An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

Multi-scale simulation of gas solid fluidization based on EMMS- DPM

This presentation will discuss some efforts to improve the speed and accuracy of discrete particle method from physical models to computational methods. For physical model, the multiscale method is used. At global scale, the particles are distributed according to global distribution predicted by the Energy Minimization Multi-Scale (EMMS) model, so that the computation domain can be decomposed non-uniformly for load balance. At grid scale, to improve accuracy, the structure dependent drag coefficient based on the EMMS is used. At particle scale, the coarse grained method is used. The size and solids concentration of the coarse-grained particles (CGP) are determined by the cluster properties which can be predicted by the EMMS model. The coefficient of restitution is modified according to the kinetic theory of granular flows (KTGF). The method thus established in called EMMS-DPM(Lu, Xu et al. 2014). As for computation, using system shared memory, the CFD computation on CPU is fully overlapped with particle computation on GPU. Also, the computation program is coupled with parallel visualization and control program, forming an online interactive simulation platform(Ge, Lu et al. 2015). This method is verified by the simulation of two different CFB risers and several orders of speedup can be achieved. A methanol to orifin (MTO) process is simulated for more than 6800s. We also simulated a CFB with 30kg 0.082mm particles in 3D full loop. Furthermore, the interactive simulation platform can also be used for education and training purpose since it allows virtual experiment on computers. REFERENCES 1.Ge, W., L. Lu, S. Liu, J. Xu, F. Chen and J. Li (2015). Multiscale Discrete Supercomputing - A Game Changer for Process Simulation? Chemical Engineering & Technology 38(4): 575-584. 2.Lu, L., J. Xu, W. Ge, Y. Yue, X. Liu and J. Li (2014). EMMS-based discrete particle method (EMMS–DPM) for simulation of gas–solid flows. Chemical Engineering Science 120(0): 67-87

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an alternative direction to avoid the caveats in the minmax two-player training of GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the 1-Lipschitz continuity of the discriminator. In this paper, we propose a novel approach to enforcing the Lipschitz continuity in the training procedure of WGANs. Our approach seamlessly connects WGAN with one of the recent semi-supervised learning methods. As a result, it gives rise to not only better photo-realistic samples than the previous methods but also state-of-the-art semi-supervised learning results. In particular, our approach gives rise to the inception score of more than 5.0 with only 1,000 CIFAR-10 images and is the first that exceeds the accuracy of 90% on the CIFAR-10 dataset using only 4,000 labeled images, to the best of our knowledge.Comment: Accepted as a conference paper in International Conference on Learning Representation(ICLR). Xiang Wei and Boqing Gong contributed equally in this wor

Nanostructured graphene:Forms, synthesis, properties and applications

This thesis describes the synthesis, properties and applications of the nanostructured graphene with different dimensionalities from 3D foams to, 2D film, and 0D graphene quantum dots (GQDs), and from micron-porous to nanoporous. The applications of the nanostructured graphene include batteries, photoluminescence and cell imaging. Specifically, the thesis comprises the following contents: (1) Nanoporous metallic templates are crucial for the synthesis of nanoporous graphene. A novel method for the synthesis of nanoporous metals was developed and is suitable for industrial production. The growth mechanism, kinetics and microstructures of porous metallic templates were investigated. In addition, the porous metals were synthesized as binder-free current collectors for high-capacity electrodes of lithium-ion batteries. (2) A new solid-state-growth approach is developed for controllable synthesis of nanoporous graphene with interconnected tubular pores and tunable porosities at relatively low temperatures. Nanoporous graphene greatly enhanced the electrochemical performances of high-energy-density Li-S batteries. (3) Large-area graphene film is successfully synthesized at near room temperatures from conversion of amorphous carbon using metallic catalysts. The nucleation, growth process and growth kinetics of graphene were investigated. The results point at several attractive strategies for the facile synthesis of graphene-based carbon films for industrial applications. (4) GQDs were successfully exfoliated from abundant carbon feedstocks such as carbon black in liquid phases with the assistance of ultrasonication. The new approach is eco-friendly and promising for large-scale production. GQDs delivered photoluminescence and light absorption properties, which are firmly associated with their microstructures. The as-synthesized GQDs show good performances as fluorescence nanoprobes for bioimaging