Journal articles on the topic 'Graph-based application'
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FOGGIA, PASQUALE, GENNARO PERCANNELLA, CARLO SANSONE, and MARIO VENTO. "A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 843–60. http://dx.doi.org/10.1142/s0218001408006557.
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In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A comparison with other four state-of-the-art graph-based algorithms was also provided, demonstrating the effectiveness of the proposed approach.
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Padole, Himanshu, Shiv Dutt Joshi, and Tapan K. Gandhi. "Graph Wavelet-Based Multilevel Graph Coarsening and Its Application in Graph-CNN for Alzheimer’s Disease Detection." IEEE Access 8 (2020): 60906–17. http://dx.doi.org/10.1109/access.2020.2983590.
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Guan, Jun, Huiying Liu, Baolei Mao, and Xu Jiang. "Android Malware Detection Based on API Pairing." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 5 (October 2020): 965–70. http://dx.doi.org/10.1051/jnwpu/20203850965.
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Aiming at the problem that the permission-based detection is too coarse-grained, a malware detection method based on sensitive application program interface(API) pairing is proposed. The method decompiles the application to extract the sensitive APIs corresponding to the dangerous permissions, and uses the pairing of the sensitive APIs to construct the undirected graph of malicious applications and undirected graph of benign applications. According to the importance of sensitive APIs in malware and benign applications, different weights on the same edge in the different graphs are assigned to detect Android malicious applications. Experimental results show that the proposed method can effectively detect Android malicious applications and has practical significance.
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Zhu, Junxiang, Heap-Yih Chong, Hongwei Zhao, Jeremy Wu, Yi Tan, and Honglei Xu. "The Application of Graph in BIM/GIS Integration." Buildings 12, no. 12 (December 7, 2022): 2162. http://dx.doi.org/10.3390/buildings12122162.
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Information exchange between building information modelling (BIM) and geographic information system (GIS) is problematic, especially in terms of semantic information. Graph-based technologies, such as the resource description framework (RDF) and the labelled property graph (LPG), are promising in solving this problem. These two technologies are different but have not been systematically investigated in the context of BIM/GIS integration. This paper presents our systematic investigation into these two technologies, trying to propose the proper one for BIM/GIS data integration. The main findings are as follows. (1) Both LPG-based databases and RDF-based databases can be generally considered graph databases, but an LPG-based database is considered a native graph database, while an RDF-based database is not. (2) RDF suits applications focusing more on linking data and sharing data, and (3) LPG-based graph database suits applications focusing more on data query and analysis. An LPG-based graph database is thus proposed for BIM/GIS data integration. This review can facilitate the use of graph technology in BIM/GIS integration.
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HOLZRICHTER, MICHAEL, and SUELY OLIVEIRA. "A GRAPH BASED DAVIDSON ALGORITHM FOR THE GRAPH PARTITIONING PROBLEM." International Journal of Foundations of Computer Science 10, no. 02 (June 1999): 225–46. http://dx.doi.org/10.1142/s0129054199000162.
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The problem of partitioning a graph such that the number of edges incident to vertices in different partitions is minimized, arises in many contexts. Some examples include its recursive application for minimizing fill-in in matrix factorizations and load-balancing for parallel algorithms. Spectral graph partitioning algorithms partition a graph using the eigenvector associated with the second smallest eigenvalue of a matrix called the graph Laplacian. The focus of this paper is the use graph theory to compute this eigenvector more quickly.
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Schmalstieg, Dieter, Gerhard Reitmayr, and Gerd Hesina. "Distributed Applications for Collaborative Three-Dimensional Workspaces." Presence: Teleoperators and Virtual Environments 12, no. 1 (February 2003): 52–67. http://dx.doi.org/10.1162/105474603763835332.
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This paper focuses on the distributed architecture of the collaborative threedimensional user interface management system, Studierstube. The system allows multiple users to experience a shared 3D workspace populated by multiple applications using see-through head-mounted displays or other presentation media such as projection systems. Building large, ubiquitous, or mobile workspaces requires distribution of applications over several hosts in varying and dynamic configurations. The system design is based on a distributed shared scene graph that alleviates the application programmer from explicitly considering distribution and that avoids a separation of graphical and application data. The idea of unifying all system data in the scene graph is taken to its logical consequence by implementing application instances as nodes in the scene graph. Through the distributed shared scene graph mechanism, consistency of scene graph replicas and the contained application nodes is assured. Dynamic configuration management is based on application migration between participating hosts and a spatial model of locales allowing dynamic workgroup management. We describe a number of experimental workspaces that demonstrate the use of these configuration management techniques.
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Wang, Jianghan, Zhu Qu, Yihan Hu, Qiyun Ling, Jingyi Yu, and Yushan Jiang. "Diagnosis and Treatment Knowledge Graph Modeling Application Based on Chinese Medical Records." Electronics 12, no. 16 (August 11, 2023): 3412. http://dx.doi.org/10.3390/electronics12163412.
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In this study, a knowledge graph of Chinese medical record data was constructed based on graph database technology. An entity extraction method based on natural language processing, disambiguation, and reorganization for Chinese medical records is proposed, and dictionaries of drugs and treatment plans are constructed. Examples of applications of the knowledge graph in diagnosis and treatment prediction are given. Experimentally, it is found that the knowledge graph based on the graph database is 116.7% faster than the traditional database in complex relational queries.
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Czerepicki, A. "Application of graph databases for transport purposes." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 3 (September 1, 2016): 457–66. http://dx.doi.org/10.1515/bpasts-2016-0051.
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Abstract The article presents an innovative concept of applying graph databases in transport information systems. The model of a graph database has been presented together with implementation of data structures and search operations in a graph. The transformation concept of relational model to a graph data model has been developed. The schema of graph database has been proposed for public transport information system purposes. The realization methods have been illustrated by the use of search function based on the Cypher query language.
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Zhang, Zeyue. "The Application of Graph Embedding Based on Random Walk." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 474–79. http://dx.doi.org/10.54097/hset.v16i.2624.
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In the historical process of scientific development, computers have a lofty position, and in recent years, graph embedding algorithms and models are one of the most popular subjects. A large number of similar data structures are indistinguishable by humans, but graph embedding can quickly compare and analyze these data structures. Existing research on random walk-based graph embedding methods is very rich. In order to summarize and classify the status quo of the more mature classical models and compare and integrate them, many different classical models are discussed in this paper. Based on different models, the problems solved, algorithm ideas, strategies, advantages, and disadvantages of the models are discussed in detail, and the application performance of some models is evaluated. DeepWalk model, Node2Vec model, HARP model are three graph embedding models based on the classical random walk model. Calculations for different data can occur by generating different node sequences. The three most important models in attribute random walk models are TriDNR model, GraphRNA model and FEATHER model. The model that only targets the information data in the shallow network is no longer suitable for the rapidly developing network. Attribute random walk models can handle data in deeper networks. At the end of this paper, the full text is summarized and the future prospect of this field is made.
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Abd Rahman, Hayati, Azrina Ashaari, and Nur Azima Alya Narawi. "STORYTELLING APPLICATION BASED ON INTERACTIVE STORY GRAPH STRUCTURE (ISGS)." MALAYSIAN JOURNAL OF COMPUTING 6, no. 1 (March 9, 2021): 715. http://dx.doi.org/10.24191/mjoc.v6i1.10370.
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Storytelling is a process of conveying series of events and information in words, images, and sound. Conventionally, storytelling developers/writers will apply the linear narrative structure approach to deliver the stories. However, that approach has some limitations; users cannot determine the path to end the story. They have no option to choose how to end the story based on their way of storytelling. Therefore, this study is about applying an Interactive Story Graph Structure (ISGS) approach to storytelling. ISGS approach is a structure used in storytelling in which users can revert their decision when going through the storytelling application implemented during the development. After completing the storytelling prototype development, a survey was conducted to test users’ enjoyment level when using the prototype. The survey was divided into four constructs: expectation, ease of navigation, understanding, and satisfaction. There were 36 respondents, and the data were collected on a random basis. Based on the survey’s result, most users (90.28%) enjoyed the storytelling application. The storytelling prototype was developed using Adobe Animate Creative Cloud and has been distributed among the respondents randomly. The analysis was conducted to determine the findings, limitations, and recommendations for future project improvement based on the results obtained. This study’s outcome is the complete production of storytelling application, which is creative and interactive with ISGS.
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Lu, Yi, Zhonghua Gou, Yu Ye, and Qiang Sheng. "Three-dimensional visibility graph analysis and its application." Environment and Planning B: Urban Analytics and City Science 46, no. 5 (November 3, 2017): 948–62. http://dx.doi.org/10.1177/2399808317739893.
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Graph-based visibility analysis, developed from space syntax and social network theory, embraces mutual visibility between locations in a spatial system. It helps designers and researchers to decode spatial cognition and behavior, but methodological constraints limit its application to two-dimensional floor plans. In this study, we propose a new visibility graph analysis that can be used in three-dimensional built environments, such as multilevel atrium buildings or urban environments with canopies or overpass bridges. Furthermore, we draw a distinction between a generic visibility graph and a targeted visibility graph. In the former, an occupiable location is considered as both the origin and target of visibility lines. In the latter, we further take into account the visible space or specific targets in a system. Visible locations are spaces people can see but cannot necessarily physically occupy. With this differentiation, the visibility graph system is more amenable to new applications in three-dimensional architectural and urban design while retaining a mapping back to the original two-dimensional visibility graph method through the generic visibility graph. Four examples illustrate the application of the proposed visibility graph analysis in complex three-dimensional building and urban environments.
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Wang, Yifei, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu, and Pengyu Hong. "Motif-Based Graph Representation Learning with Application to Chemical Molecules." Informatics 10, no. 1 (January 11, 2023): 8. http://dx.doi.org/10.3390/informatics10010008.
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This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.
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Bobrovnikova, Kira, Sergii Lysenko, Bohdan Savenko, Piotr Gaj, and Oleg Savenko. "Technique for IoT malware detection based on control flow graph analysis." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (February 23, 2022): 141–53. http://dx.doi.org/10.32620/reks.2022.1.11.
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The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats. The rapid integration of the Internet into the IoT infrastructure in various areas of human activity, including vulnerable critical infrastructure, makes the detection of malware in the Internet of Things increasingly important. Annual reports from IoT infrastructure cybersecurity companies and antivirus software vendors show an increase in malware attacks targeting IoT infrastructure. This demonstrates the failure of modern methods for detecting malware on the Internet of things. This is why there is an urgent need for new approaches to IoT malware detection and to protect IoT devices from IoT malware attacks. The subject of the research is the malware detection process on the Internet of Things. This study aims to develop a technique for malware detection based on the control flow graph analysis. Results. This paper presents a new approach for IoT malware detection based on control flow graph analysis. Control flow graphs were built for suspicious IoT applications. The control flow graph is represented as a directed graph, which contains information about the components of the suspicious program and the transitions between them. Based on the control flow graph, metrics can be extracted that describe the structure of the program. Considering that IoT applications are small due to the simplicity and limitations of the IoT operating system environment, malware detection based on control flow graph analysis seems to be possible in the IoT environment. To analyze the behavior of the IoT application for each control flow graph, the action graph is to be built. It shows an abstract graph and a description of the program. Based on the action graph for each IoT application, a sequence is formed. This allows for defining the program’s behavior. Thus, with the aim of IoT malware detection, two malware detection models based on control flow graph metrics and the action sequences are used. Since the approach allows you to analyze both the overall structure and behavior of each application, it allows you to achieve high malware detection accuracy. The proposed approach allows the detection of unknown IoT malware, which are the modified versions of known IoT malware. As the mean of conclusion-making concerning the malware presence, the set of machine learning classifiers was employed. The experimental results demonstrated the high accuracy of IoT malware detection. Conclusions. A new technique for IoT malware detection based on control flow graph analysis has been developed. It can detect IoT malware with high efficiency.
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Zuo, Cen, Anita Pal, and Arindam Dey. "New Concepts of Picture Fuzzy Graphs with Application." Mathematics 7, no. 5 (May 24, 2019): 470. http://dx.doi.org/10.3390/math7050470.
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The picture fuzzy set is an efficient mathematical model to deal with uncertain real life problems, in which a intuitionistic fuzzy set may fail to reveal satisfactory results. Picture fuzzy set is an extension of the classical fuzzy set and intuitionistic fuzzy set. It can work very efficiently in uncertain scenarios which involve more answers to these type: yes, no, abstain and refusal. In this paper, we introduce the idea of the picture fuzzy graph based on the picture fuzzy relation. Some types of picture fuzzy graph such as a regular picture fuzzy graph, strong picture fuzzy graph, complete picture fuzzy graph, and complement picture fuzzy graph are introduced and some properties are also described. The idea of an isomorphic picture fuzzy graph is also introduced in this paper. We also define six operations such as Cartesian product, composition, join, direct product, lexicographic and strong product on picture fuzzy graph. Finally, we describe the utility of the picture fuzzy graph and its application in a social network.
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Du, Xuehong, Jianxin Jiao, and Mitchell M. Tseng. "Graph Grammar Based Product Family Modeling." Concurrent Engineering 10, no. 2 (June 2002): 113–28. http://dx.doi.org/10.1177/1063293x02010002635.
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Many industries are shifting from mass production to mass customization, which demands quick response to the needs of individual customers with high quality and low costs. The development of product families has received an increasing interest in recent years because, by sharing components across products, a family of products can be derived to cater variety while maintaining the economy of scale. Aiming at the computerization, and eventual automation, of product family design, this paper tackles the formal representation issue surrounding this economically important class of engineering design problem. Breaking free from conventional understanding of product families, which is limited as shared components, the paper defines a product family as a structured system to create variety of products with shared core product technologies. It not only involves the shared base product, but also encompasses customization modules, standard designs, and primary patterns of variety to generate custom designs. The paper introduces graph grammar formalisms to the modeling of such a product family. Based on Programmed Attributed Graph Grammars (PAGG), the graph language is developed to specify the design space of the product family. The process of customizing the base product through manipulating particular modules is modeled by rewriting the starting graph using a series of productions according to the control diagram. Configuration constraints are dealt with by defining application conditions for production rules. Control diagrams are constructed to capture complex relationships among modules and used to control the application sequence of production rules. A case study of power supplies is presented to demonstrate the potential of the graph grammar based modeling approach.
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An, Jun, and Jin Song Fan. "Study on Scene Graph and its Application in Product Design." Applied Mechanics and Materials 278-280 (January 2013): 1901–5. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1901.
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Based on the study of the characteristics of scene graph and OSG, osgART and its application in product design was deeply analyzed. Organizational characteristics of scene graph, node traversal, interaction and update callback method in osgART were studied, and relevant codes were given. By adopting OSG scene graph to organize and manage scene information, the developments of AR applications with osgART can simply programming and increase application running speed. The real-time interactive capabilities of virtual objects with real environment can be enhanced, which meet the requirements of rapidly developing AR application with rich features. At the end of this article, a design review system with above approaches for product design was introduced.
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Sholikhatin, Siti Alvi, Adi Budi Prasetyo, and Ade Nurhopipah. "IMPLEMENTASI ALGORITMA KRUSKAL DAN ALGORITMA PRIM SUATU GRAPH DENGAN APLIKASI BERBASIS DESKTOP." Jurnal RESISTOR (Rekayasa Sistem Komputer) 3, no. 2 (November 6, 2020): 89–93. http://dx.doi.org/10.31598/jurnalresistor.v3i2.638.
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A graph has several algorithms in its solution, including the Kruskal algorithm and Prim algorithm, both of which are greedy algorithms for determining the minimum spanning tree. Completion of graphs is useful in various fields of life, so an accurate graph calculation is important. Making an application to solve a graph, especially the Kruskal algorithm and Prim algorithm aims to facilitate the work of the graph so as to produce an accurate final result. The flow of research carried out are: a background review of research, study of literature and relevant literature, application design, building desktop-based applications, as well as implementation and application tests. The level of technological readiness or TKT in this research is based on self-assessment which is at level 7, meaning the prototype demonstration system in the actual environment, with details of the TKT indicators as follows: TKT indicator 1 is met, TKT indicator 2 is met, TKT indicator 3 is not met, TKT indicator 4, TKT indicator 5 are met, TKT indicator 6 are met, TKT indicator 7 is met, TKT indicator 8 and 9 are not met. The application that has been built is useful for completing a graph with the Kruskal algorithm and Prim algorithm. This research was conducted to answer the crucial needs of a weighted graph settlement application.
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Zhao, Chunhui, Tengfei Tu, Cheng Wang, and Sujuan Qin. "VulPathsFinder: A Static Method for Finding Vulnerable Paths in PHP Applications Based on CPG." Applied Sciences 13, no. 16 (August 14, 2023): 9240. http://dx.doi.org/10.3390/app13169240.
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Today, as PHP application technology is becoming increasingly mature, the functions of modern multi-layer web applications are becoming more and more complete, and the complexity is also gradually increasing. While providing developers with various business functions and interfaces, multi-tier Web applications also successfully cover the details of application development. This type of web application adopts a unified entrance, many object-oriented codes are used, and features such as encapsulation, inheritance, and polymorphism bring challenges to vulnerability mining from the perspective of static analysis. A large amount of object-oriented code makes it impossible for a simple function name-matching method to build a complete call graph (CG), resulting in the inability to perform a comprehensive interprocedural analysis. At the same time, the encapsulation feature of the class makes the data hidden in the object attribute, and the vulnerability path cannot be obtained through the general data-flow analysis. In response to the above issues, we propose a vulnerability detection method that supports vulnerability detection for multi-layer web applications based on MVC (Model-View-Control) architecture. First, we improve the construction of the call graph and Code Property Graph (CPG). Then, based on the enhanced Code Property Graph, we propose a technique to support vulnerability detection for multi-layer web applications. Based on this approach, we implemented a prototype of VulPathsFinder, a security analysis tool extended from the PHP security analyzer Joern-PHP. Then, we select ten MVC based and ten non-MVC-based applications to form a test dataset and validate the effectiveness of VulPathsFinder based on this dataset. Experimental results show that, compared with currently available tools, VulPathsFinder can handle framework applications more effectively, build a complete code property map, and detect vulnerabilities in framework applications that existing tools cannot detect.
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KAPOOR, SANJIV, and XIANG-YANG LI. "PROXIMITY STRUCTURES FOR GEOMETRIC GRAPHS." International Journal of Computational Geometry & Applications 20, no. 04 (August 2010): 415–29. http://dx.doi.org/10.1142/s0218195910003360.
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In this paper we study proximity graph structures like Delaunay triangulations based on geometric graphs, i.e. graphs which are subgraphs of the complete geometric graph. Given an arbitrary geometric graph G, we define Voronoi diagrams, Delaunay triangulations, relative neighborhood graphs, Gabriel graphs which are related to the graph structure and then study their complexities when G is a general geometric graph or G is some special graph derived from the application area of wireless networks. Besides being of fundamental interest these structures have applications in topology control for wireless networks.
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Feng, Pengbin, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi, and Di Lu. "Android Malware Detection via Graph Representation Learning." Mobile Information Systems 2021 (June 4, 2021): 1–14. http://dx.doi.org/10.1155/2021/5538841.
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With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors. There is an urgent need for developing an automatic malware detection approach to prevent the spread of malware. The low code coverage and poor efficiency of the dynamic analysis limit the large-scale deployment of malware detection methods based on dynamic features. Therefore, researchers have proposed a plethora of detection approaches based on abundant static features to provide efficient malware detection. This paper explores the direction of Android malware detection based on graph representation learning. Without complex feature graph construction, we propose a new Android malware detection approach based on lightweight static analysis via the graph neural network (GNN). Instead of directly extracting Application Programming Interface (API) call information, we further analyze the source code of Android applications to extract high-level semantic information, which increases the barrier of evading detection. Particularly, we construct approximate call graphs from function invocation relationships within an Android application to represent this application and further extract intrafunction attributes, including required permission, security level, and Smali instructions’ semantic information via Word2Vec, to form the node attributes within graph structures. Then, we use the graph neural network to generate a vector representation of the application, and then malware detection is performed on this representation space. We conduct experiments on real-world application samples. The experimental results demonstrate that our approach implements high effective malware detection and outperforms state-of-the-art detection approaches.
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Balcı, Mehmet Ali, Ömer Akgüller, Larissa M. Batrancea, and Lucian Gaban. "Discrete Geodesic Distribution-Based Graph Kernel for 3D Point Clouds." Sensors 23, no. 5 (February 21, 2023): 2398. http://dx.doi.org/10.3390/s23052398.
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In the structural analysis of discrete geometric data, graph kernels have a great track record of performance. Using graph kernel functions provides two significant advantages. First, a graph kernel is capable of preserving the graph’s topological structures by describing graph properties in a high-dimensional space. Second, graph kernels allow the application of machine learning methods to vector data that are rapidly evolving into graphs. In this paper, the unique kernel function for similarity determination procedures of point cloud data structures, which are crucial for several applications, is formulated. This function is determined by the proximity of the geodesic route distributions in graphs reflecting the discrete geometry underlying the point cloud. This research demonstrates the efficiency of this unique kernel for similarity measures and the categorization of point clouds.
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Ni, Jiasheng. "A Medical Service Application Based on 3D-CNN and Knowledge Graph." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012048. http://dx.doi.org/10.1088/1742-6596/2078/1/012048.
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Abstract Remote medical prognosis application is a category of medical tests tool designed to collect patients’ body conditions and offer diagnosis results synchronously. However, most online applications are predicated on a simple chat bot which typically redirect patients to other online medical websites, which undermines the user experience and may prompt useless information for their reference. To tackle these issues, this paper proposed a medical prognosis application with deep learning techniques for a more responsive and intelligent medical prognosis procedure. This application can be break down into three parts-lung cancer detection, a database-supporting medical QA bot and a Hierarchical Bidirectional LSTM model (HBDA). A 3D-CNN model is built for the lung cancer detection, with a sequence of sliced CT images as inputs and outputs a probability scaler for tumor indications. A knowledge graph is applied in the medical QA bot implementation and the HBDA model is designed for semantic segmentation in order to better capture users’ intention in medical questions. For the performance of the medical prognosis, since we have limited computer memory, the 3D-CNN didn’t perform very well on detecting tumor conditions in the CT images with accuracy at around 70%. The knowledge graph-based medical QA bot intelligently recognize the underlying pattern in patients’ question and delivered decent medical response. The HBDA model performs well on distinguish the similarities and disparities between various medical questions, reaching accuracy at 90%. These results shed light for the feasibility of utilizing deep learning techniques such as 3D-CNN, Knowledge Graph, and Hierarchical Bi-directional LSTM to simulate the medical prognosis process.
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De Meester, Ben, Pieter Heyvaert, Dörthe Arndt, Anastasia Dimou, and Ruben Verborgh. "RDF graph validation using rule-based reasoning." Semantic Web 12, no. 1 (November 19, 2020): 117–42. http://dx.doi.org/10.3233/sw-200384.
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The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application’s supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator’s performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation “Validatrr”, based on N3Logic and the EYE reasoner. Our approach – supporting an equivalent number of constraint types compared to the state of the art – better explains the root cause of the violations due to the reasoner’s generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions.
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Shilin Zhang, Heping Li, and Shuwu Zhang. "A Graph Based Video Content Mining Method and Its Application." International Journal of Digital Content Technology and its Applications 5, no. 12 (December 31, 2011): 355–61. http://dx.doi.org/10.4156/jdcta.vol5.issue12.44.
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Idika, N., and B. Bhargava. "Extending Attack Graph-Based Security Metrics and Aggregating Their Application." IEEE Transactions on Dependable and Secure Computing 9, no. 1 (January 2012): 75–85. http://dx.doi.org/10.1109/tdsc.2010.61.
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Zhao, Xiaohong, Yang Zhao, Wenting Wang, Tiancheng Ren, and Hao Zhang. "Application of Neural Network Based Knowledge Graph in Vertical Industry." Journal of Physics: Conference Series 1584 (July 2020): 012018. http://dx.doi.org/10.1088/1742-6596/1584/1/012018.
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Liu, Kaiping, Hee Beng Kuan Tan, and Xu Chen. "Aiding Maintenance of Database Applications Through Extracting Attribute Dependency Graph." Journal of Database Management 24, no. 1 (January 2013): 20–35. http://dx.doi.org/10.4018/jdm.2013010102.
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Database is a major component of many software systems. This paper proposes a novel graph called the attribute dependency graph to reveal the dependencies between attributes in a database application and also the programs involved. The authors propose an approach to automatically extract the attribute dependency graph out of a database application from its source code through inter-procedural static program analysis. The extracted information is a straightforward indicator that benefits the maintenance process, particularly for impact analysis on the modifications in a database application. A tool has been developed to implement the proposed approach for PHP-based database applications. Case studies have also been conducted to demonstrate the use of our approach.
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Yang, Yang, Xuehui Du, Zhi Yang, and Xing Liu. "Android Malware Detection Based on Structural Features of the Function Call Graph." Electronics 10, no. 2 (January 15, 2021): 186. http://dx.doi.org/10.3390/electronics10020186.
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The openness of Android operating system not only brings convenience to users, but also leads to the attack threat from a large number of malicious applications (apps). Thus malware detection has become the research focus in the field of mobile security. In order to solve the problem of more coarse-grained feature selection and larger feature loss of graph structure existing in the current detection methods, we put forward a method named DGCNDroid for Android malware detection, which is based on the deep graph convolutional network. Our method starts by generating a function call graph for the decompiled Android application. Then the function call subgraph containing the sensitive application programming interface (API) is extracted. Finally, the function call subgraphs with structural features are trained as the input of the deep graph convolutional network. Thus the detection and classification of malicious apps can be realized. Through experimentation on a dataset containing 11,120 Android apps, the method proposed in this paper can achieve detection accuracy of 98.2%, which is higher than other existing detection methods.
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Zalkow, Frank, Julian Brandner, and Meinard Müller. "Efficient Retrieval of Music Recordings Using Graph-Based Index Structures." Signals 2, no. 2 (May 17, 2021): 336–52. http://dx.doi.org/10.3390/signals2020021.
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Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.
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Perak, Benedikt, and Tajana Ban Kirigin. "Corpus-Based Syntactic-Semantic Graph Analysis." Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, no. 2 (October 30, 2020): 957–96. http://dx.doi.org/10.31724/rihjj.46.2.27.
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This research exemplifies the corpus-based graph approach to the syntactic-semantic analysis of a concept feeling using the Construction Grammar Conceptual network methodology. by constructing a lexical network from grammatically tagged collocations of the english and the Croatian web corpora, the structure of the semantic domains is revealed as a set of sub-graphs derived from the source lexeme’s friend-of-a-friend graph. the subgraph structures, calculated with the community detection algorithm, are interpreted as the semantic domains associated with the source lexeme’s conceptual matrix. lexical structures are analyzed using a centrality algorithm that determines the overall rank of the salience and semantic relatedness to the source concept feeling. this empirical approach can be used for developing nlP methods and tasks, such as computing semantic similarity, sense disambiguation, sense structuring, as well as for comparative corpus and cross-cultural studies. ConGraCnet has a web application on the page <a target="_blank" rel="nofollow" href="http://emocnet.uniri.hr/congracnet">http://emocnet.uniri.hr/congracnet</a>.
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Xie, Chengxin, Xiumei Wen, Hui Pang, and Bo Zhang. "Application of graph auto-encoders based on regularization in recommendation algorithms." PeerJ Computer Science 9 (April 6, 2023): e1335. http://dx.doi.org/10.7717/peerj-cs.1335.
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Social networking has become a hot topic, in which recommendation algorithms are the most important. Recently, the combination of deep learning and recommendation algorithms has attracted considerable attention. The integration of autoencoders and graph convolutional neural networks, while providing an effective solution to the shortcomings of traditional algorithms, fails to take into account user preferences and risks over-smoothing as the number of encoder layers increases. Therefore, we introduce L1 and L2 regularization techniques and fuse them linearly to address user preferences and over-smoothing. In addition, the presence of a large amount of noisy data in the graph data has an impact on feature extraction. To our best knowledge, most existing models do not account for noise and address the problem of noisy data in graph data. Thus, we introduce the idea of denoising autoencoders into graph autoencoders, which can effectively address the noise problem. We demonstrate the capability of the proposed model on four widely used datasets and experimentally demonstrate that our model is more competitive by improving up to 1.3, 1.4, and 1.2, respectively, on the edge prediction task.
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Hao, Xuejie, Zheng Ji, Xiuhong Li, Lizeyan Yin, Lu Liu, Meiying Sun, Qiang Liu, and Rongjin Yang. "Construction and Application of a Knowledge Graph." Remote Sensing 13, no. 13 (June 26, 2021): 2511. http://dx.doi.org/10.3390/rs13132511.
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With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level’s ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol and resource description framework query language (SPARQL) endpoint service is used to describe functions such as query and reasoning of the knowledge graph. The graph database is then used to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation between the fields of surveying and remote sensing.
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Ma, Chao, Qi An, Zhenguo Yang, Hongguo Zhang, and Jiaxing Qu. "A Hybrid Recommendation Approach for Medical Services That Incorporates Knowledge Graphs." Processes 10, no. 8 (July 29, 2022): 1500. http://dx.doi.org/10.3390/pr10081500.
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At present, there are a large number of growing medical applications in the application market. It is difficult for users to find satisfactory medical services conveniently and efficiently. The classical collaborative filtering algorithm has some problems, such as cold start, unsatisfactory recommendation results, and so on. This paper proposes a hybrid medical service recommendation approach based on knowledge graph to solve the above problems. This approach introduces the open knowledge graph and establishes the semantic link relationship between the mobile application and the knowledge graph entity. It not only enhances the semantic feature of single application for improving the accuracy of recommendation results, but also realizes the in-depth analysis of the semantic relationship among multiple application entities in the knowledge graph through the TransHR model which can alleviate the cold start problem. Then we design a hybrid recommendation algorithm based on multi-dimensional similarity fusion. This algorithm uses the entropy method to organically integrate the calculation results of multi-dimensional semantic similarity, such as feature vector similarity, entity relation similarity, and user rating similarity. It is convenient and efficient to recommend satisfactory medical application services to target users. Finally, we test and analyze the accuracy and effectiveness of our proposed approach by experiment.
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Andriyanov, Nikita. "Application of Graph Structures in Computer Vision Tasks." Mathematics 10, no. 21 (October 29, 2022): 4021. http://dx.doi.org/10.3390/math10214021.
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On the one hand, the solution of computer vision tasks is associated with the development of various kinds of images or random fields mathematical models, i.e., algorithms, that are called traditional image processing. On the other hand, nowadays, deep learning methods play an important role in image recognition tasks. Such methods are based on convolutional neural networks that perform many matrix multiplication operations with model parameters and local convolutions and pooling operations. However, the modern artificial neural network architectures, such as transformers, came to the field of machine vision from natural language processing. Image transformers operate with embeddings, in the form of mosaic blocks of picture and the links between them. However, the use of graph methods in the design of neural networks can also increase efficiency. In this case, the search for hyperparameters will also include an architectural solution, such as the number of hidden layers and the number of neurons for each layer. The article proposes to use graph structures to develop simple recognition networks on different datasets, including small unbalanced X-ray image datasets, widely known the CIFAR-10 dataset and the Kaggle competition Dogs vs Cats dataset. Graph methods are compared with various known architectures and with networks trained from scratch. In addition, an algorithm for representing an image in the form of graph lattice segments is implemented, for which an appropriate description is created, based on graph data structures. This description provides quite good accuracy and performance of recognition. The effectiveness of this approach based, on the descriptors of the resulting segments, is shown, as well as the graph methods for the architecture search.
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Xu, Guoliang, Ming Xu, Yunzhi Chen, and Jiaqi Zhao. "A Mobile Application-Classifying Method Based on a Graph Attention Network from Encrypted Network Traffic." Electronics 12, no. 10 (May 20, 2023): 2313. http://dx.doi.org/10.3390/electronics12102313.
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Classifying mobile applications from encrypted network traffic is a common and basic requirement in network security and network management. Existing works classify mobile applications from flows, based on which application fingerprints and classifiers are created. However, mobile applications often generate concurrent flows with varying degrees of ties, such as low discriminative flows across applications and application-specific flows. So flow-based methods suffer from low accuracy. In this paper, a novel mobile application-classifying method is proposed, capturing relationships between flows and paying attention to their importance. To capture the inter-flow relationships, the proposed method slices raw mobile traffic into traffic chunks to represent flows as nodes, embeds statistical features into nodes, and adds edges according to cross-correlations between the nodes. To pay different attention to the various flows, the proposed method builds a deep learning model based on graph attention networks, implicitly assigning importance values to flows via graph attention layers. Compared to recently developed techniques on a large dataset with 101 popular apps using the Android platform, the proposed method improved by 4–20% for accuracy, precision, recall, and F1 score, and spent much less time training.
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Pietrzyk, Robert, and Andrzej Marciniak. "REPRESENTATION OF COMPLEX OBJECT AND PROCESS STRUCTURES IN GRAPH DATA BASE ON THE EXAMPLE OF AGRICULTURAL TRACTOR." Archives of Automotive Engineering – Archiwum Motoryzacji 83, no. 1 (March 29, 2019): 5–22. http://dx.doi.org/10.14669/am.vol83.art1.
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There is presented application of Graph Data Base technology in knowledge engineering task concerned with knowledge representation of complex structures. The first example is graph representation of composition of tractor gearbox. It is an example of application of static partonomy relation. Adequate codding of that relation in Graph Data Base is easily expressed in Cypher language. There were presented some applications of Graph Data Base useful in management of technical object existence in different “life” phases, as design, production, maintenance and recycling. The second example concerns the Graph representation of activity structure. Such structures was built based on agricultural tractor’s technical revisions system. Created Data Base allows for processing the queries referred to the questions of what activities in what period should be performed to maintain the agricultural tractor ready to work in good technical condition. The Graph Data Base technology is the first step to create semantic systems for data storing and processing in order to extract knowledge and information useful in precise physical process management.
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Dornaika, Fadi, and Abdelmalik Moujahid. "Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction." Algorithms 15, no. 6 (June 14, 2022): 207. http://dx.doi.org/10.3390/a15060207.
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Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods.
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Kitagawa, Daiki, Dieu Quang Nguyen, Trung Anh Dinh, and Shigeru Yamashita. "Graph-Covering-Based Architectural Synthesis for Programmable Digital Microfluidic Biochips." International Journal of Biomedical and Clinical Engineering 6, no. 2 (July 2017): 33–45. http://dx.doi.org/10.4018/ijbce.2017070103.
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Digital microfluidic technology has been extensively applied in various biomedical fields. Different from application-specific biochips, a programmable design has several advantages such as dynamic reconfigurability and general applicability. Basically, a programmable biochip divides the chip into several virtual modules. However, in the previous design, a virtual module can execute only one operation at a time. In this paper, the authors propose a new multi-functional module for programmable digital microfluidic biochips, which can execute two operations simultaneously. Moreover, they also propose a binding and scheduling algorithm for programmable biochips, which is motivated from a graph-covering problem. Experiment demonstrates that their algorithm can reduce the completion time of the applications compared with the previous approaches.
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Alshahrani, Mona, Maha A. Thafar, and Magbubah Essack. "Application and evaluation of knowledge graph embeddings in biomedical data." PeerJ Computer Science 7 (February 18, 2021): e341. http://dx.doi.org/10.7717/peerj-cs.341.
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Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, “knowledge graphs”. The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.
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Yang, Chun Shan, and Gui Jun Liu. "Research on Centrosymmetric Graph Based on DAM6416P." Advanced Materials Research 756-759 (September 2013): 962–66. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.962.
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In Order to Explore the Feasibility of Application of Virtual Technical Platform, at the Moment, Mechanical & Electrical Equipment was Used to Rolling Indicator. it is Studied the Control of Graphic Display on DSP Graphics Processing Technology, Combined with the Sensor Signal, Virtual Level Indicating Shall be Putted into Effect. Building the Development Platform as the Core of DAM6416P, Research & Analysis of Line Generating Algorithm and Match Corresponding Colors for Graphics. on this Basis, Apply Bresenham Algorithm to Digital Signal that Tilt Sensor Output, Design Centrosymmetric Graph, Output in Real Time to the Dual Plasma Display, at the same Time, in Order to more Visually Intuitive, Match Different Colors for Output.
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Manzo, Mario. "Graph-Based Image Matching for Indoor Localization." Machine Learning and Knowledge Extraction 1, no. 3 (July 15, 2019): 785–804. http://dx.doi.org/10.3390/make1030046.
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Graphs are a very useful framework for representing information. In general, these data structures are used in different application domains where data of interest are described in terms of local and spatial relations. In this context, the aim is to propose an alternative graph-based image representation. An image is encoded by a Region Adjacency Graph (RAG), based on Multicolored Neighborhood (MCN) clustering. This representation is integrated into a Content-Based Image Retrieval (CBIR) system, designed for the vision-based positioning task. The image matching phase, in the CBIR system, is managed with an approach of attributed graph matching, named the extended-VF algorithm. Evaluated in a context of indoor localization, the proposed system reports remarkable performance.
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WANG, XUEXIANG, HANLAI PU, JUN YANG, and LONGXING SHI. "EXTENDED CONTROL FLOW GRAPH BASED PERFORMANCE AND ENERGY CONSUMPTION OPTIMIZATION USING SCRATCH-PAD MEMORY." Journal of Circuits, Systems and Computers 18, no. 04 (June 2009): 697–711. http://dx.doi.org/10.1142/s0218126609005204.
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A Scratch-Pad memory (SPM) allocation method to improve the performance of a specified application while reducing its energy consumption is presented in this paper. Integrated in the design is an extended control flow graph (ECFG) built directly from the application's instruction flow. The application of the design is transformed into a directed graph that consists of nodes and relationships. Likewise, to provide a solution in decreasing the overhead of moving nodes to SPM, the design is enhanced with a refined greedy algorithm based on ECFG. An experiment is conducted to prove the feasibility and efficiency of the method. The results indicate that the method indeed improves performance by an average of 11% and consumes lesser energy by an average of 28%. This is in comparison to previous research which based on the control flow graph (CFG) method. The latter was discovered to have disregarded the relationships of nodes. In conclusion, the application's execution time and energy consumption were reduced by an average up to 56% and 69% respectively, compared to a non-SPM environment.
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Kislyakov, Alexey N. "GRAPH CONVOLUTIONAL NEURAL NETWORKS APPLICATION PERSPECTIVES IN TERRITORIES SPATIAL CLUSTERING." SOFT MEASUREMENTS AND COMPUTING 2, no. 63 (2023): 42–52. http://dx.doi.org/10.36871/2618-9976.2023.02.003.
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The paper considers the actual problem of formation of interregional clusters using spatial data analysis and models based on graph neural networks. The aim is to develop the theoretical and methodological foundations of the application of graph models and deep learning methods for the study of interregional and intermunicipal relationships in the problems of building predictive models of the economic potential of territories. The paper shows the possibility of adapting the typical architecture of spectral graph convolutional network for object clustering. Theoretical foundations for the application of a spectral graph convolutional network for spatial clustering of territories have been formulated and mathematical modeling by the example of a random graph using the tools of opensource libraries networkx, numpy of Python has been performed. The developed approaches are promising for further developments in the construction of recommendation systems in the field of interregional cooperation due to the possibility of taking into account spatial data, as well as socioeconomic indicators of territories in a single model based on the study of network structures.
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Kumar. P.K, Kishore, S.Lavanya, Sh A. Safarisabet, A. A. Talebi, and Hossein Rashmanlou. "New Concepts on Mild Balanced Vague Graphs with Application." International Journal of Fuzzy Mathematical Archive 15, no. 01 (2018): 37–53. http://dx.doi.org/10.22457/ijfma.v15n1a4.
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Recently, vague graph is a growing research topic as it is the generalization of fuzzy graphs. In this paper, we introduce intense subgraphs and feeble subgraphs based on their densities and discuss mild balanced vague graph and equally balanced vague subgraphs. The operations sum and union of subgraphs of vague graphs are analysed. Likewise, we investigated φ -complement of vague graph structure(VGS) and its isomorphic properties. Finally, an interesting application on vulnerability assessment of gas pipeline systems is given.
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Talebi, A. A., G. Muhiuddin, S. H. Sadati, and Hossein Rashmanlou. "New concepts of domination in fuzzy graph structures with application." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 3705–18. http://dx.doi.org/10.3233/jifs-211923.
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Fuzzy graphs have a prominent place in the mathematical modelling of the problems due to the simplicity of representing the relationships between topics. Gradually, with the development of science and in encountering with complex problems and the existence of multiple relationships between variables, the need to consider fuzzy graphs with multiple relationships was felt. With the introduction of the graph structures, there was better flexibility than the graph in dealing with problems. By combining a graph structure with a fuzzy graph, a fuzzy graph structure was introduced that increased the decision-making power of complex problems based on uncertainties. The previous definitions restrictions in fuzzy graphs have made us present new definitions in the fuzzy graph structure. The domination of fuzzy graphs has many applications in other sciences including computer science, intelligent systems, psychology, and medical sciences. Hence, in this paper, first we study the dominating set in a fuzzy graph structure from the perspective of the domination number of its fuzzy relationships. Likewise, we determine the domination in terms of neighborhood, degree, and capacity of vertices with some examples. Finally, applications of domination are introduced in fuzzy graph structure.
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Fu, Chao, Jihong Liu, Longxi Zhang, Yinxuan Mao, and Jie Jin. "Knowledge graph based System model configuration design." Journal of Physics: Conference Series 2029, no. 1 (September 1, 2021): 012108. http://dx.doi.org/10.1088/1742-6596/2029/1/012108.
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Abstract When solving the complexity problem between different fields, traditional document-based system engineering (TSE) is difficult to meet current research and development needs, and model-based system engineering (MBSE) is the best choice. With the implementation and application of MBSE in enterprises, the system model files are gradually accumulated. Therefore, this paper studies the reusability of the system model and builds the system model knowledge graph to achieve efficient reuse of the existing system model. A product configuration method based on subgraph matching is proposed and by configuring the product, the designer can obtain more feasible solutions, and then get the best design solution. Finally, an example of remote sensing satellite design is given to verify the effectiveness of the method.
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Zhou, Yifei, Shaoyong Li, and Yaping Liu. "Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding." Future Internet 12, no. 3 (March 20, 2020): 58. http://dx.doi.org/10.3390/fi12030058.
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Smartphones and applications have become widespread more and more. Thus, using the hardware and software of users’ mobile phones, we can get a large amount of personal data, in which a large part is about the user’s application usage patterns. By transforming and extracting these data, we can get user preferences, and provide personalized services and improve the experience for users. In a detailed way, studying application usage pattern benefits a variety of advantages such as precise bandwidth allocation, App launch acceleration, etc. However, the first thing to achieve the above advantages is to predict the next application accurately. In this paper, we propose AHNEAP, a novel network embedding based framework for predicting the next App to be used by characterizing the context information before one specific App being launched. AHNEAP transforms the historical App usage records in physical spaces to a large attributed heterogeneous network which contains three node types, three edges, and several attributes like App type, the day of the week. Then, the representation learning process is conducted. Finally, the App usage prediction problem was defined as a link prediction problem, realized by a simple neural network. Experiments on the LiveLab project dataset demonstrate the effectiveness of our framework which outperforms the three baseline methods for each tested user.
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Nikolaeva, Olesia, and Lyudmila Radchenko. "USE OF ROAPGRAPH IN NAVIGATION SYSTEMS ON THE EXAMPLE OF A COMPANY HERE TECHNOLOGIES." Interexpo GEO-Siberia 1, no. 2 (2019): 197–204. http://dx.doi.org/10.33764/2618-981x-2019-1-2-197-204.
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The road graph is the main layer of a digital navigation map. With the development of geoinformation technologies it is possible to use the road graph to solve various tasks: route construction, analysis of the use of the road network, analysis of road congestion, geomarketing research, updating the database based on the discrepancy between the data in reality and in the application. The purpose of this research is to consider in detail the applied problems that are solved on the basis of the road graph of аn navigation application. These tasks are considered on the example of HERE Technologies, which has many years of experience in the creation and use of navigation applications.
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Wu, Zhihao, and Fang Jia. "Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education." International Journal of Emerging Technologies in Learning (iJET) 17, no. 07 (April 12, 2022): 64–79. http://dx.doi.org/10.3991/ijet.v17i07.30405.
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The main problems lying in the learning process of learners in different majors are that they study blindly and do not have a complex knowledge structure, which are seriously affecting their learning effects. But if a knowledge graph can be modeled for the major-specific curriculum system using the quantitative method from the perspective of knowledge network, it may be able to improve the existing teaching problems and optimize the teaching quality. The existing major-specific knowledge graphs were all constructed in an abstract form, ignoring the inherent prior learning relationship between teaching units and curriculum knowledge. To this end, taking English major as an example, this paper studied the construction and application of a major-specific knowledge graph based on the big data in education. Firstly, the English major-specific knowledge graph was modeled, the calculation process of node importance was shown, and a localized graph of the knowledge network of English major courses was given. Then, a multi-node feature selection framework for the English major-specific knowledge graph was constructed based on the context of nodes, and the importance of the top k nodes in the constructed knowledge graph was extracted using the multi-node feature extraction technology. After that, the experimental results verified the stability and connectivity of the nodes in the constructed knowledge graph.
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Wang, Shaoting, Guigang Zhang, Phillip Sheu, Masahiro Hayakawa, Hiroyuki Shigematsu, and Atsushi Kitazawa. "A Semantic Approach to Data Reduction for Weighted Graphs and Complex Queries." International Journal of Semantic Computing 12, no. 02 (June 2018): 287–312. http://dx.doi.org/10.1142/s1793351x18500010.
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Graphs are widely used to store complex data nowadays: social networks, recommendation engines, computer networks and bioinformatics, to name a few. With a rapidly growing amount of data on the Internet in recent years, designing scalable systems to process the huge graph data efficiently has become a critical issue. In order to store and process the graph data efficiently in memory/disk, as well as to save time for transferring the data, graph compression techniques are often used. However, most of the existing graph data compression approaches are syntactic, which means they focus on graph structure and reduce it by serialization or redundancy removal. In this paper we focus on a semantic approach, namely query-based graph data reduction, which reduces a graph by preserving only the information relevant to the queries needed by an application. We study several classical graph problems and their applications, and design a suite of graph reduction algorithms to generate reduced graphs in which an application can still compute the same solutions. In addition, we design a synthesis method that can combine existing graph reduction algorithms to generate a reduced graph for a complex graph problem that includes more than one constraint. We also discuss incremental maintenance in order to update a reduced graph without reprocessing the whole graph again when the original graph is modified. We conduct experiments to compare the reduction rate of our algorithms with different sizes and types of data.