A smart contract is a piece of computer program that runs on the blockchain, which has the characteristics of automatic execution, non-tampering, and publicity. Smart contracts control the flow of large amounts of high-value data, and attackers can exploit vulnerabilities in smart contracts to steal funds or resources. Existing detection methods, such as symbol execution, have problems such as path explosion and high false positive rate, while machine learning methods are black-box and uninterpretable. In order to solve the above problems, an expert mode based on expert mode and explainable machine learning was proposed to detect vulnerabilities in smart contract code, an expert mode for vulnerabilities was designed, and shapley additive explanations (SHAP) was used to explain the weights of multiple features, and the average detection accuracy of four vulnerabilities (re-entrancy vulnerability, timestamp vulnerability, integer overflow vulnerability, and permission control vulnerability) reached 90.36% , which achieved better detection results compared with classic tools such as Oyente and Mythril.

Backscatter communication has received widespread attention in the field of the Internet of things due to its advantages of low cost and low energy consumption. However, with the gradual expansion of data services, new challenges have been posed to the transmission performance of backscatter communication. Multiple-input multiple-output (MIMO) technology has been introduced into backscatter communication due to its advantages in system reliability and transmission rate. One of them, a dual ended joint coding technology called block-level unitary query space-time block code (BUTQ-STBC) , can fully exploit the diversity potential of backscatter channels, but at the cost of sacrificing transmission rate. To combat this issue, a new signal transmission scheme that selects the optimal communication link for data transmission based on the idea of spatial matching is proposed. The simulation results show that the proposed transmission scheme is superior to the BUTQ-STBC scheme. The proposed scheme provides a solution for efficient and reliable transmission of MIMO backscatter communication.

A method is proposed to maximize the minimum user rate for full-duplex (FD) rate splitting multiple access (RSMA) systems. Firstly, the optimization problem of maximizing the minimum user rate in the system is constructed by taking the base station precoding matrix, power allocation, and common rate as optimization parameters, and the user power and rate requirements as constraint conditions. Then, the optimization objective function and the constraints are simplified by introducing slack variables respectively, and the first-order Taylor expansion is used to linearize constraints, so as to transform the non-convex optimization problem into a convex one. Finally, the optimization problem is solved based on iteration and convex (CVX) method. The simulation results show that the minimum user rate increases with the increase of the transmit power of the base station, and the minimum user rate of the proposed scheme is higher than that the existing scheme for the same scenario.

The 5th generation of mobile communications system (5G) virtual private network carrying power business not only increase system capacity, but also bring new security challenges due to the openness. Conducting security analysis on wireless power systems can effectively identify security risks and guide the design of subsequent security protection strategies. However, existing methods lack targeted framework construction and fail to provide intuitive quantitative evaluations. In this paper, a security risk assessment method for power 5G virtual private network is proposed based on the characteristics of power business. On the basis of building the assessment indicators framework, the analysis of security risks is conducted from subjective and objective aspects through analytical hierarchy process and entropy value method. Finally, the effectiveness and applicability of the proposed safety risk assessment method is proved through example analysis.

In the field of Internet of things (IoT) intrusion detection, federated learning has become an effective solution for implementing model weight integration updates. This distributed learning method allows devices to train models locally and transmit updated parameters to a central server for aggregation. However, existing intrusion detection methods based on federated learning still have limitations. In scenarios with non-independent and identically distributed data and heterogeneous client models, the intrusion detection performance of the global model will be severely affected. The significant communication overhead caused by simultaneously transmitting model parameters also hinders the actual deployment of federated learning schemes. To address the aforementioned issues, an efficient IoT intrusion detection method based on semi supervised federated learning is proposed. By utilizing unlabeled public data to enhance the model's understanding of the data, the performance of the client classifier is continuously improved. At the same time, a discriminator module is added to improve the quality of the client's predicted labels, and the combination of hard label strategy and voting mechanism effectively reduces communication overhead. The experimental results show that an accuracy of 86.97% is achieved in non-independent and identically distributed data and heterogeneous client model scenarios, which is superior to typical federated learning methods and achieves lower communication overhead.

Brain tumor segmentation is a key task in medical image analysis due to the heterogeneous and irregular nature of tumor regions. To address the limitations of existing methods in modeling long-range dependencies and reducing resource consumption, we propose a lightweight segmentation model based on a hybrid convolutional neural network (CNN) and Transformer encoder. Depthwise separable convolutions are employed in shallow layers to reduce computation, while the proposed shuffle former block (SFB) integrates Transformer and ShuffleNet v2 to effectively capture both global and local context. Furthermore, lightweightattention modules are introduced to model long-range dependencies and enhance local perception. Experimental results on the BraTS 2019 dataset demonstrate that our model achieves Dice scores of 93.1% in whole tumor (WT) , 92.2% in tumor core (TC) , and 91.2% in enhancing tumor (ET) , with only 0.98M parameters and 54.60G floating point operations per second, achieving a superior balance between segmentation accuracy and computational efficiency for deployment in resource-constrained clinical settings.

In the sixth generation (6G) mobile communication system, intelligent reflecting surfaces (IRS) enhance wireless transmission efficiency by dynamically adjusting the wireless propagation environment. In distributed IRS-assisted multiple-input multiple-output (MIMO) systems, when the number of users significantly exceeds the number of antennas at the base station (BS) , the joint optimization of user selection and beamforming is crucial for reducing BS transmission power and promoting green communication. To address this, a model is established with the objective of minimizing BS transmission power by jointly optimizing user selection, BS beamforming vectors, and phase shift matrices of distributed IRSs while ensuring user quality of service (QoS). To simplify the model, it is decoupled into two subproblems that are iteratively optimized to approximate the optimal solution of the original problem. First，the artificial bee colony（ABC）algorithm and second-order cone programming（SOCP）are employed to determine the optimal user selection strategy and BS beamforming vectors. Then，the phase shift matrices of the IRSs are optimized using the semidefinite relaxation（SDR）method. Simulation results demonstrate that the proposed algorithm not only achieves good convergence but also effectively reduces the transmission power of the BS.

Deep convolutional neural networks are widely used in structural magnetic resonance imaging (sMRI) analysis for the early diagnosis of Alzheimer's disease. To address the challenge of efficient representation learning in sMRI, this study proposes a two-pathway convolutional network that improves the computational efficiency of sMRI feature extraction by representation decoupling, and further strengthens the representation discriminability through adaptive feature fusion. The network consists of three parts：1) A high-channel-capacity slice path, which processes sparse slices to encode semantic information of slice images；2) A low-channel-capacity context path, which processes dense slices to capture inter-slice contextual information；3) An adaptive feature fusion module, which integrates the decoupled information from both paths to generate more effective sMRI representations. The proposed method was evaluated on two tasks—Alzheimer's disease classification and mild cognitive impairment conversion prediction—using the Alzheimer's disease neuroimaging initiative (ADNI) dataset. The results demonstrate that the proposed approach surpasses the baseline models in both computational efficiency and diagnostic performance, while achieving results comparable to those of current state-of-the-art methods.

Aimingat solving the problems of large amounts of computation and simplifying high complexity of M-ary low density parity check code (LDPC) decoding algorithm, a M-ary LDPC improved decoding algorithm is proposed based on the characteristics of BeiDou-3 (BD-3) B1C satellite signal. On the one hand, the proposed algorithm adopts the dimension reduction and edge complement mode to reduce the calculated amount of decoding algorithm by reducing the calculation dimension between nodes in the updating process of verification equation；On the other hand, to reduce the decoding complexity, the algorithm uses the way in which the nodes update simultaneously, which means the check node updates as the variable node updates. The simulation results show that the improved algorithm reduces the decoding computation and simplifies the decoding algorithm complexity without affecting the decoding performance in the dynamic range of receiving level of BD-3 B1C satellite signals. Compared with before optimization, the calculation amount is reduced to 25% of the original algorithm in one iteration, and 20000 times of data storaging and reading operations are saved when the calculation dimension is 32 by updating the node algorithm. Moreover, with the improvement of signal to noise ratio (SNR), the decoding computation amount and complexity can be more simplified by using smaller dimensions.

Aiming at the problems of inadequate utilization of structural semantics and poor repair results of detailed features in the existing deep learning methods for repairing ancient murals, a structure-guided diffusion generative algorithm was proposed. Firstly, a mural structure reconstruction module composed of gated convolution and fast Fourier residual block is constructed, and the edge structure after reconstruction is used to guide the repair of damaged murals, so as to overcome the problem of insufficient utilization of structural semantic repair. Then, a generative diffusion module based on stochastic differential equation is proposed, which performs forward diffusion processing on the mural image to be repaired by stochastic differential equation. Next, a mask-enhanced backward iterative reconstruction module is designed to enhance the semantic consistency between the damaged area and the intact area of the mural, and improve the repair ability of the detailed features of the mural. Finally, the digital inpainting experiments and analysis are carried out on the Dunhuang mural data set. The experimental results show that the proposed algorithm can effectively complete the mural restoration, and the objective evaluation indicators are better than the comparison algorithms.