基于费马点的网络连通性修复策略

连通性修复是保证网络有效性、可靠性的重要手段，而目前关于1-连通性修复的策略没有将图形的几何性质与网络的拓扑结构很好地结合，因此难以用最少的中继节点完成修复。将费马点、三角剖分与最小生成树有效结合，设计了一种基于费马点的网络连通性修复策略，并且从理论上证明了该策略的近似比和复杂度分别为$\frac{3\sqrt{3}}{4-\sqrt{3}}$ 与O(n log n)，而仿真实验表明该策略在中继节点消耗上明显少于其他同类型策略。     

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。     

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%。     

基于广义最大Versoria准则的稀疏自适应滤波算法

针对脉冲噪声干扰环境下传统稀疏自适应滤波稳态性能差,甚至无法收敛等问题,同时为提高稀疏参数辨识的精度的同时不增加过多计算代价,提出了一种基于广义最大Versoria准则（GMVC）的稀疏自适应滤波算法——带有CIM约束的GMVC（CIMGMVC）。首先,利用广义Versoria函数作为学习准则,其包含误差p阶矩的倒数形式,当脉冲干扰出现导致误差非常大时,GMVC将趋近于0,从而达到抑制脉冲噪声的目的。其次,将互相关熵诱导维度（CIM）作为稀疏惩罚约束和GMVC相结合来构建新代价函数,其中的CIM以高斯概率密度函数为基础,当选择合适核宽度时,可无限逼近于l_{\mathrm{0}}-范数。最后,应用梯度法推导出CIMGMVC算法,并分析了所提算法的均方收敛性。在Matlab平台上采用\alpha-stable分布模型产生脉冲噪声进行仿真,实验结果表明所提出的CIMGMVC算法能有效地抑制非高斯脉冲噪声的干扰,在稳健性方面优于传统稀疏自适应滤波,且稳态误差低于GMVC算法。     

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。     

基于地基微波辐射计数据评估不同土壤介电模型反演土壤湿度的适用性

以青藏高原开展的L波段地基微波辐射（ELBARA-III型）综合观测试验为依据,基于 \tau - \omega 辐射传输模型评估了Wang-Schmugge、Mironov、Dobson和 Four-Phase 4种土壤介电模型对L波段微波亮温模拟及土壤湿度反演的影响。结果表明：相同植被和粗糙度参数化方案条件下,4种土壤介电模型对微波亮温模拟存在明显差异,当土壤湿度小于0.23 m3·m^-3时,Wang-Schmugge模型与其他3种土壤介电模型微波亮温模拟结果差异最为显著,水平和垂直极化微波亮温模拟最大差值可达8.0 K和4.4 K;当模拟土壤湿度大于0.23 m3·m^-3时,Four-phase模型模拟的微波亮温显著高于其他3种土壤介电模型模拟结果;当土壤湿度饱和时,4种土壤介电模型间水平和垂直极化微波亮温模拟最大差值约为6.1 K和4.8 K,且4种土壤介电模型对水平极化微波亮温模拟的差异比垂直极化模拟的差异更为显著。而基于4种介电模型的土壤湿度反演对比试验表明,水平极化条件下基于Wang-Schmugge模型反演土壤湿度,较其他参数化方案,能有效减轻反演土壤湿度对观测土壤湿度的低估,Mironov模型减轻了垂直极化条件下反演土壤湿度对观测值的高估程度。在现有 \tau - \omega 模型参数化方案的基础上,总结了4种土壤介电模型在青藏高原典型草地下垫面的适用性,将为星载L波段辐射计青藏高原土壤湿度反演应用提供客观的土壤介电模型方案选取依据。     

单阶段多框检测器无人机航拍目标识别方法

无人机（UAV）航拍图像视野开阔,图像中的目标较小且边缘模糊,而现有单阶段多框检测器（SSD）目标检测模型难以准确地检测航拍图像中的小目标。为了有效地解决原有模型容易漏检的问题,借鉴特征金字塔网络（FPN）提出了一种基于连续上采样的SSD模型。改进SSD模型将输入图像尺寸调整为\mathrm{320}\times \mathrm{320},新增Conv3_3特征层,将高层特征进行上采样,并利用特征金字塔结构对VGG16网络前5层特征进行融合,从而增强各个特征层的语义表达能力,同时重新设计先验框的尺寸。在公开航拍数据集UCAS-AOD上训练并验证,实验结果表明,所提改进SSD模型的各类平均精度均值（mAP）达到了94.78%,与现有SSD模型相比,其准确率提升了17.62%,其中飞机类别提升了4.66%,汽车类别提升了34.78%。     

基于区块链的轻量化移动自组网认证方案

为了解决现有认证方式无法追踪恶意节点、集中式认证中心易受攻击的问题,基于区块链技术的去中心化、防篡改、可追溯特点,提出了一种分布式轻量化移动自组网认证方案。该方案包括对等网络建立、认证账本设计和共识机制选择,并分析讨论了智能合约的作用。在所提方案中,当给定对等节点之间最大传输时延τmax时,节点认证时间δ和网络规模Nt^all间的关系满足(Nt^all/3+1)τmax≤δ≤3(Nt^all+1)τmax.     

基于SAT方法进一步加速差分特征的搜索

回顾了孙等使用Matsui边界条件加速差分特征搜索的方法,为了进一步提高搜索效率,改进了Matsui边界条件以及利用Matsui边界条件加速差分特征自动化搜索的方法,并提出了一种改进的方法来搜索分组密码的最优差分特征。研究了线程数和询问条件的加速效果并提出了选择线程数以及询问条件的策略。使用STP和CryptoMiniSat分别搜索概率为2^-24、2^-25、2^-26的8轮SPECK96差分特征以及概率为2^-39的11轮HIGHT差分特征,并比较了在不同线程数和询问条件下求解SAT/SMT问题的耗时。研究发现线程数对搜索差分特征的耗时影响较大,而询问条件对搜索差分特征的耗时影响较小,从而提出了一种如何选择线程数和询问条件的策略。根据所提策略,使用改进的边界条件和方法搜索HIGHT的11轮最优差分特征,并首次获得了HIGHT的11轮最优差分特征的紧致概率,即2^-45。现有的11轮HIGHT最优差分特征概率的最紧致边界是P_Opt¹¹≥2     

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.     

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.     

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.     

A subgraph matching algorithm based on subgraph index for knowledge graph

The problem of subgraph matching is one fundamental issue in graph search, which is NP-Complete problem. Recently, subgraph matching has become a popular research topic in the field of knowledge graph analysis, which has a wide range of applications including question answering and semantic search. In this paper, we study the problem of subgraph matching on knowledge graph. Specifically, given a query graph q and a data graph G, the problem of subgraph matching is to conduct all possible subgraph isomorphic mappings of q on G. Knowledge graph is formed as a directed labeled multi-graph having multiple edges between a pair of vertices and it has more dense semantic and structural features than general graph. To accelerate subgraph matching on knowledge graph, we propose a novel subgraph matching algorithm based on subgraph index for knowledge graph, called as F G q_T-Match. The subgraph matching algorithm consists of two key designs. One design is a subgraph index of matching-driven flow graph ( F G q_T), which reduces redundant calculations in advance. Another design is a multi-label weight matrix, which evaluates a near-optimal matching tree for minimizing the intermediate candidates. With the aid of these two key designs, all subgraph isomorphic mappings are quickly conducted only by traversing F G q_T. Extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.     

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.