Facial diagnosis has been widely adopted over the last decade, yet the corresponding privacy concerns, particularly regarding the leakage of patient facial and biological data, have not been widely addressed. Other soft biometrics like age and gender shared common underlying features with the disease prediction task, offering a potential multitask learning paradigm for disease detection. Another critical challenge lies in the need for lightweight models to be deployed at the edge devices at medical institutions, which frequently requires limited resources and stricter real-time processing demands. Addressing these challenges, we proposed a Privacy-preserving Multitask Network with Shared Attention (PMNet-SA) for facial-based diagnosis. Here, a facial-based privacy-preserving, lightweight multitask learning network for multi-class classification of LMD subtypes (Hyperlipidemia, Fatty Liver Disease, and Dyslipidemia, and with healthy controls) is established, while concurrently estimating age and gender. The proposed lightweight model employed an attention mechanism with multitask learning ability with age and gender, with fast inference time, which is suitable for facial-based diagnosis. Furthermore, we empirically prove that there exists a mutual effect between age, gender, and disease prediction, which helps the facial-based diagnosis capability. The experiments show that our proposed model achieves an overall accuracy of 91.15%, outperforming a number of advanced models.

This paper proposes an integrated mixed-integer programming model, termed the Single-Track Railway Cyclic Scheduling Model (SRCSM), for constructing optimized periodic timetables (operating on a recurring 24-hour cycle) for bidirectional single-track railway systems. The SRCSM enhances operational efficiency by precisely considering train arrival/departure times, safety headways, platform track allocations, and meet/pass operations. Validation using real-world data demonstrates its practical applicability, flexibility, and scalability for dynamic timetable optimization, offering a robust tool for improving operational efficiency and safety in single-track railway operations.

Abstract With the widespread application of time series data, the study of classification techniques has become an important topic. Although existing multivariate time series classification (MTSC) methods have made progress, they often rely on one-dimensional (1D) time series, which limits their ability to capture complex temporal dynamics and multiscale features. To address these challenges, a Spectral Convolutional Network (SCNet) is introduced in this work. SCNet effectively transforms 1D time series data into the frequency domain using an enhanced Discrete Fourier Transform (enhanced_DFT), revealing periodicity and key frequency components while reshaping the data into a two-dimensional (2D) time series for better representation. Furthermore, it uses a Spectral Energy Prioritization method to optimize frequency domain energy distribution and a multiscale convolutional module to capture features at different scales, improving the model’s ability to analyze short-term and long-term trends. To validate the effectiveness and superiority, we conducted extensive experiments on 10 sub-datasets from the well-known UEA dataset. The results show that our proposed SCNet achieved the highest average accuracy of 74.3%, which is 2.2% higher than the current state-of-the-art models, demonstrating its potential for practical application and efficiency in MTSC task.

As Industry 4.0 continues to advance, point cloud registration technology is extensively employed in scenarios such as collaborative defect detection in products and digital twin-assisted assembly. In this paper, we present an end-to-end point cloud registration model, GTPCR, which is based on the utilization of spatial structural information within point cloud data. GTPCR employs a point-wise approach, directly solving rigid rotations based on estimated correspondences without reliance on RANSAC. It conceives of the point cloud as a graph structure in three-dimensional space, encoding nodes across various dimensions. The encoding of an individual node is dictated by its position and centrality. The geometric associations between nodes are contingent on factors such as relative position, Euclidean distance, azimuth, and elevation. Edge feature encoding is dynamically acquired through the learning process from node features. All encodings are incorporated as trainable biases input to the model. In comparison to methods transitioning from local to global, GTPCR demonstrates superior efficiency and heightened scalability. Moreover, due to its adept network architecture design, GTPCR effortlessly expands its applicability to non-rigid registration. Empirical evidence undeniably illustrates GTPCR’s competitive advantage across numerous datasets. In particular, GTPCR shows significant improvements in registration recall of 1.4% and 0.9% over baselines on the 3DMatch and 3DLoMatch datasets, respectively.

In the context of industrial anomaly detection, anomaly point detection is a challenging task due to the rarity and unpredictable nature of anomalous samples. Existing 2D image-based defect detection methods have certain advantages in capturing features such as texture, color, and shape of parts. However, traditional single-modal defect detection methods (such as using only 2D images or only 3D point cloud data) may have limitations in accurately locating abnormal points when faced with complex surface defects on parts. Therefore, a collaborative abnormal localization method (CALM) based on multi-modal images is proposed to improve the accuracy of anomaly localization by fully utilizing information from multiple data sources. First, we propose a synchronized data augmentation method for 2D and 3D images to address the issue of scarce anomalous samples. Then, feature extraction is performed separately on RGB images and 3D point clouds, leveraging the features from both 2D and 3D images and performing multi-modal feature fusion while aligning the features. Finally, anomaly point localization and segmentation are achieved based on the abnormality scores output by the decoder. To validate the effectiveness of our method, experiments are conducted on the MVTec-3D AD dataset. The Pix-AUROC and Pix-AUPRO means of the CALM method reach 0.909 and 0.739, respectively. The experimental results demonstrate that our method achieves high detection accuracy at the pixel level, outperforming some traditional anomaly localization methods.

The spatial and spectral resolution trade-off in the hyperspectral imaging is a fundamental and essential issue, and automatically generating high-resolution images in both spatial and spectral domains (HR-HS) by merging a low spatial resolution hyperspectral (LR-HS) image and a high spatial resolution RGB (HR-RGB) image, which are captured by the existing commercial sensors, has recently attracted extensive attention. Motivated by the powerful representation capability of the deep learning networks, current dominated methods have devoted to design deep and complicated network architectures, and manifested great performance progress. This study aims to exploit a simple yet effective deep model by aggregating the complementary missing information into the feature learning branches and automatically modeling the relationship between the target and observations. Specifically, we incorporate the spectral gradient of the LR-HS image with the feature learning branch of the HR-RGB image while aggregate the spatial gradient of the HR-RGB image into the learning branch of the LR-HS image to enhance the representation capability in both spatial ans spectral domains. Moreover, we reconstruct a serials of target HR-HS image from the fused features of dual branches, and employ the un-recovered residuals in the observations by automatically learning the degradation procedure to further refine the former reconstruction in an asymptotic way. Comprehensive experiments have demonstrated that our proposed deep model for HR-HS image reconstruction achieves superior SR performance over state-of-the-art methods in term of quantitative metrics and perceptive quality.

There have been researches revealing obvious associations between facial phenotypes and non-communicable diseases (NCDs), which enables effective health assessment with the integration of model-based learning methods. However, the paucity and poor quality of available datasets hinder the development of potent algorithms to detect NCDs. To meet this challenge, we propose a method called Exemplar Diffusion Model (EDM), the objective of proposed EDM is to generate facial images that illustrate simulated non-communicable diseases, utilizing a normal facial image as input. Extensive experimental results show that the proposed EDM method outperforms the state-of-the-art methods in terms of Frechet Inception Distance (FID) and Quality Score (QS), with improvements of 0.11 and 0.74, respectively. Furthermore, comprehensive ablation studies and comparative experiments prove the value of proposed EDM method in large-scale facial image dataset generation and non-communicable disease detection.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus with an envelope frequently altered by unstable genetic material, making it extremely difficult for vaccines, drugs, and diagnostics to work. Understanding SARS-CoV-2 infection mechanisms requires studying gene expression changes. Deep learning methods are often considered for large-scale gene expression profiling data. Data feature-oriented analysis, however, neglects the biological process nature of gene expression, making it difficult to describe gene expression behaviors accurately. In this article, we propose a novel scheme for modeling gene expression during SARS-CoV-2 infection as networks (gene expression modes, GEM), to characterize their expression behaviors. On this basis, we investigated the relationships among GEMs to determine SARS-CoV-2's core radiation mode. Our final experiments identified key COVID-19 genes by gene function enrichment, protein interaction, and module mining. Experimental results show that ATG10, ATG14, MAP1LC3B, OPTN, WDR45, and WIPI1 genes contribute to SARS-CoV-2 virus spread by affecting autophagy.

The application of machine vision techniques in Vehicle-to-Everything (V2X) scenarios within Intelligent Connected Systems (ICS) has gained increasing importance with advancements in 6G communication technology. However, the stringent latency and bandwidth requirements of most machine vision applications pose significant challenges to the existing infrastructure. Hence, there is a dearth of prior research examining whether the latency of real applications in ICS aligns with the needs of machine vision scenarios, let alone any performance evaluations conducted in this regard. In this paper, we conduct a comprehensive literature review and proposed a novel machine vision architecture that can analyze traffic data in real-time in the V2X scenario within ICS. Furthermore, based on the end-to-end latency assessment of the system, we outline a plan to optimize the latency as per the requirements of the machine vision application. Our findings show that with appropriate algorithms and architecture, the ICS system can meet the stringent needs of machine vision applications. Our research can provide valuable insights as a guideline on ICS with high latency requirements and therefore pave the way for future explorations in this field.

Video classification is a challenging task because of the intricate spatiotemporal information present within videos. Current models often rely on 2D or 3D convolutional neural networks. However, convolutional neural networks are difficult to solve the long-range dependency problem. In addition, they are computationally expensive and memory-intensive. To address the challenges, a Multi-layer Transformer is proposed for video classification. The proposed method takes advantage of the high correlation between adjacent frames by grouping them and learning local and global information with a multi-layer structure based on Transformer. First, different frame sampling rates and grouping strategies are tested in the experiments, then comparing the method with state-of-the-art models. The results demonstrate that the proposed method has advanced performance with TOP1 accuracy of 77.8% on the Kinetics-400 dataset and 64.9% on the Something-Something v2 dataset.

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.

Predicting Drug-Drug Interactions (DDIs) is usually a time-consuming and labour-intensive task. The undetected adverse interactions between drugs are a common cause of medical injuries. Recently, with the assistance of deep learning algorithms, the accuracy of prediction in DDIs significantly improved. However, previous methods do not take well into account both adapting to datasets and extracting neighbourhood information on the graph-structured data. In this study, we propose a new framework to fill this gap, named Interaction Prediction Graph Attention Network(IPGAT). This framework consists of two modules, i.e., the embedding module and the prediction module. Inspired by the Graph Embedding and Graph Attention Networks, the embedding module extracts features from graph-structured data with high-order neighbourhoods. Then, it directly transfers to the prediction module without an intermediate process. Our proposed IPGAT presents advantages compared to existing DDI prediction methods. Experimental results on the public DrugBank dataset reveal that IPGAT significantly outperforms the state-of-the-art methods such as AMF&AMFP, Conv-LSTM, Graph Auto-Encoder, etc. The corresponding results increase 6.9% in AUROC and at least 8.5% in AUPR for the retrospective experiment. Further studies verify the efficacy of multi-layer and multi-head in the model. The codes are available at https://github.com/yytfy/IPGAT.

Text restoration plays a vital role in Chinese automatic speech recognition (ASR), which includes punctuation prediction and error correction. However, there are two inevitable challenges for this task. On the one hand, there are no public dataset and model for Chinese punctuation prediction. On the other hand, current text restoration methods for automatic speech recognition only focus on Chinese error correction instead of combining with Chinese punctuation prediction task. To address these problems, a BERT-based text restoration method called TRCA is proposed for Chinese ASR consisting of a Chinese punctuation prediction model and a Chinese error correction model. Experiments demonstrate that the proposed TRCA method outperforms state-of-the-art methods for both punctuation prediction and error correction tasks, among which the proposed TRCA improves the average accuracy to 98% in Chinese punctuation prediction.

As a component widely used in electronic products, Printed Circuit Board(PCB) plays an extremely important role in our life. Due to technical limitations, PCB with defects will inevitably appear in the production process. In order to ensure high yield and save labor cost, this paper applied two kinds of target detection network to PCB defect detection and classification tasks. Experiments show that the two methods used in the two different distribution of data sets achieved high accuracy.

Fog computing is a promising solution to enable delay-sensitive applications in the Internet of Things (IoT). In this article, based on the simultaneous wireless information and power transfer (SWIPT) technology, we investigate the joint offloading of tasks and energy (JOTE) in fog-enabled IoT networks. Specifically, the task node is allowed to offload energy and tasks to multiple neighboring helper nodes in a time-division multiple access (TDMA) manner. When there are no task queues in the nodes, the offloading decision for each task is independent. We first find the offloading strategy to minimize the task execution delay as well as the energy consumption for a specific task and then, analyze the condition under which the JOTE is beneficial. We show that it becomes more and more desirable to offload both the tasks and the energy from the task node as the number of helper nodes gets large. When there are task queues in the nodes, the offloading decision for each task becomes temporally correlated. We then characterize the optimal strategies to offload the tasks and energy jointly over multiple time slots. An online offloading policy based on the Lyapunov optimization is then proposed to minimize the time average expected delay while stabilizing the system operation. Comprehensive numerical results corroborate our theoretical results and demonstrate the superior performance of the proposed JOTE algorithms.

The unmanned surface vehicle (USV) has been widely used to accomplish tasks that cannot be completed by ships with human drivers on certain sea areas. It is not only necessary but essential to obtain a robust strategy in order to ensure multiple USVs accomplish collaborative tasks successfully and efficiently. To meet the challenge, a deep reinforcement learning method is proposed, which is combined with an improved A star algorithm. A statistically promising collaborative strategy is achieved by the proposed method under the guidance from the unmanned aerial vehicles (UAVs). After the collaborative strategy is generated, the improved A star algorithm is used to navigate the USVs. To verify the proposed algorithm, several tasks are tested on a simulation platform. Experimental results demonstrate that the proposed method outperforms state-of-the-art reinforcement learning methods such as DQN and DeepSarsa. © 2019 The authors and IOS Press. All rights reserved.

The unmanned surface vehicles (USVs) have become a major trend in the construction of naval equipment and its flexibility and intelligence making it widely used in real-scenes. For cooperative defense with multiple USVs to intercept intruders, it is proposed that planning the path with obstacle avoidance and protecting the target by task allocation actions. The particle swarm optimization based on probe mechanism (PM-PSO) is proposed for pathing planning with obstacle avoidance. With the consideration of the constraints of different defense schemes such as the path cost, the interception loss, the defense income and so on, it is proposed that the dispersed particle swarm optimization based on genetic algorithm (GA-PSO) for the interception task allocation. Furthermore, the fitness function is proposed to evaluate the feasibility of the interception path and the quality of the allocation scheme. Extensive simulation experiments are conducted and demonstrated the effectiveness, rationality and superiority of the proposed methods.

Based on the multi-agents system control theory and technology, this paper presents the cooperative defense process of multiple unmanned surface vehicles (USVs) operating in complicated sea environment, and explains the quantification of the battle effectiveness, cooperative strategy, task allocation and finally describes in detail the cooperative strategies on random graph. Then we point out the problems in the current cooperative defense process and the future development direction. The cooperative defense research of USVs in the sea environment has a great significance to the effective promotion of social and military efficiency.

Unmanned combat system is one of the development trend of modern weapons and equipment and has applied to military affairs. The major goal of the Unmanned Surface Vehicles (USVs in short) is to destroy protected targets in the shortest time. This paper originates from biology, putting forward a new attack strategy—The USV combat Based on Wolves Attack with Weight. Namely, using the characteristics of the wolves attack to study the process of attacking. It also discusses the attack measures from weights, velocity and firepower of USVs through weight distribution and summarizes the advantages of wolves attack in USV combat.

Based on the multi-agents system control theory and technology, this paper presents the construction of cooperative defense system of multiple unmanned surface vehicles (USVs) operating in complicated sea environment, develops the mathematical formula describing the trajectory equation when the USV intercepts the intruder, and explains the proposed coordination control method used in the corresponding defense system. Then we point out the future development requirement of the cooperative defense system. The cooperative defense system research of USVs in the sea environment has a great significance to the effective promotion of social and military efficiency, and it is the basis of the implement about cooperative strategies.

Cloud computing is a promising technology, where the infrastructure, developing platform, software and storage are delivered as a service. With the development of cloud computing, more and more cloud service providers emerge. However, there are no metrics can be referred to compare these providers, so it is difficult for cloud consumers to select the most reliable providers or resources. Thus we present a platform of Services Level Agreement (SLA) in cloud computing. In the platform, we propose a Reputation System to evaluate the reliability of providers or resources to address this challenge, and we also propose a SLA template pool in order to make the SLA negotiation between cloud providers and cloud consumers become more equitable, transparent and convenient.