Trajectory, which is the route **of** the movement **of** an object, contains significant spatial information that is necessary in studying the behaviors **of** **vessels**. With the development **of** information techniques, a growing amount **of** vessel movement information can be monitored, and voluminous records **of** historical **trajectories** can be stored [1]. Several new and efficient methods have been proposed to utilize big data in promoting the development **of** maritime intelligent traffic systems (ITS) [2–4]. Notably, **similarity** measurement **between** the **trajectories** **of** **vessels** is a fundamental issue that needs to be solved in these methods [5–7]. The raw **trajectories** **of** **vessels** usually include many redundant points, outliers, and other elements [8–9]. When the volume **of** trajectory data is large, **similarity** measurement requires the feature points to be extracted from **trajectories** [10–13]. Moreover, to study the traffic characteristics **of** **vessels**, the **similarity** measurement result must be consistent with the actual motion **of** **vessels** [14–17]. The trajectory spatial distance describes the motion position information, and the trajectory shape shows the changes in the motion direction. Therefore, an efficient model is necessary to consider both factors to solve the aforementioned problem.

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Experimental results are shown in Figs 1 and 2 (for 1000 dimensional feature vectors) and Figs 3 and 4 (for 10,000 dimensional feature vectors). As an overall trend in both Figures, we see that all methods are performing equally when the dimensionality **of** the projected space is very small. However, the correlation coefficients for such low dimensional projections are also very low because most **of** the important features **of** the original space are lost as a result **of** the aggressive lower dimensional projections. When we increase the dimensionality both Pearson and Kendall correlation coefficients improve. However, SVD and NMF methods quickly saturates to almost fixed correlations and by further increasing the dimensionality we cannot improve their performance. On the other hand, the correlation coefficients with PVP continu- ously increase. Because SVD and NMF are computing low rank approximations to the matrix defined by the feature vectors, the correlation does not improve when we have reached the rank **of** the data matrix. Moreover, minimization **of** the Frobenius norm **of** the approximation as done by SVD does not guarantee a high correlation **between** **similarity** scores computed using the lower dimensional projections **of** the feature vectors. In the larger 10,000 dimensional setting depicted in Figs 3 and 4, we see that Kendall’s τ drops for SVD and NMF methods when the dimensionality is increased beyond 300 dimensions. In practice, it is difficult to determine the optimal value **of** the dimensionality for the projection. Therefore, in practice projection methods that do not loose performance due to extra dimensions are desirable. Per- formance **of** the L2 baseline varies and is not robust. For example, in the 10,000 dimensional case (Fig 3), L2 method reports the worst Pearson correlation among the four methods compared.

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neutrosophic sets, then propose a new method to construct entropy **of** interval-valued neutrosophic sets based on the **similarity** **measure** **between** the two single valued neutrosophic sets, finally we give an example to show that our method is effective and reasonable.

Motivation: The search results returned by the most popular search engines are not satisfactory. Because **of** the vastly numerous documents and the high growth rate **of** the Web, it is time consuming to analyze each document separately. It is not uncommon that search engines return a lot **of** Web page links that have nothing to do with the user’s need. Information retrieval such as search engines has the most important use **of** semantic **similarity** is the main problem to retrieve all the documents that are semantically related to the queried term by the user. Web search engines provide an efficient interface to this vast information. Page counts and snippets are two useful information sources provided by most Web search engines. Hence, accurately measuring the semantic **similarity** **between** words is a very challenging task.

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More and more researchers focus on Web usage mining for the past recent years [3,4,5]. However, the topic **of** clustering web sessions has recently become popular in the field **of** practical application **of** clustering techniques. In [1] Anil K.J worked out on different algorithms for data and it performance. Few researchers in the past applied OPTICS clustering with noise on the different datasets and analyze on various ways. Ester .M.et.al[6] introduced about the various issues that related to the Dbscan for discovering clusters in large databases with noise. Cao.F, Estery .M, Qian .W [5],worked on Density based clustering over an evolving data stream with noise. “M Ankerst, M. Breunig, H.Kriegel, J.Sander” [11] introduce OPTICS algorithm on density based clustering structure. In [2] the authors described the various ways **of** scaling the Dbscan algorithm in the application **of** spatial database. Many researchers carried out their works on web usage clustering using Density based algorithms. The Density-based notion is a common approach for clustering. Density-based clustering algorithms are based on the idea that objects which form a dense regions should be grouped together into one cluster. They use a fixed threshold value to determine dense regions. Mobasher[15] used the Cosine coefficient and a threshold **of** 0.5 to cluster on a web log. Banerjee and Ghosh[9] introduced a new method for measuring **similarity** **between** web sessions. The longest common sub-sequence **between** two sessions is first found through dynamic programming, then the **similarity** **between** two sessions is defined through their relative time spent on the longest common sub-sequences.

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The proposed method in this paper is different from the above existing methods. In the proposed method two community graphs with possibly equal number **of** nodes (communities) and different number **of** edges for **similarity** check. Each node (community) is labeled with a unique community number. Based on the community number **of** node, the **similarity** **measure** takes place by considering the weight **of** self-loop **of** community as well as the weight **of** edge **between** the communities. After **similarity** **between** two community graphs, it finally returns a **similarity** value i.e., a number from 0 to 3. Based on this number, the **similarity** **of** two community graphs can be judged. The proposed algorithm has capable **of** showing **similarity** and five different ways **of** dissimilarity. The five different dissimilarities are "similar on dissimilar edges", "similar on similar edges", "communities same but different edges", "communities not same", and "number **of** communities are different". Moreover, the proposed method is completely based on labeled community graphs and simple graph-theoretic model. So the authors conclude that the proposed community graph **similarity** is simply different from the above existing methods and fast since the time complexity is O(n 3 ).

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We can distinguish three main approaches for the **similarity** identification measures **between** the taxonomy objects. The first type is based on the nodes [2] [7] [8]. Works under the banner **of** these approaches used the typically information based content to determine the conceptual **similarity**. Moreover, the **similarity** **between** two concepts is obtained by the degree **of** sharing information. The second type is based only on the hierarchy or the edge distances [1] [9] [10] [11]. The problem with this approach is that the taxonomy arcs represent uniform distances, i.e. all the semantic links have the same weight. Finally, the hybrid approach [12] [13] [14] [15] which combines the two approaches presented above. With these approaches, there exist several manners **of** detecting conceptual **similarity** **of** two words in a hierarchical semantic network. The following section presents some measures which are listed under these approaches.

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Abstract: Clustering web sessions is to group web sessions based on **similarity** and consists **of** minimizing the intra-group **similarity** and maximizing the inter-group **similarity**. Here in this paper we developed a new **similarity** **measure** named SSM(Sequence **Similarity** **Measure**) and enhanced an existing DBSCAN and OPTICS clustering techniques namely SSM-DBSCAN, and SSM-OPTICS for clustering web sessions for web personalization. Then we adopted various **similarity** measures like Euclidean distance, Jaccard, Cosine and Fuzzy **similarity** measures to **measure** the **similarity** **of** web sessions using sequence alignment to determine learning behaviors **of** web usage data. This new **measure** has significant results when comparing similarities **between** web sessions with other previous measures. We performed a variety **of** experiments in the context **of** density based clustering, using existing DBSCAN and OPTICS and developed SSM-DBSCAN and SSM-OPTICS based on sequence alignment to **measure** similarities **between** web sessions where sessions are chronologically ordered sequences **of** page visits. Finally the time and the memory required to perform clustering using SSM is less when compared to other **similarity** measures.

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Given the increasing importance **of** ontologies in biological settings, mechanisms enabling users to **measure** the similar- ity **between** the concepts represented by the ontologies or be- tween the objects linked to these concepts are required. In computational linguistics, recent research on this topic has emphasized the use **of** semantic **similarity** measures. These measures compute distances **between** terms structured in a hierarchical taxonomy. Two kinds **of** approaches are preva- lent: information content (node based) and conceptual dis- tance (edge based). Information content considers the similar- ity **between** two terms the amount **of** information they share, where a term contains less information when it occurs very **of**- ten. Conceptual distance is a more intuitive approach. It iden- tifies the shortest topological distance **between** two terms in the scheme taxonomy. Budanitsky et al. experimentally com- pared five different proposed semantic **similarity** measures in WordNet [Budanitsky and Hirst2001]. The comparison shows that Jiang and Conrath’s semantic **similarity** **measure** provides the best results overall [Jiang and Conrath1997]. This seman- tic **similarity** **measure** is a hybrid approach, i.e. it combines information content and conceptual distance with some pa- rameters that control the degree **of** each factor’s contribution. The conceptual distance is based on the node depth and den- sity factors. The node depth factor relies on the argument that **similarity** increases as we descend the hierarchy, since the re-

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Thus if we prove that ΔL/ΔF =(r+a)/(r-a) then the theorem is proved. Now let us consider a circle with centre at origin and radius ‘r’. Thus the equation **of** circle is x²+y²=r².The equation **of** the chord at ‘a’ distance from center is ax-ry- ar=0 or Y= a/r(x-r).

is the customer who gives the technical specification **of** the prod- uct and it is the customer who has to decide whether he can or cannot accept the price dictated by the manufacturer. Based on these restrictions, it is now the manufacturer who must decide whether he is able to execute the order (within the deadline ap- pointed and using the available facilities) and what will be the price **of** the product as dictated by the cost **of** production. Improv- ing the procedure **of** production costs estimation should improve the contract negotiations and make them more efficient. On the other hand, estimating the cost **of** production involves, among others, also the need to determine the type **of** material and treat- ment. The manufacturer can choose what materials he will use for the product and at what price, providing he can check which

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Optimisation **of** the turbine mixer’s performance during the preparations **of** the sand mix still remains an important issue as this mixer type is now in widespread use. Monitoring techniques **of** the system sand mixing include the analysis **of** electric power demand by the mixer’s drive based on measurements **of** power components. This study shows the operating characteristics **of** turbine mixers as the function **of** electric power demand by the drive system.

Essence **of** modification **of** silumins boils to change **of** form or size **of** silicon crystals present as eutectic or primary ones. Perfect sliding properties and high abrasion resistance **of** hypereutectoid silumins result from their structure, which can be characterized by precipitations **of** primary crystals **of** silicon in soft eutectic groundmass. Primary crystals **of** silicon are unfavorable due to their impact on machinability **of** material. They bring about considerable wear **of** tools and have negative effect on conditions **of** machined surface (big roughness). In case **of** hypereutectic silumins, by introduction **of** active nucleuses **of** crystallization are refined mainly a brittle, hard precipitations **of** primary silicon [1]. High content o silicon results in necessity **of** superheating **of** the alloy in limits **of** 850 – 900 C and keeping it

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The quality **of** the links, as measured by Pagerank, is a good choice for ranking nodes but we think there are some other features that can incorporate the activity **of** the node. We propose to incorporate the features via the link **similarity** taking into account contact times **of** the node. The idea behind is that the node has higher **similarity** must be prized with a higher value. Our main idea consists in constructing the link vector that records the contact times **of** the nodes, defining a link **similarity** function to **measure** the **similarity** **of** the nodes according to the link vector, and then reconstructing Pagerank model by considering the link **similarity**. These ideas are a work in progress.

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The control program starts the cooling process in the 1. zone as soon as possible after filling the mold with the liquid metal, and at the latest at the beginning **of** crystallization **of** silumin, i.e. 330 C. Then, after the silumin crystallization is finished, the program begins with the cooling **of** the other zones, which will no longer supply the zone 1 with the liquid metal,, but instead accelerate the process **of** cooling **of** the entire cast. The program ends cooling **of** the chill after reaching the temperature **of** 60 C for casting. The program also contains a condition **of** water pulsation after temperature reduction by mold below 150 C and then 100 C. This condition reduces the amount **of** water in a mist along with the decreasing ability **of** the evaporation on the cooled wall **of** the pre-chill and thereby reduces the possibility **of** water gathering at the casting station.. Moreover, research shows that reducing the amount **of** water at this stage did not affect the cooling rate and the total time **of** casting. The cooling process ends when they reach the permanent molds temperature below 60 ° C.

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large precipitates in the size range **of** 100-200nm, present as single objects or as clusters, characterised by different shapes (oval and oblong), sizes and chemical compositions, fine-dispersed precipitates **of** carbides in the size range **of**

Abstract: This article discusses the impact **of** inter-ethnic conflict in 1999 to the multi-ethnic community life in Sambas and offers a concept **of** education as a modified formulation **of** the local wisdom in the communication aspect that the Malay ethnic community in Sambas have in responding relations **between** ethnic groups post-conflict **of** ethnics in 1999. The methodology used is literature review, observation, interview and documentation-based qualitative analysis. The result is that ethnic conflict 1999 in Sambas, West Kalimantan causes a number **of** problems or moral and social impacts in some small communities **of** Malay. By gaining the value **of** local wisdom into a new form **of** education, an effort to respond the post-conflict negative impact through cultural communication greeting **of** sapa and base that shows a polite language education in Malay Sambas society and even the culture is believed to be an alternative solution that can deal with inter-ethnic conflicts and prevent conflict to happen again

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It should be stressed that, according to the relationships given in Table 1, the solidification character **of** both **of** examined duplex cast steel compositions should be pure ferritic. The degree **of** segregation **of** the alloying elements resulting from the partition coefficients and empirically determined by EDX method [14], has confirmed the change **of** the solidification mechanism from pure ferritic to the ferritic-austenitic one only if the relationship (2) is employed. An addition **of** copper results in occurring the structural constituent **of** different morphology, which is Ȗ’ phase.

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application, among others, in the modification **of** moulding sands. These nanoparticles introduced into the systems **of** a multi- particle matrix (binder) can change their properties by way **of** a physico-chemical or chemical reaction [6, 7].

Experimental castings were prepared in moulds made **of** two types **of** plaster. Cast temperatures were 1120 and 1200°C for bronzes and 700 and 800°C for silumin. Temperatures **of** the mould were 500 and 600°C for bronzes and 200 and 300°C for aluminum alloy. The roughness measurements were carried out with use **of** Hommelwerke Tester T1000. The average arithmetic deviation **of** roughness profile Ra, the ten-point height **of** irregularities Rz and maximum peak to valley height Rm, were measured.