LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling

Information Systems Frontiers - System logs that trace system states and record valuable events comprise a significant component of any computer system in our daily life. Each log contains...     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

Research on Action Strategies and Simulations of DRL and MCTS-based Intelligent Round Game

International Journal of Control, Automation and Systems - The reinforcement learning problem of complex action control in multiplayer online battlefield games has brought considerable interest in...     

Optimization of uncertain acoustic metamaterial with Helmholtz resonators based on interval model

Uncertainties existing in the acoustic metamaterial may strongly affect its unusual properties. Aiming at this actuality, the interval model is introduced to treat with uncertainties existing in the acoustic metamaterial with Helmholtz resonators. Frequency intervals in which the sound intensity transmission coefficients are certainly less than the required value and the effective bulk moduli are certainly negative are defined as conservative approximations. Frequency intervals in which the sound intensity transmission coefficients may be less than the required value and the effective bulk moduli may be negative are defined as unsafe approximations. The proportion of the conservative approximation and the unsafe approximation is defined as an approximate precision. Based on the quantification of uncertainties of the sound intensity transmission coefficients and the negative effective bulk moduli, an optimization model for the interval acoustic metamaterial with Helmholtz resonators is constructed. Numerical results showed that even suffering from effects of interval parameters, unusual properties of the optimized acoustic metamaterial (such as the bandgap of the sound transmission and the negative effective bulk modulus) could be improved.     

《中德四季晨昏杂咏》（之八）的译文鉴赏

在诗歌翻译中,译文会因为译者的不同在风格上表现出差异.然而,译文的质量不外乎经受形美、音美和意美的考量.译者应从本国读者的文化背景上结合时代的脉搏来进行“再创造”,做到形式、音律和意蕴的完美统一.歌德的诗歌《中德四季晨昏杂咏》（之八）的四种中文译本各有特色,均在不同程度上实现了“三美”的融合.     

激光熔覆含SiC金属陶瓷涂层显微组织特征

采用连续ＣＯ２激光器在钢表面进行熔覆含ＳｉＣ金属陶瓷涂层。对不同工艺条件下激光熔属层组织结构、成分与硬度进行了研究，对试验结果和凝固过程进行了传热分析。     

校园网资源整合与校园云服务模式研究

云计算应用正以势如破竹的态势进入到教育领域,云计算的推广有利于教育资源的共建共享,方便师生快捷地获取所需资源和服务,更高效地开展学习和科研工作.分析了现有网络资源的具体实际情况,利用云计算技术,探究资源整合的有效方式,整合校园网资源,探索适合于教师教学与科研、学生学习与成长,以及学校提升社会服务能力的校园私有云服务模式.     

Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays在大规模系统上优化 TPC-C 评测程序

Transaction processing performance council benchmark C (TPC-C) is the de facto standard for evaluating the performance of high-end computers running on-line transaction processing applications. Differing from other standard benchmarks, the transaction processing performance council only defines specifications for the TPC-C benchmark, but does not provide any standard implementation for end-users. Due to the complexity of the TPC-C workload, it is a challenging task to obtain optimal performance for TPC-C evaluation on a large-scale high-end computer. In this paper, we designed and implemented a large-scale TPC-C evaluation system based on the latest TPC-C specification using solid-state drive (SSD) storage devices. By analyzing the characteristics of the TPC-C workload, we propose a series of system-level optimization methods to improve the TPC-C performance. First, we propose an approach based on SmallFile table space to organize the test data in a round-robin method on all of the disk array partitions; this can make full use of the underlying disk arrays. Second, we propose using a NOOP-based disk scheduling algorithm to reduce the utilization rate of processors and improve the average input/output service time. Third, to improve the system translation lookaside buffer hit rate and reduce the processor overhead, we take advantage of the huge page technique to manage a large amount of memory resources. Lastly, we propose a locality-aware interrupt mapping strategy based on the asymmetry characteristic of non-uniform memory access systems to improve the system performance. Using these optimization methods, we performed the TPC-C test on two large-scale high-end computers using SSD arrays. The experimental results show that our methods can effectively improve the TPC-C performance. For example, the performance of the TPC-C test on an Intel Westmere server reached 1.018 million transactions per minute.