Lung cancer is one of the leading causes of global mortality, and accurate lung nodule segmentation in thoracic medical images is crucial for lung cancer analysis and diagnosis. In recent years, Vision Transformer (ViT) have been increasingly applied to medical image segmentation, demonstrating advantages in accuracy and scalability. However, existing ViT-based models still face limitations in segmenting heterogeneous targets with significant variations in shape and size. To address this, we propose a novel Transformer module, T-Unet, which leverages deformable convolutions. Our innovations include: (1) A deformable convolution-based attention mechanism that uses multi-scale feature fusion to generate offsets and improve attention precision; (2) Unique deformable patch embedding and position embedding layers designed for the Transformer module to enhance feature extraction; (3) Integration of this module into the UNet architecture to form T-Unet. We conducted experiments on the LIDC-IDRI, NSCLC and LUNA16 datasets. The results indicate that T-Unet performs remarkably in the lung nodule segmentation task. On the LIDC-IDRI dataset, it achieved a Dice coefficient of 91.89% and an IoU of 83.57%. On the NSCLC dataset, it achieved a Dice coefficient of 92.26% and an IoU of 82.73%. Additionally, on the LUNA16 dataset, T-Unet achieved a Dice coefficient of 92.08% and an IoU of 82.53%.

The intelligent monitoring and classification of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis of cardiovascular diseases. Despite significant progress in deep learning-based ECG analysis, existing models still struggle to effectively capture key information from critical signal regions while neglecting the impact of patient-specific attributes on ECG signals, thereby limiting their accuracy and generalization. To address these issues, we propose MFCA-UNet, a multi-source feature fusion cross-attention enhancement network. Specifically, we integrate a multi-scale feature attention module into the UNet backbone to improve the model’s sensitivity to crucial features. Meanwhile, the lead fusion module is optimized to facilitate effective interaction between local and global information across different leads. Additionally, we design a cross-fusion encoder to represent and extract patient-specific demographic features through feature mapping, which are then cross-fused with ECG signal data. Extensive experiments on the public datasets PTB-XL and Chapman show that the proposed method performs better than current advanced electrocardiogram classification models.

In recent years, cloud storage has been favored by users and operators due to its high availability, scalability, and low cost. However, due to the physical control of data being transferred to cloud servers, using only a single cloud service provider (CSP) poses security risks. Therefore, multi cloud storage solutions are becoming an increasingly popular choice for users.However, in current multi-cloud storage solutions, data integrity is still at risk of being compromised. To address this problem, this article designs a cloud data auditing scheme based on blockchain in a multi-cloud storage environment to ensure data integrity, as well as the reliability and credibility of the interaction process. This article proposes a data auditing scheme based on the improved Merkle tree algorithm, which addresses the potential malicious behavior of cloud servers and ensures the repeatability and reliability of auditing. This article also designs a data audit arbitration scheme based on smart contracts to solve disputes between users and CSP over auditing results, and to address the pain points of the current lack of such a solution. The final theoretical and experimental analysis results indicate that our scheme has higher security and faster verification process in multi cloud storage.

The widespread spread of disinformation has a significant negative impact on individuals and even society as a whole. Therefore, this paper proposes a novel hybrid model that organically combines blockchain technology with machine learning models. The hybrid model adopts a decentralized blockchain framework and smart contracts prior to information release to ensure its independence, and uses the incentive characteristics and trust scores of the blockchain to ensure that auditors send the correct audit results, and then uses machine learning technology to detect error information. We selected two public datasets, MediaEval-2015 and LIAR. Through experimental comparative analysis, the classification model with high accuracy was selected to effectively prevent the release of false information.

VNDN (Vehicular Named Data Networking) applies completely new communication mechanism to the VANET (Vehicular Ad hoc Network), which can effectively improve the efficiency of vehicle communication and reduce communication overhead. However, the dynamic of the vehicle network topology causes malicious nodes to join the network and send fake messages to legitimate nodes, which brings many security challenges to the reliable communication between vehicles in VNDN. Trust management in VNDN is mainly used to evaluate the security and reliability of messages and nodes in the network and is an important technical means in the field of security of VANET. In this paper, we proposed a trust management mechanism, including two parts: a fake message detection mechanism based on multi-attribute decision-making and a malicious node detection mechanism based on LSTM (Long Short-Term Memory). Firstly, the fake message in the network is detected by the vehicle node using multiple attributes, and then the malicious node is detected by LSTM based on the multiple features of the node. Finally, the proposed mechanism is tested by using the data obtained from the simulation experiment on the simulation platform. The experimental results show that the mechanism proposed in this paper can effectively improve the detection effect of fake messages and malicious nodes, and build a reliable trust relationship between nodes, thereby improving the security of VNDN.

Social media, with its immediacy and convenience, has become an important channel for people to exchange information. However, this freedom of information dissemination also provides a breeding ground for the spread of disinformation. Because the image will inevitably leave traces in the process of tampering and compression. Therefore, this paper proposes a convolutional neural network model SE-CNN, which, combined with the SE attention module, designs two inputs, on the one hand accepting the original image and on the other hand converting the image into error Level Analysis (ELA) format. The open source dataset CASIA V1.0 was selected for testing in this article. The experimental results show that the accuracy of this model on this dataset is 99.22%, which has stronger generalization ability than the traditional CNN model.

Web 3.0, an integral component of the future internet, aims to offer users more diversified and engaging web services. Blockchain as a crucial technology, addresses decentralization, data security, and tamper resistance, laying the groundwork for the next generation of the internet. The blockchain consensus algorithm plays a pivotal role in supporting the infrastructure and services of Web 3.0. However, the widely adopted PBFT consensus algorithm in blockchain suffers from significant communication overhead and arbitrary selection of primary nodes, resulting in low consensus efficiency and compromised security. To address this, we propose an enhanced PBFT consensus mechanism based on the k-medoids clustering algorithm and a reputation evaluation mechanism. Initially, nodes are hierarchically clustered based on location features using the improved K-medoids algorithm before the consensus process begins, thereby simplifying the three-phase consensus process into two- phase. Additionally, a dynamic reputation evaluation mechanism is introduced to enhance the election process of primary nodes. Experimental results demonstrate that the KR-PBFT algorithm effectively reduces communication overhead and consensus latency while maintaining a low view change frequency and certain fault tolerance, thereby ensuring system security and stability.

The Vehicle Named Data Networking(V-NDN) is a vehicular ad-hoc network with the Named Data Networking(NDN) as the architecture, and the most advantageous feature is the in-network cache, which caches the content in the intermediate nodes of the network and can quickly satisfy the requests of subsequent consumers for the same content. Since the cache space of nodes is limited, the cached content should be the popular content frequently requested by users in the network, so the most important problem is accurately finding out the future popular content in the network. This paper designs a multi-feature content popularity prediction algorithm to address this problem based on the attention mechanism and GRU (GRU-Attention). According to the characteristics of multiple historical requests for content, the GRU-Attention model is used to predict the future popularity of content. Through experimental verification, the content popularity prediction algorithm proposed in this paper effectively improves the accuracy of prediction.

With the explosive development of technology and Internet communication, it has become an inevitable trend to realize the secure storage and sharing of electronic medical data among hospitals. In recent researches, there are also many problems in realizing secure storage and sharing of electronic medical data, such as "data silos", leakage of patient sensitive information due to data sharing and having no reliability about the original data uploaded by patients. To solve the above problems, we propose a blockchain-based medical data storage and secure sharing scheme. In the scheme, we utilize IPFS-based Web3.Storage for medical data storage, propose a sensitivity classification and access control strategy for sensitive data leakage and present a blockchain-based original data reliability checking strategy to check the reliability of the original data. Our scheme is explained in detail in the paper, and the performance analysis of this scheme is carried out to prove the feasibility of this scheme.

With the rapid development and popularization of block chain technology, some social platforms based on block chain technology have emerged. Due to the characteristics of block chain technology, the social behaviors and ways of users on the network have been affected, thus changing the process of information transmission. Based on the characteristics of information transmission in blockchain social networks, this paper proposes a new information transmission model by comprehensively considering the influence of factors such as blockchain incentive mechanism, node intimacy in traditional social networks and individual differences on the state transition probability. A new voting node has been added to the model, representing users on the blockchain network who are affected by the incentive mechanism. In the simulation experiment, by adjusting the weight of each factor, the influence on the number of spreaders in the information transmission model is analyzed. Moreover, by comparing the model proposed in this paper with the traditional social network model, the number of propagation nodes is reduced by 17.32%. The experimental results show that the blockchain environment can effectively curb the spread and spread of false news, and achieve a good ecological network public opinion communication environment.

V-NDN (Vehicular Named Data Networking) applies completely new communication mechanism to the Internet of Vehicles, which can effectively improve the efficiency of vehicle communication and reduce communication overhead. However, the dynamic characteristics of the vehicle network topology cause malicious nodes to join the network and send fake messages to normal nodes, which brings many security challenges to the reliable communication between vehicles in the V-NDN. In this paper, we propose a method based on machine learning to detect fake messages and malicious nodes. The proposed method simplifies the detection process into a classification process and combines with a variety of machine learning algorithms such as logistic regression, KNN. It can intelligently identify fake messages and malicious nodes in the V-NDN. The simulation results show that the method can effectively increase the accuracy and efficiency of identifying fake messages and malicious nodes and improve the security in the V-NDN.

The Vehicular Named Data Networking (V-NDN) improves the speed of message acquisition between vehicles and reduces network overhead by using a in-network caching mechanism. The vehicles in V-NDN have the capability of built-in caching, in other words, they can cache contents passing by and provide content services for users. However, malicious nodes in V-NDN might apply fake messages for malicious purposes, which is one of the major risks of network security. In this paper, we build a trust management mechanism based on blockchain to solve the above problems. In the proposed mechanism, vehicles first judge the credibility of the received message based on the vehicle reputation value and the feature of the message itself. Then the vehicle reputation value is updated according to the message credibility. Finally, the blockchain is used to realize the consensus of the message credibility and the vehicle reputation value. We conduct experiments on the simulation platform and simulation results show that the proposed mechanism can effectively improve the accuracy of message credibility judgment and malicious vehicles detection, thereby improving the security of the V-NDN.

Named Data Networking (NDN), as one of the most promising future Internet architectures, can effectively relieve the bandwidth pressure caused by the rapid growth of network users and network traffic. It uses a content-based communication framework, in which the routers in NDN have the capacity of caching contents passing by and provide data services for incoming requests. The caching strategies, however, currently widely used in NDN are facing several challenges including low cache utilization, long service response time and so on. To address the above issue, considering the dynamic changes of routers’ location in different request/reply processes, a caching placement strategy based on dynamic router hierarchy (DRH) is proposed to determine the caching routers on the data return path. The strategy includes a dynamic hierarchical algorithm of routers, a content popularity classification algorithm and a cache-matching algorithm. By analyzing the users' requests for different contents and the location of routers, DRH determines the content popularity and importance of routers, and then caches the contents of different popularity levels on the corresponding routers. The level of each router is determined only in each request/reply process, that is, the same router has different levels in different request/reply paths. We implement DRH and conduct performance evaluations based on ndnSIM. The simulation result shows that DRH outperforms baseline solutions in terms of cache-hit rate, network throughput and average cache-hit distance.

Named Data Networking (NDN) is one of the future Internet architectures and has recently attracted a great deal of attention. NDN adopts named routing and caches all contents passing by. So it helps to reduce network traffic, accelerate content distribution and reduces content retrieval latency. However, the default caching strategy named Leave Copies Everywhere (LCE) also brings a large number of redundant data and the higher number of content replacements in NDN. In this paper, we present a Dynamic Popularity-Based Caching Permission strategy (DPCP). DPCP takes advantage of interest packet and data packet to carry content popularity, so routers in the path can obtain the information about the content popularity and uses dynamic popularity threshold to make cache permission policy. It also introduces cache control flag to avoid caching the same redundant copies in adjacent routers. The simulation results show that the DPCP strategy can effectively reduce the amount of redundant data in the network as well as reduce the number of the content replacement while acquiring the similar cache hit ratio compared to LCE.