Current deep learning-based medical image classification methods face challenges in effectively learning correct classification boundaries when dealing with imbalanced data. Traditional data augmentation methods often suffer from insufficient diversity, leading to limited performance improvement. Based on this, this paper proposes a Boundary and Diversity-aware Generative Adversarial Network (BDGAN) for data augmentation, focusing on class boundaries and intra-class diversity. First, to enhance the diversity of generated samples, we design a multi-generator GAN architecture, where each generator learns and generates different data patterns. Second, to further generate more diverse and higher-quality samples, we introduce mutual exclusion loss and Hausdorff loss. Finally, for downstream classification tasks, we design a sampling method based on One-Class SVM (OCS), enabling the GAN to focus more on training and generating boundary samples during the training process. Experimental results on two real-world medical image datasets demonstrate that the proposed method can generate more diverse and higher-quality augmented samples, effectively improving the performance of downstream classification tasks.

Addressing the persistent challenge of learning from imbalanced datasets is crucial in advancing machine learning applications. Standard machine learning algorithms typically assume that the input data is balanced, and they often struggle to effectively learn the distribution of minority class data when dealing with imbalanced data. To address this, our study designed an improved Generative Adversarial Networks (GANs) model, named MDGAN, for tabular sample synthesis to augment samples and balance the data distribution. MDGAN employs a multi-generator and multi-discriminator structure to capture non-connected subspace manifolds, thereby better fitting the complete data distribution. To enhance the diversity among the multiple generators, an exclusive loss among generators was designed, ensuring that each generator produces data of different modalities. Additionally, a contrastive loss was introduced to ensure that the generated samples better fit the minority class distribution and are separated from the majority class distribution, preventing blurred classification boundaries. Qualitative and quantitative tests were conducted on 25 real datasets, and the experimental results indicate that MDGAN outperforms traditional classical models and current advanced oversampling models.

The big data of Internet of Vehicles contributes to the development of intelligent transportation. Privacy protection in vehicular ad hoc networks (VANETs) is the core factor to improve user and vehicle participation. This article proposes a novel blockchain-based dynamic extensible privacy protection and message authentication scheme for VANETs. It minimizes the computation cost of message authentication based on an elliptic curve and message batch verification. Based on the Chinese remainder theorem, this scheme protects transmitted message security by adaptively and dynamically responding to vehicles and roadside units accessing the VANET. It offers a smart contract-based forensics and tracing solution from the accident vehicle. In addition, strict security proof and analysis that the scheme meets the security requirements for the VANET. It evaluates the efficiency of the scheme, and the results show its practicality.

The barriers of food enterprises and departments caused information asymmetry, which is the root cause of food safety incidents. Simultaneously, it is challenging to solve the information asymmetry by the existing cloud-based food supply-chain regulation system. Establishing a secure and reliable data sharing environment is an effective solution to the information island. Blockchain can construct a security network based on mathematical algorithms, eliminating the third party’s potential security risk, and realize transparently share data. In this paper, on the principle of metadata remaining in the food enterprises, we propose a blockchain-cloud fusion scheme based on Decentralized Attribute-Based Signature (DABS) to realize secure data sharing between departments. It constructs a decentralized and trusting environment for data owners to share data and achieves social co-governance of food safety based on the smart contract. It can also preserve the existing system architecture and complement the performance disadvantage of blockchain and cloud storage. The result achieved from security analysis shows that our scheme supports unconditional full anonymity and can resist collusion attacks of N-1 out of N corrupted attribute authorities.

Attribute Based Encryption (ABE) scheme can achieve information sharing of one-to-many users, without considering the number of users and the users identity. But, the traditional single Attribute Authority (AA) ABE scheme can hardly meet requirements of different agencies in distributed application environment and it is easy to form the system performance bottlenecks. Based on ciphertext-policy ABE scheme, this paper proposes a multi-authority revocable ABE scheme, where the classification manages user attributes, effectively relieving the management burden of single organization. In addition, it can achieve fine grained access control of shared information by adopting tree access strategy and secret sharing scheme, and support system attribute revocation. Finally, we show that the scheme is secure against chosen plaintext attack under the Decisional Bilinear Diffie-Hellman (DBDH) assumption.

Deals with basic research on underwater docking of flexible underwater structures using an active control system. The actively controlled underwater docking has such potentials on the installation or construction of underwater structures at deep water depth that the structures can be installed precisely on the desired point of seabed. For very large structure automated construction, it can be possible to assemble the onshore fabricated partitioned structures on site. In the paper, the H/sup /spl infin// controller is used for flexible structural control to avoid spillovers. Basin tests were executed using two types of neutrally buoyant flexible models with ultrasound ranging system and thrusters.