaotic image ciphers are being cryptanalyzed. In this article, we evaluate the security of a family of image ciphers whose encryption kernel consists of a bit-level or pixel-level permutation and a bit-wise exclusive OR substitution. After investigating the intrinsic linearity inside the outfitted structures and encryption techniques, we find that each ciphertext-plaintext pair can be represented as a combination of a set of ciphertext-plaintext bases. A chosen-ciphertext attack is proposed to construct the ciphertext-plaintext bases rather than the traditional solution to retrieve equivalent encryption elements. We further reveal that such weakness cannot be remedied by common enhancements such as more chaotic dynamics, complex permutation methods, and random pixel insertion during encryption. In addition, applications of the proposed attack to break 12 ciphers are theoretically presented and experimentally verified.

In recent years, the use of permutation-diffusion architecture for digital image encryption has become increasingly popular. The permutation procedure scrambles the pixel locations, while the diffusion phase modifies the pixel values and gives rise to the avalanche effect. Various diffusion techniques have been developed, and their strength strongly impacts the security of the overall cryptosystem. In this paper, we re-evaluate the security of a family of image diffusion mechanisms that are based on mixing modulo addition with bitwise exclusive OR operations. The recovery of the encryption element of these diffusion mechanisms is comprehensively demonstrated, and the accuracy bounds under various conditions are proved mathematically. Compared to the state-of-the-art methods, our work improves the recovery accuracy of the encryption element while the required prior knowledge is decreased. The proposed analysis of the diffusion mechanisms is further used to cryptanalyze the whole cryptosystem theoretically and experimentally.

In recent decades, there has been considerable popularity in employing nonlinear dynamics and permutation-substitution structures for image encryption. Three procedures generally exist in such image encryption schemes: the key schedule module for producing encryption elements, permutation for image scrambling and substitution for pixel modification. This paper cryptanalyzes a family of image encryption schemes that adopt pixel-level permutation and modular addition-based substitution. The security analysis first reveals a common defect in the studied image encryption schemes. Specifically, the mapping from the differentials of the ciphertexts to those of the plaintexts is found to be linear and independent of the key schedules, permutation techniques and encryption rounds. On this theory basis, a universal chosen-ciphertext attack is further proposed. Experimental results demonstrate that the proposed attack can recover the plaintexts of the studied image encryption schemes without a security key or any encryption elements. Related cryptographic discussions are also given.

With the development of Internet of Things technology, the will to make cities smarter is growing. In the context of smart cities, where people are surrounded by a tremendous number of points of interests (POIs), POI recommendation is of great significance. The massive amount of user check-in data collected by location-based social networks (LBSNs) can help users explore new places via POI recommendations. With rich auxiliary data becomeing available in LBSNs, purely exploiting users’ check-in information for POI recommendation is not sufficient. Although several attempts have been done to employ geographical influence to enhance POI recommendation, they simply use a certain distribution function to measure the geographical influence between POIs, which may lead to biased results. To this end, this article proposes a geography-aware inductive matrix completion (GAIMC) approach for personalized POI recommendation. Specifically, the GAIMC model consists of two parts, including geographic feature extraction via a Gaussian mixture model (GMM) and inductive matrix completion for recommendation. The GAIMC model first captures the geographical influence among users and POIs by the GMM, which can mine clustering information with hierarchical structures based on the users’ check-in data and POIs’ location information. Then, a matrix-completion method called inductive matrix completion, which can incorporate geographical features with the user-POI association metric, is utilized to recommend POIs. The experimental results on two real-world LBSN data sets demonstrate that our proposed model can achieve the best recommendation performance in comparison with the state-of-the-art counterparts.

Predicting the user's trajectory behavior sequence based on point of interests (POIs) recommendation is of great significance in the realization of the smart city with the emerging of social Internet of Things technology. One of the widely adopted frameworks is the user-based collaborative filtering, where the explicit POI rating is calculated based on similar users’ preference. However, the trust between users is seldom considered. We believe that if two users show similar preferences or personality traits, the trust level between them should be high. To this end, we propose to calculate the trust-enhanced user similarity in user-based collaborative filtering based on network representation learning. Meanwhile, due to the significance of geographic influence and temporal influence, we integrate these two factors into POI recommendation by a fusion model. Therefore, our proposed POI recommendation system is unified collaborative recommendation framework, which fuses trust-enhanced users’ preferences to potential POIs with geographic influences and temporal influence for POI recommendation. Finally, we conduct extensive experiments on two real-world datasets by comparing with several state-of-the-art methods in terms of precision@k and recall@k. Experimental results indicate that our proposed trust-enhanced collaborative filtering method outperforms other recommendation approaches.

This paper proposes a joint selective encryption and data hiding scheme based on compressed sensing (CS), with a focus to its application in secure imaging. Specifically, working with a semantic-secure stream cipher, we suggest to selectively encrypt the sign bits of the CS measurements during its quantization stage and insert the authentication information using a nonseparable histogram-shifting based data hiding scheme. The rationale behind the sign encryption is that CS measurements, when measured by random subspace projection, is random in nature and thus, from both theoretical and experimental points of view, the mean squared errors associated with authorized users and attackers are significant. Due to the indistinguishability of the output ciphertext and the nonlinearity of the CS decoder, it is robust, when comparing with the existing selective encryption system of multimedia data, against known error concealment attacks. When applied in imaging, we demonstrate that it could effectively degrade the visual quality level while saving the computation load by at least 90%. Moreover, we further show that a state-of-the-art data hiding system can be seamlessly incorporated into the sign encryption, thus allowing soft data authentication without heavy computation. The proposed scheme is expected to strengthen the security of applications in the field where both energy and privacy are the concerns, such as sensitive information protection for multimedia data in wireless sensor networks.

Internet of Multimedia Things (IoMT) faces the challenge of how to realize low-cost data acquisition while still preserve data confidentiality. In this paper, we present a low-cost and confidentiality-preserving data acquisition framework for IoMT. First, we harness chaotic convolution and random subsampling to capture multiple image signals. The measurement matrix is under the control of chaos, ensuring the security of the sampling process. Next, we assemble these sampled images into a big master image, and then encrypt this master image based on Arnold transform and single value diffusion. The computation of these two transforms only requires some low-complexity operations. Finally, the encrypted image is delivered to cloud servers for storage and decryption service. Experimental results demonstrate the security and effectiveness of the proposed framework.

Exploiting the Security Aspects of Compressive Sampling