In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent"brain"to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm still has limitations.K-means algorithm is widely used to solve traffic clustering problems,but it has limitations,such as sensitivity to initial points and poor robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA)and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA)to solve the problem of urban taxi-stand locations.Based on the traffic trajectory data of Xi’an and Chengdu provided by Didi Chuxing,the clustering results of our algorithm and K-means algorithm are compared.We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means,and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%,up to approximately 83%,with higher robustness.QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum,and brain-inspired cognitive computing provides search feedback and direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.
Defending against return-oriented programing(ROP) attacks is extremely challenging for modern operating systems.As the most popular mobile OS running on ARM,Android is even more vulnerable to ROP attacks due to its weak implementation of ASLR and the absence of effective control-flow integrity enforcement.In this paper,leveraging specific ARM features,an instruction randomization strategy to mitigate ROP attacks in Android even with the threat of single pointer leakage vulnerabilities is proposed.By popping out more registers in functions' epilogue instructions and reallocating registers in function scopes,branch targets in all(direct and indirect) branch instructions potential to be ROP gadgets are changed randomly.Without the knowledge of binaries' runtime instructions layout,adversary's repeated control flow transfer in ROP exploits will be subverted.Furthermore,this instruction randomization idea has been implemented in both Android Dalvik runtime and ART.Corresponding evaluations proved it is capable to introduce enough randomness for more than 99% discovered functions and thwart about 95% ROP gadgets in application's shared libraries and oat file compiled from Dalvik bytecode.Besides,evaluations on real-world exploits also confirmed its effectiveness on mitigating ROP attacks within acceptable performance overhead.
With the slow progress of universal quantum computers,studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important.The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations.This study constructs a new Quantum-Inspired Annealing(QIA)framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one.Through various configurations of the 1 D Ising model,the new framework can achieve ground state,corresponding to the optimum of classical problems,with higher probability up to 28%versus classical counterpart(22%in case).This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian,but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.
