Traditional Multi-View Stereo (MVS) methods, which rely on hand-crafted features, often fail to capture overall scene structures, particularly under varying lighting conditions, in low-textured regions, or in reflective areas. Recent learning-based approaches, although demonstrating state-of-the-art (SOTA) performance, still face generalization challenges due to their reliance on fine-tuning. In this paper, we propose SF-MVSNet to mitigates this issue. SF-MVSNet enhances the diversity and isolation of features by introducing a Siamese-like Fusion network in the depth estimation stage, thereby improving generalizability. More specifically, we divide the correlation volume workflow into two parallel branches with shared weights, which can be viewed as group-wise correlation, providing unique insights for measuring feature similarities. Then, we introduce a Three-dimensional Attentional Feature Fusion network (3D AFF) to combine the similarities and differences. This process augments the network’s capability to handle intricate scenes, which is particularly important when confronted with the unseen and more challenging dataset such as the Tanks and Temples benchmarks. Experimental results demonstrate that our method achieves competitive results compared with recent SOTA approaches. Results can be found on the Tanks and Temples official evaluation website leaderboard section. Ablation studies further validate the efficiency of the designed module. Code is available at https://github.com/AsDeadAsAD-odo/SF-MVSNet.

When a single gene exhibits an insignificant association with complex disease, applying gene–gene interactions as biomarkers may achieve striking findings. However, it is still a barrier to measuring the strength of gene–gene interaction at the individual level and further applying gene–gene pairs as biomarkers. To overcome this challenge, we introduce iCKI, namely individualized co-expression-like index, quantifying the interaction strength of a gene–gene pair for each individual. The higher the absolute value of iCKI, the stronger the co-expression of two biomarkers. iCKI makes co-expression variations as novel individual biomarkers possible, enabling advanced applications in disease diagnosis, survival analysis, and more. Applying iCKI to rheumatoid arthritis early prediction and pancreatic cancer survival analysis, we demonstrated that co-expression achieved substantial improvements compared to single biomarkers. Overall, iCKI offers an innovative and efficient indicator for considering gene–gene interactions as biomarkers and represents a starting point for individual co-expression.

Federated learning coordinates model training in a distributed manner within Internet of Things (IoT) systems and ensures the privacy of local client data simultaneously. Nonetheless, traditional federated learning relies primarily on a unified global model and focuses on local feature extraction, failing to accommodate the diversity and personalized needs of clients in non-independent and identically distributed (non-IID) environments. To mitigate the decline in model accuracy posed by these challenges, we propose a discrepancy-aware federated learning method based on multi-branch feature fusion (FedDM). Firstly, we design a differential-aware aggregation strategy (DA), which adjusts the contribution of each client during model aggregation using Gaussian distribution statistics, to generate personalized local models. Next, we propose a multi-branch feature fusion mechanism (MFF) that integrates diverse feature representations through multi-scale pooling and feature enhancement, enabling the incorporation of features across both spatial and channel dimensions for a more holistic representation. Experimental results demonstrate that FedDM enhances model accuracy and robustness, while exhibiting adaptability when facing challenges posed by data distribution heterogeneity.

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction.

Accurate traffic flow prediction is crucial for alleviating traffic congestion and optimizing intelligent transportation systems. However, traffic flow is subject to uncertainties and exhibits complex spatial and temporal dependence and dynamic change characteristics. Moreover, many efforts rely on a single view, which makes it difficult to comprehensively capture multiple levels of spatial and temporal correlations, thus limiting the accuracy of predictions. Therefore, we propose the multi-view spatio-temporal dynamic graph convolution framework MVSTDG for more comprehensively exploring and fusing the multi-view spatio-temporal features. Firstly, we design a dual-path Time-Patch Convolution (TPConv) module to separately model short-term fluctuations and long-term periodic trends, enabling effective extraction of dynamic features at multiple temporal scales. Secondly, we construct a data-driven traffic pattern library to generate dynamic adjacency matrices and integrate them with static topologies view. An Adaptive Diffusion Graph Convolutional Network (ADGCN) is then employed to model both local and global spatial correlations. In addition, we design a cross-gated spatio-temporal fusion mechanism that adaptively adjusts the contribution of short-term and long-term information, enhances the interaction of spatio-temporal information, and improves the model’s adaptive capability under different time scales. The experimental results show that MVSTDG outperforms the state-of-the-art baselines in several evaluation metrics and demonstrates higher prediction accuracy and stability on the four real datasets.

Imbalanced problem is concerned with the performance of classifiers on the data set with severe class imbalance distribution. For two-class, the examples can be categorized into majority class or minority class, and the cost of misclassifying minority class examples is often much higher than the co ntrary cases. However, traditional classifiers do not work well on the imbalanced problem due to the assumption that the number of each class examples is similar to each other. To handle these problems, this paper proposes a simple but effective ensemble learning method based on feature projection and under-sampling (EFPUS). EFPUS learns an ensemble through the following two steps: (1) under-sampling several subset from majority class and learning a novel projection matrix from each subset, and (2) constructing new training sets by projecting the original training set to different spaces defined by the matrixes and learning a class-imbalance oriented classifier from each new training set. For the first step, feature projection and under-sampling mainly aim to improve the diversity between ensemble members. With respect to the second step, the base models can be learned by any traditional class-imbalance oriented learning method such as USBagging, SMOTEBoost and BalanceCascade. Experimental results show that, compared with other state-of-the-art methods, EFPUS shows significantly better performance on measures of g-mean, f-measure, AUC, recall and accuracy.

In multi-domain service-based systems, services from different domains are composed together to accomplish critical tasks. In these systems, data flow from one domain to another through the composed services. Thus, security and trustworthiness are the major concerns. Many access control models have been developed for service-based systems. Also, many data provenance schemes have been proposed in recent years to support data quality assessment and enhancement, data reproduction, etc. However, none of the existing mechanisms consider both access control and data provenance in an integrated model. In this paper, we propose an integrated role-based access control and data provenance model to secure the cross-domain interactions. We develop a role-based data provenance scheme which tracks the roles of originators/contributors of a data object and uses this information to help evaluate data trustworthiness. We also make use of the data provenance information and the derived data quality attributes to assist with cross domain access and information flow control. This integrated model mutually enhances data provenance and access control, providing better security and trustworthiness for many multi-domain service-based applications.

Enforcing access control in composite services is essential in distributed multidomain environment. Many advanced access control models have been developed to secure web services at execution time. However, they do not consider access control validation at composition time, resulting in high execution-time failure rate of composite services due to access control violations. Performing composition-time access control validation is not straightforward. First, many candidate compositions need to be considered and validating them can be costly. Second, some service composers may not be trusted to access protected policies and validation has to be done remotely. Another major issue with existing models is that they do not consider information flow control in composite services, which may result in undesirable information leakage. To resolve all these problems, we develop a novel three-phase composition protocol integrating information flow control. To reduce the policy evaluation cost, we use historical information to efficiently evaluate and prune candidate compositions and perform local/remote policy evaluation only on top candidates. To achieve effective and efficient information flow control, we introduce the novel concept of transformation factor to model the computation effect of intermediate services. Experimental studies show significant performance benefit of the proposed mechanism.

In this paper, we propose a kind of extended fuzzy Petri net (EFPN) which is used in building the fault diagnosis model. First, we define a weighted mathematical expectation μ to calculate the distribution center of point-in-time with weight, and give the definition of weighted variance v to evaluate the indexes of discrete degree of the point-in-time with weight. Second, the maximum membership function ϕ and the fuzzy timestamp function τ are proposed, and these functions are used for calculating the temporal uncertainty and the different temporal distribution of TSSs. Finally, by using μ, v, ϕ and τ, the fault diagnosis model of substation equipments is constructed to analyze the confidence degree of all possible faults. Moreover, the repetitiveness of signals is analyzed to assess the influence degree caused by uncertainty factors which affect the results of reasoning. Simulation experiment shows that the EFPN is an effective substation fault diagnosis model, and it can be used to deal with the temporal uncertainty problem which is presented among the TSSs. More important is that the EFPN offers a new approach to make qualitative and quantitative analysis on the temporal relationship of the TSSs.

To analyze the composite fault in Discrete Event System (DES), a Probabilistic Petri net (PPN) and a fault diagnosis method for power system are proposed. Firstly, the PPN models are established on every fault spread direction. Secondly, the failed component is determined by the application of Petri net reasoning and probabilistic calculation. At last, the result is given by fusing all parties' results using mean method. Diagnosis analysis shows that the method can adapt to topology changes and obtain satisfying diagnosis results with incomplete information. In the PPN reasoning, calculating the fault probability of component is based on the prior probability from statistics, so the subjectivity of setting related parameters can be avoided.

Service cloud provides added value to customers by allowing them to compose services from multiple providers. Most existing web service security models focus on the protection of individual web services. When multiple services from different domains are composed together, it is critical to ensure the proper information flow on the chain of services. In a service chain, each service needs to determine whether the sensitive information can be directly or indirectly disseminated to the subsequent services. Also, each service in the chain needs to decide whether to accept the data passed to it directly or indirectly from prior services. Moreover, the input data that service si receives from si-1, si. InF, may cause certain side effects inside si, such as updating si's backend database using data computed from si. InF. Service si may wish to allow such side effects in one situation while reject some side effects in another situation. All these decisions should be made based on the service's information flow control policies. To achieve fine-grained information flow control, it is also necessary to analyze the flow and processing of the data and derive the dependencies between the data dynamically generated or used in a service chain. In this paper, we develop a run-time information flow control model for service cloud. First, we develop a run-time dependency analysis mechanism which enables each service in the service chain to determine the correlation between the locally accessed data and the data dynamically generated by the services in the service chain. Then, we develop a model to enable each service in a service chain to specify policies on how its sensitive information can be released to its subsequent services and what types of input data from prior services can be accepted and how they can flow within the services. Finally, we design a run-time protocol to enforce these policies in a service chain.

Ensuring secure information flow is a critical task for service composition in multi-domain systems. Research in security-aware service composition provides some preliminary solutions to this problem, but there are still issues to be addressed. In this paper, we develop a service composition mechanism specifically focusing on the secure information flow control issues. We first introduce a general model for information flow control in service chains, considering the transformation factors of services and security classes of data resources in a service chain. Then, we develop general rules to guide service composition satisfying secure information flow requirements. Finally, to achieve efficient service composition, we develop a three-phase protocol to allow rapid filtering of candidate compositions that are unlikely to satisfy the information flow constraints and thorough evaluation of highly promising candidates. Our approach can achieve effective and efficient service composition considering secure information flow.

Existing Web service access control models focus on individual Web services, and do not consider service composition. In composite services, a major issue is information flow control. Critical information may flow from one service to another in a service chain through requests and responses and there is no mechanism for verifying that the flow complies with the access control policies. In this paper, we propose an innovative access control model to empower the services in a service chain to control the flow of their sensitive information. Our model supports information flow control through a back-check procedure and pass-on certificates. We also introduce additional factors such as the carry-along policy, security class, and transformation factor, to improve the protocol efficiency. A formal analysis is also presented to show the power and complexity of our protocol.

In recent years, the issues in web service security have been widely investigated and various security standards have been proposed. But most of these studies and standards focus on the access control policies for individual web services and do not consider the access issues in composed services. Consider a simple service chain where service s1accesses s2, and s2, in turn, accesses service s3. The information returned from s3to s2may be used to compute some results that are further returned to s1. The current web service security framework does not provide any mechanisms to control such an information flow, and hence, sensitive information may be leaked to s1without the consensus of s3. In this paper, we propose an enhanced security model to facilitate the control of information flow through service chains. It extends the basic security models by introducing the concepts of delegation and pass-on. Based on these concepts, new certificates, certificate chain, delegation and pass-on policies, and how they are used to control the information flow are discussed.