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heterogeneous similarity</title> <meta name="description" content="Search results for: heterogeneous similarity"> <meta name="keywords" content="heterogeneous similarity"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="heterogeneous similarity"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1378</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: heterogeneous similarity</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1378</span> Map Matching Performance under Various Similarity Metrics for Heterogeneous Robot Teams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Akay">M. C. Akay</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Aybakan"> A. Aybakan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Temeltas"> H. Temeltas </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aerial and ground robots have various advantages of usage in different missions. Aerial robots can move quickly and get a different sight of view of the area, but those vehicles cannot carry heavy payloads. On the other hand, unmanned ground vehicles (UGVs) are slow moving vehicles, since those can carry heavier payloads than unmanned aerial vehicles (UAVs). In this context, we investigate the performances of various Similarity Metrics to provide a common map for Heterogeneous Robot Team (HRT) in complex environments. Within the usage of Lidar Odometry and Octree Mapping technique, the local 3D maps of the environment are gathered.  In order to obtain a common map for HRT, informative theoretic similarity metrics are exploited. All types of these similarity metrics gave adequate as allowable simulation time and accurate results that can be used in different types of applications. For the heterogeneous multi robot team, those methods can be used to match different types of maps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=common%20maps" title="common maps">common maps</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20robot%20team" title=" heterogeneous robot team"> heterogeneous robot team</a>, <a href="https://publications.waset.org/abstracts/search?q=map%20matching" title=" map matching"> map matching</a>, <a href="https://publications.waset.org/abstracts/search?q=informative%20theoretic%20similarity%20metrics" title=" informative theoretic similarity metrics"> informative theoretic similarity metrics</a> </p> <a href="https://publications.waset.org/abstracts/99098/map-matching-performance-under-various-similarity-metrics-for-heterogeneous-robot-teams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99098.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1377</span> Resource-Constrained Heterogeneous Workflow Scheduling Algorithms in Heterogeneous Computing Clusters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lei%20Wang">Lei Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahao%20Zhou"> Jiahao Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of heterogeneous computing clusters provides a strong computility guarantee for large-scale workflows (e.g., scientific computing, artificial intelligence (AI), etc.). However, the tasks within large-scale workflows have also gradually become heterogeneous due to different demands on computing resources, which leads to the addition of a task resource-restricted constraint to the workflow scheduling problem on heterogeneous computing platforms. In this paper, we propose a heterogeneous constrained minimum makespan scheduling algorithm based on the idea of greedy strategy, which provides an efficient solution to the heterogeneous workflow scheduling problem in a heterogeneous platform. In this paper, we test the effectiveness of our proposed scheduling algorithm by randomly generating heterogeneous workflows with heterogeneous computing platform, and the experiments show that our method improves 15.2% over the state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20computing" title="heterogeneous computing">heterogeneous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=workflow%20scheduling" title=" workflow scheduling"> workflow scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=constrained%20resources" title=" constrained resources"> constrained resources</a>, <a href="https://publications.waset.org/abstracts/search?q=minimal%20makespan" title=" minimal makespan"> minimal makespan</a> </p> <a href="https://publications.waset.org/abstracts/190199/resource-constrained-heterogeneous-workflow-scheduling-algorithms-in-heterogeneous-computing-clusters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190199.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">34</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1376</span> Comparative Study of Poetics of Ancient China and Greece</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Junwu%20Tian">Junwu Tian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chinese poetics originated in the pre-Qin period, while Western poetics came into being in the Hellenistic period. Although there was no mutual communication and influence between the two kinds of poetics due to both geographical distance and chronological displacement, the Sino-Western thinkers shared much in common, particularly in the social function of literature and art, the pursuit of unified and harmonious aesthetics, the advocacy of poets’ subjective initiative in the creative process of literature and art. In the sphere of rhetoric, the poetics of the pre-Qin scholars and their Greek counterparts also had heterogeneous similarities. By comparing the aesthetic ideas of Confucius, Mencius, Xun Zi, and Deng Xi with those of Plato, Aristotle, and Protagoras, this paper intends to reveal the common concerns of Chinese and Western poetics in the context of heterogeneous cultures and in their respective origin periods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pre-Qin%20poetics" title="Pre-Qin poetics">Pre-Qin poetics</a>, <a href="https://publications.waset.org/abstracts/search?q=ancient%20Greek%20poetics" title=" ancient Greek poetics"> ancient Greek poetics</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20similarity" title=" heterogeneous similarity"> heterogeneous similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=origin%20period" title=" origin period"> origin period</a> </p> <a href="https://publications.waset.org/abstracts/167723/comparative-study-of-poetics-of-ancient-china-and-greece" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1375</span> Multi-Objective Optimal Threshold Selection for Similarity Functions in Siamese Networks for Semantic Textual Similarity Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kriuk%20Boris">Kriuk Boris</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriuk%20Fedor"> Kriuk Fedor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=siamese%20networks" title="siamese networks">siamese networks</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20textual%20similarity" title=" semantic textual similarity"> semantic textual similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20functions" title=" similarity functions"> similarity functions</a>, <a href="https://publications.waset.org/abstracts/search?q=STS%20benchmark%20dataset" title=" STS benchmark dataset"> STS benchmark dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20selection" title=" threshold selection"> threshold selection</a> </p> <a href="https://publications.waset.org/abstracts/187407/multi-objective-optimal-threshold-selection-for-similarity-functions-in-siamese-networks-for-semantic-textual-similarity-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187407.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">38</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1374</span> Approximately Similarity Measurement of Web Sites Using Genetic Algorithms and Binary Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doru%20Anastasiu%20Popescu">Doru Anastasiu Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20R%C4%83dulescu"> Dan Rădulescu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we determine the similarity of two HTML web applications. We are going to use a genetic algorithm in order to determine the most significant web pages of each application (we are not going to use every web page of a site). Using these significant web pages, we will find the similarity value between the two applications. The algorithm is going to be efficient because we are going to use a reduced number of web pages for comparisons but it will return an approximate value of the similarity. The binary trees are used to keep the tags from the significant pages. The algorithm was implemented in Java language. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tag" title="Tag">Tag</a>, <a href="https://publications.waset.org/abstracts/search?q=HTML" title=" HTML"> HTML</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20page" title=" web page"> web page</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20value" title=" similarity value"> similarity value</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20tree" title=" binary tree"> binary tree</a> </p> <a href="https://publications.waset.org/abstracts/50460/approximately-similarity-measurement-of-web-sites-using-genetic-algorithms-and-binary-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1373</span> Measuring Text-Based Semantics Relatedness Using WordNet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madiha%20Khan">Madiha Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidrah%20Ramzan"> Sidrah Ramzan</a>, <a href="https://publications.waset.org/abstracts/search?q=Seemab%20Khan"> Seemab Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Hassan"> Shahzad Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Saeed"> Kamran Saeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Graphviz%20representation" title="Graphviz representation">Graphviz representation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relatedness" title=" semantic relatedness"> semantic relatedness</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measurement" title=" similarity measurement"> similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=WordNet%20similarity" title=" WordNet similarity"> WordNet similarity</a> </p> <a href="https://publications.waset.org/abstracts/95106/measuring-text-based-semantics-relatedness-using-wordnet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">238</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1372</span> Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20A.%20Y.%20Mustafa">Adnan A. Y. Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20images" title="big images">big images</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20images" title=" binary images"> binary images</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20similarity" title=" image similarity"> image similarity</a> </p> <a href="https://publications.waset.org/abstracts/89963/quick-similarity-measurement-of-binary-images-via-probabilistic-pixel-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1371</span> A Context-Sensitive Algorithm for Media Similarity Search </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guang-Ho%20Cha">Guang-Ho Cha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context-sensitive%20search" title="context-sensitive search">context-sensitive search</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20search" title=" image search"> image search</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20ranking" title=" similarity ranking"> similarity ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20search" title=" similarity search"> similarity search</a> </p> <a href="https://publications.waset.org/abstracts/65150/a-context-sensitive-algorithm-for-media-similarity-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1370</span> Review and Suggestions of the Similarity between Employee and Its Workplace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gi%20Ryung%20Song">Gi Ryung Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyoung%20Seok%20Kim"> Kyoung Seok Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study reviewed the literature that focused on similarity of various characteristics such as values, personality, or demographics between employee and other elements in its organization for example employee with leader, job, and organization. We divided a body of this study into two parts and organized and demonstrated recent studies in first part. Three issues appeared in this part, which are statistical ways of measuring similarity, supervisor-subordinate similarity, and person-organization fit with person-job fit. In the latter part, based on the three issues of recent studies, we suggested three propositions about points that the recent studies missed or the studies did not orient. First proposition argued about the direction of similarity, which could also be interpreted as there is causal relation between employee and its workplace environments. Second, we suggested a consideration of eliminating common variance buried in one’s characteristics or its profiles. Third proposition was about the similarity of extra role behavior between individual and organization, and we treated this organization’s level of extra role behavior as a kind of its culture. In doing so, similarity of individual’s extra role behavior and organization’s has the meaning that individual’s congruence against their organization culture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=similarity" title="similarity">similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=person-organization%20fit" title=" person-organization fit"> person-organization fit</a>, <a href="https://publications.waset.org/abstracts/search?q=supervisor-subordinate%20similarity" title=" supervisor-subordinate similarity"> supervisor-subordinate similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=literature%20review" title=" literature review"> literature review</a> </p> <a href="https://publications.waset.org/abstracts/54492/review-and-suggestions-of-the-similarity-between-employee-and-its-workplace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54492.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1369</span> Heterogeneous Artifacts Construction for Software Evolution Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zekkaoui">Mounir Zekkaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhadi%20Fennan"> Abdelhadi Fennan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The software evolution control requires a deep understanding of the changes and their impact on different system heterogeneous artifacts. And an understanding of descriptive knowledge of the developed software artifacts is a prerequisite condition for the success of the evolutionary process. The implementation of an evolutionary process is to make changes more or less important to many heterogeneous software artifacts such as source code, analysis and design models, unit testing, XML deployment descriptors, user guides, and others. These changes can be a source of degradation in functional, qualitative or behavioral terms of modified software. Hence the need for a unified approach for extraction and representation of different heterogeneous artifacts in order to ensure a unified and detailed description of heterogeneous software artifacts, exploitable by several software tools and allowing to responsible for the evolution of carry out the reasoning change concerned. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20software%20artifacts" title="heterogeneous software artifacts">heterogeneous software artifacts</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20evolution%20control" title=" software evolution control"> software evolution control</a>, <a href="https://publications.waset.org/abstracts/search?q=unified%20approach" title=" unified approach"> unified approach</a>, <a href="https://publications.waset.org/abstracts/search?q=meta%20model" title=" meta model"> meta model</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20architecture" title=" software architecture"> software architecture</a> </p> <a href="https://publications.waset.org/abstracts/13647/heterogeneous-artifacts-construction-for-software-evolution-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13647.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1368</span> Nazca: A Context-Based Matching Method for Searching Heterogeneous Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karine%20B.%20de%20Oliveira">Karine B. de Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Carina%20F.%20Dorneles"> Carina F. Dorneles</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The structure level matching is the problem of combining elements of a structure, which can be represented as entities, classes, XML elements, web forms, and so on. This is a challenge due to large number of distinct representations of semantically similar structures. This paper describes a structure-based matching method applied to search for different representations in data sources, considering the similarity between elements of two structures and the data source context. Using real data sources, we have conducted an experimental study comparing our approach with our baseline implementation and with another important schema matching approach. We demonstrate that our proposal reaches higher precision than the baseline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context" title="context">context</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20source" title=" data source"> data source</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=search" title=" search"> search</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=structure" title=" structure"> structure</a> </p> <a href="https://publications.waset.org/abstracts/4417/nazca-a-context-based-matching-method-for-searching-heterogeneous-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">364</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1367</span> 2D Fingerprint Performance for PubChem Chemical Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatimah%20Zawani%20Abdullah">Fatimah Zawani Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Shereena%20Mohd%20Arif"> Shereena Mohd Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Malim"> Nurul Malim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of molecular similarity search in chemical database is increasingly widespread, especially in the area of drug discovery. Similarity search is an application in the field of Chemoinformatics to measure the similarity between the molecular structure which is known as the query and the structure of chemical compounds in the database. Similarity search is also one of the approaches in virtual screening which involves computational techniques and scoring the probabilities of activity. The main objective of this work is to determine the best fingerprint when compared to the other five fingerprints selected in this study using PubChem chemical dataset. This paper will discuss the similarity searching process conducted using 6 types of descriptors, which are ECFP4, ECFC4, FCFP4, FCFC4, SRECFC4 and SRFCFC4 on 15 activity classes of PubChem dataset using Tanimoto coefficient to calculate the similarity between the query structures and each of the database structure. The results suggest that ECFP4 performs the best to be used with Tanimoto coefficient in the PubChem dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=2D%20fingerprints" title="2D fingerprints">2D fingerprints</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanimoto" title=" Tanimoto"> Tanimoto</a>, <a href="https://publications.waset.org/abstracts/search?q=PubChem" title=" PubChem"> PubChem</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20searching" title=" similarity searching"> similarity searching</a>, <a href="https://publications.waset.org/abstracts/search?q=chemoinformatics" title=" chemoinformatics"> chemoinformatics</a> </p> <a href="https://publications.waset.org/abstracts/15097/2d-fingerprint-performance-for-pubchem-chemical-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15097.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">293</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1366</span> Study of the Vertical Handoff in Heterogeneous Networks and Implement Based on Opnet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Benaatou">Wafa Benaatou</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnane%20Latif"> Adnane Latif </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this document we studied more in detail the Performances of the vertical handover in the networks WLAN, WiMAX, UMTS before studying of it the Procedure of Handoff Vertical, the whole buckled by simulations putting forward the performances of the handover in the heterogeneous networks. The goal of Vertical Handover is to carry out several accesses in real-time in the heterogeneous networks. This makes it possible a user to use several networks (such as WLAN UMTS and WiMAX) in parallel, and the system to commutate automatically at another basic station, without disconnecting itself, as if there were no cut and with little loss of data as possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vertical%20handoff" title="vertical handoff">vertical handoff</a>, <a href="https://publications.waset.org/abstracts/search?q=WLAN" title=" WLAN"> WLAN</a>, <a href="https://publications.waset.org/abstracts/search?q=UMTS" title=" UMTS"> UMTS</a>, <a href="https://publications.waset.org/abstracts/search?q=WIMAX" title=" WIMAX"> WIMAX</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous" title=" heterogeneous"> heterogeneous</a> </p> <a href="https://publications.waset.org/abstracts/12140/study-of-the-vertical-handoff-in-heterogeneous-networks-and-implement-based-on-opnet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12140.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">394</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1365</span> Enhancement of Indexing Model for Heterogeneous Multimedia Documents: User Profile Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Aggoune">Aicha Aggoune</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkrim%20Bouramoul"> Abdelkrim Bouramoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Khiereddine%20Kholladi"> Mohamed Khiereddine Kholladi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent research shows that user profile as important element can improve heterogeneous information retrieval with its content. In this context, we present our indexing model for heterogeneous multimedia documents. This model is based on the combination of user profile to the indexing process. The general idea of our proposal is to operate the common concepts between the representation of a document and the definition of a user through his profile. These two elements will be added as additional indexing entities to enrich the heterogeneous corpus documents indexes. We have developed IRONTO domain ontology allowing annotation of documents. We will present also the developed tool validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indexing%20model" title="indexing model">indexing model</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20profile" title=" user profile"> user profile</a>, <a href="https://publications.waset.org/abstracts/search?q=multimedia%20document" title=" multimedia document"> multimedia document</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20of%20sources" title=" heterogeneous of sources"> heterogeneous of sources</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a> </p> <a href="https://publications.waset.org/abstracts/41159/enhancement-of-indexing-model-for-heterogeneous-multimedia-documents-user-profile-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41159.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1364</span> Similarity Based Membership of Elements to Uncertain Concept in Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kamel%20El-Sayed">M. Kamel El-Sayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The process of determining the degree of membership for an element to an uncertain concept has been found in many ways, using equivalence and symmetry relations in information systems. In the case of similarity, these methods did not take into account the degree of symmetry between elements. In this paper, we use a new definition for finding the membership based on the degree of symmetry. We provide an example to clarify the suggested methods and compare it with previous methods. This method opens the door to more accurate decisions in information systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20system" title="information system">information system</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20concept" title=" uncertain concept"> uncertain concept</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20relation" title=" similarity relation"> similarity relation</a>, <a href="https://publications.waset.org/abstracts/search?q=degree%20of%20similarity" title=" degree of similarity"> degree of similarity</a> </p> <a href="https://publications.waset.org/abstracts/88086/similarity-based-membership-of-elements-to-uncertain-concept-in-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88086.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1363</span> Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walid%20Cherif">Walid Cherif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery" title=" knowledge discovery"> knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measurement" title=" similarity measurement"> similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20classification" title=" supervised classification"> supervised classification</a> </p> <a href="https://publications.waset.org/abstracts/79268/hybrid-reliability-similarity-based-approach-for-supervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79268.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">465</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1362</span> Agglomerative Hierarchical Clustering Using the Tθ Family of Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salima%20Kouici">Salima Kouici</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Khelladi"> Abdelkader Khelladi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20data" title="binary data">binary data</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measure" title=" similarity measure"> similarity measure</a>, <a href="https://publications.waset.org/abstracts/search?q=T%CE%B8%20measures" title=" Tθ measures"> Tθ measures</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title=" agglomerative hierarchical clustering"> agglomerative hierarchical clustering</a> </p> <a href="https://publications.waset.org/abstracts/13108/agglomerative-hierarchical-clustering-using-the-tth-family-of-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1361</span> Empirical Study of Partitions Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelkrim%20Alfalah">Abdelkrim Alfalah</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahcen%20Ouarbya"> Lahcen Ouarbya</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Howroyd"> John Howroyd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates and compares the performance of four existing distances and similarity measures between partitions. The partition measures considered are Rand Index (RI), Adjusted Rand Index (ARI), Variation of Information (VI), and Normalised Variation of Information (NVI). This work investigates the ability of these partition measures to capture three predefined intuitions: the variation within randomly generated partitions, the sensitivity to small perturbations, and finally the independence from the dataset scale. It has been shown that the Adjusted Rand Index performed well overall, with regards to these three intuitions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=comparing%20partitions" title=" comparing partitions"> comparing partitions</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measure" title=" similarity measure"> similarity measure</a>, <a href="https://publications.waset.org/abstracts/search?q=partition%20distance" title=" partition distance"> partition distance</a>, <a href="https://publications.waset.org/abstracts/search?q=partition%20metric" title=" partition metric"> partition metric</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20between%20partitions" title=" similarity between partitions"> similarity between partitions</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20comparison." title=" clustering comparison."> clustering comparison.</a> </p> <a href="https://publications.waset.org/abstracts/143607/empirical-study-of-partitions-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143607.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1360</span> Human Tracking across Heterogeneous Systems Based on Mobile Agent Technologies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tappei%20Yotsumoto">Tappei Yotsumoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Atsushi%20Nomura"> Atsushi Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kozo%20Tanigawa"> Kozo Tanigawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenichi%20Takahashi"> Kenichi Takahashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takao%20Kawamura"> Takao Kawamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazunori%20Sugahara"> Kazunori Sugahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a human tracking system, expanding a monitoring range of one system is complicating the management of devices and increasing its cost. Therefore, we propose a method to realize a wide-range human tracking by connecting small systems. In this paper, we examined an agent deploy method and information contents across the heterogeneous human tracking systems. By implementing the proposed method, we can construct a human tracking system across heterogeneous systems, and the system can track a target continuously between systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20tracking%20system" title="human tracking system">human tracking system</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agent" title=" mobile agent"> mobile agent</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20systems" title=" heterogeneous systems"> heterogeneous systems</a> </p> <a href="https://publications.waset.org/abstracts/11702/human-tracking-across-heterogeneous-systems-based-on-mobile-agent-technologies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11702.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">536</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1359</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1358</span> Research of Seepage Field and Slope Stability Considering Heterogeneous Characteristics of Waste Piles: A Less Costly Way to Reduce High Leachate Levels and Avoid Accidents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serges%20Mendomo%20Meye">Serges Mendomo Meye</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Guowei"> Li Guowei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Zhenzhong"> Shen Zhenzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Gan%20Lei"> Gan Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Liqun"> Xu Liqun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the characteristics of high-heap and large-volume, the complex layers of waste and the high-water level of leachate, environmental pollution, and slope instability are easily produced. It is therefore of great significance to research the heterogeneous seepage field and stability of landfills. This paper focuses on the heterogeneous characteristics of the landfill piles and analyzes the seepage field and slope stability of the landfill using statistical and numerical analysis methods. The calculated results are compared with the field measurement and literature research data to verify the reliability of the model, which may provide the basis for the design, safe, and eco-friendly operation of the landfill. The main innovations are as follows: (1) The saturated-unsaturated seepage equation of heterogeneous soil is derived theoretically. The heterogeneous landfill is regarded as composed of infinite layers of homogeneous waste, and a method for establishing the heterogeneous seepage model is proposed. Then the formation law of the stagnant water level of heterogeneous landfills is studied. It is found that the maximum stagnant water level of landfills is higher when considering the heterogeneous seepage characteristics, which harms the stability of landfills. (2) Considering the heterogeneity weight and strength characteristics of waste, a method of establishing a heterogeneous stability model is proposed, and it is extended to the three-dimensional stability study. It is found that the distribution of heterogeneous characteristics has a great influence on the stability of landfill slope. During the operation and management of the landfill, the reservoir bank should also be considered while considering the capacity of the landfill. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20characteristics" title="heterogeneous characteristics">heterogeneous characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=leachate%20levels" title=" leachate levels"> leachate levels</a>, <a href="https://publications.waset.org/abstracts/search?q=saturated-unsaturated%20seepage" title=" saturated-unsaturated seepage"> saturated-unsaturated seepage</a>, <a href="https://publications.waset.org/abstracts/search?q=seepage%20field" title=" seepage field"> seepage field</a>, <a href="https://publications.waset.org/abstracts/search?q=slope%20stability" title=" slope stability"> slope stability</a> </p> <a href="https://publications.waset.org/abstracts/140053/research-of-seepage-field-and-slope-stability-considering-heterogeneous-characteristics-of-waste-piles-a-less-costly-way-to-reduce-high-leachate-levels-and-avoid-accidents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140053.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">251</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1357</span> Hybrid Weighted Multiple Attribute Decision Making Handover Method for Heterogeneous Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohanad%20Alhabo">Mohanad Alhabo</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Zhang"> Li Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Nawaz"> Naveed Nawaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Small cell deployment in 5G networks is a promising technology to enhance capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn will result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers, and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision according to Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this paper, we propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method shows better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=handover" title="handover">handover</a>, <a href="https://publications.waset.org/abstracts/search?q=HetNets" title=" HetNets"> HetNets</a>, <a href="https://publications.waset.org/abstracts/search?q=interference" title=" interference"> interference</a>, <a href="https://publications.waset.org/abstracts/search?q=MADM" title=" MADM"> MADM</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20cells" title=" small cells"> small cells</a>, <a href="https://publications.waset.org/abstracts/search?q=TOPSIS" title=" TOPSIS"> TOPSIS</a>, <a href="https://publications.waset.org/abstracts/search?q=weight" title=" weight"> weight</a> </p> <a href="https://publications.waset.org/abstracts/129187/hybrid-weighted-multiple-attribute-decision-making-handover-method-for-heterogeneous-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1356</span> Tool for Determining the Similarity between Two Web Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doru%20Anastasiu%20Popescu">Doru Anastasiu Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Raducanu%20Dragos%20Ionut"> Raducanu Dragos Ionut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the presentation of a tool which measures the similarity between two websites is made. The websites are compound only from webpages created with HTML. The tool uses three ways of calculating the similarity between two websites based on certain results already published. The first way compares all the webpages within a website, the second way compares a webpage with all the pages within the second website and the third way compares two webpages. Java programming language and technologies such as spring, Jsoup, log4j were used for the implementation of the tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Java" title="Java">Java</a>, <a href="https://publications.waset.org/abstracts/search?q=Jsoup" title=" Jsoup"> Jsoup</a>, <a href="https://publications.waset.org/abstracts/search?q=HTM" title=" HTM"> HTM</a>, <a href="https://publications.waset.org/abstracts/search?q=spring" title=" spring"> spring</a> </p> <a href="https://publications.waset.org/abstracts/48293/tool-for-determining-the-similarity-between-two-web-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48293.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1355</span> Improving Similarity Search Using Clustered Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deokho%20Kim">Deokho Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Wonwoo%20Lee"> Wonwoo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaewoong%20Lee"> Jaewoong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Teresa%20Ng"> Teresa Ng</a>, <a href="https://publications.waset.org/abstracts/search?q=Gun-Ill%20Lee"> Gun-Ill Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiwon%20Jeong"> Jiwon Jeong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20search" title="visual search">visual search</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/92185/improving-similarity-search-using-clustered-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92185.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1354</span> Impact of Similarity Ratings on Human Judgement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ian%20A.%20McCulloh">Ian A. McCulloh</a>, <a href="https://publications.waset.org/abstracts/search?q=Madelaine%20Zinser"> Madelaine Zinser</a>, <a href="https://publications.waset.org/abstracts/search?q=Jesse%20Patsolic"> Jesse Patsolic</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Ramos"> Michael Ramos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ratings" title="ratings">ratings</a>, <a href="https://publications.waset.org/abstracts/search?q=rankings" title=" rankings"> rankings</a>, <a href="https://publications.waset.org/abstracts/search?q=crowdsourcing" title=" crowdsourcing"> crowdsourcing</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20studies" title=" empirical studies"> empirical studies</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20studies" title=" user studies"> user studies</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measures" title=" similarity measures"> similarity measures</a>, <a href="https://publications.waset.org/abstracts/search?q=human-centered%20computing" title=" human-centered computing"> human-centered computing</a>, <a href="https://publications.waset.org/abstracts/search?q=novelty%20in%20information%20retrieval" title=" novelty in information retrieval"> novelty in information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/163910/impact-of-similarity-ratings-on-human-judgement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163910.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1353</span> Text Similarity in Vector Space Models: A Comparative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omid%20Shahmirzadi">Omid Shahmirzadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Lugowski"> Adam Lugowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20Younge"> Kenneth Younge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=patent" title=" patent"> patent</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20embedding" title=" text embedding"> text embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20similarity" title=" text similarity"> text similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space%20model" title=" vector space model"> vector space model</a> </p> <a href="https://publications.waset.org/abstracts/102930/text-similarity-in-vector-space-models-a-comparative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1352</span> Static vs. Stream Mining Trajectories Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Musaab%20Riyadh">Musaab Riyadh</a>, <a href="https://publications.waset.org/abstracts/search?q=Norwati%20Mustapha"> Norwati Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Dina%20Riyadh"> Dina Riyadh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20distance%20measure" title="global distance measure">global distance measure</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20distance%20measure" title=" local distance measure"> local distance measure</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20trajectory" title=" semantic trajectory"> semantic trajectory</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20dimension" title=" spatial dimension"> spatial dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20data%20mining" title=" stream data mining"> stream data mining</a> </p> <a href="https://publications.waset.org/abstracts/94763/static-vs-stream-mining-trajectories-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94763.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1351</span> Hydrogen: Contention-Aware Hybrid Memory Management for Heterogeneous CPU-GPU Architectures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiwei%20Li">Yiwei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingyu%20Gao"> Mingyu Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Integrating hybrid memories with heterogeneous processors could leverage heterogeneity in both compute and memory domains for better system efficiency. To ensure performance isolation, we introduce Hydrogen, a hardware architecture to optimize the allocation of hybrid memory resources to heterogeneous CPU-GPU systems. Hydrogen supports efficient capacity and bandwidth partitioning between CPUs and GPUs in both memory tiers. We propose decoupled memory channel mapping and token-based data migration throttling to enable flexible partitioning. We also support epoch-based online search for optimized configurations and lightweight reconfiguration with reduced data movements. Hydrogen significantly outperforms existing designs by 1.21x on average and up to 1.31x. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20memory" title="hybrid memory">hybrid memory</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20systems" title=" heterogeneous systems"> heterogeneous systems</a>, <a href="https://publications.waset.org/abstracts/search?q=dram%20cache" title=" dram cache"> dram cache</a>, <a href="https://publications.waset.org/abstracts/search?q=graphics%20processing%20units" title=" graphics processing units"> graphics processing units</a> </p> <a href="https://publications.waset.org/abstracts/183187/hydrogen-contention-aware-hybrid-memory-management-for-heterogeneous-cpu-gpu-architectures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1350</span> Discovering the Dimension of Abstractness: Structure-Based Model that Learns New Categories and Categorizes on Different Levels of Abstraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgi%20I.%20Petkov">Georgi I. Petkov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20I.%20Vankov"> Ivan I. Vankov</a>, <a href="https://publications.waset.org/abstracts/search?q=Yolina%20A.%20Petrova"> Yolina A. Petrova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analogy-making" title="analogy-making">analogy-making</a>, <a href="https://publications.waset.org/abstracts/search?q=categorization" title=" categorization"> categorization</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20of%20categories" title=" learning of categories"> learning of categories</a>, <a href="https://publications.waset.org/abstracts/search?q=abstraction" title=" abstraction"> abstraction</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20structure" title=" hierarchical structure"> hierarchical structure</a> </p> <a href="https://publications.waset.org/abstracts/94222/discovering-the-dimension-of-abstractness-structure-based-model-that-learns-new-categories-and-categorizes-on-different-levels-of-abstraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94222.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1349</span> Graph Similarity: Algebraic Model and Its Application to Nonuniform Signal Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nileshkumar%20Vishnav">Nileshkumar Vishnav</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Tatu"> Aditya Tatu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A recent approach of representing graph signals and graph filters as polynomials is useful for graph signal processing. In this approach, the adjacency matrix plays pivotal role; instead of the more common approach involving graph-Laplacian. In this work, we follow the adjacency matrix based approach and corresponding algebraic signal model. We further expand the theory and introduce the concept of similarity of two graphs. The similarity of graphs is useful in that key properties (such as filter-response, algebra related to graph) get transferred from one graph to another. We demonstrate potential applications of the relation between two similar graphs, such as nonuniform filter design, DTMF detection and signal reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20signal%20processing" title="graph signal processing">graph signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=algebraic%20signal%20processing" title=" algebraic signal processing"> algebraic signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20similarity" title=" graph similarity"> graph similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=isospectral%20graphs" title=" isospectral graphs"> isospectral graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=nonuniform%20signal%20processing" title=" nonuniform signal processing"> nonuniform signal processing</a> </p> <a href="https://publications.waset.org/abstracts/59404/graph-similarity-algebraic-model-and-its-application-to-nonuniform-signal-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=heterogeneous%20similarity&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=heterogeneous%20similarity&page=3">3</a></li> <li class="page-item"><a class="page-link" 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