Rethinking the architecture design of data center networks

In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore＇s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidi- mensional characteristics. The huge amount of data brings many challenges on the management, transportation, and pro- cessing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. How- ever, how to build a good data center remains an open chal- lenge. Concurrently, the architecture design, which signifi- cantly affects the total performance, is of great research inter- est. This paper surveys advances in data center network de- sign. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today＇s data center network architectures. In the third part, we present some up-to-date data center frame- works and make a comprehensive comparison of them. Dur- ing the comparison, we observe that there is no so-called op- timal data center and the design should be different referring to the data placement, replication, processing, and query pro- cessing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.

A fast and efficient mesh segmentation method based on improved region growing

Mesh segmentation is one of the important issues in digital geometry processing. Region growing method has been proven to be a efficient method for 3D mesh segmentation. However, in mesh segmentation, feature line extraction algorithm is computationally costly, and the over-segmentation problem still exists during region merging processing. In order to tackle these problems, a fast and efficient mesh segmentation method based on improved region growing is proposed in this paper. Firstly, the dihedral angle of each non-boundary edge is defined and computed simply, then the sharp edges are detected and feature lines are extracted. After region growing process is finished, an improved region merging method will be performed in two steps by considering some geometric criteria. The experiment results show the feature line extraction algorithm can obtain the same geometric information fast with less computational costs and the improved region merging method can solve over-segmentation well.

Test-Driven Feature Extraction of Web Components

With the growing requirements of web applications,web components are developed to package the implementation of commonly-used features for reuse.In some cases,the developer may want to reuse some features which cannot be customized by the component’s APIs.He/she has to extract the implementation by hand.It is labor-intensive and error-prone.Considering the widely-used test cases which can be useful to specify the software features,a test-driven approach is proposed to extract the implementation of the desired features in web components.The satisfaction of the user^requirements is transformed into the passing rate of user-specified test cases.In this way,the quality of the extraction result can be evaluated automatically.Meanwhile,a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI appearance.To extract the feature implementation,a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest size.We compare our method with two existing feature extraction methods.The result shows that our method can extract the correct implementation with the minimum size.A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

Automatic Cage Building with Quadric Error Metrics

Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computational cost during deformations, a dense model is often manipulated through a simplified structure, called cage, which envelops the model. However, cages are usually built interactively by users, which is tedious and time-consuming. In this paper, we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes. The method consists of two steps： 1） simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage; 2） removing the self-intersections of the coarse cage with Delaunay partitions. With this new method, a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies. Experimental results show that, compared to other cage building methods with the same number of vertex, cages built by our method are more similar to the input models. Thus, the dense models can be manipulated with higher accuracy through our cages.

Automatic 3D Shape Co-Segmentation Using Spectral Graph Method

Co-analyzing a set of 3D shapes is a challenging task considering a large geometrical variability of the shapes. To address this challenge, this paper proposes a new automatic 3D shape co-segmentation algorithm by using spectral graph method. Our method firstly represents input shapes as a set of weighted graphs and extracts multiple geometric features to measure the similarities of faces in each individual shape. Secondly all graphs are embedded into the spectral domain to find meaningful correspondences across the set, After that we build a joint weighted matrix for the graph set and then apply normalized cut criterion to find optimal co-segmentation of the input shapes. Finally we evaluate our approach on different categories of 3D shapes, and the experimental results demonstrate that our method can accurately co-segment a wide variety of shapes, which may have different poses and significant topology changes.

The Cayley-Bacharach Theorem for Continuous Piecewise Algebraic Curves over Cross-cut Triangulations

A piecewise algebraic curve is a curve determined by the zero set of a bivariate spline function. In this paper, we propose the Cayley-Bacharach theorem for continuous piecewise algebraic curves over cross-cut triangulations. We show that, if two continuous piecewise algebraic curves of degrees m and n respectively meet at ranT distinct points over a cross-cut triangulation, where T denotes the number of cells of the triangulation, then any continuous piecewise algebraic curve of degree m ＋ n - 2 containing all but one point of them also contains the last point.

Learning Human-Written Commit Messages to Document Code Changes

Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message:"fixing 'null' pointer exception"). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.

Example-based web page recoloring method

1 Introduction The user interface(UI)is very important for a software product,and a well-designed UI will attract users and make a good impression.It has been shown that color plays an important role in invoking emotional reactions[1],influencing usability[2],forming first impressions[3],and affecting the energy cost of the UI[4].Generating satisfying color schemes has high requirements for experience,aesthetic,and creative inspiration of designers[5].