具有时滞的利率-流通量方程的建立及其应用

本文对金融网络建立了带有时滞的利率-流通量方程，应用时滞微分方程理论，我们得到金融网络中，各节点利率最终将稳定于其基本利率的充分必要条件；给出了各节点利率均振动的充分必要条件以及存在周期解的充分必要条件。     

Fine-grained forward-secure signature schemes without random oracles

We propose the concept of fine-grained forward-secure signature schemes. Such signature schemes not only provide non-repudiation w.r.t. past time periods the way ordinary forward-secure signature schemes do but, in addition, allow the signer to specify which signatures of the current time period remain valid when revoking the public key. This is an important advantage if the signer produces many signatures per time period as otherwise the signer would have to re-issue those signatures (and possibly re-negotiate the respective messages) with a new key.Apart from a formal model for fine-grained forward-secure signature schemes, we present practical schemes and prove them secure under the strong RSA assumption only, i.e., we do not resort to the random oracle model to prove security. As a side-result, we provide an ordinary forward-secure scheme whose key-update time is significantly smaller than that of known schemes which are secure without assuming random oracles.     

A Bayesian record linkage model incorporating relational data

In this article, we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allows us to jointly characterize the network and linkage structures relying on both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter , Facebook , and YouTube .     

Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.     

Multiple re-configurable intelligent surfaces based physical layer eavesdropper detection for V2I communications

Inspired by the promising potential of re-configurable intelligent surface (RIS)-aided transmission in achieving the vision of 6th Generation (6G) network, we analyze the security model for a vehicular-to-infrastructure (V2I) network by considering multiple RISs (M-RIS) on buildings to act as passive relays at fixed distances from a source. In addition, multiple eavesdroppers are presented in the vicinity of the intended destination. Our aim is to enhance the secrecy capacity (SC) and to minimize secrecy outage probability (SOP) in presence of multiple eavesdroppers with the help of M-RIS in V2I communications. We propose a key-less physical layer security using beam-forming by exploiting M-RIS. The proposed approach assumes the concept of detecting eavesdroppers before the information can be transmitted via beam-forming by utilizing M-RIS. The results reveal that with consideration of M-RIS and beam-forming, the achievable SC and SOP performance is significantly improved while imposing minimum power consumption and fewer RIS reflectors.     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

基于个体仿真的机场安检系统优化研究

建立了安检流程的网络流模型,模型中包含了不稳定的乘客到达情形、嫌疑乘客以及反馈机制。分析了安检过程中的瓶颈所在,并给出了相应的优化方案,包括整个安检过程的通行规则,A区和B区内部预检节点与普通节点的适当比例等。仿真结果显示,为了充分利用安检资源并兼顾预检乘客的利益,通行规则应采用连通方案。依照统计数据中计算出的各种参数,在A区预检节点和普通节点比例设置为2∶2,B区预检节点和普通节点比例设置为3∶5的情况下,旅客平均等待时间最短,等待时间的标准差也最小,同时高峰期的通行量也相对较优。进一步分析了不同的文化背景及机场情况。对于美国人和瑞士人,通过改变预检乘客的选择概率来满足他们不同的行为偏好。仿真结果说明,预检乘客选择哪类节点进行安检对平均等待时间、等待时间的标准差和高峰期通行量的影响不大。分析了插队情形,插队对平均等待时间和高峰期通行量几乎没有影响,但当插队比例非常大时,旅客等待时间标准差会增加,影响乘客到达的准确性。最后,针对具体情况提出了一些合理的建议,并给出了进一步的研究计划。     

可持续供应链管理实践对企业绩效的影响:基于中国的实证研究

本文基于我国172家企业的问卷调查数据,运用结构方程模型,对企业可持续供应链管理实践及其对绩效的影响进行实证分析。结果表明,企业实现内部可持续管理能够对企业管理供应商的行为有着显著的影响;同时,企业管理供应商的行为作为企业外部管理实践对企业的绩效有着显著的正向影响;在企业绩效中,环境绩效和社会绩效对经济绩效有着显著的正向影响,企业的外部管理实践能够通过环境绩效和社会绩效的提高实现最终经济绩效的明显增加。     

Compressing strongly connected subgroups in social networks: An entropy-based approach

To detect and study cohesive subgroups of actors is a main objective in social network analysis. What are the respective relations inside such groups and what separates them from the outside. Entropy-based analysis of network structures is an up-and-coming approach. It turns out to be a powerful instrument to detect certain forms of cohesive subgroups and to compress them to superactors without loss of information about their embeddedness in the net: Compressing strongly connected subgroups leaves the whole net’s and the (super-)actors’ information theoretical indices unchanged; i.e., such compression is information-invariant. The actual article relates on the reduction of networks with hundreds of actors. All entropy-based calculations are realized in an expert system shell.     

Forecasting the integration of immigrants

In this research, we develop and introduce a theoretical and mathematical forecasting framework of immigrant integration using immigrant density as a single driver. First, we introduce the integration concepts we aim at forecasting. Thereafter, we introduce a theoretical and mathematical model of the relationship between integration and immigrant density. Based on this model, we develop a methodological forecasting framework. We test the framework using immigrant integration data from Spain. We produce the forecasts, and conduct the proper evaluation of them. Finally, we conclude with a brief discussion of the wider implications of our results.