兰州市削山造地区域生态变化遥感评价方法

针对现有方法无法准确提取削山造地范围以及定量评价削山造地区域生态变化问题,由此提出了基于三维地形变化的削山造地范围提取方法;同时,基于1991、2001、2009和2016年遥感影像,以绿度、湿度、热度和干度指标构建遥感生态指数（Remote Sensing Ecological Index, RSEI）,对比分析4个时期生态质量时空变化。结果表明：\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}不仅能够定量反映生态环境质量,而且可以空间化可视化。1991—2016年,兰州市周边削山造地工程面积不断增加,RSEI值由1991年的0.297上升至2016年的0.406,兰州市生态环境质量整体呈上升趋势,但是具有明显的空间差异。生态变差区域集中在兰州主城区北部山区、兰州新区东南部的削山造地工程范围,削山造地工程致使建筑用地和裸土地激增,植被覆盖度降低,表明削山造地工程对区域生态环境质量有一定影响,因此应该控制削山造地工程规模,加强保护生态脆弱区域环境。     

基于Sentinel-1数据的黑河中游土壤水分反演

基于Sentinel-1合成孔径雷达 (SAR) 数据及相同时段的中分辨率成像光谱仪（MODIS）和Landsat 8两种归一化植被指数（NDVI）,构建变化检测模型以估算黑河中游的高分辨率土壤水分,并探讨模型中具体参数设置对估算精度的影响。结果表明：①在对后向散射系数时间序列的差值 ( \mathrm{\Delta } \sigma ) 和植被指数 ( \mathrm{V} \mathrm{I} ) 进行线性建模过程中,MODIS NDVI和Landsat 8 NDVI这两种植被产品所构建的模型在 \mathrm{\Delta } \sigma - \mathrm{V} \mathrm{I} 空间中所选取的采样点比例分别为2%和4%时,各自取得最优精度; ②以土壤水分反演为目标,使用Landsat 8 NDVI构建的变化检测模型略优于使用MODIS NDVI构建的变化检测模型,两种模型的均方根误差RMSE分别为0.040 m³/m³和0.044 m³/m³,相关系数R分别为0.86和0.83; ③对于变化检测方法的关键参数,若使用低分辨率的SMAP/Sentinel-1 L2_SM_SP土壤水分数据分别代替站点观测的土壤水分初始值和缩放因子 (即两个连续时相土壤水分变化的最大值 \mathrm{\Delta } M s_{max} ) 这两个参数,则土壤水分RMSE将分别增加0.01 m³/m³和0.04 m³/m³。即土壤水分缩放因子这一参数的误差对反演结果的影响大于土壤水分初始值误差对反演结果的影响,故采用高精度的缩放因子进行变化检测估算。研究结论对于利用新兴的Sentinel-1 SAR数据,通过变化检测算法准确获取高分辨率土壤水分信息具有实际参考价值。     

大数据上函数查询解答的复杂度分析

函数查询是大数据应用中重要的操作,查询解答问题一直是数据库理论中的核心问题。为了分析大数据上函数查询解答问题的复杂度,首先,使用映射归约方法将函数查询语言归约到已知的可判定语言,证明了函数查询解答问题的可计算性;其次,使用一阶语言描述函数查询,并分析了一阶语言的复杂度;在此基础上,使用NC-factor归约方法将函数查询类归约到已知的\mathrm{\Pi }{\mathrm{{\rm T}}}_Q-complete类中。证明函数查询解答问题经过PTIME（多项式时间）预处理后,可以在NC（并行多项式-对数）时间内求解。通过以上证明可以推出,函数查询解答问题在大数据上是可处理的。     

基于阵列处理器的最小均方误差检测算法并行设计与实现

针对大规模多输入多输出（MIMO）系统中,最小均方误差（MMSE）检测算法在可重构阵列结构上适应性差、计算复杂度高和运算效率低的问题,基于项目组开发的可重构阵列处理器,提出了一种基于MMSE算法的并行映射方法。首先,利用Gram矩阵计算时较为简单的数据依赖关系,设计时间上和空间上可以高度并行的流水线加速方案;其次,根据MMSE算法中Gram矩阵计算和匹配滤波计算模块相对独立的特点,设计模块化并行映射方案;最后,基于Xilinx Virtex-6开发板对映射方案进行实现并统计其性能。实验结果表明,该方法在MIMO规模为\mathrm{128}\times \mathrm{4}、\mathrm{128}\times \mathrm{8}和\mathrm{128}\times \mathrm{16}的正交相移键控（QPSK）上行链路中,加速比分别2.80、4.04和5.57;在\mathrm{128}\times \mathrm{16}的大规模MIMO系统中,可重构阵列处理器比专用硬件减少了42.6%的资源消耗。     

基于神经正切核的多核学习方法

多核学习方法是一类重要的核学习方法,但大多数多核学习方法存在如下问题：多核学习方法中的基核函数大多选择传统的具有浅层结构的核函数,在处理数据规模大且分布不平坦的问题时表示能力较弱;现有的多核学习方法的泛化误差收敛率大多为O\left(\mathrm{1}/\sqrt[]{n}\right),收敛速度较慢。为此,提出了一种基于神经正切核（NTK）的多核学习方法。首先,将具有深层次结构的NTK作为多核学习方法的基核函数,从而增强多核学习方法的表示能力。然后,根据主特征值比例度量证明了一种收敛速率可达O\left(\mathrm{1}/n\right)的泛化误差界;在此基础上,结合核对齐度量设计了一种全新的多核学习算法。最后,在多个数据集上进行了实验,实验结果表明,相比Adaboost和K近邻（KNN）等分类算法,新提出的多核学习算法具有更高的准确率和更好的表示能力,也验证了所提方法的可行性与有效性。     

海南岛海岸侵蚀脆弱性评价

开展海南岛海岸侵蚀脆弱性评价对于沿海地区生态资源保护和灾害预防有着重要意义。采用生态系统服务和权衡的综合评估模型（Integrated Valuation of Ecosystem Services and Trade-offs, InVEST）中的沿海脆弱性模型合成暴露度指数\mathrm{E}\mathrm{I}（Exposure Index）对海南岛海岸侵蚀脆弱性开展评价。针对典型研究区建立海岸特征—海岸动力—经济社会指标评价体系,优选海岸带特色评价因子包括海岸侵蚀速率、海岸类型、海岸生境等,进一步利用综合指数法量化脆弱性指数,最终获得海南岛沿海不同情境下的脆弱性,以及重点区域海岸侵蚀速率和侵蚀脆弱性等级。研究结果表明：①海南岛侵蚀脆弱性空间分布呈现东低西高,其中西南部市县脆弱性最高,东南部市县脆弱性最低,其余地区脆弱性适中。无生境情境下的沿海脆弱性远高于处于生境保护情境。②典型研究区海口东西海岸2016年至2020年砂质海岸受较多侵蚀,最多处超20 m/a。海口市龙华、美兰等主要城区海岸侵蚀脆弱性高,西海岸、东海岸分别次之,东寨港地区脆弱性最低。③研究发现在红树林等生境保护下的海岸可以有效得到保护,脆弱性极低,而退化的砂质岸线表现较高的脆弱性。因此保护沿海生境和防止沿海泥沙流失具有必要性。     

基于公证人组的跨链交互安全模型

针对公证人机制中存在的公证人节点职能集中以及跨链交易效率较低等问题,提出一种基于公证人组的跨链交互安全模型。首先,将公证人节点分为三类角色,即交易验证者、连接者和监督者,由交易验证组成员打包经过共识的多笔交易成一笔大的交易,并利用门限签名技术对它进行签名;其次,被确认的交易会被置于跨链待转账池中,连接者随机选取多笔交易,利用安全多方计算和同态加密等技术判断交易的真实性;最后,若打包所有符合条件的交易的哈希值真实可靠且被交易验证组验证过,则连接者可以继续执行多笔跨链交易的批处理任务,并与区块链进行信息交互。安全性分析表明,该跨链机制有助于保护信息的机密性和数据的完整性,实现数据在不出库的情况下的协同计算,保障区块链跨链系统的稳定性。与传统的跨链交互安全模型相比,所提模型的签名次数和需要分配公证人组数的复杂度从O(n)降为O(\mathrm{1})。     

基于差分表的Blow-CAST-Fish算法的密钥恢复攻击

针对Blow-CAST-Fish算法攻击轮数有限和复杂度高等问题,提出一种基于差分表的Blow-CAST-Fish算法的密钥恢复攻击。首先,对S盒的碰撞性进行分析,分别基于两个S盒和单个S盒的碰撞,构造6轮和12轮差分特征;然后,计算轮函数f₃的差分表,并在特定差分特征的基础上扩充3轮,从而确定密文差分与f₃的输入、输出差分的关系;最后,选取符合条件的明文进行加密,根据密文差分计算f₃的输入、输出差分值,并查寻差分表找到对应的输入、输出对,从而获取子密钥。在两个S盒碰撞的情况下,所提攻击实现了9轮Blow-CAST-Fish算法的差分攻击,比对比攻击多1轮,时间复杂度由2^107.9降低到2⁷⁴;而在单个S盒碰撞的情况下,所提攻击实现了15轮Blow-CAST-Fish算法的差分攻击,与对比攻击相比,虽然攻击轮数减少了1轮,但弱密钥比例由{\mathrm{2}}^{-\mathrm{52.4}}提高到{\mathrm{2}}^{-\mathrm{42}},数据复杂度由2⁵⁴降低到2⁴⁷。测试结果表明,在相同差分特征基础上,基于差分表的攻击的攻击效率更高。     

基于L-Metric重叠子图发现的B细胞表位预测模型

针对现有表位预测方法对抗原中存在的重叠表位预测能力不佳的问题,提出了将基于局部度量（L-Metric）的重叠子图发现算法用于表位预测的模型。首先,利用抗原上的表面原子构建原子图并升级为氨基酸残基图;然后,利用基于信息流的图划分算法将氨基酸残基图划分为互不重叠的种子子图,并使用基于L-Metric的重叠子图发现算法对种子子图进行扩展以得到重叠子图;最后,利用由图卷积网络（GCN）和全连接网络（FCN）构建的分类模型将扩展后的子图分类为抗原表位和非抗原表位。实验结果表明,所提出的模型在相同数据集上的F_{\mathrm{1}}值与现有表位预测模型DiscoTope 2、ElliPro、EpiPred和Glep相比分别提高了267.3%、57.0%、65.4%和3.5%。同时,消融实验结果表明,所提出的重叠子图发现算法能够有效改善预测能力,使用该算法的模型相较于未使用该算法的模型的F_{\mathrm{1}}值提高了19.2%。     

基于新极化特征参数的SAR海洋溢油检测

为了提升海上油膜与其他目标的可分离程度,提出基于特征值分解的一种新的极化特征G,该特征不仅能够反映集合中不同目标之间的极化状态,还能够描述不同散射类型在统计意义上的不纯度。若某个区域中去极化状态越弱,不纯度越低,则该区域中新极化特征G的值越低。利用两景Radarsat-2全极化SAR (Synthetic Aperture Radar)影像对新特征的有效性进行实验验证。结果表明：海水具有较小的特征值,油膜具有较大的特征值,生物膜的特征值介于两者之间。且与span、\bar{\mathrm{\alpha }}、P、A、CPD等5种经典的极化特征相比,新特征在图像对比度、局部标准偏差及概率密度曲线等三个指标上均有更好的表现,不仅能区分生物膜（植物油模拟）与原油,且具有更好的抑噪性。     

A primal-dual approximation algorithm for the k-prize-collecting minimum vertex cover problem with submodular penalties

In this paper, we consider the k-prize-collecting minimum vertex cover problem with submodular penalties, which generalizes the well-known minimum vertex cover problem, minimum partial vertex cover problem and minimum vertex cover problem with submodular penalties. We are given a cost graph G=(V,E;c) and an integer k. This problem determines a vertex set S\subseteq V such that S covers at least k edges. The objective is to minimize the total cost of the vertices in S plus the penalty of the uncovered edge set, where the penalty is determined by a submodular function. We design a two-phase combinatorial algorithm based on the guessing technique and the primal-dual framework to address the problem. When the submodular penalty cost function is normalized and nondecreasing, the proposed algorithm has an approximation factor of 3. When the submodular penalty cost function is linear, the approximation factor of the proposed algorithm is reduced to 2, which is the best factor if the unique game conjecture holds.     

Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query

Secure k-Nearest Neighbor (k-NN) query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas, such as privacy-preserving machine learning and secure biometric identification. Several solutions have been put forward to solve this challenging problem. However, the existing schemes still suffer from various limitations in terms of efficiency and flexibility. In this paper, we propose a new encrypt-then-index strategy for the secure k-NN query, which can simultaneously achieve sub-linear search complexity (efficiency) and support dynamical update over the encrypted database (flexibility). Specifically, we propose a novel algorithm to transform the encrypted database and encrypted query points in the cloud. By indexing the transformed database using spatial data structures such as the R-tree index, our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted database. To the best of our knowledge, the proposed strategy is the first to simultaneously provide these two properties. Through theoretical analysis and extensive experiments, we formally prove the security and demonstrate the efficiency of our scheme.     

Properties of the satisfiability threshold of the strictly d-regular random (3, 2s)-SAT problem

A k-CNF (conjunctive normal form) formula is a regular (k, s)-CNF one if every variable occurs s times in the formula, where k≥2 and s>0 are integers. Regular (3, s)- CNF formulas have some good structural properties, so carrying out a probability analysis of the structure for random formulas of this type is easier than conducting such an analysis for random 3-CNF formulas. Some subclasses of the regular (3, s)-CNF formula have also characteristics of intractability that differ from random 3-CNF formulas. For this purpose, we propose strictly d-regular (k, 2s)-CNF formula, which is a regular (k, 2s)-CNF formula for which d≥0 is an even number and each literal occurs s-\frac{d}{2} or s+\frac{d}{2} times (the literals from a variable x are x and \neg x, where x is positive and \neg x is negative). In this paper, we present a new model to generate strictly d-regular random (k, 2s)-CNF formulas, and focus on the strictly d-regular random (3, 2s)-CNF formulas. Let F be a strictly d-regular random (3, 2s)-CNF formula such that 2s>d. We show that there exists a real number s₀ such that the formula F is unsatisfiable with high probability when s>s₀, and present a numerical solution for the real number s₀. The result is supported by simulated experiments, and is consistent with the existing conclusion for the case of d= 0. Furthermore, we have a conjecture: for a given d, the strictly d-regular random (3, 2s)-SAT problem has an SAT-UNSAT (satisfiable-unsatisfiable) phase transition. Our experiments support this conjecture. Finally, our experiments also show that the parameter d is correlated with the intractability of the 3-SAT problem. Therefore, our research maybe helpful for generating random hard instances of the 3-CNF formula.     

Zero-correlation linear attack on reduced-round SKINNY

At ToSC 2019, Ankele et al. proposed a novel idea for constructing zero-correlation linear distinguishers in a related-tweakey model. This paper further clarifies this principle and gives a search model for zero-correlation distinguishers. As a result, for the first time, the authors construct 15-round and 17-round zero-correlation linear distinguishers for SKINNY-n-2n and SKINNY-n-3n, respectively, which are both two rounds longer than Anekele et al.’s. Based on these distinguishers, the paper presents related-tweakey zero-correlation linear attacks on 22-round SKINNY-n-2n and 26-round SKINNY-n-3n, respectively.     

ReCSA: a dedicated sort accelerator using ReRAM-based content addressable memory

With the increasing amount of data, there is an urgent need for efficient sorting algorithms to process large data sets. Hardware sorting algorithms have attracted much attention because they can take advantage of different hardware’s parallelism. But the traditional hardware sort accelerators suffer “memory wall” problems since their multiple rounds of data transmission between the memory and the processor. In this paper, we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array simultaneously. Using this designed ReCAM array, we present ReCSA, which is the first dedicated ReCAM-based sort accelerator. Besides hardware designs, we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting performance. The sorting algorithm in ReCSA can process various data types, such as integer, float, double, and strings. We also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort accelerators. The experimental results show that ReCSA has 90.92\times, 46.13\times, 27.38\times, 84.57\times, and 3.36\times speedups against CPU-, GPU-, FPGA-, NDP-, and PIM-based platforms when processing numeric data sets. ReCSA also has 24.82\times, 32.94\times, and 18.22\times performance improvement when processing string data sets compared with CPU-, GPU-, and FPGA-based platforms.