Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection

Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication

Deep convolutional neural networks have made outstanding contributions in many fields such as computer vision in the past few years and many researchers published well-trained network for downloading. But recent studies have shown serious concerns about integrity due to model-reuse attacks and backdoor attacks. In order to protect these open-source networks, many algorithms have been proposed such as watermarking. However, these existing algorithms modify the contents of the network permanently and are not suitable for integrity authentication. In this paper, we propose a reversible watermarking algorithm for integrity authentication. Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift. As shown in the experiments, the influence of embedding reversible watermarking on the classification performance is less than 0.5% and the parameters of the model can be fully recovered after extracting the watermarking. At the same time, the integrity of the model can be verified by applying the reversible watermarking: if the model is modified illegally, the authentication information generated by original model will be absolutely different from the extracted watermarking information.Comment: Accepted to ACM MM 202

AmbiguityVis: Visualization of Ambiguity in Graph Layouts

Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graphlayout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteriasimultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attentionto these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visualambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguityin edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in communitystructures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others arethen displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualizationapproaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a userto explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectivenessof the technique is demonstrated through case studies and expert reviews

Decoupled measurement and modeling of interface reaction kinetics of ion-intercalation battery electrodes

Ultrahigh rate performance of active particles used in lithium-ion battery electrodes has been revealed by single-particle measurements, which indicates a huge potential for developing high-power batteries. However, the charging/discharging behaviors of single particles at ultrahigh C-rates can no longer be described by the traditional electrochemical kinetics in such ion-intercalation active materials. In the meantime, regular kinetic measuring methods meet a challenge due to the coupling of interface reaction and solid-state diffusion processes of active particles. Here, we decouple the reaction and diffusion kinetics via time-resolved potential measurements with an interval of 1 ms, revealing that the classical Butler-Volmer equation deviates from the actual relation between current density, overpotential, and Li+ concentration. An interface ion-intercalation model is developed which considers the excess driving force of Li+ (de)intercalation in the charge transfer reaction for ion-intercalation materials. Simulations demonstrate that the proposed model enables accurate prediction of charging/discharging at both single-particle and electrode scales for various active materials. The kinetic limitation processes from single particles to composite electrodes are systematically revealed, promoting rational designs of high-power batteries

Frequency and distribution of AP-1 sites in the human genome

The AP-1-binding sequences are promoter/enhancer elements that play an essential role in the induction of many genes in mammalian cells; however, the number of genes containing AP-1 sites remains unknown. In order to better address the overall effect of AP-1 on expression of genes encoded by the entire genome, a genome-wide analysis of the frequency and distribution of AP-1 sites would be useful; yet to date, no such analysis of AP-1 sites or any other promoter/enhancer elements has been performed. We present here our study of the consensus AP-1 site and two single-bp variants showing that the frequency of AP-1 sites in promoter regions is significantly lower than their average rate of occurrence in the whole genomic sequence, as well as the frequency of a random heptanucleotide suggesting that nature has selected for a decrease in the frequency of AP-1 sites in the regulatory regions of genes. In addition, genes containing multiple AP-1 sites are more prevalent than those containing only one copy of an AP-1 site, which again may have evolved to allow for greater signal amplification or integration in the regulation of AP-1 target genes. However, the number of AP-1-regulated genes identified in various studies is far smaller than the number of genes containing potential AP-1 sites, indicating that not all AP-1 sites are activated in a given cell under a given condition, and is consistent with the prediction by others that cellular context determines which AP-1 sites are targeted by AP-1