data flow graph representation

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PAPER Variable Data-Flow Graph for Lightweight Program Slicing and Visualization

PAPER Variable Data-Flow Graph for Lightweight Program Slicing and Visualization

ysis. Previously, J´asz [13] proposed an alternative ap- proximation — Static Execute After / Before dependencies. That approach is a control-flow-based approximation with- out data-flow analysis, while our approach performs approx- imated data and control flow analysis. The weak point of J´asz’ approach is a handling loop that connects control paths among all functions, e.g., a message loop in the GUI. Our analysis can extract data dependencies in such applications. Nguyen [14] has proposed a flow-insensitive data-flow analysis for mining source code patterns. The analysis con- structs a directed acyclic graph named groum whose nodes represent method calls and field accesses in a Java method. A data dependency edge between two nodes is generated if the two nodes share at least one common variable. Note that groum is an intra-procedural representation to extract a coding pattern in a method. On the contrary, our approach aims to visualize inter-procedural information, e.g. data- dependencies among methods.
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Evaluation of Flow Graph and Dependence Graphs for Program Representation

Evaluation of Flow Graph and Dependence Graphs for Program Representation

technique for dynamic slicing of Object-Oriented programs, which extends the System Dependence Graph (SDG) [20]. The graph is known as Extended System Dependence Graph (ESDG) that handles the features of object oriented programs such as polymorphism, inheritance etc. Their algorithm is named as Edge Marking Dynamic Slicing (EMDS) because it is based on marking and unmarking the edges of the ESDG. Zhao presented a Java-based graph that encapsulates the benefits offered by the earlier approaches of SDG. The Graph was named as Java System Dependence Graph (JSDG) and it enables the representation of Java-specific features such as interfaces, packages and single inheritance [21]. Walkinshaw et al. extended this Java-based graph that is known as Java System Dependence Graph (JSysDG) [22]. A JSysDG is a multi-graph that maps out the control and data dependencies among the statements of a Java program. Xi et al. presented an approach of Coarse-grained Dynamic Slice for Java Program [23]. This technique uses AspectJ code tracing tactic to gather method execution traces, which comprises information of method calls. Dynamic Java System Dependence Graph (DJSDG) is used for the intermediate representation and the slice computation has also been implemented on this graph.
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Representation and Analysis of Object Oriented Graph (OOG): A Graph Algorithmic Approach

Representation and Analysis of Object Oriented Graph (OOG): A Graph Algorithmic Approach

combination of use cases and cause effect graphing has lead to the development of a rigorous approach for acceptance testing which ensures function coverage as well. In [12] Bixin Li describes new techniques based on object oriented program slicing techniques that compute the amount and width of information flow, correlation coefficient and coupling among basic components. In [13] dynamic data flow analysis in Java programs has been presented to detect data flow anomalies. Bertolino et al. Al presents a generalized algorithm in [13] that generates a set of paths that covers every arc in the program flow graph for branch testing of program code. In [3, 5] we have proposed an extension of McCabe’s CFG by Extended Control Flow Graph (ECFG).
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Korean wide area differential global navigation satellite system augments global navigation satellite system by broadcasting additional signals from geostationary satellites and providing differential correction messages and integrity data for the GNSS satellites. It includes a network of wide area reference stations, wide area master station, ground earth station and geostationary satellites. Wide area reference stations are widely dispersed GNSS data collection sites that monitor and process satellite data to determine satellite orbit and clock drift plus delays caused by atmosphere and ionosphere. This information is then transmitted to wide area master station which creates and broadcasts correction messages through geostationary satellites. The user segment receives and applies correction messages to improve position accuracy and reliability. This study presents a flexible and robust software design and data processing algorithms of user segment of Korean wide area differential global navigation satellite system. The user segment software performs numerous functions such as calculation of ionosphere and troposphere delays, processing of correction messages, and data quality monitoring. It implements numerous tropospheric, ionospheric and position models, supports RINEX and BINEX data exchange formats, and is designed to work in real time and post processing modes. It can also be used in precision and non-precision approach modes. The software is divided into several layers such as Data Processing and Visualization, and can be easily extended to support various interfaces such as web interface and mobile device interface. The current version processes global positioning system and wide area differential global navigation satellite system data but can be easily extended to support various global navigation satellite systems such as GLONASS and Galileo.
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Image matching using relational graph representation

Image matching using relational graph representation

Over the years, a broad range of image matching techniques has been proposed for various types of data and many domains of application, resulting in a large body of research. Some interesting areas are recovering 3-D structure from stereo images or image sequence for autonomous vehicle navigation, industrial automation and augmented reality.

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Advances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library. This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship and meanings of words in the document. As a result the sparsity and semantic problem that is prevalent in textual document are not resolved. In this study, the problem of sparsity and semantic is reduced by proposing a graph based text representation method, namely dependency graph with the aim of improving the accuracy of document clustering. The dependency graph representation scheme is created through an accumulation of syntactic and semantic analysis. A sample of 20 news groups, dataset was used in this study. The text documents undergo pre-processing and syntactic parsing in order to identify the sentence structure. Then the semantic of words are modeled using dependency graph. The produced dependency graph is then used in the process of cluster analysis. K-means clustering technique was used in this study. The dependency graph based clustering result were compared with the popular text representation method, i.e. TFIDF and Ontology based text representation. The result shows that the dependency graph outperforms both TFIDF and Ontology based text representation. The findings proved that the proposed text representation method leads to more accurate document clustering results.
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A Graph Kernel based on Jensen-Shannon Representation

A Graph Kernel based on Jensen-Shannon Representation

Clearly, the JS matching kernel is related to the DB represen- tation defined in [Bai and Hancock, 2014]. However, there are two significant differences. First, the DB representation is computed by measuring the entropies of subgraphs rooted at the centroid vertex. The centroid vertex is identified by evaluating the minimum shortest path length variance to the remaining vertices. By contrast, in our work, we first compute the h-layer DB representation rooted at each vertex, and then compute the resulting m-layer JS representation. For a ver- tex, its m-layer JS representation is computed by summing its DB representation and the JSD measure between its DB rep- resentation and that of the vertices from its m-sphere. Sec- ond, in [Bai and Hancock, 2014] the DB representation from the centroid vertex is a vectorial signature of a graph, i.e., it can be seen as an embedding vector for the graph. Embed- ding a graph into a vector tends to approximate the structural correlations in a low dimensional space, and thus leads to in- formation loss. By contrast, the JS matching kernel aligning the m-layer JS representation represents graphs in a high di- mensional space and thus better preserves graph structures.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Wireless sensor network (WSN) consists of sensor nodes, which are small devices equipped with sensors, wireless transceiver, battery and microcontroller, the major function of this nodes is to monitors a physical phenomenon and measure physical factors. WSNs are applied to various fields of science and technology that have applications starting from military surveillance and reconnaissance to civilian application area like traffic controlling, environment monitoring, home automation and healthcare applications[1].Due to restricted characteristics of this kind of network, such as data storage, limited power supply, small memory size, low transmission bandwidth, and according to simplicity of sensor nodes, dynamic network topology, open and unprotected area of deployment, Security is a big concern. Thus, all security mechanisms for WSNs must take into consideration these constraints. Many traditional security mechanisms have been proposed for securing WSN such as data aggregation protocols
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PSK Modulator Design Implementation using GNU Radio

PSK Modulator Design Implementation using GNU Radio

The underlying principle of wireless communication is digital modulation. With the availability of limited spectrum and yet there are unused spectrum. Here comes the main goal of modulation to squeeze as much data into the amount of spectrum available. Cognitive Radio can be programmed and configured to use the best wireless channels in its environment to avoid user interference and congestion. The main function of Cognitive Radios is to detect and share unused spectrum with other systems without any harmful interferences.

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Context dependent random walk graph kernels and tree pattern graph matching kernels with applications to action recognition

Context dependent random walk graph kernels and tree pattern graph matching kernels with applications to action recognition

effectively combines context-dependent graph kernels of different orders. In our tree-pattern graph matching kernel, more topological structural information is exploited. We have recursively computed the similarity between affinal tree-pattern groups in a dynamic programming formulation and applied a sparse constraint to match the tree pattern groups. The errors caused by falsely matched affinal tree-pattern groups are suppressed and the discriminative power of the tree pattern graph matching is increased. We have applied the proposed kernels to recognize human actions by constructing the concurrent graph and the causal graph to capture the spatiotemporal relations among local feature vectors. Experimental results on several datasets have demonstrated that the two graphs for representing actions are complementary and the proposed context-dependent random walk graph kernel and tree-pattern graph matching kernel are effective at improving the performance of action recognition. Our tree pattern graph matching kernel yields more accurate results than our context-dependent random walk kernel.
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The field testing of a vortex sewage overflow

The field testing of a vortex sewage overflow

Technical Committees set up to investigate the sewerage system and storm sewage overflows. These have highlighted the serious problems of deterioration of the existing sewers, and the large numbers of unsatisfactory overflows. Older designs exercised poor control over the flows through them, and were ineffective at preventing polluting material from spilling from the system. They which were causing much pollution to the recieving watercourses. The investigators have made various increasingly refined recommendations concerning overflows. Such structures should be used sparingly, and not spill so much that they 'cause a nuisance'. Initially it was suggested that they not begin to spill until the inflow rose above a setting of 6 multiples of the dry weather flow (DWF), with greater multiples to protect more sensitive watercourses. In 1970 'Formula A' was put forward :
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Sequence-to-sequence modeling for graph representation learning

Sequence-to-sequence modeling for graph representation learning

We now discuss supervised methods that learn graph representations for the graph classification task. The representations obtained by these approaches are tailored for a supervised task and are not based solely on the graph topology. Most of existing approaches (Niepert et al. 2016; Zhang et al. 2018; Ying et al. 2018; Gilmer et al. 2017; Duvenaud et al. 2015; Li et al. 2015b; Bruna et al. 2013; Henaff et al. 2015; Defferrard et al. 2016; Scarselli et al. 2009) are variations of Graph Neural Networks (GNNs) and rely on the idea of message propagation around the neighbors. Niepert et al. (2016) devel- oped a framework (PSCN) to learn graph representations by defining receptive fields of neighborhoods and using canonical node ordering. Deep Graph Convolutional Neural Network (DGCNN) (Zhang et al. 2018) is another model that extracts multi-scale node features and applies a consistent pooling layer on unordered nodes. The main difference between DGCNN and PSCN is the way they deal with the node-ordering problem. We compare our models to them in our experiments, outperforming them on all datasets. Ying et al. (2018) proposed a hierarchical representation learning framework via hier- archical GNNs pooling layers. Gilmer et al. (2017) proposed a message passing neural network framework, and explored the existing supervised approaches (Gilmer et al. 2017; Duvenaud et al. 2015; Li et al. 2015b) that have been recently used for graph-structured data in chemistry applications, such as molecular property prediction. Duvenaud et al. (Duvenaud et al. 2015) introduced a GNN to create “fingerprints" (vectors that encode molecule structure) for graphs derived from molecules. The information about each atom and its neighbors are fed to the neural network, and neural fingerprints are used to predict new features for the graphs. Bruna et al. (2013) proposed spectral networks, generaliza- tions of GNNs on low-dimensional graphs via graph Laplacians. Henaff et al. (2015) and Defferrard et al. (2016) extended spectral networks to high-dimensional graphs. Scarselli et al. (2009) proposed a GNN which extends recursive neural networks and find node rep- resentations using random walks. Li et al. (2015b) extended GNNs with gating recurrent neural networks to predict sequences from graphs. In general, neural message passing approaches can suffer from high computational and memory costs, since they perform multiple iterations of updating hidden node states in graph representations. However, our supervised approach obtains strong performance without the requirement of passing messages between nodes for multiple iterations.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

The diversity of natural language for meaning representation in documents is one of the causes of information overload in information retrieval. Headline generation is an automatic text summarization technique that can reduce or address such a problem. This research discusses an experimental study on the determination of Malay language characteristics from news genre documents. A Malay news corpus comprising 140 news documents was chosen from the BERNAMA news archive. The selection criteria were limited to hard news, a word count of 50 to 250 words, published between 2007 and 2012, and with news genres of economy, crime, education, or sports only. Three Malay linguistic experts were selected to produce a reference headline for each news document manually. Experiment results identify three characteristics. First, the first two sentences of a news document are suitable candidates for the most important sentences; second, sentences that contain an acronym definition also have the potential to become the most important sentences; and third, the ideal length of a headline is six words. Considering these characteristics will generate intelligent headlines for Malay news.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

A major drawback was the network degradation as it worked well with simulators but not using real data set. To address this issues, Intrusion Detection and Adaptive Responsive (IDAR) [5] mechanism was designed based on the level of attack, network degradation and so on. However, the accuracy of intrusion being detected was unaddressed. Hybrid Intrusion Detection (HID) model [6] addressed the intrusion detection rate with respect to accuracy using Tree Augmented Naïve Bayes (TAN). Reduced Error Pruning (REP) algorithm was used for efficient classification of intrusion being detected.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

The real-time hardware application is developed around a FPGA hardware architecture that includes embedded processor MicroBlaze on the field programmable gate array (FPGA).This paper introduces a design of a Micro Blaze soft-core processor system that can be running the output pins (XGI Expansion Headers Protocol) as such as clock generator to feed external circuits. The designed processor system is programmed in C language to manage, and specify the number of clock generators selected. It focuses on implementation XGI Expansion Headers Protocol and an interface RS232 serial communication on a Xilinx FPGA, which allows bidirectional data transfer with an external application.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Table 4.5 shows that four path relations representing four hypotheses were significant. Graphical image of paths is presented in figure 4.1 and 4.2 .The results of boot strapping method (Table 4.5) show a p-value for each relation. All structural model relationships were significant considering a p-value = 0.05. In the model all IV’s had a significant a positive coefficients which means, companies with higher level of BA will tend to achieve a better SC performance. Among BA dimensions the highest coefficient belonged to Plan (β=0.268, p<0.05) followed by Source (β=0.253, p<0.05) and Make (β=0.258, p<0.05). Compare to the other BA components delivery had a lower influence on SC performance (β=0.436, p<0.05). It is important to note that contrary to confirmative SEM models (e.g., LISREL), explorative PLS models still do not have such global indicators that would assess the overall goodness of the model, to evaluate the goodness of fit for models. The criterion of global fitness (GoF) was calculated. The GoF is a geometric average of all communalities and R2 in the model. The GoF is an index that can be used to validate models with PLS. The R2 coefficient is 0.628, which demonstrates that the indicator of analytical businesses was able to explain 62.8% of the variability in the performance results. A value higher than the GoF> 0.5 shows that the set of structural equations is well defined and offers a good representation of the dataset and is valid. GoF of current model was 0.647 which is ready to consider 64.7 % of the reachable fitness.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Algorithm Shortest Path Using Candidates mainly uses the matrices Reverse Matrix, Weighted Graph Matrix, and Mark Matrix in Figures 4, 5, and 7 respectively. The Reverse Matrix is generated from original unweighted directed Graph Matrix representation in Fig. 2. The two given source and destination nodes are assumed to exist in the graph G. An efficient algorithm called Path Existence Query that aids in finding the existence of the path in a directed graph from <s> to <t> is presented in ref. [1]. The algorithm proceeds by finding the candidate nodes starting from the destination node <t> visiting all predecessors towards the source node <s>. This is the main advantage of using the reverse representation in Fig. 4. Marking nodes is possible by updating Mark Matrix as shown in Fig. 7. It starts by initializing marked vertices to unmark tag equals to zero. Then the algorithm finds the shortest path among the marked nodes as of Weighted Graph Matrix representation. The function keeps in each entry of the main values; Vertex, Dist, and Pred Node. These values are updated as the procedure proceeds. Specifically, it starts from the source node <s> checking the marked nodes and calculating the path distance horizontally then diagonally. The function stores in Dist (when first visit the node) the accumulated
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

The paper also presents the comparative analysis of proposed algorithm with existing round robin scheduling algorithm on the basis of varying time quantum, average waiting time, average [r]

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Graph based text representation for document clustering

Graph based text representation for document clustering

Advances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library.This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship and meanings of words in the document. As a result the sparsity and semantic problem that is prevalent in textual document are not resolved. In this study, the problem of sparsity and semantic is reduced by proposing a graph based text representation method, namely dependency graph with the aim of improving the accuracy of document clustering. The dependency graph representation scheme is created through an accumulation of syntactic and semantic analysis. A sample of 20 news group, dataset was used in this study. The text documents undergo pre-processing and syntactic parsing in order to identify the sentence structure. Then the semantic of words are modeled using dependency graph. The produced dependency graph is then used in the process of cluster analysis. K-means clustering technique was used in this study. The dependency graph based clustering result were compared with the popular text representation method, i.e. TFIDF and Ontology based text representation. The result shows that the dependency graph outperforms both TFIDF and Ontology based text representation. The findings proved that the proposed text representation method leads to more accurate document clustering results.
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A Review on: Automatic Movie Character Annotation by Robust Face Name Graph Matching

A Review on: Automatic Movie Character Annotation by Robust Face Name Graph Matching

the co-occurrence between the detected faces and names extracted from the video transcript [5]. The system which built a database of named faces by video optical character recognition (VOCR) is called Named Faces system [6]. Yang et al. have developed a closed caption and speech transcript, based on that he built model. Thus which is improved their methods doing dynamic captioning which usages multiple instance of learning for partially labeled faces to reduce collected data by users. Speech transcript was also used for finding people those are frequently appearing in the news videos. In news videos, candidate names are made available from local matching while in TV and movie videos, the characters names are rarely directly produced in the subtitle or closed caption which contains names and no time stamps for aligning the video. The proposed system for the same frame work has shown in Fig. 2 which will use clustering method?
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