支持全同态密文计算的访问控制加密方案

提出支持全同态密文计算的访问控制加密（FH-ACE）方案,并给出基于带错学习（Learning with Error）困难性问题的具体构造.首先,根据全同态加密（Fully Homomorphic Encryption）概念和访问控制加密（Access Control Encryption）概念,给出支持全同态密文计算的访问控制加密方案的定义以及需要满足的安全模型;其次,提出以满足特定条件的全同态加密方案为基本模块的黑盒构造,并分析基于目前的全同态加密方案,具体构造所面临的困难点以及解决方法;最后,基于带错学习困难性问题,给出支持全同态密文计算的访问控制加密方案的具体构造.     

人工智能技术发展概述

近年来,以深度学习为核心的人工智能技术,取得了一系列重大突破.本文将就人工智能的产业化热潮,主要研究流派及发展历史,以深度学习为核心的成功应用,以及存在的一些问题和今后的可能研究方向做一个介绍.     

Machine learning predictions of passive microwave brightness temperature over snow-covered land using the special sensor microwave imager (SSM/I)

Abstract

Two different forms of machine learning – an artificial neural network (ANN) and a support vector machine (SVM) – are used to estimate passive microwave (PMW) brightness temperatures (Tb) as observed by the special sensor microwave imager (SSM/I) satellite sensor over snow- covered land in North America. Both techniques reasonably reproduce unbiased estimates of SSM/I observations at 19.35 and 37.0 GHz for both vertically- and horizontally-polarized channels. When compared against SSM/I observations not used during training, domain-averaged statistics from 1 September 1987 to 1 September 2002 yielded a root mean squared error (RMSE) of less than 9 K for all frequency and polarization combinations examined in this study. Even though both ML techniques reasonably reproduced SSM/I Tb observations, the SVM outperformed the ANN because the SVM: (1) better captured the high-frequency (i.e. day-to-day) temporal characteristics in the Tb observations across the majority of the study domain, (2) better reproduced the spatial variability as a function of snow classification, and (3) yielded greater sensitivity to snow-related input variables during the estimation of PMW Tb. These findings reinforce previous research of SVM-based estimation of PMW Tb employing observations from the advanced microwave scanning radiometer.     

Turning Contention into Collaboration: Engaging Power,Trust, and Learning in Collaborative Networks

Given the complexity and multiplicity of goals in natural resource governance, it is not surprising that policy debates are often characterized by contention and competition. Yet at times adversaries join together to collaborate to find creative solutions not easily achieved in polarizing forums. We employed qualitative interviews and a quantitative network analysis to investigate a collaborative network that formed to develop a resolution to a challenging natural resource management problem, the conservation of vernal pools. We found that power had become distributed among members, trust had formed across core interests, and social learning had resulted in shared understanding and joint solutions. Furthermore, institutions such as who and when new members joined, norms of inclusion and openness, and the use of small working groups helped create the observed patterns of power, trust, and learning.     

Ecological modernisation and policy learning in Hong Kong

Whilst ecological modernisation theory emphasizes the potential for modern societies to recognize and respond to their environmental impacts by finding new ways of governing environment-economy relations, concepts of policy learning focus on the scope for new forms of environmental policy to be generated within and transferred between different contexts. Within this paper we explore the conceptual and practical linkages between the two areas of debate - a hitherto neglected area in the literature - and we set this discussion in the context of environmental policy-making in Hong Kong. We suggest that the practical relevance of the concepts of ecological modernisation and policy learning depends upon the presence of a reflexive society with rational, responsive institutions. While many theorists assume that such institutions exist, our analysis of policies for water and air quality management in Hong Kong highlights the need for theories to consider the embeddedness of existing institutions and the significance of the capacities for, and the barriers to, change more fully. We find that capacities for some forms of ecological modernisation and policy learning do exist in Hong Kong. However, we argue that the nature of these capacities often limits the potential for change to those local environmental problems that can be addressed through more technically and economically viable forms of policy intervention and that can be easily accommodated within existing political and economic structures. We also conclude that the capacities for ecological modernisation and policy learning that are needed if Hong Kong is to tackle the effects of the trans-boundary environmental problems that it is increasingly encountering have yet to emerge.     

非开挖水平定向钻机钻头的类型及适应性分析

非开挖水平钻机是非开挖铺管技术的核心机械,直接关系到铺设工程的成败;钻头作为钻机的一个部件,主要起到导向成孔、扩孔和清孔的作用。钻头的选择直接关系到后续拖管的难易程度,因此,根据不同的地质和施工要求应选择相应的钻头。本文就非开挖钻机扩孔钻头的类型及适应性作了详细地分析。     

Optimize taxi driving strategies based on reinforcement learning

The efficiency of taxi services in big cities influences not only the convenience of peoples’ travel but also urban traffic and profits for taxi drivers. To balance the demands and supplies of taxicabs, spatio-temporal knowledge mined from historical trajectories is recommended for both passengers finding an available taxicab and cabdrivers estimating the location of the next passenger. However, taxi trajectories are long sequences where single-step optimization cannot guarantee the global optimum. Taking long-term revenue as the goal, a novel method is proposed based on reinforcement learning to optimize taxi driving strategies for global profit maximization. This optimization problem is formulated as a Markov decision process for the whole taxi driving sequence. The state set in this model is defined as the taxi location and operation status. The action set includes the operation choices of empty driving, carrying passengers or waiting, and the subsequent driving behaviors. The reward, as the objective function for evaluating driving policies, is defined as the effective driving ratio that measures the total profit of a cabdriver in a working day. The optimal choice for cabdrivers at any location is learned by the Q-learning algorithm with maximum cumulative rewards. Utilizing historical trajectory data in Beijing, the experiments were conducted to test the accuracy and efficiency of the method. The results show that the method improves profits and efficiency for cabdrivers and increases the opportunities for passengers to find taxis as well. By replacing the reward function with other criteria, the method can also be used to discover and investigate novel spatial patterns. This new model is prior knowledge-free and globally optimal, which has advantages over previous methods.     

An intelligent spatial land use planning support system using socially rational agents

This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained.

This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents.     

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.     

基于迁移学习的卫星云图云分类

针对在基于机器学习的云图识别中,由于不存在公认的云分类样本库的现实条件下,带来的训练样本数量不足和不平衡,从而难以获得可靠的分类模型的问题,利用迁移学习中的多源加权Tradaboost算法(内部采用极限学习机作为分类器)来进行卫星云图云的检测。利用多人(多源)标注的大量厚云的样本,构成多源辅助样本集;利用少量标注的薄云样板构成目标样本集。使用迁移学习和辅助样本集,对仅在薄云样本集下的训练获得的极限学习机分类器进行辅助训练,提高其薄云识别率。基于国家卫星气象中心的HJ-1A/B的卫星数据实验结果表明,迁移学习可以充分利用容易获得的大样本厚云辅助样本知识,对同类型有关联的小样本薄云分类器进行识别提高。实验表明,迁移学习算法可以进一步用于更多多源样本和其他云分类的任务。