There is still a lack of well-performing approaches for **learning** the **representation** for an entire **graph**. There are several challenges that need to be addressed within this area. First, the choice of the subgraph structures to be incorporated in the **graph** **representation** **learning** has a significant impact on the expressiveness power of the embeddings of an entire **graph**. Second, choosing the appropriate granularity level of this substructure (e.g., whether to include first or second order neighborhoods of a node when building node sequences), which is necessary to preserve the **graph** embedding, is an open problem. The choice may depend on many factors, such as the **graph** domain, scale, density, and its various structural properties. The types of the substructures, from fine-to-coarse, such as nodes, edges, trees, graphlets, random walks, and communities, can capture local and global features of the **graph**. The question is what types of substructures with what level of granularity are informative enough to capture the general **graph** structure and recognize similarity between graphs, while reducing the loss of information? The additional chal- lenge is, of course, the efficiency of **learning** the **representation** of the substructures and aggregating them into a **graph** embedding. In this work, we investigate these challenges within the context of our proposed architectures.

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Previous **representation** **learning** techniques for knowledge **graph** **representation** usually represent the same entity or relation in differ- ent triples with the same **representation**, with- out considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowl- edge **graph** **representation** **learning** method, which can represent a relation/entity with dif- ferent representations in different triples by ex- ploiting additional textual information. Specif- ically, our method enhances representations by exploiting the entity descriptions and triple- specific relation mention. And a mutual atten- tion mechanism between relation mention and entity description is proposed to learn more accurate textual representations for further improving knowledge **graph** **representation**. Experimental results show that our method achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge **representation** mod- els.

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Many data driven organizations such as Google and Microsoft take the approach of constructing a unified super-**graph** by integrating data from multi- ple sources. Such unification has shown to signifi- cantly help in various applications, such as search, question answering, and personal assistance. To this end, there exists a rich body of work on linking entities and relations, and conflict resolution (e.g., knowledge fusion (Dong et al., 2014). Still, the problem remains challenging for large scale knowl- edge graphs and this paper proposes a deep **learning** solution that can play a vital role in this construc- tion process. In real-world setting, we envision our method to be integrated in a large scale system that would include various other components for tasks like conflict resolution, active **learning** and human-in-loop **learning** to ensure quality of con- structed super-**graph**. However, we point out that our method is not restricted to such use cases—one can readily apply our method to directly make infer- ence over multiple graphs to support applications like question answering and conversations.

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Therefore, recent methods, especially those on headline generation, require a profound understanding of the natural language system characteristics that represent the information in documents. Understanding these characteristics involves identifying the morphology in a particular sentence structure and information on sentence syntax and syntax formulas that must be used to generate a perfect sentence. Comprehending the characteristics of the natural language stem, the natural language processing technique, and the machine **learning** technique allows the development of intelligent headline generation techniques. Subsequently, these techniques are expected to execute the generation task perfectly and produce results similar to those generated by humans.

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The k-means algorithm is used to recognize data into different classes (known as clusters). This unsupervised **learning** algorithm is widely used in sensor node clustering problem due to its linear complexity and simple implementation [10].Loo et al. [11], present an intrusion detection scheme for sensor networks based on anomaly detection. They use a fixed width clustering algorithm to allow for the detection of previously unseen attacks. They also came up with 12 general features for detecting sinkholes and periodic route error attacks. Generally -means is used to detect novel intrusions in WSN by dividing or clustering the network connection’s data to collect the majority of the intrusions together in one or several clusters, the figure below present the K-means clustering algorithm:

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Intermittent errors in signature databases, in the code of the antivirus tool or in file compression and encryption algorithms used frequently used by antivirus tools are the main cause [r]

Initially Trapezoidal Notch band monopole antenna is constructed from the basic design of trapezoidal monopole wideband antenna and the corresponding antenna parameters and Radiation cha[r]

With the most flexible in operation, mobile ad hoc networks (MANETs) are increasingly gaining higher amount of reception with respect to next-generation network arena. Also with the increasing mobility one of the key issues to be addressed is the anomaly detection rate. Anomaly-detection [13] based on dynamic **learning** process was designed to perform the process of identifying the intrusion at specific time intervals using multidimensional statistics. However, security remained unaddressed. To provide security, a fuzzy model was introduced in [14] by increasing the identification of intrusion rate. But, classification of normal and abnormal activities was not performed. Separate classification of normal and abnormal activities was concentrated on [15] with the help of proactive and reactive protocol. An enhanced protocol called as the Secured AODV (SAODV) was introduced in [16] using a Trust Based Mechanism (TBM) to improve the throughput level.

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• Aspect-oriented software development: Aspect-oriented software development (AOSD) is a new approach to software development that addresses limitations inherent in other approaches, including object-oriented programming. AOSD aims to address crosscutting concerns by providing means for systematic identification, separation, **representation** and composition. Crosscutting concerns are encapsulated in separate modules, known as aspects, so that localization can be promoted. This results in better support for modularization hence reducing development, maintenance and evolution costs [27].

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!"| | # (2) where E is the expected value. NL equidistant samples are taken from equation 2 to find the discrete **representation** of PAPR, $ is known as the oversampling factor. It is shown that L = 4 is a good value to have accurate results of PAPR for simulation purposes [2]. Discrete version of Equation 2 has the following mathematical form:

Nowadays data extracted from various or large database are transformed into meaningful structure. This transformed structure is used for various purpose and its powerful and produces an intended result. The size and difficulty of performances in the datasets are increased. The KDD(knowledge discovery dataset) are cleaning the missing values ,inconsistent and incomplete data, integrating multiple values ,selecting the relevant data, transforming to suitable format, knowledge extraction from intelligence ,some interesting measures or thresholds are applied and exact pattern returned ,presentation in **graph** trees etc.. The above KDD are represented in many ways. More functionality is also used. Clustering is used by many applications. It is said to be an attractive task in data mining. The major uses of clustering are marketing ,land use ,insurance, city planning and earth –quake

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Can define ontology as a set of domain concepts interest of the organization representing the hierarchical structure. Where [17] defined ontology, as a catalogue of the kinds of things that are supposed to exist in the area. Ontology helps to know what it means for a specific term. Ontologies provide a way to describe the meaning of the terms and relationships so that a common understanding or consensus can be obtained between machines and people [18]. A number of studies have dealt with this type of text **representation**. For instance, In [11], the authors proposed a system that utilizes the concept of weight for text clustering. It was developed with a k-means algorithm based on the principle of ontology. The system is used to identify irrelevant or redundant features that may reduce strength, thereby achieving more accurate document clustering.

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USER SEGMENT OF KOREAN WIDE AREA GLOBAL NAVIGATION SATELLITE SYSTEM SAYED CHHATTAN SHAH Assistant Professor Department of Information Communications Engineering Hankuk University of Fore[r]

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Experimental Results: In terms of the classification accu- racies, our JSMK kernel can easily outperform all the alter- native **graph** kernels on any dataset. The classification accu- racies of our JSMK kernel are obviously higher than those of all the alternative kernels. The reasons for the effective- ness are threefold. First, compared to the WLSK, SPGK, GCGK and JTQK kernels that require decomposing graphs into substructures, our JSMK kernel can establish the sub- structure location correspondence which is not considered in these kernels. Second, compared to the JSGK and QJSK ker- nels that rely on the similarity measure between global graphs in terms of the classical or quantum JSD, our JSMK kernel can identify the correspondence information between both the vertices and the substructures, and can thus reflect richer in- terior topological characteristics of graphs. By contrast, the JSGK and QJSK kernels can only reflect the global similarity information between graphs. Third, compared to the DBMK kernel that can also reflect the correspondence information between substructures, our JSMK kernel can identify more pairs of aligned isomorphic substructures. Moreover, as we have stated in Section 3.3, the m-layer JS **representation** can reflect richer characteristics than the h-layer DB representa- tion. As a result, the JSMK kernel using the JS **representation** can capture more information for graphs than the DBMK ker- nel using the DB **representation**.

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The methodology is consists of six steps: (1) input image, (2) line segment extraction from the image, (3) the interpretation and derivation of structural descriptions from the line-ex[r]

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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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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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PROPOSED METHOD In this journal we perform a comparison using three methods which are clustering algorithms, Fuzzy C-Means FCM, Standard K-means SKM and Enhanced K-Means EKM.. After we p[r]

ABSTRACT 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 pa[r]

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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