Privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

The widespread use of Location-Based Services(LBSs),which allows untrusted service providers to collect large quantities of information regarding users’locations,has raised serious privacy concerns.In response to these issues,a variety of LBS Privacy Protection Mechanisms(LPPMs)have been recently proposed.However,evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics.In particular,the relationships between these metrics have not been examined in depth under a common adversarial model,leading to a possible selection of the inappropriate metric,which runs the risk of wrongly evaluating LPPMs.In this paper,we address these issues by proposing a privacy quantification model,which is based on Bayes conditional privacy,to specify a general adversarial model.This model employs a general definition of conditional privacy regarding the adversary’s estimation error to compare the different LBS privacy metrics.Moreover,we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics.We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective.Our results contribute to a better understanding of how privacy properties can be measured,as well as to the better selection of the most appropriate metric for any given LBS application.     

省级公共医疗卫生供给绩效评估实证研究——基于全面绩效管理的视角

为了研究安徽省公共医疗卫生供给绩效及其影响因素,从公共服务供给压力、状态和响应3个维度筛选了15个相关指标,构建了评估指标体系,利用最新数据和熵值-Tobit模型对安徽省公共医疗卫生供给绩效及其影响因素进行综合评估,认为样本考察期内安徽省公共医疗卫生供给绩效呈现"快速-平稳-快速-平稳"的动态波动趋势,且供给压力依然较大,供给状态不容乐观,但供给响应指数提升明显; Tobit回归显示,安徽省财政医疗支出占比呈现规模报酬递减效应,人口密度与公共医疗卫生供给绩效呈现负相关关系,而人均地区生产总值、城镇化水平和财政自给率与公共医疗卫生供给绩效呈现正相关关系,但财政自给率的影响不显著,据此,提出了促进省级公共医疗卫生供给绩效提升的相关对策建议。     

Secure pairing with wearable devices by using ambient sound and light

Wearable devices usually work together with smart phones. To ensure only legitimate smart phones can read the data, they must conduct pairing to establish a shared key. Traditional pairing methods require that the pairing devices have a keyboard or screen for user interaction. However, due to the size limitation, keyboards or screens are hard to be installed in the wearable devices. To solve this problem, we propose a novel pairing method by using ambient sound and light. In this new scheme, any pairing request from smart phone will trigger wearable device vibration. Only after users press the confirm key on the device can the pairing process continues. Then pairing devices collect ambient sound and light at the predetermined time and establish a shared key by using the Diffie-Hellman protocol. To protect against potential man-in-the-middle attacks in the key establishment process, an improved interlock protocol with sound and light comparison is conducted to authenticate the key. If both the sound and light collected by the pairing devices are similar enough, the key is accepted. Otherwise, it is rejected. Compared with current context based pairing methods, our scheme does not impose strict synchronization on devices to collect ambient context data. Moreover, our scheme need not collect and exchange contextual information for multiple times to resist offline brute force attacks. The experimental results and security analysis prove the effectiveness of our scheme.     

面向无人自主平台的战场地理环境模型研究

战场环境模型是复杂战场环境面向特定需求的抽象和描述,用以支撑战场环境特性和演变规律的研究和应用。现有的战场环境模型主要面向人类战争活动来描述战场环境,缺乏以无人自主平台为认知主体的设计。提出了一种耦合人机各自优势的多层次战场地理环境模型结构,充分发挥机器在特征层面的快速计算能力,以及人在要素和关系层面的认知经验。提出了一种集环境观测、模型构建和空间决策为一体的建模方法,支撑无人自主平台根据任务和环境实现自主空间决策。     

碳化细菌纤维素在钙钛矿太阳能电池对电极中的研究

在高效的钙钛矿太阳能电池(PSCs)中，通常采用贵金属对电极(Au、Ag)和昂贵的空穴传输材料(HTM)，导致了高成本和不稳定等问题.该研究使用稳定的无HTM的CsPbBr3纯无机钙钛矿的PSCs，将结构优异的天然纳米生物材料细菌纤维素(BC)经过高温碳化得到具有纳米精细结构、大表面积和孔容的多介孔碳化细菌纤维素(CBC)材料，对该材料进行SEM、XRD、FT-IR、比表面积、电导率测试以证实该材料在PSCs上的应用潜质.根据PSCs对电极的应用需要，将CBC及其与商业导电碳浆(CS)混合物作为PSCs对电极材料，通过J-V、IPCE等测试，发现纯CBC材料的电池效率高于纯CS，在作为PSCs对电极方面具有很大的潜力.该研究不仅扩大了生物质碳材料的应用领域，而且有望将CBC作为低成本稳定高效PSCs对电极材料.     

In situ backscattered electron imaging study of the effect of annealing on the deformation behaviors of Ni electroformed from additive-free and saccharin-containing sulfamate solutions

The Ni samples were electroformed from additive-free(AF) and saccharin-containing(SC) sulfamate solutions, respectively. In situ backscattered electron(BSE) imaging, electron backscatter diffraction(EBSD), and electron-probe microanalysis(EPMA) were used to investigate the effect of annealing on the deformation behaviors of the AF and SC samples. The results indicate that columnar grains of the as-deposited AF sample had an approximated average width of 3 μm and an approximated aspect ratio of 8. The average width of columnar grains of the as-deposited SC sample was reduced to approximately 400 nm by the addition of saccharin to the electrolyte. A few very-large grains distributed in the matrix of the SC sample after annealing. No direct evidence indicated that S segregated at the grain boundaries before or after annealing. The average value of the total elongations of the SC samples decreased from 16% to 6% after annealing, whereas that of the AF samples increased from 18% to 50%. The dislocation recovery in grain-boundary areas of the annealed AF sample was reduced, which contributed to the appearance of microvoids at the triple junctions. The incompatibility deformation between very-large grains and fine grains contributed to the brittle fracture behavior of the annealed SC Ni.     

高校“双困”毕业生就业状况调研及对策建议--以福建省厦门市高校毕业生为例

对福建省厦门市5所高校的223名家庭经济困难且就业存在困难的毕业生进行了问卷调查，系统调研了“双困生”的就业意向、就业信心、求职渠道、职业认知、职业规划等，为社会、政府、企业、高校等机构科学、高效地开展“双困生”就业援助工作提供合理建议。     

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and patternrecognition for decades.A two stage automatic image annotation method based on Gaussian mixturemodel (GMM) and random walk model (abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization (RPEM) algorithm is employed to estimatethe posterior probabilities of each annotation keyword.Subsequently, a random walk processover the constructed label similarity graph is implemented to further mine the potential correlations ofthe candidate annotations so as to capture the refining results, which plays a crucial role in semanticbased image retrieval.The contributions exhibited in this work are multifold.First, GMM is exploitedto capture the initial semantic annotations, especially the RPEM algorithm is utilized to train themodel that can determine the number of components in GMM automatically.Second, a label similaritygraph is constructed by a weighted linear combination of label similarity and visual similarity ofimages associated with the corresponding labels, which is able to avoid the phenomena of polysemyand synonym efficiently during the image annotation process.Third, the random walk is implementedover the constructed label graph to further refine the candidate set of annotations generated byGMM.Conducted experiments on the standard Corel5k demonstrate that GMM-RW is significantlymore effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.