Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks' location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies ε-differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.

Traffic sign detection has a wide range of applications. In the wild, the existing detectors are difficult to balance the requirements for robustness, real time, and accuracy. In this paper, we propose a one-level feature anchor-free traffic sign detector based on adaptive image enhancement and explicit objectness branch. Firstly, aiming to weather variations in outdoor scenes, we design an Image Enhancement Module to achieve strong robustness, including exposure, tone, and luminance three filters. We integrate a small convolutional network to predict filter parameters, which is jointly trained end-to-end and does not exist during inference. Secondly, we propose a Feature Encoder which consists of a One-Level Feature Extractor and a Receptive Fields Expander. In One-Level Feature Extractor, we introduce the Multilayer Perceptron (MLP) Block and design a Feature Aggregator (FA) to fully fuse feature maps of different resolutions. Receptive Fields Expander is used to make up for the deficiency of one-level feature map and generates output features with various receptive fields. Finally, to improve the accuracy based on the pixel-wise prediction, we train an explicit objectness branch of the detection head and improve the dynamic selection strategy of positive and negative samples. We weight Generalized Intersection over Union (GIoU) and Smooth L1 to propose a new localization loss. During inference, we multiply the predicted objectness value by the classification score as the final classification confidence to alleviate sample imbalance. To comprehensively evaluate the proposed method, we conduct experiments on GTSDB, TT100 K and CCTSDB 2021 datasets, and all achieve state-of-the-art results.

In this paper, we first present a simple seven-term 4D hyperchaotic system based on the classical Sprott-C 3D chaotic system. This novel system is inspired by the simple 4D hyperchaotic system based on Sprott-B proposed by A. T. Sheet (2022). We discuss the phenomenon of premature divergence brought about by the improper choice of coupling parameters in that paper and describe the basic properties of the new system with phase diagrams, Lyapunov exponential spectra and bifurcation diagrams. Then, we find that the dynamical behaviors of the system suffer from the limitation of the control parameters and cannot represent the process of motion in detail. To improve the system, we expand the dimensionality and add the control parameters and memristors. A 5D memristive hyperchaotic system with hidden attractors is proposed, and the basic dynamical properties of the system, such as its dissipation, equilibrium point, stability, Lyapunov exponential spectra and bifurcation diagram, are analyzed. Finally, the hardware circuits of the 4D Sprott-C system and the 5D memristive hyperchaotic system were realized by a field programmable gate array (FPGA) and verified by an experiment. The experimental results are consistent with the numerical simulation results obtained in MATLAB, which demonstrates the feasibility and potential of the system.

On the basis of the chaotic system proposed by Wang et al. in 2023, this paper constructs a 5D fractional-order memristive hyperchaotic system (FOMHS) with multiple coexisting attractors through coupling of magnetic control memristors and dimension expansion. Firstly, the divergence, Kaplan–Yorke dimension, and equilibrium stability of the chaotic model are studied. Subsequently, we explore the construction of the 5D FOMHS, introducing the definitions of the Caputo differential operator and the Riemann–Liouville integral operator and employing the Adomian resolving approach to decompose the linears, the nonlinears, and the constants of the system. The complex dynamic characteristics of the system are analyzed by phase diagrams, Lyapunov exponent spectra, time-domain diagrams, etc. Finally, the hardware circuit of the proposed 5D FOMHS is performed by FPGA, and its randomness is verified using the NIST tool.