QoS Recommendation in Cloud Services

As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud services. The approach is used to predict both QoS ratings and rankings for cloud services. To evaluate the effectiveness of the approach, we conduct extensive simulations. Results show that the approach can achieve more reliable rankings, yet less accurate ratings, than a collaborative filtering approach using the Pearson coefficient

Fixed trace β\beta-Hermite ensembles: Asymptotic eigenvalue density and the edge of the density

In the present paper, fixed trace $\beta$-Hermite ensembles generalizing the fixed trace Gaussian Hermite ensemble are considered. For all $\beta$, we prove the Wigner semicircle law for these ensembles by using two different methods: one is the moment equivalence method with the help of the matrix model for general $\beta$, the other is to use asymptotic analysis tools. At the edge of the density, we prove that the edge scaling limit for $\beta$-HE implies the same limit for fixed trace $\beta$-Hermite ensembles. Consequently, explicit limit can be given for fixed trace GOE, GUE and GSE. Furthermore, for even $\beta$, analogous to $\beta$-Hermite ensembles, a multiple integral of the Konstevich type can be obtained.Comment: 16 page

The Applications of Green Building Rating System in Property Management

In the time of Low-carbon economy，the thought of sustainable development has influenced every aspects of life, and the ideas of green service and environmental management has become increasingly popular in property management .Green property management is now a trend, yet necessarily the only way to meet the owner’s needs. Responding to the current call of building energy efficiency, it is inevitable in the development of property management to introduce the idea of green management, advocate green service management, and apply the green building rating system to property management, which is one distinguishing feature of modern property services.Key words: Green Building Rating System; Green Property Management; Application

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling

Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively. The key component in our approach is the adaptive sampling (AS) module. It first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. To further capture the neighbor and long-range dependencies of the sampled point, we proposed a local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL module enables the learning process insensitive to noise. Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise. Specifically, PointASNL achieves state-of-the-art robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise. Our code is released through https://github.com/yanx27/PointASNL.Comment: To appear in CVPR 2020. Also seen in http://kaldir.vc.in.tum.de/scannet_benchmark