Design Pattern Mining Using Graph Matching

The identification of design pattern instances is important for program understanding and software maintenance. Aiming at the mining of design patterns in existing systems, this paper proposes a subgraph isomorphism approach to discover several design patterns in a legacy system at a time. The attributed relational graph is used to describe design patterns and legacy systems. The sub-graph isomorphism approach consists of decomposition and composition process. During the decomposition process, graphs corresponding to the design patterns are decomposed into sub-graphs, some of which are graphs corresponding to the elemental design patterns. The composition process tries to get sub-graph isomorphism of the matched graph if sub-graph isomorphism of each subgraph is obtained. Due to the common structures between design patterns, the proposed approach can reduce the matching times of entities and relations. Compared with the existing methods, the proposed algorithm is not linearly dependent on the number of design pattern graphs. Key words design pattern mining - attributed relational graph - subgraph isomorphism CLC number TP 311.5 Foundation item: Supported by the National Natural Science Foundation of China (60273075) and the Science Foundation of Naval University of Engineering (HGDJJ03019)Biography: LI Qing-hua (1940-), male, Professor, research direction: parallel computing.

Matching spatial relation graphs using a constrained partial permutation strategy

A constrained partial permutation strategy is proposed for matching spatial relation graph (SRG), which is used in our sketch input and recognition system Smart Sketchpad for representing the spatial relationship among the components of a graphic object. Using two kinds of matching constraints dynamically generated in the matching process, the proposed approach can prune most improper mappings between SRGs during the matching process. According to our theoretical analysis in this paper, the time complexity of our approach is O(n 2) in the best case, and O(n!) in the worst case, which occurs infrequently. The spatial complexity is always O(n) for all cases. Implemented in Smart Sketchpad, our proposed strategy is of good performance.

Graph-Segmenter:graph transformer with boundary-aware attention for semantic segmentation

Thetransformer-based semantic segmentation approaches,which divide the image into different regions by sliding windows and model the relation inside each window,have achieved outstanding success.However,since the relation modeling between windows was not the primary emphasis of previous work,it was not fully utilized.To address this issue,we propose a Graph-Segmenter,including a graph transformer and a boundary-aware attention module,which is an effective network for simultaneously modeling the more profound relation between windows in a global view and various pixels inside each window as a local one,and for substantial low-cost boundary adjustment.Specifically,we treat every window and pixel inside the window as nodes to construct graphs for both views and devise the graph transformer.The introduced boundary-awareattentionmoduleoptimizes theedge information of the target objects by modeling the relationship between the pixel on the object's edge.Extensive experiments on three widely used semantic segmentation datasets(Cityscapes,ADE-20k and PASCAL Context)demonstrate that our proposed network,a Graph Transformer with Boundary-aware Attention,can achieve state-of-the-art segmentation performance.

Graph reasoning over explicit semantic relation

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research,particularly with the reliance on deep neural networks.These networks are integral to text reasoning processes,yet they present challenges in extracting and representing domain or commonsense knowledge,and they often lack robust logical reasoning capabilities.To address these issues,we introduce an innovative text reasoning framework.This framework is grounded in the use of a semantic relation graph and a graph neural network,designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning.Our framework operates by extracting knowledge from a broad range of texts.It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process.Beginning with the core question,the framework methodically deduces key knowledge,using it as a guide to iteratively establish a complete evidence chain,thereby determining the final answer.Leveraging the advanced reasoning capabilities of the graph neural network,this approach is adept at multi-hop logical reasoning.It demonstrates strong performance in tasks like machine reading comprehension and question answering,while also clearly delineating the path of logical reasoning.

Architecture and algorithm for web phishing detection

A phishing detection system, which comprises client-side filtering plug-in, analysis center and protected sites, is proposed. An image-based similarity detection algorithm is conceived to calculate the similarity of two web pages. The web pages are first converted into images, and then divided into sub-images with iterated dividing and shrinking. After that, the attributes of sub-images including color histograms, gray histograms and size parameters are computed to construct the attributed relational graph（ARG）of each page. In order to match two ARGs, the inner earth mover＇s distances（EMD）between every two nodes coming from each ARG respectively are first computed, and then the similarity of web pages by the outer EMD between two ARGs is worked out to detect phishing web pages. The experimental results show that the proposed architecture and algorithm has good robustness along with scalability, and can effectively detect phishing.

Development of efficient and accurate skeletal mechanisms for hydrocarbon fuels and kerosene surrogate

In this paper,the methodology of the directed relation graph with error propagation and sensitivity analysis（DRGEPSA）,proposed by Niemeyer et al.（Combust Flame 157：1760-1770.2010）.and its differences to the original directed relation graph method are described.Using DRGEPSA,the detailed mechanism of ethylene containing 71 species and 395 reaction steps is reduced to several skeletal mechanisms with different error thresholds.The 25-species and 131-step mechanism and the 24-species and115-step mechanism are found to be accurate for the predictions of ignition delay time and laminar flame speed.Although further reduction leads to a smaller skeletal mechanism with 19 species and 68 steps,it is no longer able to represent the correct reaction processes.With the DRGEPSA method,a detailed mechanism for n-dodecane considering low-temperature chemistry and containing 2115 species and8157 steps is reduced to a much smaller mechanism with249 species and 910 steps while retaining good accuracy.If considering only high-temperature（higher than 1000 K）applications,the detailed mechanism can be simplified to even smaller mechanisms with 65 species and 340 steps or48 species and 220 steps.Furthermore,a detailed mechanism for a kerosene surrogate having 207 species and 1592 steps is reduced with various error thresholds and the results show that the 72-species and 429-step mechanism and the66-species and 392-step mechanism are capable of predicting correct combustion properties compared to those of the detailed mechanism.It is well recognized that kinetic mechanisms can be effectively used in computations only after they are reduced to an acceptable size level for computation capacity and at the same time retaining accuracy.Thus,the skeletal mechanisms generated from the present work are expected to be useful for the application of kinetic mechanisms of hydrocarbons to numerical simulations of turbulent or supersonic combustion.

Parametric message passing-based relative navigation in joint tactical information distribution system

Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Multi-Robot Cooperative Localization:Time-Varying Unobservable Subspace Analysis and Consistent Algorithm Design

This paper investigates the problem of cooperative localization(CL)for a multi-robot system(MRS)under dynamic measurement topology,which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements.The authors provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm.First,the authors introduce the relative measurement graph(RMG)to represent the relative pose measurements obtained by the MRS at each instant.Then,the authors derive the local observability matrix over a time interval.An equivalent relationship is established between the local observability matrix and the spectral matrices of the RMG.Moreover,the authors present a method for constructing the unobservable subspace based on the RMG under different topology conditions.Based on this analysis,the authors design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace.An analytical optimal solution is derived for the constrained optimization problem.Monte Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.

Reduced Methane Combustion Mechanism and Verification,Validation,and Accreditation(VV&A)in CFD for NO Emission Prediction

In order to obtain a reduced methane combustion mechanism for predicting combustion field and pollutants accurately in CFD simulations with a lower computational cost,a reduced mechanism with 22 species and 65 steps of reactions from GRI-Mech 3.0 was obtained by direct relation graph method and sensitivity analysis.The ideal reactor calculation and VV&A(Verification,Validation,and Accreditation)in CFD were carried out using the proposed mechanism.The results showed that the proposed mechanism agrees well with the detailed mechanism in a wide range of operating conditions;the temperature field and species can be predicted accurately in CFD simulations(RANS and LES models),and the NO prediction error of an industrial gas turbine combustor outlet is less than 2×10-6.The proposed mechanism has high engineering values.

A Class of Association Schemes and Their Automorphisms

In this paper,we construct a class of association schemes by using pairs of subspaces of vector spaces and determine their full automorphism groups.