基于城市群城市间创新联系和创新差异水平的改进引力模型,运用社会网络分析方法探究了2006-2016年长江中游城市群协同创新空间关联网络结构及其时空演变趋势.研究发现:研究期内长江中游城市群城市间协同创新水平整体逐渐提升,但强寡弱多的非均衡格局改变并不显著;空间上形成了以武汉、长沙为中心辐射邻近地区的协同创新高值区,环鄱阳湖城市群相对落后;长江中游城市群协同创新空间关联网络整体密度和效率提升较快,而三大子城市群城市间协同创新发展相对滞后;网络多中心在发育,逐渐由最初的双核主导发展为“3+5”多核心格局;随着创新内外环境的变化,网络内部四个凝聚子群不断重构,结构和功能更加复杂化和层次化.

GPUs have been widely used in multi-user environments such as the cloud due to its rich thread-level parallelism. In such an environment, multiple kernels can execute in parallel. However, it is possible that a kernel leverages buffer overflow to attack other kernels on the same GPU. The limited existing work focuses on the detection of buffer overflows instead of prevention. Address randomization is an effective approach to preventing memory-related attacks on the CPU. However, current GPUs lack similar support to defend the increasing threats of memory overflow. In this paper, we propose an address randomization method for dynamic memory allocation on the GPU. We have implemented and compared different pseudo-random algorithms on the GPU, and integrated the address randomization into an existing allocator. Elaborate discussions are presented to analyze the security of our proposed address randomization. Experimental evaluations show that the overhead incurred by our randomized algorithm is less than 20% on top of existing memory allocators.

The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor as compared with the interpolation methods based on a 2-D matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose a Reshape-Align scheme to form the regular tensor with data from variable rate measurements, and introduce user-domain and temporal-domain factor matrices which take full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CANDECOMP/PARAFAC decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of several metrics: error ratio, mean absolute error, and root mean square error.

The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a three-dimensional tensor as compared to interpolation methods based on two-dimensional matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose Reshape-Align scheme to form the regular tensor with data from dynamic measurements, and introduce user-domain and temporal-domain factor matrices which takes full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CP decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of two metrics: error ratio and mean absolute error (MAE).