Due to the ambiguity of text and the lack of location information in training data， current state-of-the-art diffusion model cannot accurately control the locations of generated objects in the image under the condition of text prompts. To address this issue， a spatial condition of the object’s location range was introduced， and an attention-guided method was proposed based on the strong correlation between the cross-attention map in U-Net and the image spatial layout to control the generation of the attention map， thus controlling the locations of the generated objects. Specifically， based on the Stable Diffusion （SD） model， in the early stage of the generation of the cross-attention map in the U-Net layer， a loss was introduced to stimulate high attention values in the corresponding location range， and reduce the average attention value outside the range. The noise vector in the latent space was optimized step by step in each denoising step to control the generation of the attention map. Experimental results show that the proposed method can significantly control the locations of one or more objects in the generated image， and when generating multiple objects， it can reduce the phenomenon of object omission， redundant object generation， and object fusion.

Exact cover problems are NP complete problems in combinatorial optimization， and it is difficult to solve them in polynomial time by using classical algorithms. In order to solve this problem， on the open source quantum computing framework qiskit， a quantum circuit solution based on Quantum Approximate Optimization Algorithm （QAOA） was proposed， and Constrained Optimization BY Linear Approximation （COBYLA） algorithm based on the simplex method was used to optimize the parameters in the quantum logic gates. Firstly， the classical Ising model was established through the mathematical model of the exact cover problem. Secondly， the classical Ising model was quantized by using the rotation variable in quantum theory， and then the Pauli rotation operator was used to replace the rotation variable to obtain the quantum Ising model and the problem Hamiltonian， which improved the speed of QAOA in finding the optimal solution. Finally， the expected expression of the problem Hamiltonian was obtained by the accumulation of the product of the unitary transformation with the mixed Hamiltonian as the generator and the unitary transformation with the problem Hamiltonian as the generator， and the generative quantum circuit was designed accordingly. In addition， the classical processor was used to optimize the parameters in the two unitary transformations to adjust the expected value of the problem Hamiltonian， thereby increasing the probability of solution. The circuit was simulated on qiskit， IBM’s open source quantum computing framework. Experimental results show that the proposed scheme can obtain the solution of the problem in polynomial time with a probability of 95.6%， which proves that the proposed quantum circuit can find a solution to the exact cover problem with a higher probability.

Aiming at the problems that the traditional Support Vector Machine (SVM) classifier is sensitive to outliers and has the large number of Support Vectors (SV) and the parameter of its separating hyperplane is not sparse, the Truncated hinge loss SVM with Smoothly Clipped Absolute Deviation (SCAD) penalty (SCAD-TSVM) was put forward and was used for constructing the financial early-warning model. At the same time, an iterative updating algorithm was proposed to solve the SCAD-TSVM model. Experiments were implemented on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets. Compared to the T-2 and T-3 models constructed by SVM with L1 norm penalty (L1-SVM), SVM with SCAD penalty (SCAD-SVM) and Truncated hinge loss SVM (TSVM), the T-2 and T-3 model constructed by the SCAD-TSVM had the best sparseness and the highest accuracy of prediction, and its average accuracies of prediction with different number of training samples were higher than those of the L1-SVM, SCAD-SVM and TSVM algorithms.

To deal with the increasingly serious situation of document's security and better protect the controlled documents, in this paper, an identity-based On-The-Fly Encryption (OTFE) and decryption scheme was proposed for the controlled documents, which combined an Identity-Based Encryption (IBE) algorithm with an on-the-fly encryption technique. In the scheme, file system filter driver technology was used to monitor program's behaviors on the controlled documents; meanwhile, the IBE algorithm was used to encrypt and decrypt the controlled documents. Specifically, a new algorithm that associated the original ciphertext and divided the associated ciphertext into two parts stored in different locations was proposed. Therefore, it is impossible for an adversary to obtain the whole ciphertext and further recover the original plaintext. Finally, an elaborate description was made on the scheme from system level and algorithm level. The security analysis indicates that the proposed scheme is able to effectively protect the controlled documents.