Near-field (NF) communications have attracted considerable interest, particularly with the implementation of extremely large-scale antenna arrays (ELAA). Additionally, the increase in communication frequencies and the expansion of reconfigurable intelligent surface (RIS) apertures contribute to this growing field. This paper investigates the synergy of simultaneously transmitting and reflecting (STAR)-RIS and non-orthogonal multiple access (NOMA) for secure transmission under hybrid far-field (FF) and NF scenarios. The secrecy sum rate (SSR) maximization problem is formulated by joint optimization of the power allocation, the beamforming at access point (AP), and the transmission/reflection coefficients (TRCs). Specifically, we consider the transmit power budget, unit-norm conditions, coupled phase shifts (CPS), quality of service requirements, and decoding order. To tackle this extremely challenging problem, we combine the successive convex approximation (SCA), Riemannian exact penalty method via smoothing, and penalty dual decomposition (PDD) and successfully develop an efficient iterative algorithm. Simulation results reveal that the proposed design exhibits superior effectiveness when compared to other traditional benchmarks.

Accurately predicting human future trajectories remains a core challenge in autonomous driving and human-robot interaction, primarily due to the inherent uncertainty of human motion and real-time requirements of decision-making. While generative models have shown promise in multimodal trajectory prediction, they often fail to simultaneously achieve high fidelity, high diversity, and fast sampling efficiency. To address this, we propose a unified trajectory prediction framework based on goal-guided truncated flow matching, which jointly optimizes these objectives. Specifically, we introduce a truncated flow matching strategy that initializes the denoising process from a context-aware intermediate state closer to the true trajectory distribution, reducing the number of sampling steps from 100 to just 2 and significantly enhancing inference efficiency. To enhance intent awareness and behavioral plausibility, we design an internal intention module that incorporates goal-guided cross-attention and is supervised by both destination regression and physical feasibility constraints, leading to more accurate and realistic predictions. Furthermore, to promote behavioral diversity and mitigate mode collapse, we propose a modality-aware diversity modeling mechanism that explicitly disentangles semantic differences among trajectory modes. Meanwhile, we introduce a diversity metric to quantitatively evaluate the diversity of the generated predictions. Extensive experiments on three public datasets show that our method achieves competitive accuracy, significantly enhances motion diversity, and delivers superior inference efficiency compared to existing models. These results highlight its strong potential for real-world deployment in safety-critical applications.

The computing power network (CPN) overcomes the limitations of single-point computing performance by integrating computing and network resources. Latency and energy consumption during the task offloading process are key performance indicators for evaluating the CPN. However, most existing algorithms execute tasks based on their order of arrival, which fails to meet the requirements of latency-sensitive tasks. Additionally, they lack adjustment of offloading preferences when the number of network devices dynamically increases, leading to uneven task distribution and increased energy consumption. To address these issues, this paper proposes a priority-based multiobjective weight-adjusted Q-value task offloading optimization algorithm (PMWQ). First, tasks are prioritized before offloading to ensure that latency-sensitive tasks are processed first, thereby reducing latency. Second, an adaptive dynamic weight Q-value adjustment strategy is employed to balance the load among devices at different levels and reduce energy consumption. Moreover, the algorithm optimizes the feedback mechanism through a dual Q-function table to improve decision accuracy. Simulations show that, with 210 devices, the PMWQ algorithm outperforms baseline methods by increasing task success rates by 3.72%-17.47%, reducing average waiting delays by 58.81%-75.93%, lowering average energy consumption by 4.32%-10.24%, and enhancing average CPU utilization by 14.82%-27.92%.

Fire detection technology based on deep learning methods has become a prevalent practice. However, the performance of current YOLO-based detection models is limited by NMS, and DETR-based detection models struggle with real-time performance. To address these challenges, a new fire detection model, FasterGold-DETR, is proposed. Firstly, this model introduces an innovative backbone network, FasterRepNet, which efficiently captures and retains feature information, thereby accelerating the model’s convergence speed. Secondly, we propose the AIFI-GD hybrid encoder to reduce information loss in intra-scale and cross-scale feature interactions and improve the ability to detect fire of different sizes. Furthermore, to adapt the complex fire scenarios, we extend the dataset based on the KMU Fire and Smoke database and replace the loss function with WIoU to enhance the model’s robustness. Experiments show that our proposed model outperforms mainstream object detection models in terms of accuracy and complexity.

Consistency learning is considered one of the most effective approaches for leveraging unlabeled data in semi-supervised medical image segmentation(SSMIS). However, existing methods often rely on a single perturbation strategy or only apply perturbations to unlabeled data, resulting in limited exploration of the perturbation space and an inability to learn precise decision boundaries. Moreover, excessive perturbations tend to cause training instability due to the accumulation of errors. To address the aforementioned issue, this paper proposes the BlenMatch method, which ensures the effect of consistency learning by using high perturbations initially, while striving to maintain the stability of the training. Specifically, BlenMatch applies random complementary interchange (RCI) and feature-level mixed perturbations on unlabeled data alongside labeled data, it ensures comprehensive exploration of the perturbation space while mitigating the negative effects of noisy pseudo-labels, leveraging ground truth guidance to counteract excessive perturbations. Additionally, SL loss is introduced to enhance training stability and prevent overfitting. Experimental results demonstrate that BlenMatch outperforms current state-of-the-art techniques on the ACDC and PROMISE12 datasets.

Semi-supervised learning methods combine a small amount of labeled data with a large amount of unlabeled data to achieve high-precision segmentation while reducing the annotation cost. However, existing semi-supervised learning methods usually focus on data-level perturbations or improvements in network structures, ignoring the problem of insufficient information interaction between different branches and regions in complex texture tasks. In addition, in scenarios that require high-resolution processing (such as 3D medical images), traditional Transformer-based methods are not only computationally expensive but also prone to overfitting problems. Therefore, to address the above problems, we propose a Mamba based Consistency Constraints network (MCC-Net) for semi-supervised 3D medical image segmentation. Specifically, the model is organized by a shared encoder and three-branch decoder architecture. First, a consistency regularization constraint mechanism is introduced to combine the segmentation map of one decoder with the pseudo-labels of other decoders, thereby capturing more valuable features in high-uncertainty areas and generating stable, low-entropy predictions; second, a new consistency loss function is designed to construct constraints between the signed distance map (SDM) of the two auxiliary decoders and the segmentation map of the main decoder to enhance the learning ability of the target geometric structure. In addition, a Fusion Mamba(FM) Block is proposed to improve the model’s capabilities in deep semantic feature extraction and computational efficiency by modeling long-distance dependent features. Experimental results on public dataset show that, compared with six state-of-the-art semi-supervised segmentation methods, our method achieves Dice scores of 89.48%, 91.46% and 91.96%, respectively, when 10%, 20% and 30% of labeled data are used for training, significantly outperforming the other methods. The experimental results show that the model has strong advantages in both segmentation accuracy and utilization of unlabeled data.

As a crucial component of autonomous vehicles and intelligent robots, human trajectory prediction provides route planning for intelligent agents. Recent studies have shown that human/pedestrian trajectory prediction models are vulnerable to adversarial attacks. However, previous studies failed to consider the practicality of perturbations, generating adversarial trajectory with the length equal to the input length required by the prediction model. It is hard for an attacker (signed as candidate agent) to precisely walk on such many nodes in adversarial trajectory to execute an attack. Moreover, when selecting a target agent to approach for candidate agent, the attack is based solely on the distance to the candidate agent, ignoring the target agents with high relative velocity. This paper proposes a two-stage attack called AGNS aimed at minimizing the number of perturbing trajectory nodes meanwhile keeping the attack effectiveness. In the first stage, we propose a node-selection method to select the "important" nodes to reduce the number of nodes that we need to perturb. In the second stage, the selected nodes are perturbed with the attention-guided loss to generate a partial adversarial trajectory. Experiments on four models demonstrate that AGNS causes an average collision rate of over 80% while perturbing only 56% of the trajectory nodes, and even achieves an average collision rate exceeding 70% when perturbing just a single node. Our code can be obtained on Github: https://anonymous.4open.science/r/AGNS.

Deep learning is widely used in the early diagnosis of Alzheimer ’s disease (AD) in recent years, and has achieved better performance than traditional machine learning methods. The current deep methods for early diagnosis of AD mainly adopt a fully supervised method, which relies too much on a large number of labeled high-quality medical image data, and has a large performance loss in under-labeled data scenarios. Meanwhile,the existing semi-supervised methods ignore the reliability of pseudo-labeling. Aiming at the above problems, a Dual Augmentation Semi-supervised Learning (DASSL) method is proposed. DASSL designs a feature enhancement method based on attention mechanism and combines it with a spatial enhancement method more suitable for medical images, which provides a new solution for early feature recognition of Alzheimer ’s disease. At the same time, a new loss function is designed, and combined with the threshold, high confidence samples are selected to cope with the challenge of high sample impurity rate. The DASSL method proposed in this paper was evaluated in 518 subjects on the ADNI-1 dataset. The experimental results show that DASSL achieves the best accuracy and stability in classifying AD, MCI, and NC (more than 95% accuracy for classifying AD and CN task, and more than 90% accuracy for classifying MCI and CN) compared to other semi-supervised methods.

The rapid advancement of technologies such as virtual and augmented reality has garnered substantial attention from both academia and industry. As the foundation of immersive multimedia content, omnidirectional image generation necessitates the development of robust and efficient image stitching algorithms. Unlike planar image stitching, omnidirectional image stitching involves stitching images captured by binocular or quadrinocular camera systems, introducing lager parallaxes (e.g., 90° or even 180°), which lead to pronounced distortions and hard-to-remove artifacts. Conventional planar image stitching methods based on optical flow typically employ unidirectional models. However, the intrinsic spatial relationships between sub-images in omnidirectional image necessitate the use of bidirectional optical flow. Existing approaches often estimate optical flow for each branch independently through simple feature mapping, neglecting the latent correlations between bidirectional flow. To address these challenges, we propose using a seam-driven approach to replace the traditional weighted blending strategy to effectively minimize artifacts. Additionally, we incorporate an advanced attention mechanism to establish a "bridge" that enables joint optimization of the two optical flow branches. Finally, we lightweight the model at a small cost of precision. Experimental results demonstrate that our method comprehensively outperforms the baseline model and generates natural omnidirectional images.

Monocular Depth Estimation (MDE) is vital for autonomous driving, enabling 3D perception without costly LiDAR, yet existing 2D white-box adversarial attacks against MDE lack robustness to viewpoint and real-world variations. We propose DepthHack, the first 3D black-box adversarial attack framework for MDE, using probabilistic sampling and score-based optimization to craft robust 3D adversarial textures. DepthHack ensures robustness across diverse weather conditions and viewpoints, achieving an average depth estimation error of 8.88 m on Monodepth2 (Carla), surpassing HardBeat by 2.1%, with only 60k queries. This work reveals MDE vulnerabilities, enhancing the safety of autonomous systems. Experiments on four MDE models and two datasets validate its superior performance, efficiency, and cross-dataset generalization.

The core challenge in location privacy protection for location-based services (LBS) remains balancing location privacy and data utility. Differential privacy, backed by mathematical proofs, offers an effective framework for location protection. However, existing extended differential privacy methods for this purpose have some limitations. On one hand, most such methods focus on Euclidean spaces, making them ill-suited for road network-based LBS. They fail to align with road network contexts, potentially disrupting path planning, and often introduce excessive noise that inflates distance loss and degrades service quality. On the other hand, location semantics, a critical component of data utility, are frequently overlooked. Their degradation directly undermines utility. To address these issues, this paper introduces Semantic-Graph-Indistinguishability (SEM-G-IND) within the differential privacy paradigm, aiming to enhance location protection under road network and semantic constraints. First, a POI-based location semantic hash is designed to quantify location semantics. Then, integrating semantic distance and shortest path distance, a novel graph-based metric, Semantic-Graph-Distance (SGD), is proposed to measure inter-location distances. Finally, based on SGD, we propose a semantic penalty-based differential privacy (SPDP) location protection mechanism under road networks that satisfies ϵ-SEM-G-IND. We validated on real-world datasets that the SPDP mechanism effectively reduces the semantic loss of locations while ensuring minimal path distance loss, and it is feasible in terms of time overhead.

Ensuring product quality through automated anomaly detection is crucial in manufacturing. Traditional methods often struggle to capture both local and global features effectively, relying heavily on predefined templates that limit their adaptability and accuracy. To address these challenges, this study propose NN2ViT, a novel approach that integrates a Single Shot Detector (SSD) for local feature detection and the Segment Anything Model (SAM) for global feature segmentation. This integration allows for a comprehensive analysis of anomalies in industrial images. Our method improves anomaly segmentation performance by fine-tuning SAM for precise segmentation in industrial product images. Experiments on the MVTec benchmark dataset demonstrate that NN2ViT outperforms traditional models and achieved the highest 95.54% and 96.23% Image AUROC and AP scores, respectively thus enhancing interpretability and adaptability to various anomaly patterns. This research presents a significant advancement in manufacturing quality control , contributing to improved product quality and operational efficiency.

With the proliferation of trajectory data generated by many Internet of Things (IoT) devices in the AI-driven transportation field, trajectory representation is crucial for extracting individual behavioral characteristics from intelligent IoT systems. Neural network-based trajectory representation learning methods excel at obtaining consistent representations of individual movements and mining spatio-temporal autocorrelation features of trajectories. However, existing methods achieve high accuracy in reliable experimental data, and their high performance is not always available in real-world wild scenarios, which is not available to reveal the performance bounds of trajectory representation learning models when confronted with real-world phenomena. Given this, we propose a universal trajectory adversarial attack framework that investigates the robustness vulnerabilities of trajectory representation learning models by generating adversarial trajectory examples. We reveal the impact of the attack surface in practical deployment and the adversarial perturbation budget on the trajectory adversarial attack performance. Guided by the new framework, we propose a universal black-box adversarial attack for trajectories, named Universal Trajectory Customized Iteration Attack (UTCIA). Specifically, we simulate trajectories through single-point perturbations to obtain a vulnerability-dependent set of victim points, selecting and perturbing only a small subset of points to degrade the entire model performance. Furthermore, we aggregate the discrete set of salient target trajectory points into a high-dimensional space for iterative direction estimation, and relax the constraint region to further compress noise. We generate adversarial trajectory examples for two trajectory representation downstream tasks with fundamentally different objectives using multiple large-scale real-world datasets. Extensive experiments demonstrate the feasibility and generalizability of our proposed framework in exploring the robustness vulnerabilities of trajectory representation learning models.

The cloud-edge–terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This article proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN’s intricate features. Lyapunov optimization, integrated into a multithreaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the tradeoff between resource utilization efficiency and system stability follows the relationship [O(1/V), O(V)]. Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies.

Remote sensing road extraction is a key technology in spatial geoinformation analysis. It provides essential data for smart city development, traffic optimization, disaster emergency response, and ecological protection. Existing deep learning-based road extraction models were affected by gradual feature loss with increasing network depth, and complex backgrounds with diverse road morphologies further challenged road connectivity. To address these issues, HyperCARE was proposed as a Hierarchical Cross-Attention Road Extractor with Multi-Spectral Augmentation and Topology-Consistent Refinement. HSV color space transformation and edge detection were applied for fusion-based enhancement. Background noise was suppressed, and the contrast between the road and the background was increased. A Hierarchical Cross-Attention Road Extractor was designed by constructing a Multi-Scale Feature Interaction Module (MSIA) and a Coordinate-Channel Dual Attention (CCPA). High-level and low-level features were dynamically fused, and spatial coordinates with channel features were jointly optimized. Morphological operations and a scoring mechanism was employed to repair breakpoints in the initial segmentation results, thereby addressing road discontinuities caused by shadows or vegetation. Experiments on the DeepGlobe, Massachusetts Roads, and CHN6-CUG datasets demonstrated consistent gains over the baseline, confirming the method’s robustness and practical value in complex scenarios. Code and models will be released upon acceptance at https://github.com/0225zyk/HyperCARE.

Automatic nuclei segmentation and classification (NSC) is a fundamental prerequisite in digital pathology analysis as it enables the quantification of biomarkers and histopathological features for precision medicine. Nuclei appear to be small, however, global spatial distribution and brightness contrast, or color correlation between the nucleus and background, have been recognized as key rationales for accurate nuclei segmentation in actual clinical practice. Although recent great breakthroughs in medical image segmentation have been achieved by Transformer-based methods, the adaptability of segmenting and classifying nuclei from histopathological images is rarely investigated. Also, the severe overlap of nuclei and the large intra-class variability are common in clinical wild data. Prevailing methods based on polygonal representations or distance maps are limited by empirically designed post-processing strategies, resulting in ineffective segmentation of large irregular nuclei instances. To address these challenges, we propose a keypoint-guided tri-decoder Transformer (PointFormer) for NSC simultaneously. Specifically, the overall NSC task is decoupled to a multi-task learning problem, where a tri-decoder structure is employed for decoding nuclei instance, edges, and types, respectively. The nuclei detection and classification (NDC) subtask is reformulated as a semantic keypoint estimation problem. Meanwhile, introduces a novel attention-guiding strategy to capture strong inter-branch correlations and mitigate inconsistencies between multi-decoder predictions. Finally, a multi-local perception module is designed as the base building block of PointFormer to achieve local and global trade-offs and reduce model complexity. Comprehensive quantitative and qualitative experimental results on three datasets of different volumes have demonstrated the superiority of the proposed method over prevalent methods, especially for the PanNuke dataset with an achievement of 70.6% on bPQ.

In 3D point cloud-based object detection, attention mechanism in Group-Free [1] learns direct relationships between proposals and all seed points, providing each proposal with a global context in the form of a cross-attention map. However, our analysis and experimental comparison show that the attention mechanism assigns inappropriately large attention weights to certain seed points far from a proposal, which is not conducive to detecting objects correctly. In this work, we alleviate the above problem by proposing a mask method. For an initial proposal, our method first calculates a spatial distance-based mask, which measures the spatial relationship between all seed points and the proposal. Then, we fuse the mask into cross-attention layers in stacked attention modules and get a refined cross-attention map. In essence, our mask gives each proposal a local context; after it is fused with the global context given by the attention mechanism, the refined cross-attention map could suppress the negative impact of some distant seed points on a proposal. We present two alternative strategies to compute the mask, a hard mask, and a soft mask. Experimental results demonstrate that the soft mask brings better performance. In the soft mask, for each initial proposal's 3D-box shape, we use a parametric approximate ellipsoid as the basis of the mask's calculation, which has only two learnable parameters. Experimental results show our work could outperform Group-Free 0.7 [email protected] at the cost of increasing inference time by less than 1%. The performance of our algorithm on the public dataset SUN RGB-D is 63.7 [email protected] and 45.5 [email protected], which is the best performance among algorithms that preserve the irregular of seed points.

Deep neural networks are extensively employed in CAD (computer-aided diagnosis) to enhance diagnostic efficiency, accuracy, and reliability. However, relying on manually annotation, labeling medical images is costly and time-consuming. Consequently, obtaining labeled data of sufficient scale and high quality poses a challenge. In the field of data science, semi-supervised learning (SSL) methods have significantly reduced the burden of dataset labeling. However, two primary issues exist: (1) the multi-classification challenge involving simultaneous classification of multiple lesions and diagnosis of multiple diseases; (2) data imbalance results in significant variations in disease prevalence rates. In this paper, the design of Curriculum Pseudo-labels (CPL) methods is adopted to overcome the limitations of applying SSL methods across data science and CAD. Additionally, a novel strategy called MedAugment is proposed for enhancing the model's robustness and generalization ability. Experimental results show that the design of MedMatch model reaches 94.21% in AUC score compared with state-of-the-art SSL methods on CAD task.

With the deep integration of computing and network development, as a new type of network infrastructure, computing power network (CPN) has become a research hotspot in the industry. Computing resource metrics integrates the computing resources connected to the CPN, realizes the collaborative management of heterogeneous resources through the measurement of multi-dimensional computing resource, and provides an accurate resource view for resource matching, which has become an important part of the CPN. The traditional measurement methods are too single to measure computing resources from a single dimension, which is difficult to adapt to the development of CPN. The existing methods of computing resource metrics need to be improved in the accuracy of resource matching and cannot reflect the comprehensive performance of computing resources. In this paper, a multi-dimensional computing resource measurement method based on entropy weight TOPSIS is designed to score the comprehensive performance of computing resources, storage resources and communication resources of computing nodes, then the nodes are divided into different categories of comprehensive performance according to the score, so as to narrow the scope of resource matching for different user requirements. At the same time, a multi-dimensional resource matching algorithm based on deep reinforcement learning is proposed. The resource matching process is constructed as a Markov decision process to realize the matching of tasks and nodes. The simulation results show that the proposed algorithm can better solve the matching problem of multi-dimensional resources, and the utilization rate of all kinds of resources reaches more than 90%.

Computing Power Network (CPN) overcome the performance limitations of computing power islands by integrating computing and network resources, dynamically scheduling business traffic to optimal nodes. However, effectively and collaboratively utilizing computing resources to reduce the delay of computing tasks has become a challenging issue in CPN due to the heterogeneity of resources and dynamic load. Existing works often treat workflow tasks as atomic tasks for offloading, disregarding subtask dependencies within a task. This approach leads to increased overall waiting time due to varying execution delays of each subtask. To address this problem, this paper proposes an optimization algorithm for workflow tasks based on slack quantity (CSA_WTO). The algorithm optimizes the arrival order of workflow task graphs before offloading, considering subtask dependencies and arranging them appropriately for subsequent offloading. This effectively reduces waiting delays for subtasks. Additionally, we utilize the Dependent Task Offloading algorithm (DTO) based on A3C to offload optimized workflow tasks, thereby improving execution efficiency in CPN. Simulation results demonstrate that compared with other algorithms, CSA_WTO significantly reduces task waiting delays by up to 85%, and DTO achieves a request acceptance rate of up to 96% while reducing the average completion time by 71%.

Recent advances in single-cell RNA sequencing(scRNA-seq) technologies have shown significant improvements in cellular-level biological research. Clustering individual cells into subpopulations is a pivotal procedure of scRNA-seq data analysis. The high sparsity and dimensionality of scRNA-seq data still limit the performance of cell clustering. Recent graph-based scRNA-seq clustering methods overlook the expression of cells, leading to suboptimal representation of cell embeddings. In this study, we propose single-cell Combined Graph Attentional Clustering (scCAT), a novel unsupervised clustering method for scRNA-seq data. scCAT first learns cell embeddings through a joint dimensionality reduction (JDR). Then, a graph attention autoencoder (GATE) is designed to analyze the cell topological information. Finally, the clustering operation is executed through a self-optimizing unit. Experimental results on five scRNA-seq datasets show that scCAT significantly improves clustering accuracy over existing methods with an average increase of 11.2% in the Adjusted Rand Index (ARI) and 9.6% in the Normalized Mutual Information (NMI) parameters.

Traffic flow prediction assumes a pivotal role in aiding governments and companies accurately forecast changes in vehicle volume, consequently enhancing transportation efficiency and facilitating vehicle travel. Presently, the majority of traffic flow prediction methods rely on centralized learning strategies, which entail the transmission of substantial data and may jeopardize user privacy. To address this issue, we propose a Federated Learning-based Attention Graph Convolutional Network (FLSTAGCN) algorithm for traffic flow prediction. Firstly, we develop a Spatial-Temporal Attention Graph Convolutional Network (STAGCN) method that employs attention mechanism to proficiently extract spatial-temporal features from traffic flow data, augmenting the model's learning capabilities. Subsequently, within the aggregation mechanism of Federated learning, we devise a bespoke optimal selection to enhance training accuracy and reduce communication costs in traffic flow prediction scenarios. Finally, we integrate Federated Learning with STAGCN and utilize the optimal selection protocol to designate participants for transmitting optimal parameters. The Experimental results substantiate that our approach outperforms advanced deep learning approaches in terms of traffic flow prediction performance while ensuring the privacy and security of traffic data.

Federated learning is an emerging machine learning framework, which is commonly used in the structure of distributed machine learning due to its characteristic of “data immutable model motion”. In practical scenarios, the data samples and hardware conditions between clients are highly heterogeneous. The traditional simple aggregation can cause the global model to unintentionally favor certain clients. There is a significant performance gap between vulnerable groups and groups with richer training resources in the global model. This paper proposes Dynamic Momentum-based Federated Learning (DMFL) to address this issue. It dynamically adjusts the client aggregation weight based on historical performance and current round losses in each round. Experimental results show that DMFL can improve the effectiveness of the overall model while reducing the variance of the client accuracy distribution. Compared to existing baselines, the proposed algorithm performs superior fairness in results.

Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.

Leaderless rational individuals often lead the group into a resource decision dilemma in resource competition. Reducing the cost of resource competition while avoiding group decision dilemmas is a challenging task. Inspired by multiagent systems (MASs) and repeated games, we propose a decision-making reward discrimination (DRD) framework to address the resource competition dilemma of leaderless group formation. We aim to model the leaderless group’s resource gaming process using MAS and achieve optimal rewards for the group while minimizing conflict in resource competition. The proposed framework consists of three modules: 1) the decision-making module; 2) the reward module; and 3) the discriminative module. The decision-making module defines the agents and models the decision-making process, while the reward module calculates the group reward in each round using the reward matrix. The discriminative module compares the group reward with the target reward while providing the agent with environmental information. We verify the feasibility of the model through numerous experiments. The results show that agents adopt a revenge strategy to avoid resource competition dilemmas and achieve group reward optimality.

In this article, glioma segmentation in the glioma grading computer-aided diagnosis (CAD) system requires manual delineation from radiologists, adding substantially to their workload. Although automatic segmentation is powerful, it cannot fully delegate power to artificial intelligence. We propose an AI-powered radiomics algorithm based on slice pooling (AI-RASP). AI-RASP generated compress images by compressing the gray value of each magnetic resonance imaging slice for radiologists to segment manually. In addition, AI-RASP integrated radiomics models to verify the glioma grading effect and the availability of compressed images. AI-RASP significantly reduce the time of manual segmentation. Results reported on multicenter datasets reveal that our architecture is better than the traditional manual segmentation while being over five times faster. The radiomics model with slice pooling mechanism achieves an area under the curve values of 0.86, 086, and 0.83 in the validation cohorts. Radiologists and patients can benefit from a CAD system integrated with AI-RASP.

The security issue has been a challenging concern for cloud computing because of the multitenant usage model. In cloud, each application normally runs on a dynamic coalition that is composed by multiple virtual machines (VMs) running on different virtualised service nodes, which the authors called logic virtual domain (LVD). Moreover, the owners of cloud applications, who are also the tenants of cloud, would specify some security policies to control the access to those resources that they have paid for. Therefore the owners of cloud infrastructures have to provide the tenants with the mechanism to correctly configure and enforce the access control policies on resources that are from multiple service nodes, to meet the security requirements from cloud applications. To address the above challenge, this study presents the design and implementation about a multilayer access control architecture for LVD, named CloudAC, aiming to provide isolation control, information flow control and resource‐sharing control among multiple VMs on Xen virtualisation platforms in cloud computing environment. The theory and technology this research formed will provide reliable security guarantee for resource configuration and application deployment on LVDs.

Cloud computing is believed to be the next major paradigm of computing because it will substantially reduce the cost of IT systems. Ensuring security in the cloud-end is necessary because customers' data are stored and processed there. Previous studies have mainly focused on secure cloud-end storage, whereas secure cloud-end computing is much less investigated. The current practice is solely based on Virtual Machines (VM), and cannot offer adequate security because the guest Operating Systems (OS) often can be easily breached (e.g., by exploiting their vulnerabilities). This motivates the need of solutions for more secure cloud-end computing. This poster presents the design, implementation and analysis of a candidate solution, called Trusted Execution Environment (TEE), which takes advantage of both virtualization and trusted computing technologies simultaneously. The novelty behind TEE is the virtualization of the Dynamic Root of Trust for Measurement (DRTM).

We design and implement a Mandatory Access Control (MAC) system in distributed virtual computing environment, named DVM-MAC, aiming to provide distributed trust through enforcing MAC policies. In DVM-MAC, Prioritized Chinese Wall (PCW) model is implemented to control potential covert channels between VMs in both single node and distributed environment. A policy enforcement module locates inside Xen VMM for better enforcing MAC locally rather than outside the VMM. DVM-MAC adopts centralized architecture for multi-level management and secure transmission of inter-node policy information. For performance consideration, a specific policy decision and enforcement module for controlling inter-node behaviors is moved out of Xen VMM and up to user space. DVM-MAC authorizes a specific center node named Central Security Server (CSS) to be responsible for the decision making between the nodes as well as leaves the inter-node policy enforcement module in each node. Through our experiments and data analysis, we verify the correctness, effectiveness, and efficiency in our prototype when implementing PCW model.

Web prefetching is a promising solution used to reduce user's latency and improve the QOS. This paper presents a popularity-based selective Markov prefetching model for predicting the forthcoming Web pages. We make use of teh Zipf's law to model the Web objects' popularity. An experimental evaluation of the prefetching mechanism is presented using real server logs. Our trace-driven simulation results show that the popularity-based selective. Markov prefetching model can achieve a good hit ratio with reducing the traffic load to some degree.