arXiv subjects

Zhibin Lei

Publications and source records attributed to Zhibin Lei.

5 recordsLinked to original sources

arXiv2023

Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing

The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multi-classification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and R_y, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover-Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to O(sqrt(M)), and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to O(sqrt(M)). Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.

cs.LG↗

arXiv2023

Quantum Searchable Encryption for Cloud Data Based on Full-Blind Quantum Computation

Searchable encryption (SE) is a positive way to protect users sensitive data in cloud computing setting, while preserving search ability on the server side, i.e., it allows the server to search encrypted data without leaking information about the plaintext data. In this paper, a multi-client universal circuit-based full-blind quantum computation (FBQC) model is proposed. In order to meet the requirements of multi-client accessing or computing encrypted cloud data, all clients with limited quantum ability outsource the key generation to a trusted key center and upload their encrypted data to the data center. Considering the feasibility of physical implementation, all quantum gates in the circuit are replaced with the combination of π/8 rotation operator set {Rz(π/4), Ry(π/4), CRz(π/4), CRy(π/4), CCRz(π/4), CCRy(π/4)}. In addition, the data center is only allowed to perform one π/8 rotation operator each time, but does not know the structure of the circuit (i.e., quantum computation), so it can guarantee the blindness of computation. Then, through combining this multi-client FBQC model and Grover searching algorithm, we continue to propose a quantum searchable encryption scheme for cloud data. It solves the problem of multi-client access mode under searchable encryption in the cloud environment, and has the ability to resist against some quantum attacks. To better demonstrate our scheme, an example of our scheme to search on encrypted 2-qubit state is given in detail. Furthermore, the security of our scheme is analysed from two aspects: external attacks and internal attacks, and the result indicates that it can resist against such kinds of attacks and also guarantee the blindness of data and computation.

cs.ET↗

arXiv2019

An Augmented Transformer Architecture for Natural Language Generation Tasks

The Transformer based neural networks have been showing significant advantages on most evaluations of various natural language processing and other sequence-to-sequence tasks due to its inherent architecture based superiorities. Although the main architecture of the Transformer has been continuously being explored, little attention was paid to the positional encoding module. In this paper, we enhance the sinusoidal positional encoding algorithm by maximizing the variances between encoded consecutive positions to obtain additional promotion. Furthermore, we propose an augmented Transformer architecture encoded with additional linguistic knowledge, such as the Part-of-Speech (POS) tagging, to boost the performance on some natural language generation tasks, e.g., the automatic translation and summarization tasks. Experiments show that the proposed architecture attains constantly superior results compared to the vanilla Transformer.

cs.CL↗

arXiv2017

Cascaded Face Alignment via Intimacy Definition Feature

In this paper, we present a random-forest based fast cascaded regression model for face alignment, via a novel local feature. Our proposed local lightweight feature, namely intimacy definition feature (IDF), is more discriminative than landmark pose-indexed feature, more efficient than histogram of oriented gradients (HOG) feature and scale-invariant feature transform (SIFT) feature, and more compact than the local binary feature (LBF). Experimental results show that our approach achieves state-of-the-art performance when tested on the most challenging datasets. Compared with an LBF-based algorithm, our method can achieve about two times the speed-up and more than 20% improvement, in terms of alignment accuracy measurement, and save an order of magnitude of memory requirement.

cs.CV↗

arXiv2016

Efficient Likelihood Bayesian Constrained Local Model

The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM). Fitting the model to an object involves two steps. A response map, which represents the likelihood of the location of a landmark, is first computed for each landmark using local-texture detectors. Then, an optimal PDM is determined by jointly maximizing all the response maps simultaneously, with a global shape constraint. This global optimization can be considered as a Bayesian inference problem, where the posterior distribution of the shape parameters, as well as the pose parameters, can be inferred using maximum a posteriori (MAP). In this paper, we present a cascaded face-alignment approach, which employs random-forest regressors to estimate the positions of each landmark, as a likelihood term, efficiently in the CLM model. Interpretation from CLM framework, this algorithm is named as an efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in each stage of the regressors, the PDM non-rigid parameters of previous stage can work as shape clues for training each stage regressors. Experimental results on benchmarks show our approach achieve about 3 to 5 times speed-up compared with CLM models and improve around 10% on fitting quality compare with the same setting regression models.

cs.CV↗