Currently, most quantum homomorphic encryption (QHE) schemes only allow a single evaluator (server) to accomplish computation tasks on encrypted data shared by the data owner (user). In addition, the quantum computing capability of the evaluator and the scope of quantum computation it can perform are usually somewhat limited, which significantly reduces the flexibility of the scheme in quantum network environments. In this paper, we propose a novel $(t,n)$-threshold QHE (TQHE) network scheme based on the Shamir secret sharing protocol, which allows $k(t\le k\le n)$ evaluators to collaboratively perform evaluation computation operations on each qubit within the shared encrypted sequence. Moreover, each evaluator, while possessing the ability to perform all single-qubit unitary operations, is able to perform arbitrary single-qubit gate computation task assigned by the data owner. We give a specific (3, 5)-threshold example, illustrating the scheme’s correctness and feasibility, and simulate it on IBM quantum computing cloud platform. Finally, it is shown that the scheme is secure by analyzing encryption/decryption private keys, ciphertext quantum state sequences during transmission, plaintext quantum state sequence, and the result after computations on the plaintext quantum state sequence.

Attribute-based encryption mechanism is widely used in outsourcing systems because of its characteristics of 'one-to-many' communication. However, users' attributes often change dynamically. In order to solve the problem of attribute revocation in the attribute-based encryption scheme in the outsourcing systems, a revocable ciphertext policy attribute-based encryption in data outsourcing systems from lattices is proposed. The scheme uses the LWE problem to construct the encryption and decryption algorithm, which can resist the quantum attack. Tree-access structure is adopted to realise flexible fine-grained access strategy. In addition, with the help of the data outsourcing management server, the attribute key and ciphertext are updated to achieve immediate attribute revocation. The scheme is proved to be secure under the selective attribute and selective plaintext attacks. The comparative analysis shows that the scheme has a significant improvement in performance, and it supports immediate attribute revocation, which is more suitable for the dynamic change of users in the outsourcing systems, such as social network platforms.

Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-cl...

Head-Related Transfer Function (HRTF) describes the acoustic reflection and diffraction effect caused by the influence of the human body (head, torso, etc.) in the transmission of sound waves to the human ear. In Virtual Reality(VR) / Augmented Reality(AR), HRTF is often used to generate virtual 3D audio due to its ability to recreate perceptions of natural sound scenes realistically. However, HRTF varies from person to person due to the differences in anthropometric features. Using non-individualized HRTF to produce 3D sounds may lead to hearing localization bias in users. Therefore, how to obtain individualized HRTF is a hot topic in the field of VR / AR. This paper proposes an effective method to establish the relationship between anthropometric features and HRTF. At first, a method based on multimodal principal component analysis is proposed for the representation of HRTF models with low dimensions. Then a nonlinear mapping representation model between the low-dimensional features of HRTF and anthropometric features is established using Radial Basis Function Neural Network (RBFNN). Objective experiments show that the proposed HRTF Individualization method can reduce the spectral distortion as low as 4.48 dB. The subjective listening experiments based on the principal sagittal plane show that the individualized HRTF obtained using this method can effectively improve the accuracy of subjective listening (about 33%).

To address the challenges in forecasting crude oil and hot-rolled coil futures prices, the aim is to transcend the constraints of conventional approaches. This involves effectively predicting short-term price fluctuations, developing quantitative trading strategies, and modeling time series data. The goal is to enhance prediction accuracy and stability, thereby supporting decision-making and risk management in financial markets. A novel approach, the multi-dimensional fusion feature-enhanced (MDFFE) prediction method has been devised. Additionally, a data augmentation framework leveraging multi-dimensional feature engineering has been established. The technical indicators, volatility indicators, time features, and cross-variety linkage features are integrated to build a prediction system, and the lag feature design is used to prevent data leakage. In addition, a deep fusion model is constructed, which combines the temporal feature extraction ability of the convolution neural network with the nonlinear mapping advantage of an extreme gradient boosting tree. With the help of a three-layer convolution neural network structure and adaptive weight fusion strategy, an end-to-end prediction framework is constructed. Experimental results demonstrate that the MDFFE model excels in various metrics, including mean absolute error, root mean square error, mean absolute percentage error, coefficient of determination, and sum of squared errors. The mean absolute error reaches as low as 0.0068, while the coefficient of determination can be as high as 0.9970. In addition, the significance and stability of the model performance were verified by statistical methods such as a paired t-test and ANOVA analysis of variance. This MDFFE algorithm offers a robust and practical approach for predicting commodity futures prices. It holds significant theoretical and practical value in financial market forecasting, enhancing prediction accuracy and mitigating forecast volatility.