基于QoE感知的星地一体化网络公平资源分配策略

星地一体化网络是卫星网络和地面网络互联，实现卫星网络和地面网络优势互补的一体化网络。随着无线通信的快速发展，卫星网络和地面网络的同频复用技术可有效提升资源利用效率，满足日益增长的无线数据业务需求。但由于无线数据业务类型的多样化和频谱资源的稀缺性，如何实现用户数据服务的服务体验质量（Quality of experience， QoE）和公平性提升也成为星地一体化网络亟待解决的关键问题。本文综合考虑信道时变性、服务体验多样性和服务公平性，提出了一种基于QoE感知的星地一体化网络资源分配策略。为此，将目标问题描述为基于时间平均的服务体验质量的网络公平效用最大化，并利用李雅普诺夫优化理论，将复杂的动态原始优化问题进一步转化并分解为3个瞬时优化子问题，每个子问题可以在每个时隙独立求解。同时，由于无线资源分配为典型的非凹混合组合优化问题，推导了一个低复杂度的两步算法来求解第三个资源分配子问题。仿真结果揭示了在不同的公平性参数下用户的长时QoE与公平性之间的折衷情况。     

Taxonomy of digital community currency platforms*

ABSTRACT

Community currencies are used to pay for products or services within specific groups defined by geographical boundaries or specific common interests. Financial crises, social emergence in developing countries, and increased access to digital devices have stimulated a growing number of communities worldwide to develop digital currency projects. These projects use technologies ranging from traditional plastic cards to mobile phones and blockchain technologies. Following the design science research approach, this paper analyzes digital community currencies (DCCs) by developing a taxonomy based on platform architecture, governance, transactionality and virtuality. By investigating 22 DCC platforms around the world, 4 groups were distinguished: local, proprietary, commons and cyber. The identification of these four different groups of digital community currencies allows us to better discuss the potentials and limitations of each one of them. The presented taxonomy can be useful to researchers and practitioners both to explain and to design DCC platforms. Discussing each of the emerging categories from the proposed taxonomy helps us to provide insights into DCCs, offering a new theoretical frame for investigating the particular case of digital payment platforms.     

Effect of temperature on raw whole milk density and its potential impact on milk payment in the dairy industry

The objective of this study was to determine the effect of temperature on whole milk density measured at four different temperatures: 5, 10, 15, and 20 °C. A total of ninety-three individual milk samples were collected from morning milking of thirty-two Holstein Friesian dairy cows, of national average genetic merit, once every two weeks over a period of 4 weeks and were assessed by Fourier transform infrared spectroscopy for milk composition analysis. Density of the milk was evaluated using two different analytical methods: a portable density meter DMA35 and a standard desktop model DMA4500M (Anton Paar GmbH, UK). Milk density was analysed with a linear mixed model with the fixed effects of sampling period, temperature and analysis method; triple interaction of sampling period x analysis method x temperature; and the random effect of cow to account for repeated measures. The effect of temperature on milk density (ρ) was also evaluated including temperature (t) as covariate with linear and quadratic effects within each analytic method. The regression equation describing the curvature and density–temperature relationship for the DMA35 instrument was ρ = 1.0338−0.00017T−0.0000122T² (R² = 0.64), while it was ρ = 1.0334 + 0.000057T−0.00001T² (R² = 0.61) for DMA4500 instrument. The mean density determined with DMA4500 at 5 °C was 1.0334 g cm⁻³, with corresponding figures of 1.0330, 1.0320 and 1.0305 g cm⁻³ at 10, 15 and 20 °C, respectively. The milk density values obtained in this study at specific temperatures will help to address any bias in weight–volume calculations and thus may also improve the financial and operational control for the dairy processors in Ireland and internationally.     

Femtocell网络的鲁棒议价博弈功率控制

针对具有不完美信道状态信息的Femtocell双层网络，为了保证网络效用最大化、最小服务质量要求及Femtocell用户之间的效用公平，提出了基于议价博弈的鲁棒功率控制方案.在保证宏小区用户正常传输情况下，考虑到Femtocell家庭用户的最小服务质量要求，将Femtocell网络的鲁棒功率控制问题转化为议价博弈功率控制问题.为了保证Femtocell家庭用户之间的公平性，给出了Kalai-Smorodinsky（K-S）议价博弈解，证明了议价博弈解的存在性和唯一性，并提出了一种基于二分查找算法的分布式鲁棒功率控制方案.仿真结果表明，所提出的算法不仅具有较好的收敛性能，而且能够较好地保证用户之间的公平性.     

Ex post demand information sharing between differentiated suppliers and a common retailer

We consider ex post demand information sharing in a two-echelon supply chain in which two suppliers sell differentiated products through a common retailer. We model three scenarios of information sharing to characterise the conditions under which information sharing may benefit or hurt different players: (1) no supplier is informed; (2) only one supplier is informed; and (3) both suppliers are informed. We find that the retailer may voluntarily share the low demand information when the product differentiation and demand magnitude satisfy certain conditions. In contrast, the retailer has no incentive to share the high demand information, even though this information benefits the suppliers. To achieve information sharing, we propose a side payment mechanism, which allows benefitted players to subsidise hurtful players. Finally, we extend our model by considering a situation where the suppliers have limited capacity and may invest to ramp up production capacity to satisfy a high demand.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Bracketing or reinforcing? Socio-economic status,network power,and online deliberation

The extent to which the deliberative scenario brackets socio-economic inequalities (i.e., functioning as an ideal public sphere) has been long-debating. This study empirically addresses this question in the online setting. We test whether offline advantages (i.e., economic and cultural capitals) can be translated into power in online deliberation and how their dynamic relations further influence interlocutors’ deliberation experience (i.e., perceived procedural fairness and validity claim). Power is conceptualized as network power, including prestige (indegree), leadership (outdegree), and brokering power (betweenness). A national sample of Singaporeans participated in a three-week online deliberation, and network power is measured based on the web log. The participants answered a two-wave (i.e., pre- and post-deliberation) survey to indicate individual attributes. Structural equation modeling finds that online deliberation brackets as well as reinforces offline status. Economic capital is suppressed while cultural capital translates into network power, which further influences interlocutors’ evaluation of the deliberation.     

Throughput-based fair bandwidth allocation in OBS networks

Fair bandwidth allocation (FBA) has been studied in optical burst switching (OBS) networks, with the main idea being to map the max-min fairness in traditional IP networks to the fair-loss probability in OBS networks. This approach has proven to be fair in terms of the bandwidth allocation for differential connections, but the use of the ErlangB formula to calculate the theoretical loss probability has made this approach applicable only to Poisson flows. Furthermore, it is necessary to have a reasonable fairness measure to evaluate FBA models. This article proposes an approach involving throughput-based-FBA, called TFBA, and recommends a new fairness measure that is based on the ratio of the actual throughput to the allocated bandwidth. An analytical model for the performance of the output link with TFBA is also proposed.     

基于公平偏好理论的PPP项目收益风险分配机制研究

PPP 模式对于社会资本参与基础设施建设项目具有积极的推动作用。然而,由于基础设施建设周期长、回报低,社会投资方将面临较高的收益风险,因此,我国大部分 PPP 项目的社会资本方为国企或国企与民企的联营体,纯粹的私人资本参与 PPP 项目的积极性并不高。为了激发私人资本参与基础设施项目建设的投资热情,当政府与不同投资方合作时,有必要基于项目参与方的公平偏好程度设置相应风险分配机制,一方面降低私人资本投资方所面临的收益风险,另一方面改变政府承担项目付费的现状,实现风险的合理分配。基于委托代理理论模拟项目收益风险分配机制,在考察投资方公平偏好的前提下,对传统的收益函数进行了调整,构建了基于公平偏好理论的 PPP 项目收益风险分配模型。通过数据分析得出,社会效益会随着投资方公平偏好的增大而增加,投资方承担风险比例亦与其公平偏好正相关     

浅谈工程款支付与工程成本的关系

通过对四种工程类型工程成本投入情况的分析,介绍了工程资金投入的特点,阐述了承发包双方各自承担的工程风险,探讨了在工程产品的阶段性移交时产生预付款回扣问题应考虑的要点,提出了有关工程预付款回扣及工程款支付的建议,以体现工程预付款回扣的合理性。