Regulation of Digital Financial Services in China: Last Mover or First Mover?

Since 1979, China has made tremendous progress in its transformation to a socialist market economy. As part of this process, China’s financial system has evolved to one characterised by a high degree of marketization. At the same time, China today faces new challenges to growth and development, particularly from the necessity of restructuring its economy to focus increasingly on innovation and away from government led investment and low wage labour. In the context of digital financial services, China has been a late mover but this has changed dramatically in the past five years, to the point today where China is one of the major centres for digital financial services and financial technology (“fintech”). Looking forward, China needs to provide an appropriate regulatory basis for the future development of digital financial services and fintech, balancing growth and innovation with financial stability. China today is exhibiting signs of a last mover advantage in this respect that may see it leaping regulatory developments elsewhere.postprin

Elastic Switch Migration for Control Plane Load Balancing in SDN

© 2013 IEEE. Software-defined network (SDN) provides a solution for the scalable network framework with decoupled control and data plane. Migrating switches can balance the resource utilization of controllers and improve network performance. Switch migration problem has to date been formulated as a resource utilization maximization problem to address the scalability of the control plane. However, this problem is NP-hard with high-computational complexities and without addressing the security challenges of the control plane. In this paper, we propose a switch migration method, which interprets switch migration as a signature matching problem and is formulated as a 3-D earth mover's distance model to protect strategically important controllers in the network. Considering the scalability, we further propose a heuristic method which is time-efficient and suitable to large-scale networks. Simulation results show that our proposed methods can disguise strategically important controllers by diminishing the difference of traffic load between controllers. Moreover, our proposed methods can significantly relieve the traffic pressure of controllers and prevent saturation attacks

Energy efficient duty cycle design based on quantum immune clonal evolutionary algorithm in body area networks

© 2015 ICST. Duty cycle design is an important topic in body area networks. As small sensors are equipped with the limited power source, the extension of network lifetime is generally achieved by reducing the network energy consumption, for instance through duty cycle schemes. However, the duty cycle design is a highly complex NP-hard problem and its computational complexity is too high with exhaustive search algorithm for practical implementation. In order to extend the network lifetime, we proposed a novel quantum immune clonal evolutionary algorithm (QICEA) for duty cycle design while maintaining full coverage in the monitoring area. The QICEA is tested, and a performance comparison is made with simulated annealing (SA) and genetic algorithm (GA). Simulation results show that compared to the SA and the GA, the proposed QICEA can extending the lifetime of body area networks and enhancing the energy efficiency effectively

Low energy clustering in BAN based on fuzzy simulated evolutionary computation

© 2015 ICST. A low energy clustering method of body area networks based on fuzzy simulated evolutionary computation is proposed in this paper. To reduce communication energy consumption, we also designed a fuzzy controller to dynamically adjust the crossover and mutation probability. Simulations are conducted by using the proposed method, the clustering methods based on the particle swarm optimization and the method based on the quantum evolutionary algorithm. Results show that the energy consumption of the proposed method decreased compared with the other two methods, which means that the proposed method significantly improves the energy efficiency

Scalable Node-Centric Route Mutation for Defense of Large-Scale Software-Defined Networks

© 2017 Yang Zhou et al. Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problem where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover's distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose that a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks

Delay-Guaranteed Admission Control for LAA Coexisting with WiFi

© 2012 IEEE. Licensed-assisted-access (LAA) is used to extend the LTE link into the unlicensed band. How to guarantee the quality-of-service (QoS) for LTE devices in the unlicensed band is a challenging problem due to the listen-before-talk contention access in 5-GHz unlicensed bands. In this letter, we quantitatively analyze the medium access control delay for tagged LAA eNBs and propose a delay-guaranteed admission control scheme. We consider the freezing time of busy slots caused by collision or successful transmission, and introduce the exponential backoff mechanism for delay analysis. Validated by simulation results, our method provides important insights into the system admission performance and fairness of access

Modified elite chaotic artificial fish swarm algorithm for PAPR reduction in OFDM systems

© 2014 IEEE. Orthogonal frequency division multiplexing (OFDM) is a leading technology in the field of broadband wireless communications. In OFDM systems, a high peak-to-average power ratio (PAPR) is a critical issue, which may cause a nonlinear distortion and reduce power efficiency. To reduce the PAPR, partial transmit sequences (PTS) technique can be applied to the transmit data. However, the phase factor sequence selection in PTS technique is a non-linear optimization problem and it suffers from high complexity and memory use when there is a large number of non-overlapping sub-blocks in one symbol. In this paper a novel modified elite chaotic artificial fish swarm algorithm for PTS method (MECAFSA-PTS) is proposed to generate the optimum phase factors. The MECAFSA-PTS method is evaluated with extensive simulations and its performance is compared with quantum evolutionary and selective mapping algorithms. Our results show that the proposed MECAFSA-PTS algorithm is efficient in PAPR reduction

A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms

Particle dynamics near extreme Kerr throat and supersymmetry

The extreme Kerr throat solution is believed to be non-supersymmetric. However, its isometry group SO(2,1) x U(1) matches precisely the bosonic subgroup of N=2 superconformal group in one dimension. In this paper we construct N=2 supersymmetric extension of a massive particle moving near the horizon of the extreme Kerr black hole. Bosonic conserved charges are related to Killing vectors in a conventional way. Geometric interpretation of supersymmetry charges remains a challenge.Comment: V2: 10 pages; discussion in sect. 4 and 5 extended, acknowledgements and references adde