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non-sensitive attribute</title> <meta name="description" content="Search results for: non-sensitive attribute"> <meta name="keywords" content="non-sensitive attribute"> <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="non-sensitive attribute"> <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> 326</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: non-sensitive attribute</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">326</span> Design Elements: Examining Product Design Attribute That Make Sweets Appear More Delicious to Foreign Patrons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuko%20Sakamoto">Kazuko Sakamoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Keiichiro%20Kawarabayashi"> Keiichiro Kawarabayashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoji%20Kitani"> Yoji Kitani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Japanese sweets are one of the important elements of the Chur Japan strategy. In this research, we investigated what kind of sweets are liked to the Chinese tourist. What is generally eaten is influenced by culture, a sense of values, and business practice. Therefore, what was adapted there is sold. However, when traveling, what its country does not have is called for. Then, how far should we take in Chinese people's taste in a design?　This time, the design attribute (a color and a form) which leads to sweets "being delicious" was clarified by rough aggregate theory.As a result, the difference in the taste of Chinese people and Japanese people became clear. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20attribute" title="design attribute">design attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=international%20comparison" title=" international comparison"> international comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=taste%20by%20appearance" title=" taste by appearance"> taste by appearance</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20attribute" title=" design attribute"> design attribute</a> </p> <a href="https://publications.waset.org/abstracts/10772/design-elements-examining-product-design-attribute-that-make-sweets-appear-more-delicious-to-foreign-patrons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10772.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">422</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">325</span> RAPDAC: Role Centric Attribute Based Policy Driven Access Control Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamil%20Ahmed">Jamil Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Access control models aim to decide whether a user should be denied or granted access to the user‟s requested activity. Various access control models have been established and proposed. The most prominent of these models include role-based, attribute-based, policy based access control models as well as role-centric attribute based access control model. In this paper, a novel access control model is presented called “Role centric Attribute based Policy Driven Access Control (RAPDAC) model”. RAPDAC incorporates the concept of “policy” in the “role centric attribute based access control model”. It leverages the concept of "policy‟ by precisely combining the evaluation of conditions, attributes, permissions and roles in order to allow authorization access. This approach allows capturing the "access control policy‟ of a real time application in a well defined manner. RAPDAC model allows making access decision at much finer granularity as illustrated by the case study of a real time library information system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authorization" title="authorization">authorization</a>, <a href="https://publications.waset.org/abstracts/search?q=access%20control%20model" title=" access control model"> access control model</a>, <a href="https://publications.waset.org/abstracts/search?q=role%20based%20access%20control" title=" role based access control"> role based access control</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute%20based%20access%20control" title=" attribute based access control"> attribute based access control</a> </p> <a href="https://publications.waset.org/abstracts/129022/rapdac-role-centric-attribute-based-policy-driven-access-control-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129022.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">159</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">324</span> An Attribute Based Access Control Model with POL Module for Dynamically Granting and Revoking Authorizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gang%20Liu">Gang Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Huimin%20Song"> Huimin Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Can%20Wang"> Can Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Runnan%20Zhang"> Runnan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Lu%20Fang"> Lu Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, resource sharing and system security are critical issues. This paper proposes a POL module composed of PRIV ILEGE attribute (PA), obligation and log which improves attribute based access control (ABAC) model in dynamically granting authorizations and revoking authorizations. The following describes the new model termed PABAC in terms of the POL module structure, attribute definitions, policy formulation and authorization architecture, which demonstrate the advantages of it. The POL module addresses the problems which are not predicted before and not described by access control policy. It can be one of the subject attributes or resource attributes according to the practical application, which enhances the flexibility of the model compared with ABAC. A scenario that illustrates how this model is applied to the real world is provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=access%20control" title="access control">access control</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute%20based%20access%20control" title=" attribute based access control"> attribute based access control</a>, <a href="https://publications.waset.org/abstracts/search?q=granting%20authorizations" title=" granting authorizations"> granting authorizations</a>, <a href="https://publications.waset.org/abstracts/search?q=privilege" title=" privilege"> privilege</a>, <a href="https://publications.waset.org/abstracts/search?q=revoking%20authorizations" title=" revoking authorizations"> revoking authorizations</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20security" title=" system security"> system security</a> </p> <a href="https://publications.waset.org/abstracts/65275/an-attribute-based-access-control-model-with-pol-module-for-dynamically-granting-and-revoking-authorizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65275.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">359</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">323</span> Proficient Estimation Procedure for a Rare Sensitive Attribute Using Poisson Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Suman">S. Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Singh"> G. N. Singh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present manuscript addresses the estimation procedure of population parameter using Poisson probability distribution when characteristic under study possesses a rare sensitive attribute. The generalized form of unrelated randomized response model is suggested in order to acquire the truthful responses from respondents. The resultant estimators have been proposed for two situations when the information on an unrelated rare non-sensitive characteristic is known as well as unknown. The properties of the proposed estimators are derived, and the measure of confidentiality of respondent is also suggested for respondents. Empirical studies are carried out in the support of discussed theory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title="Poisson distribution">Poisson distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=randomized%20response%20model" title=" randomized response model"> randomized response model</a>, <a href="https://publications.waset.org/abstracts/search?q=rare%20sensitive%20attribute" title=" rare sensitive attribute"> rare sensitive attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=non-sensitive%20attribute" title=" non-sensitive attribute"> non-sensitive attribute</a> </p> <a href="https://publications.waset.org/abstracts/95219/proficient-estimation-procedure-for-a-rare-sensitive-attribute-using-poisson-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95219.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">266</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">322</span> Clustering-Based Computational Workload Minimization in Ontology Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansir%20Abubakar">Mansir Abubakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hazlina%20Hamdan"> Hazlina Hamdan</a>, <a href="https://publications.waset.org/abstracts/search?q=Norwati%20Mustapha"> Norwati Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Teh%20Noranis%20Mohd%20Aris"> Teh Noranis Mohd Aris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attribute%20correspondence" title="attribute correspondence">attribute correspondence</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20workload" title=" computational workload"> computational workload</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids%20clustering" title=" k-medoids clustering"> k-medoids clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20matching" title=" ontology matching"> ontology matching</a> </p> <a href="https://publications.waset.org/abstracts/78369/clustering-based-computational-workload-minimization-in-ontology-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78369.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">248</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">321</span> Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mafarja%20Majdi">Mafarja Majdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salwani%20Abdullah"> Salwani Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20S.%20Jaddi"> Najmeh S. Jaddi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20set%20theory" title="rough set theory">rough set theory</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute%20reduction" title=" attribute reduction"> attribute reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=memetic%20algorithms" title=" memetic algorithms"> memetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=record%20to%20record%20algorithm" title=" record to record algorithm"> record to record algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=great%20deluge%20algorithm" title=" great deluge algorithm"> great deluge algorithm</a> </p> <a href="https://publications.waset.org/abstracts/33663/fuzzy-population-based-meta-heuristic-approaches-for-attribute-reduction-in-rough-set-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33663.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">454</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">320</span> Evaluation of Aggregate Risks in Sustainable Manufacturing Using Fuzzy Multiple Attribute Decision Making</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gopinath%20Rathod">Gopinath Rathod</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinod%20Puranik"> Vinod Puranik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sustainability is regarded as a key concept for survival in the competitive scenario. Industrial risk and diversification of risk type’s increases with industrial developments. In the context of sustainable manufacturing, the evaluation of risk is difficult because of the incomplete information and multiple indicators. Fuzzy Multiple Attribute Decision Method (FMADM) has been used with a three level hierarchical decision making model to evaluate aggregate risk for sustainable manufacturing projects. A case study has been presented to reflect the risk characteristics in sustainable manufacturing projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sustainable%20manufacturing" title="sustainable manufacturing">sustainable manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=aggregate%20risk" title=" aggregate risk"> aggregate risk</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20multiple%20attribute%20decision%20method" title=" fuzzy multiple attribute decision method"> fuzzy multiple attribute decision method</a> </p> <a href="https://publications.waset.org/abstracts/17324/evaluation-of-aggregate-risks-in-sustainable-manufacturing-using-fuzzy-multiple-attribute-decision-making" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17324.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">519</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">319</span> Intelligent Recognition of Diabetes Disease via FCM Based Attribute Weighting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat">Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C-means%20clustering" title="fuzzy C-means clustering">fuzzy C-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C-means%20clustering%20based%20attribute%20weighting" title=" fuzzy C-means clustering based attribute weighting"> fuzzy C-means clustering based attribute weighting</a>, <a href="https://publications.waset.org/abstracts/search?q=Pima%20Indians%20diabetes" title=" Pima Indians diabetes"> Pima Indians diabetes</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/46171/intelligent-recognition-of-diabetes-disease-via-fcm-based-attribute-weighting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46171.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">413</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">318</span> A Multi-Attribute Utility Model for Performance Evaluation of Sustainable Banking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonia%20Rebai">Sonia Rebai</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Naceur%20Azaiez"> Mohamed Naceur Azaiez</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhafer%20Saidane"> Dhafer Saidane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we develop a performance evaluation model based on a multi-attribute utility approach aiming at reaching the sustainable banking (SB) status. This model is built accounting for various banks’ stakeholders in a win-win paradigm. In addition, it offers the opportunity for adopting a global measure of performance as an indication of a bank’s sustainability degree. This measure is referred to as banking sustainability performance index (BSPI). This index may constitute a basis for ranking banks. Moreover, it may constitute a bridge between the assessment types of financial and extra-financial rating agencies. A real application is performed on three French banks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-attribute%20utility%20theory" title="multi-attribute utility theory">multi-attribute utility theory</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20banking" title=" sustainable banking"> sustainable banking</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20rating" title=" financial rating "> financial rating </a> </p> <a href="https://publications.waset.org/abstracts/10643/a-multi-attribute-utility-model-for-performance-evaluation-of-sustainable-banking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10643.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">467</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">317</span> Mask-Prompt-Rerank: An Unsupervised Method for Text Sentiment Transfer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yufen%20Qin">Yufen Qin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20model" title="language model">language model</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=prompt" title=" prompt"> prompt</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20sentiment%20transfer" title=" text sentiment transfer"> text sentiment transfer</a> </p> <a href="https://publications.waset.org/abstracts/173904/mask-prompt-rerank-an-unsupervised-method-for-text-sentiment-transfer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173904.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">81</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">316</span> Attribute Index and Classification Method of Earthquake Damage Photographs of Engineering Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming%20Lu">Ming Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaojun%20Li"> Xiaojun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bodi%20Lu"> Bodi Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Juehui%20Xing"> Juehui Xing</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthquake damage phenomenon of each large earthquake gives comprehensive and profound real test to the dynamic performance and failure mechanism of different engineering structures. Cognitive engineering structure characteristics through seismic damage phenomenon are often far superior to expensive shaking table experiments. After the earthquake, people will record a variety of different types of engineering damage photos. However, a large number of earthquake damage photographs lack sufficient information and reduce their using value. To improve the research value and the use efficiency of engineering seismic damage photographs, this paper objects to explore and show seismic damage background information, which includes the earthquake magnitude, earthquake intensity, and the damaged structure characteristics. From the research requirement in earthquake engineering field, the authors use the 2008 China Wenchuan M8.0 earthquake photographs, and provide four kinds of attribute indexes and classification, which are seismic information, structure types, earthquake damage parts and disaster causation factors. The final object is to set up an engineering structural seismic damage database based on these four attribute indicators and classification, and eventually build a website providing seismic damage photographs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attribute%20index" title="attribute index">attribute index</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20method" title=" classification method"> classification method</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20damage%20picture" title=" earthquake damage picture"> earthquake damage picture</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering%20structure" title=" engineering structure"> engineering structure</a> </p> <a href="https://publications.waset.org/abstracts/66126/attribute-index-and-classification-method-of-earthquake-damage-photographs-of-engineering-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66126.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">765</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">315</span> An Information-Based Approach for Preference Method in Multi-Attribute Decision Making</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serhat%20Tuzun">Serhat Tuzun</a>, <a href="https://publications.waset.org/abstracts/search?q=Tufan%20Demirel"> Tufan Demirel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multi-Criteria Decision Making (MCDM) is the modelling of real-life to solve problems we encounter. It is a discipline that aids decision makers who are faced with conflicting alternatives to make an optimal decision. MCDM problems can be classified into two main categories: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM), based on the different purposes and different data types. Although various MADM techniques were developed for the problems encountered, their methodology is limited in modelling real-life. Moreover, objective results are hard to obtain, and the findings are generally derived from subjective data. Although, new and modified techniques are developed by presenting new approaches such as fuzzy logic; comprehensive techniques, even though they are better in modelling real-life, could not find a place in real world applications for being hard to apply due to its complex structure. These constraints restrict the development of MADM. This study aims to conduct a comprehensive analysis of preference methods in MADM and propose an approach based on information. For this purpose, a detailed literature review has been conducted, current approaches with their advantages and disadvantages have been analyzed. Then, the approach has been introduced. In this approach, performance values of the criteria are calculated in two steps: first by determining the distribution of each attribute and standardizing them, then calculating the information of each attribute as informational energy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=literature%20review" title="literature review">literature review</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-attribute%20decision%20making" title=" multi-attribute decision making"> multi-attribute decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=operations%20research" title=" operations research"> operations research</a>, <a href="https://publications.waset.org/abstracts/search?q=preference%20method" title=" preference method"> preference method</a>, <a href="https://publications.waset.org/abstracts/search?q=informational%20energy" title=" informational energy"> informational energy</a> </p> <a href="https://publications.waset.org/abstracts/73395/an-information-based-approach-for-preference-method-in-multi-attribute-decision-making" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73395.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">224</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">314</span> Attribute Based Comparison and Selection of Modular Self-Reconfigurable Robot Using Multiple Attribute Decision Making Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manpreet%20Singh">Manpreet Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20P.%20Agrawal"> V. P. Agrawal</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurmanjot%20Singh%20Bhatti"> Gurmanjot Singh Bhatti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> From the last decades, there is a significant technological advancement in the field of robotics, and a number of modular self-reconfigurable robots were introduced that can help in space exploration, bucket to stuff, search, and rescue operation during earthquake, etc. As there are numbers of self-reconfigurable robots, choosing the optimum one is always a concern for robot user since there is an increase in available features, facilities, complexity, etc. The objective of this research work is to present a multiple attribute decision making based methodology for coding, evaluation, comparison ranking and selection of modular self-reconfigurable robots using a technique for order preferences by similarity to ideal solution approach. However, 86 attributes that affect the structure and performance are identified. A database for modular self-reconfigurable robot on the basis of different pertinent attribute is generated. This database is very useful for the user, for selecting a robot that suits their operational needs. Two visual methods namely linear graph and spider chart are proposed for ranking of modular self-reconfigurable robots. Using five robots (Atron, Smores, Polybot, M-Tran 3, Superbot), an example is illustrated, and raking of the robots is successfully done, which shows that Smores is the best robot for the operational need illustrated, and this methodology is found to be very effective and simple to use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-reconfigurable%20robots" title="self-reconfigurable robots">self-reconfigurable robots</a>, <a href="https://publications.waset.org/abstracts/search?q=MADM" title=" MADM"> MADM</a>, <a href="https://publications.waset.org/abstracts/search?q=TOPSIS" title=" TOPSIS"> TOPSIS</a>, <a href="https://publications.waset.org/abstracts/search?q=morphogenesis" title=" morphogenesis"> morphogenesis</a>, <a href="https://publications.waset.org/abstracts/search?q=scalability" title=" scalability"> scalability</a> </p> <a href="https://publications.waset.org/abstracts/90771/attribute-based-comparison-and-selection-of-modular-self-reconfigurable-robot-using-multiple-attribute-decision-making-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90771.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">222</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">313</span> Emotion Mining and Attribute Selection for Actionable Recommendations to Improve Customer Satisfaction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaishree%20Ranganathan">Jaishree Ranganathan</a>, <a href="https://publications.waset.org/abstracts/search?q=Poonam%20Rajurkar"> Poonam Rajurkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Angelina%20A.%20Tzacheva"> Angelina A. Tzacheva</a>, <a href="https://publications.waset.org/abstracts/search?q=Zbigniew%20W.%20Ras"> Zbigniew W. Ras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=actionable%20pattern%20discovery" title="actionable pattern discovery">actionable pattern discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute%20selection" title=" attribute selection"> attribute selection</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20data" title=" business data"> business data</a>, <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=emotion" title=" emotion"> emotion</a> </p> <a href="https://publications.waset.org/abstracts/126886/emotion-mining-and-attribute-selection-for-actionable-recommendations-to-improve-customer-satisfaction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126886.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">199</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">312</span> Research on Urban Point of Interest Generalization Method Based on Mapping Presentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chengming%20Li">Chengming Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong%20Yin"> Yong Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Peipei%20Guo"> Peipei Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoli%20Liu"> Xiaoli Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Without taking account of the attribute richness of POI (point of interest) data and spatial distribution limited by roads, a POI generalization method considering both attribute information and spatial distribution has been proposed against the existing point generalization algorithm merely focusing on overall information of point groups. Hierarchical characteristic of urban POI information expression has been firstly analyzed to point out the measurement feature of the corresponding hierarchy. On this basis, an urban POI generalizing strategy has been put forward: POIs urban road network have been divided into three distribution pattern; corresponding generalization methods have been proposed according to the characteristic of POI data in different distribution patterns. Experimental results showed that the method taking into account both attribute information and spatial distribution characteristics of POI can better implement urban POI generalization in the mapping presentation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=POI" title="POI">POI</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20network" title=" road network"> road network</a>, <a href="https://publications.waset.org/abstracts/search?q=selection%20method" title=" selection method"> selection method</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20information%20expression" title=" spatial information expression"> spatial information expression</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20pattern" title=" distribution pattern"> distribution pattern</a> </p> <a href="https://publications.waset.org/abstracts/70683/research-on-urban-point-of-interest-generalization-method-based-on-mapping-presentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70683.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">410</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">311</span> The Effectiveness of Computerized Dynamic Listening Assessment Informed by Attribute-Based Mediation Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaru%20Meng">Yaru Meng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study contributes to the small but growing literature around computerized approaches to dynamic assessment (C-DA), wherein individual items are accompanied by mediating prompts. Mediation in the current computerized dynamic listening assessment (CDLA) was informed by an attribute-based mediation model (AMM) that identified the underlying L2 listening cognitive abilities and associated descriptors. The AMM served to focus mediation during C-DA on particular cognitive abilities with a goal of specifying areas of learner difficulty. 86 low-intermediate L2 English learners from a university in China completed three listening assessments, with an experimental group receiving the CLDA system and a control group a non-dynamic assessment. As an assessment, the use of the AMM in C-DA generated detailed diagnoses for each learner. In addition, both within- and between-group repeated ANOVA found greater gains at the level of specific attributes among C-DA learners over the course of a 5-week study. Directions for future research are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computerized%20dynamic%20assessment" title="computerized dynamic assessment">computerized dynamic assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=effectiveness" title=" effectiveness"> effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20as%20foreign%20language%20listening" title=" English as foreign language listening"> English as foreign language listening</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute-based%20mediation%20model" title=" attribute-based mediation model"> attribute-based mediation model</a> </p> <a href="https://publications.waset.org/abstracts/132564/the-effectiveness-of-computerized-dynamic-listening-assessment-informed-by-attribute-based-mediation-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132564.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">224</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">310</span> Simple Multiple-Attribute Rating Technique for Optimal Decision-Making Model on Selecting Best Spiker of World Grand Prix</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen%20Chih-Cheng">Chen Chih-Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20I-Cheng"> Chen I-Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Lee%20Yung-Tan"> Lee Yung-Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuo%20Yen-Whea"> Kuo Yen-Whea</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Chin-Hung"> Yu Chin-Hung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to construct a model for best spike player selection in a top volleyball tournament of the world. Data consisted of the records of 2013 World Grand Prix declared by International Volleyball Federation (FIVB). Simple Multiple-Attribute Rating Technique (SMART) was used for optimal decision-making model on the best spike player selection. The research results showed that the best spike player ranking by SMART is different than the ranking by FIVB. The results demonstrated the effectiveness and feasibility of the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simple%20multiple-attribute%20rating%20technique" title="simple multiple-attribute rating technique">simple multiple-attribute rating technique</a>, <a href="https://publications.waset.org/abstracts/search?q=World%20Grand%20Prix" title=" World Grand Prix"> World Grand Prix</a>, <a href="https://publications.waset.org/abstracts/search?q=best%20spike%20player" title=" best spike player"> best spike player</a>, <a href="https://publications.waset.org/abstracts/search?q=International%20Volleyball%20Federation" title=" International Volleyball Federation"> International Volleyball Federation</a> </p> <a href="https://publications.waset.org/abstracts/3166/simple-multiple-attribute-rating-technique-for-optimal-decision-making-model-on-selecting-best-spiker-of-world-grand-prix" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3166.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">474</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">309</span> Combining the Deep Neural Network with the K-Means for Traffic Accident Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Celso%20L.%20Fernando">Celso L. Fernando</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshio%20Yoshii"> Toshio Yoshii</a>, <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Tsubota"> Takahiro Tsubota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accident%20risks%20estimation" title="accident risks estimation">accident risks estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</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=k-mean" title=" k-mean"> k-mean</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20safety" title=" road safety"> road safety</a> </p> <a href="https://publications.waset.org/abstracts/148822/combining-the-deep-neural-network-with-the-k-means-for-traffic-accident-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148822.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">163</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">308</span> Research of Data Cleaning Methods Based on Dependency Rules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Bao">Yang Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shi%20Wei%20Deng"> Shi Wei Deng</a>, <a href="https://publications.waset.org/abstracts/search?q=WangQun%20Lin"> WangQun Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSQL), and gives 6 data cleaning methods based on these algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20cleaning" title="data cleaning">data cleaning</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20rules" title=" dependency rules"> dependency rules</a>, <a href="https://publications.waset.org/abstracts/search?q=violation%20data%20discovery" title=" violation data discovery"> violation data discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20repair" title=" data repair"> data repair</a> </p> <a href="https://publications.waset.org/abstracts/31348/research-of-data-cleaning-methods-based-on-dependency-rules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31348.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">564</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">307</span> Construction Contractor Pre-Qualification Using Multi-Attribute Utility Theory: A Multiplicative Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Vikram">B. Vikram</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Anu%20Leena"> Y. Anu Leena</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Anu%20Neena"> Y. Anu Neena</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Krishna%20Rao"> M. V. Krishna Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20S.%20Kumar"> V. S. S. Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The industry is often criticized for inefficiencies in outcomes such as time and cost overruns, low productivity, poor quality and inadequate customer satisfaction. To enhance the chances for construction projects to be successful, selecting an able contractor is one of the fundamental decisions to be made by clients. The selection of the most appropriate contractor is a multi-criteria decision making (MCDM) process. In this paper, multi-attribute utility theory (MAUT) is employed utilizing the multiplicative form of utility function for ranking the prequalified contractors. Performance assessment criteria covering contracting company attributes, experience record, past performance, performance potential, financial stability and project specific criteria are considered for contractor evaluation. A case study of multistoried building for which four contractors submitted bids is considered to illustrate the applicability of multiplicative approach of MAUT to rank the prequalified contractors. The proposed MAUT decision making methodology can also be employed to other decision making situations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-attribute%20utility%20theory" title="multi-attribute utility theory">multi-attribute utility theory</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20industry" title=" construction industry"> construction industry</a>, <a href="https://publications.waset.org/abstracts/search?q=prequalification" title=" prequalification"> prequalification</a>, <a href="https://publications.waset.org/abstracts/search?q=contractor" title=" contractor"> contractor</a> </p> <a href="https://publications.waset.org/abstracts/14928/construction-contractor-pre-qualification-using-multi-attribute-utility-theory-a-multiplicative-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14928.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">435</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">306</span> Efficacy of Agrobacterium Tumefaciens as a Possible Entomopathogenic Agent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fouzia%20Qamar">Fouzia Qamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahida%20Hasnain"> Shahida Hasnain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of the present study was to evaluate the possible role of Agrobacterium tumefaciens as a possible insect biocontrol agent. Pests selected for the present challenge were adult males of Periplaneta americana and last instar larvae of Pieris brassicae and Spodoptera litura. Different ranges of bacterial doses were selected and tested to score the mortalities of the insects after 24 hours, for the lethal dose estimation studies. Mode of application for the inoculation of the bacteria, was the microinjection technique. The evaluation of the possible entomopathogenic carrying attribute of bacterial Ti plasmid, led to the conclusion that the loss of plasmid was associated with the loss of virulence against target insects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agrobacterium%20tumefaciens" title="agrobacterium tumefaciens">agrobacterium tumefaciens</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity%20assessment" title=" toxicity assessment"> toxicity assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=biopesticidal%20attribute" title=" biopesticidal attribute"> biopesticidal attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=entomopathogenic%20agent" title=" entomopathogenic agent"> entomopathogenic agent</a> </p> <a href="https://publications.waset.org/abstracts/17439/efficacy-of-agrobacterium-tumefaciens-as-a-possible-entomopathogenic-agent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17439.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">378</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">305</span> Application of Data Mining Techniques for Tourism Knowledge Discovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Teklu%20Urgessa">Teklu Urgessa</a>, <a href="https://publications.waset.org/abstracts/search?q=Wookjae%20Maeng"> Wookjae Maeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Joong%20Seek%20Lee"> Joong Seek Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title="classification algorithms">classification algorithms</a>, <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=tourism" title=" tourism"> tourism</a> </p> <a href="https://publications.waset.org/abstracts/59419/application-of-data-mining-techniques-for-tourism-knowledge-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59419.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">295</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">304</span> Antecedents of Regret and Satisfaction in Electronic Commerce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chechen%20Liao">Chechen Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Pui-Lai%20To"> Pui-Lai To</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuang-Chun%20Liu"> Chuang-Chun Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online shopping has become very popular recently. In today’s highly competitive online retail environment, retaining existing customers is a necessity for online retailers. This study focuses on the antecedents and consequences of Internet buyer regret and satisfaction in the online consumer purchasing process. This study examines the roles that online consumer’s purchasing process evaluations (i.e., search experience difficulty, service-attribute evaluations, product-attribute evaluations and post-purchase price perceptions) and alternative evaluation (i.e., alternative attractiveness) play in determining buyer regret and satisfaction in e-commerce. The study also examines the consequences of regret, satisfaction and habit in regard to repurchase intention. In addition, this study attempts to investigate the moderating role of habit in attaining a better understanding of the relationship between repurchase intention and its antecedents. Survey data collected from 431 online customers are analyzed using structural equation modeling (SEM) with partial least squares (PLS) and support provided for the hypothesized links. These results indicate that online consumer’s purchasing process evaluations (i.e., search experience difficulty, service-attribute evaluations, product-attribute evaluations and post-purchase price perceptions) have significant influences on regret and satisfaction, which in turn influences repurchase intention. In addition, alternative evaluation (i.e., alternative attractiveness) has a significant positive influence on regret. The research model can provide a richer understanding of online customers’ repurchase behavior and contribute to both research and practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20shopping" title="online shopping">online shopping</a>, <a href="https://publications.waset.org/abstracts/search?q=purchase%20evaluation" title=" purchase evaluation"> purchase evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=regret" title=" regret"> regret</a>, <a href="https://publications.waset.org/abstracts/search?q=satisfaction" title=" satisfaction"> satisfaction</a> </p> <a href="https://publications.waset.org/abstracts/70522/antecedents-of-regret-and-satisfaction-in-electronic-commerce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70522.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">283</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">303</span> Studies of Rule Induction by STRIM from the Decision Table with Contaminated Attribute Values from Missing Data and Noise — in the Case of Critical Dataset Size —</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tetsuro%20Saeki">Tetsuro Saeki</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuichi%20Kato"> Yuichi Kato</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoutarou%20Mizuno"> Shoutarou Mizuno</a> </p> <p class="card-text"><strong>Abstract:</strong></p> STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rule%20induction" title="rule induction">rule induction</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20table" title=" decision table"> decision table</a>, <a href="https://publications.waset.org/abstracts/search?q=missing%20data" title=" missing data"> missing data</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a> </p> <a href="https://publications.waset.org/abstracts/22621/studies-of-rule-induction-by-strim-from-the-decision-table-with-contaminated-attribute-values-from-missing-data-and-noise-in-the-case-of-critical-dataset-size" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22621.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">302</span> Multi-source Question Answering Framework Using Transformers for Attribute Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prashanth%20Pillai">Prashanth Pillai</a>, <a href="https://publications.waset.org/abstracts/search?q=Purnaprajna%20Mangsuli"> Purnaprajna Mangsuli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</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=transformers" title=" transformers"> transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/142889/multi-source-question-answering-framework-using-transformers-for-attribute-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142889.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">193</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">301</span> Engagement Resources Use by Expert and Novice EFL Academic Writers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moharram%20Sharifi">Moharram Sharifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study was to show how expert and novice writers take positions and stances in Research Articles and Master of Art theses Introductions, so Engagement resources were investigated in 30 Research Articles and 30 Master of Art theses written by Iranian non-native speakers. Through paired samples t-test analysis, we found out that the mean occurrences of heteroglossic items in both RA and Master thesis Introductions were larger than those of monoglossic items, indicating the awareness of both groups of writers to ‘engage’ alternative positions in Introduction sections. The results also revealed that expansive choices were preferred over contractive options in both corpora, implying both groups of writers respect alternative voices cautiously by welcoming rather than closing down the possibility of different perspectives and stances. Furthermore, unlike novice academic writers who used more Attribute features than Entertainment ones in their MATs introduction sections, expert academic writers employed a balanced number of Entertainment and Attribute in their RA introduction sections. The balanced deployment of entertaining and Attribute features in RA Introductions by expert writers might be characteristics of the writers’ demonstration of politeness, which is commonly accepted as an essential feature in academic writing discourse. Finally, through qualitative analysis, it was demonstrated that MAT writers, as novice academic writers, suffered from lacking appropriate evaluative stances and authorial voices toward propositions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=novice" title="novice">novice</a>, <a href="https://publications.waset.org/abstracts/search?q=expert" title=" expert"> expert</a>, <a href="https://publications.waset.org/abstracts/search?q=engagement" title=" engagement"> engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=RA%20Introductions" title=" RA Introductions"> RA Introductions</a>, <a href="https://publications.waset.org/abstracts/search?q=MA%20Thesis" title=" MA Thesis"> MA Thesis</a> </p> <a href="https://publications.waset.org/abstracts/184605/engagement-resources-use-by-expert-and-novice-efl-academic-writers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184605.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">43</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">300</span> Application of Multilinear Regression Analysis for Prediction of Synthetic Shear Wave Velocity Logs in Upper Assam Basin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Triveni%20Gogoi">Triveni Gogoi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rima%20Chatterjee"> Rima Chatterjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Castagna%27s%20equation" title="Castagna's equation">Castagna's equation</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20linear%20regression" title=" multi linear regression"> multi linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20attribute%20analysis" title=" multi attribute analysis"> multi attribute analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=shear%20wave%20logs" title=" shear wave logs"> shear wave logs</a> </p> <a href="https://publications.waset.org/abstracts/80705/application-of-multilinear-regression-analysis-for-prediction-of-synthetic-shear-wave-velocity-logs-in-upper-assam-basin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80705.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">229</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">299</span> Oman’s Position in U.S. Tourists’ Mind: The Use of Importance-Performance Analysis on Destination Attributes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Gamil%20Montasser">Mohammed Gamil Montasser</a>, <a href="https://publications.waset.org/abstracts/search?q=Angelo%20Battaglia"> Angelo Battaglia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tourism is making its presence felt across the Sultanate of Oman. The story is one of the most recognized phenomena as a sustainable solid growth and is considered a remarkable outcome for any destination. The competitive situation and challenges within the tourism industry worldwide entail a better understanding of the destination position and its image to achieve Oman’s aspiration to retain its international reputation as one of the most desirable destinations in the Middle East. To access general perceptions of Oman’s attributes, their importance and their influences among U.S. tourists, an online survey was conducted with 522 American travelers who have traveled internationally, including non-visitors, virtual-visitors and visitors to Oman. This research involved a total of 36 attributes in the survey. Participants were asked to rate their agreement on how each attribute represented Oman and how important each attribute was for selecting destinations on 5- point Likert Scale. They also indicated if each attribute has a positive, neutral or negative influence on their destination selection. Descriptive statistics and importance performance analysis (IPA) were conducted. IPA illustrated U.S. tourists’ perceptions of Oman’s destination attributes and their importance in destination selection on a matrix with four quadrants, divided by actual mean value in each grid for importance (M=3.51) and performance (M=3.57). Oman tourism organizations and destination managers may use these research findings for future marketing and management efforts toward the U.S. travel market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20importance" title="analysis of importance">analysis of importance</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=destination%20attributes" title=" destination attributes"> destination attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=Oman%27s%20position" title=" Oman's position"> Oman's position</a>, <a href="https://publications.waset.org/abstracts/search?q=U.S.%20tourists" title=" U.S. tourists"> U.S. tourists</a> </p> <a href="https://publications.waset.org/abstracts/49595/omans-position-in-us-tourists-mind-the-use-of-importance-performance-analysis-on-destination-attributes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49595.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">306</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">298</span> Ant-Tracking Attribute: A Model for Understanding Production Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prince%20Suka%20Neekia%20Momta">Prince Suka Neekia Momta</a>, <a href="https://publications.waset.org/abstracts/search?q=Rita%20Iheoma%20Achonyeulo"> Rita Iheoma Achonyeulo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant Tracking seismic attribute applied over 4-seconds seismic volume revealed structural features triggered by clay diapirism, growth fault development, rapid deltaic sedimentation and intense drilling. The attribute was extracted on vertical seismic sections and time slices. Mega tectonic structures such as growth faults and clay diapirs are visible on vertical sections with obscured minor lineaments or fractures. Fractures are distinctively visible on time slices yielding recognizable patterns corroborating established geologic models. This model seismic attribute enabled the understanding of fluid flow characteristics and production responses. Three structural patterns recognized in the field include: major growth faults, minor faults or lineaments and network of fractures. Three growth faults mapped on seismic section form major deformation bands delimiting the area into three blocks or depocenters. The growth faults trend E-W, dip down-to-south in the basin direction, and cut across the study area. The faults initiating from about 2000ms extended up to 500ms, and tend to progress parallel and opposite to the growth direction of an upsurging diapiric structure. The diapiric structures form the major deformational bands originating from great depths (below 2000ms) and rising to about 1200ms where series of sedimentary layers onlapped and pinchout stratigraphically against the diapir. Several other secondary faults or lineaments that form parallel streaks to one another also accompanied the growth faults. The fracture networks have no particular trend but form a network surrounding the well area. Faults identified in the study area have potentials for structural hydrocarbon traps whereas the presence of fractures created a fractured-reservoir condition that enhanced rapid fluid flow especially water. High aquifer flow potential aided by possible fracture permeability resulted in rapid decline in oil rate. Through the application of Ant Tracking attribute, it is possible to obtain detailed interpretation of structures that can have direct influence on oil and gas production. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=seismic" title="seismic">seismic</a>, <a href="https://publications.waset.org/abstracts/search?q=attributes" title=" attributes"> attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=production" title=" production"> production</a>, <a href="https://publications.waset.org/abstracts/search?q=structural" title=" structural"> structural</a> </p> <a href="https://publications.waset.org/abstracts/183152/ant-tracking-attribute-a-model-for-understanding-production-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183152.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">70</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">297</span> Applications for Accounting of Inherited Object-Oriented Class Members </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jehad%20Al%20Dallal">Jehad Al Dallal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A class in an Object-Oriented (OO) system is the basic unit of design, and it encapsulates a set of attributes and methods. In OO systems, instead of redefining the attributes and methods that are included in other classes, a class can inherit these attributes and methods and only implement its unique attributes and methods, which results in reducing code redundancy and improving code testability and maintainability. Such mechanism is called Class Inheritance. However, some software engineering applications may require accounting for all the inherited class members (i.e., attributes and methods). This paper explains how to account for inherited class members and discusses the software engineering applications that require such consideration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=class%20flattening" title="class flattening">class flattening</a>, <a href="https://publications.waset.org/abstracts/search?q=external%20quality%20attribute" title=" external quality attribute"> external quality attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=inheritance" title=" inheritance"> inheritance</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20quality%20attribute" title=" internal quality attribute"> internal quality attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=object-oriented%20design" title=" object-oriented design"> object-oriented design</a> </p> <a href="https://publications.waset.org/abstracts/40730/applications-for-accounting-of-inherited-object-oriented-class-members" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40730.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">272</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=non-sensitive%20attribute&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=non-sensitive%20attribute&page=3">3</a></li> <li class="page-item"><a class="page-link" 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