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learning</title> <meta name="description" content="Search results for: category learning"> <meta name="keywords" content="category learning"> <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 class="d-block d-lg-none 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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="category learning"> <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> 7886</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: category learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7886</span> Role-Governed Categorization and Category Learning as a Result from Structural Alignment: The RoleMap Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yolina%20A.%20Petrova">Yolina A. Petrova</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgi%20I.%20Petkov"> Georgi I. Petkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents a symbolic model for category learning and categorization (called <em>RoleMap</em>). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in <em>RoleMap</em> while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization. <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=category%20learning" title=" category learning"> category learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20modeling" title=" cognitive modeling"> cognitive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=role-governed%20categories" title=" role-governed categories"> role-governed categories</a> </p> <a href="https://publications.waset.org/abstracts/94200/role-governed-categorization-and-category-learning-as-a-result-from-structural-alignment-the-rolemap-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94200.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">142</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">7885</span> Auditory and Visual Perceptual Category Learning in Adults with ADHD: Implications for Learning Systems and Domain-General Factors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yafit%20Gabay">Yafit Gabay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Attention deficit hyperactivity disorder (ADHD) has been associated with both suboptimal functioning in the striatum and prefrontal cortex. Such abnormalities may impede the acquisition of perceptual categories, which are important for fundamental abilities such as object recognition and speech perception. Indeed, prior research has supported this possibility, demonstrating that children with ADHD have similar visual category learning performance as their neurotypical peers but use suboptimal learning strategies. However, much less is known about category learning processes in the auditory domain or among adults with ADHD in which prefrontal functions are more mature compared to children. Here, we investigated auditory and visual perceptual category learning in adults with ADHD and neurotypical individuals. Specifically, we examined learning of rule-based categories – presumed to be optimally learned by a frontal cortex-mediated hypothesis testing – and information-integration categories – hypothesized to be optimally learned by a striatally-mediated reinforcement learning system. Consistent with striatal and prefrontal cortical impairments observed in ADHD, our results show that across sensory modalities, both rule-based and information-integration category learning is impaired in adults with ADHD. Computational modeling analyses revealed that individuals with ADHD were slower to shift to optimal strategies than neurotypicals, regardless of category type or modality. Taken together, these results suggest that both explicit, frontally mediated and implicit, striatally mediated category learning are impaired in ADHD. These results suggest impairments across multiple learning systems in young adults with ADHD that extend across sensory modalities and likely arise from domain-general mechanisms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ADHD" title="ADHD">ADHD</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20learning" title=" category learning"> category learning</a>, <a href="https://publications.waset.org/abstracts/search?q=modality" title=" modality"> modality</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20modeling" title=" computational modeling"> computational modeling</a> </p> <a href="https://publications.waset.org/abstracts/185848/auditory-and-visual-perceptual-category-learning-in-adults-with-adhd-implications-for-learning-systems-and-domain-general-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185848.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">47</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">7884</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">7883</span> Building a Dynamic News Category Network for News Sources Recommendations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swati%20Gupta">Swati Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Shagun%20Sodhani"> Shagun Sodhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhaval%20Patel"> Dhaval Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Biplab%20Banerjee"> Biplab Banerjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is generic that news sources publish news in different broad categories. These categories can either be generic such as Business, Sports, etc. or time-specific such as World Cup 2015 and Nepal Earthquake or both. It is up to the news agencies to build the categories. Extracting news categories automatically from numerous online news sources is expected to be helpful in many applications including news source recommendations and time specific news category extraction. To address this issue, existing systems like DMOZ directory and Yahoo directory are mostly considered though they are mostly human annotated and do not consider the time dynamism of categories of news websites. As a remedy, we propose an approach to automatically extract news category URLs from news websites in this paper. News category URL is a link which points to a category in news websites. We use the news category URL as a prior knowledge to develop a news source recommendation system which contains news sources listed in various categories in order of ranking. In addition, we also propose an approach to rank numerous news sources in different categories using various parameters like Traffic Based Website Importance, Social media Analysis and Category Wise Article Freshness. Experimental results on category URLs captured from GDELT project during April 2016 to December 2016 show the adequacy of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=news%20category" title="news category">news category</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20network" title=" category network"> category network</a>, <a href="https://publications.waset.org/abstracts/search?q=news%20sources" title=" news sources"> news sources</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking" title=" ranking"> ranking</a> </p> <a href="https://publications.waset.org/abstracts/74473/building-a-dynamic-news-category-network-for-news-sources-recommendations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74473.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">386</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">7882</span> SEM Image Classification Using CNN Architectures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G%C3%BCzi%CC%87n%20Ti%CC%87rke%C5%9F">Güzi̇n Ti̇rkeş</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zge%20Teki%CC%87n"> Özge Teki̇n</a>, <a href="https://publications.waset.org/abstracts/search?q=Kerem%20Kurtulu%C5%9F"> Kerem Kurtuluş</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Yekta%20Yurtseven"> Y. Yekta Yurtseven</a>, <a href="https://publications.waset.org/abstracts/search?q=Murat%20Baran"> Murat Baran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</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=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=scanning%20electron%20microscope" title=" scanning electron microscope"> scanning electron microscope</a> </p> <a href="https://publications.waset.org/abstracts/160332/sem-image-classification-using-cnn-architectures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160332.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">125</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">7881</span> Survey of Neonatologists’ Burnout on a Neonatal Surgical Unit: Audit Study from Cairo University Specialized Pediatric Hospital</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Tarek">Mahmoud Tarek</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaa%20Obeida"> Alaa Obeida</a>, <a href="https://publications.waset.org/abstracts/search?q=Mai%20Magdy"> Mai Magdy</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Hussein"> Khalid Hussein</a>, <a href="https://publications.waset.org/abstracts/search?q=Aly%20Shalaby"> Aly Shalaby</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: More doctors are complaining of burnout than before, Burnout is a state of physical and mental exhaustion caused by the doctor’s lifestyle, unfortunately, Medical errors are also more likely in those suffering from burnout and these may result in malpractice suits. Methodology: It is a retrospective audit of burnout response on all neonatologists over a 9 months period. We gathered data using burnout questionnaire, it was obtained from 23 physicians, the physicians divided into 5 categories according to the final score of the 28 questions in the questionnaire. Category 1 with score from 28-38 with almost no work stress, category 2 with score (38-50) who express a low amount of job related stress, category 3 with score (51-70) with moderate amount of stress, category 4 with score (71-90) those express a high amount of job stress and begun to burnout, category 5 with score (91 and above) who are under a dangerous amount of stress and advanced stage of burnout. Results: 33 neonatologists have received the questionnaire, 23 responses were sent back with a response rate of 69.6%. The results showed that 61% of physicians fall in category 4, 31% of the physician in category 5, while 8% of physicians equally distributed between category 2 and 3 (4% each of them). On the other hand, there is no physician present in category 1. Conclusion: Burnout is prevalent in SNICUs, So interventions to minimize burnout prevalence may be of greater importance as this may be reflected indirectly on medical conditions of the patients and physicians, efforts should be done to decrease this high rate of burnout. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cairo" title="Cairo">Cairo</a>, <a href="https://publications.waset.org/abstracts/search?q=work%20overload" title=" work overload"> work overload</a>, <a href="https://publications.waset.org/abstracts/search?q=exhaustion" title=" exhaustion"> exhaustion</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a>, <a href="https://publications.waset.org/abstracts/search?q=neonatal%20ICU" title=" neonatal ICU"> neonatal ICU</a> </p> <a href="https://publications.waset.org/abstracts/93428/survey-of-neonatologists-burnout-on-a-neonatal-surgical-unit-audit-study-from-cairo-university-specialized-pediatric-hospital" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93428.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">213</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">7880</span> A Stylistic Analysis of the Short Story ‘The Escape’ by Qaisra Shahraz</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huma%20Javed">Huma Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stylistics is a broad term that is concerned with both literature and linguistics, due to which the significance of the stylistics increases. This research aims to analyze Qaisra Shahraz's short story ‘The Escape’ from the stylistic analysis viewpoint. The focus of this study is on three aspects grammar category, lexical category, and figure of speech of the short story. The research designs for this article are both explorative and descriptive. The analysis of the data shows that the writer has used more nouns in the story as compared to other lexical items, which suggests that story has a descriptive style rather than narrative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=The%20Escape" title="The Escape">The Escape</a>, <a href="https://publications.waset.org/abstracts/search?q=stylistics" title=" stylistics"> stylistics</a>, <a href="https://publications.waset.org/abstracts/search?q=grammatical%20category" title=" grammatical category"> grammatical category</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical%20category" title=" lexical category"> lexical category</a>, <a href="https://publications.waset.org/abstracts/search?q=figure%20of%20speech" title=" figure of speech"> figure of speech</a> </p> <a href="https://publications.waset.org/abstracts/138820/a-stylistic-analysis-of-the-short-story-the-escape-by-qaisra-shahraz" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138820.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">237</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">7879</span> Advertising Message Strategy on Ghana’s TV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisha%20Iddrisu">Aisha Iddrisu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ferruh%20Uztu%C4%9F"> Ferruh Uztuğ</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is a quantitative content analysis of advertising message strategies used in Ghana’s TV commercials (2020-2021) using the modified strategy of Wang and Praet (2016) with the objective of exploring the various advertising message strategies used in Ghana’s TV advertisement, its variation according to product category including the most widely used message strategy. The findings indicate that, out of the 220 commercials used in the study, the Affective message strategy (n=122, 55%) was the dominant message strategy used in Ghana’s TV commercials. The most advertised product category in Ghana’s TV commercials (2020-2021) was the food category, and a significant relationship was observed between message strategy and product category as well as message strategy and brand type. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advertising" title="advertising">advertising</a>, <a href="https://publications.waset.org/abstracts/search?q=message%20strategy" title=" message strategy"> message strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghana" title=" Ghana"> Ghana</a>, <a href="https://publications.waset.org/abstracts/search?q=television" title=" television"> television</a> </p> <a href="https://publications.waset.org/abstracts/170306/advertising-message-strategy-on-ghanas-tv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170306.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">184</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">7878</span> Evaluation Metrics for Machine Learning Techniques: A Comprehensive Review and Comparative Analysis of Performance Measurement Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed-Ali%20Sadegh-Zadeh">Seyed-Ali Sadegh-Zadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaveh%20Kavianpour"> Kaveh Kavianpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Atashbar"> Hamed Atashbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Heidari"> Elham Heidari</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Shiry%20Ghidary"> Saeed Shiry Ghidary</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20M.%20Hajiyavand"> Amir M. Hajiyavand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evaluation%20metrics" title="evaluation metrics">evaluation metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20measurement" title=" performance measurement"> performance measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20robustness%20and%20stability" title=" model robustness and stability"> model robustness and stability</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title=" comparative analysis"> comparative analysis</a> </p> <a href="https://publications.waset.org/abstracts/184552/evaluation-metrics-for-machine-learning-techniques-a-comprehensive-review-and-comparative-analysis-of-performance-measurement-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184552.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">74</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">7877</span> A Targeted Maximum Likelihood Estimation for a Non-Binary Causal Variable: An Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Raouf%20Benmakrelouf">Mohamed Raouf Benmakrelouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Rynkiewicz"> Joseph Rynkiewicz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Targeted maximum likelihood estimation (TMLE) is well-established method for causal effect estimation with desirable statistical properties. TMLE is a doubly robust maximum likelihood based approach that includes a secondary targeting step that optimizes the target statistical parameter. A causal interpretation of the statistical parameter requires assumptions of the Rubin causal framework. The causal effect of binary variable, E, on outcomes, Y, is defined in terms of comparisons between two potential outcomes as E[YE=1 − YE=0]. Our aim in this paper is to present an adaptation of TMLE methodology to estimate the causal effect of a non-binary categorical variable, providing a large application. We propose coding on the initial data in order to operate a binarization of the interest variable. For each category, we get a transformation of the non-binary interest variable into a binary variable, taking value 1 to indicate the presence of category (or group of categories) for an individual, 0 otherwise. Such a dummy variable makes it possible to have a pair of potential outcomes and oppose a category (or a group of categories) to another category (or a group of categories). Let E be a non-binary interest variable. We propose a complete disjunctive coding of our variable E. We transform the initial variable to obtain a set of binary vectors (dummy variables), E = (Ee : e ∈ {1, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when its category is not present, and the value of 1 when its category is present, which allows to compute a pairwise-TMLE comparing difference in the outcome between one category and all remaining categories. In order to illustrate the application of our strategy, first, we present the implementation of TMLE to estimate the causal effect of non-binary variable on outcome using simulated data. Secondly, we apply our TMLE adaptation to survey data from the French Political Barometer (CEVIPOF), to estimate the causal effect of education level (A five-level variable) on a potential vote in favor of the French extreme right candidate Jean-Marie Le Pen. Counterfactual reasoning requires us to consider some causal questions (additional causal assumptions). Leading to different coding of E, as a set of binary vectors, E = (Ee : e ∈ {2, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when the first category (reference category) is present, and the value of 1 when its category is present, which allows to apply a pairwise-TMLE comparing difference in the outcome between the first level (fixed) and each remaining level. We confirmed that the increase in the level of education decreases the voting rate for the extreme right party. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20inference" title="statistical inference">statistical inference</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a>, <a href="https://publications.waset.org/abstracts/search?q=super%20learning" title=" super learning"> super learning</a>, <a href="https://publications.waset.org/abstracts/search?q=targeted%20maximum%20likelihood%20estimation" title=" targeted maximum likelihood estimation"> targeted maximum likelihood estimation</a> </p> <a href="https://publications.waset.org/abstracts/147591/a-targeted-maximum-likelihood-estimation-for-a-non-binary-causal-variable-an-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147591.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">103</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">7876</span> Sparse Coding Based Classification of Electrocardiography Signals Using Data-Driven Complete Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20Noman">Fuad Noman</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh-Hussain%20Salleh"> Sh-Hussain Salleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadri%20Hussain"> Hadri Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Rasul"> Syed Rasul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20coding" title=" sparse coding"> sparse coding</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/52418/sparse-coding-based-classification-of-electrocardiography-signals-using-data-driven-complete-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52418.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">386</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">7875</span> Lexical-Semantic Processing by Chinese as a Second Language Learners</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Hsiu%20Lai">Yi-Hsiu Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lexical-semantic%20processing" title="lexical-semantic processing">lexical-semantic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandarin%20Chinese" title=" Mandarin Chinese"> Mandarin Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=naming" title=" naming"> naming</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20effects" title=" category effects "> category effects </a> </p> <a href="https://publications.waset.org/abstracts/43848/lexical-semantic-processing-by-chinese-as-a-second-language-learners" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43848.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">462</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">7874</span> A Study on Bilingual Semantic Processing: Category Effects and Age Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lai%20Yi-Hsiu">Lai Yi-Hsiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study addressed the nature of bilingual semantic processing in Mandarin Chinese and Southern Min and examined category effects and age effects. Nineteen bilingual adults of Mandarin Chinese and Southern Min, nine monolingual seniors of Mandarin Chinese, and ten monolingual seniors of Southern Min in Taiwan individually completed two semantic tasks: Picture naming and category fluency tasks. The instruments for the naming task were sixty black-and-white pictures, including thirty-five object pictures and twenty-five action pictures. The category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). The reaction time for each picture/question was additionally calculated and analyzed. Oral productions in Mandarin Chinese and in Southern Min were compared and discussed to examine the category effects and age effects. The results of the category fluency task indicated that the content of information of these seniors was comparatively deteriorated, and thus they produced a smaller number of semantic-lexical items. Significant group differences were also found in the reaction time results. Category effects were significant for both adults and seniors in the semantic fluency task. The findings of the present study will help characterize the nature of the bilingual semantic processing of adults and seniors, and contribute to the fields of contrastive and corpus linguistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bilingual%20semantic%20processing" title="bilingual semantic processing">bilingual semantic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=aging" title=" aging"> aging</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandarin%20Chinese" title=" Mandarin Chinese"> Mandarin Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=Southern%20Min" title=" Southern Min"> Southern Min</a> </p> <a href="https://publications.waset.org/abstracts/43219/a-study-on-bilingual-semantic-processing-category-effects-and-age-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43219.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">571</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">7873</span> Lifelong Learning in Applied Fields (LLAF) Tempus Funded Project: Assessing Constructivist Learning Features in Higher Education Settings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dorit%20Alt">Dorit Alt</a>, <a href="https://publications.waset.org/abstracts/search?q=Nirit%20Raichel"> Nirit Raichel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Educational practice is continually subjected to renewal needs, due mainly to the growing proportion of information communication technology, globalization of education, and the pursuit of quality. These types of renewal needs require developing updated instructional and assessment practices that put a premium on adaptability to the emerging requirements of present society. However, university instruction is criticized for not coping with these new challenges while continuing to exemplify the traditional instruction. In order to overcome this critical inadequacy between current educational goals and instructional methods, the LLAF consortium (including 16 members from 8 countries) is collaborating to create a curricular reform for lifelong learning (LLL) in teachers' education, health care and other applied fields. This project aims to achieve its objectives by developing, and piloting models for training students in LLL and promoting meaningful learning activities that could integrate knowledge with the personal transferable skills. LLAF has created a practical guide for teachers containing updated pedagogical strategies and assessment tools based on the constructivist approach for learning. This presentation will be limited to teachers' education only and to the contribution of a pre-pilot research aimed at providing a scale designed to measure constructivist activities in higher education learning environments. A mix-method approach was implemented in two phases to construct the scale: The first phase included a qualitative content analysis involving both deductive and inductive category applications of students' observations. The results foregrounded eight categories: knowledge construction, authenticity, multiple perspectives, prior knowledge, in-depth learning, teacher- student interaction, social interaction and cooperative dialogue. The students' descriptions of their classes were formulated as 36 items. The second phase employed structural equation modeling (SEM). The scale was submitted to 597 undergraduate students. The goodness of fit of the data to the structural model yielded sufficient fit results. This research elaborates the body of literature by adding a category of in-depth learning which emerged from the content analysis. Moreover, the theoretical category of social activity has been extended to include two distinctive factors: cooperative dialogue and social interaction. Implications of these findings for the LLAF project are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constructivist%20learning" title="constructivist learning">constructivist learning</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=mix-methodology" title=" mix-methodology"> mix-methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=lifelong%20learning" title=" lifelong learning"> lifelong learning</a> </p> <a href="https://publications.waset.org/abstracts/22465/lifelong-learning-in-applied-fields-llaf-tempus-funded-project-assessing-constructivist-learning-features-in-higher-education-settings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22465.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">334</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">7872</span> Debussy's Piano Music: Style Characteristics in Three Categories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rika%20Uchida">Rika Uchida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Claude Debussy's piano works can be divided into three categories in terms of style characteristics. The first category includes works which are strongly impressionistic, evoking a mood or an atmosphere, rather than making a direct, clear statement. These works often depict nature, and they are descriptive and sensitive in their character. Harmonic vocabulary is often complex, and the sense of tonality is often ambiguous in those works. Examples which belong to this category are ‘Clair de lune’ from Suite Bergamasque, Deux Arabesques, and ‘Reflets dans l'eau’ from Images Book 2. The second category shows little or no trace of impressionism. Works are not descriptive; rather, they are classical or absolute. Examples which belong to this category are Pour le Piano, ‘Hommage à Rameau’ and ‘Movement’ from Images Book 1 and Etudes. The third category can be called exotic. Debussy had a great interest in foreign lands such as the Far and Near East, and Spain. He employs pentatonic and quartal harmonies to describe the Orient, occasionally using the effect of the Javanese gamelan, which impressed him at the Paris Exhibition. His compositions in the Spanish style evoke the atmosphere of Spain. Though he borrowed some techniques from Spanish composers whom he knew, the tonal experimentation which occurs in these works sets them apart. Examples which belong to this category are ‘Pagodes’ and ‘la Soiree dans Grenade’ from Estampes, ‘la Puerta del Vino’ from Preludes Book 2. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music" title="music">music</a>, <a href="https://publications.waset.org/abstracts/search?q=piano" title=" piano"> piano</a>, <a href="https://publications.waset.org/abstracts/search?q=Debussy" title=" Debussy"> Debussy</a>, <a href="https://publications.waset.org/abstracts/search?q=style" title=" style"> style</a> </p> <a href="https://publications.waset.org/abstracts/133673/debussys-piano-music-style-characteristics-in-three-categories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133673.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">156</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">7871</span> A New Measurement for Assessing Constructivist Learning Features in Higher Education: Lifelong Learning in Applied Fields (LLAF) Tempus Project</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dorit%20Alt">Dorit Alt</a>, <a href="https://publications.waset.org/abstracts/search?q=Nirit%20Raichel"> Nirit Raichel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although university teaching is claimed to have a special task to support students in adopting ways of thinking and producing new knowledge anchored in scientific inquiry practices, it is argued that students' habits of learning are still overwhelmingly skewed toward passive acquisition of knowledge from authority sources rather than from collaborative inquiry activities.This form of instruction is criticized for encouraging students to acquire inert knowledge that can be used in instructional settings at best, however cannot be transferred into real-life complex problem settings. In order to overcome this critical inadequacy between current educational goals and instructional methods, the LLAF consortium (including 16 members from 8 countries) is aimed at developing updated instructional practices that put a premium on adaptability to the emerging requirements of present society. LLAF has created a practical guide for teachers containing updated pedagogical strategies and assessment tools, based on the constructivist approach for learning that put a premium on adaptability to the emerging requirements of present society. This presentation will be limited to teachers' education only and to the contribution of the project in providing a scale designed to measure the extent to which the constructivist activities are efficiently applied in the learning environment. A mix-method approach was implemented in two phases to construct the scale: The first phase included a qualitative content analysis involving both deductive and inductive category applications of students' observations. The results foregrounded eight categories: knowledge construction, authenticity, multiple perspectives, prior knowledge, in-depth learning, teacher- student interaction, social interaction and cooperative dialogue. The students' descriptions of their classes were formulated as 36 items. The second phase employed structural equation modeling (SEM). The scale was submitted to 597 undergraduate students. The goodness of fit of the data to the structural model yielded sufficient fit results. This research elaborates the body of literature by adding a category of in-depth learning which emerged from the content analysis. Moreover, the theoretical category of social activity has been extended to include two distinctive factors: cooperative dialogue and social interaction. Implications of these findings for the LLAF project are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constructivist%20learning" title="constructivist learning">constructivist learning</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=mix-methodology" title=" mix-methodology"> mix-methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modeling" title=" structural equation modeling "> structural equation modeling </a> </p> <a href="https://publications.waset.org/abstracts/22467/a-new-measurement-for-assessing-constructivist-learning-features-in-higher-education-lifelong-learning-in-applied-fields-llaf-tempus-project" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22467.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">315</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">7870</span> A Methodology for Automatic Diversification of Document Categories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dasom%20Kim">Dasom Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Liu"> Chen Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Myungsu%20Lim"> Myungsu Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Su-Hyeon%20Jeon"> Su-Hyeon Jeon</a>, <a href="https://publications.waset.org/abstracts/search?q=ByeoungKug%20Jeon"> ByeoungKug Jeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Kee-Young%20Kwahk"> Kee-Young Kwahk</a>, <a href="https://publications.waset.org/abstracts/search?q=Namgyu%20Kim"> Namgyu Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we previously proposed a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. In this paper, we design a survey-based verification scenario for estimating the accuracy of our automatic categorization methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analysis" title="big data analysis">big data analysis</a>, <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=multi-category" title=" multi-category"> multi-category</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20analysis" title=" topic analysis"> topic analysis</a> </p> <a href="https://publications.waset.org/abstracts/36754/a-methodology-for-automatic-diversification-of-document-categories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36754.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7869</span> Semantic Processing in Chinese: Category Effects, Task Effects and Age Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Hsiu%20Lai">Yi-Hsiu Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed to elucidate the nature of semantic processing in Chinese. Language and cognition related to the issue of aging are examined from the perspective of picture naming and category fluency tasks. Twenty Chinese-speaking adults (ranging from 25 to 45 years old) and twenty Chinese-speaking seniors (ranging from 65 to 75 years old) in Taiwan participated in this study. Each of them individually completed two tasks: a picture naming task and a category fluency task. Instruments for the naming task were sixty black-and-white pictures: thirty-five object and twenty-five action pictures. Category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). Participants were asked to report as many items within a category as possible in one minute. Scores of action fluency and of object fluency were a summation of correct responses in these two categories. Category effects (actions vs. objects) and age effects were examined in these tasks. Objects were further divided into two major types: living objects and non-living objects. Actions were also categorized into two major types: action verbs and process verbs. Reaction time to each picture/question was additionally calculated and analyzed. Results of the category fluency task indicated that the content of information in Chinese seniors was comparatively deteriorated, thus producing smaller number of semantic-lexical items. Significant group difference was also found in the results of reaction time. Category Effect was significant for both Chinese adults and seniors in the semantic fluency task. Findings in the present study helped characterize the nature of semantic processing in Chinese-speaking adults and seniors and contributed to the issue of language and aging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20processing" title="semantic processing">semantic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=aging" title=" aging"> aging</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinese" title=" Chinese"> Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20effects" title=" category effects"> category effects</a> </p> <a href="https://publications.waset.org/abstracts/43216/semantic-processing-in-chinese-category-effects-task-effects-and-age-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43216.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">361</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">7868</span> Enhancing Sell-In and Sell-Out Forecasting Using Ensemble Machine Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishal%20Das">Vishal Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyi%20Mao"> Tianyi Mao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhicheng%20Geng"> Zhicheng Geng</a>, <a href="https://publications.waset.org/abstracts/search?q=Carmen%20Flores"> Carmen Flores</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Pelloso"> Diego Pelloso</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Wang"> Fang Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sell-in%20and%20sell-out%20forecasting" title="sell-in and sell-out forecasting">sell-in and sell-out forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20planning" title=" demand planning"> demand planning</a>, <a href="https://publications.waset.org/abstracts/search?q=DeepAR" title=" DeepAR"> DeepAR</a>, <a href="https://publications.waset.org/abstracts/search?q=retail" title=" retail"> retail</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20machine%20learning" title=" ensemble machine learning"> ensemble machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series" title=" time-series"> time-series</a> </p> <a href="https://publications.waset.org/abstracts/169497/enhancing-sell-in-and-sell-out-forecasting-using-ensemble-machine-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169497.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">274</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">7867</span> Literature Review: Adversarial Machine Learning Defense in Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leidy%20M.%20Aldana">Leidy M. Aldana</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20E.%20Camargo"> Jorge E. Camargo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malware" title="Malware">Malware</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial" title=" adversarial"> adversarial</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=defense" title=" defense"> defense</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a> </p> <a href="https://publications.waset.org/abstracts/177946/literature-review-adversarial-machine-learning-defense-in-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177946.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">63</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">7866</span> A Review of Machine Learning for Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aravindraj%20D."> Aravindraj D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadathulla%20A."> Sadathulla A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <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=deep%20learning" title=" deep learning"> deep learning</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/72161/a-review-of-machine-learning-for-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72161.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">446</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">7865</span> Analyzing the Association between Physical Activity and Sleep Quality in College Students: Cross-Sectional Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fildzah%20Badzlina">Fildzah Badzlina</a>, <a href="https://publications.waset.org/abstracts/search?q=Mega%20Puspa%20Sari"> Mega Puspa Sari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To rest the body after a full day of activities, the body needs sleep. During sleep, the body's response to external stimuli will be reduced and relatively inactive so that it is used to optimize the body's biological functions that cannot be done when awake. College students often experience poor sleep quality because of the dense activities carried out during the day. In addition, the level of physical activity of college students is also relatively low. Based on previous research, college students who have low physical activity have poor sleep quality. Therefore, the purpose of this study was to determine the relationship between physical activity and sleep quality in college students of the University of Muhammadiyah Prof. Dr. Hamka. This study used a cross-sectional research design with 107 respondents as research subjects. Samples were taken using the purposive sampling technique. The data was taken using a google form which was distributed to all college students in September 2021. The statistical test used was Chi-square. The results of this study showed that 85 (79.4%) college students experienced poor sleep quality during the Covid-19 Pandemic Period. Most respondents were 96 women (89.7%) and 32.7% (35 people) aged 20 years. In the pocket money category, most college students (71%) got pocket money less than 500.000 rupiahs per month. A total of 52 respondents (48.6%) had a moderate level of physical activity category. Poor sleep quality was more common in male students (90.9%) compared to female students (78.1%) (p>0.05). In the group with poor sleep quality, 88.9% of students were categorized in Rp. 500.001 to Rp. 1.000.000 for pocket money, 80.3% of students included in the category Rp. 500.000 or less, and 61.5% of students are included in the category of Rp. 1.000.000 or more. Poor sleep quality was more common among students in the age category 20 years (84.1%), compared to students in the age category > 20 years (71.1%). For the level of physical activity in the poor sleep quality group, 87% were included in the category of heavy physical activity, 82.7% included in the moderate level of physical activity, and 68.8% included in the category of low-level physical activity. There was no significant relationship between gender, pocket money, age, and physical activity with sleep quality (p>0.05). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=college%20students" title="college students">college students</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20activity" title=" physical activity"> physical activity</a>, <a href="https://publications.waset.org/abstracts/search?q=sleep%20quality" title=" sleep quality"> sleep quality</a>, <a href="https://publications.waset.org/abstracts/search?q=university%20students" title=" university students"> university students</a> </p> <a href="https://publications.waset.org/abstracts/144468/analyzing-the-association-between-physical-activity-and-sleep-quality-in-college-students-cross-sectional-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144468.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">140</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">7864</span> Russian Spatial Impersonal Sentence Models in Translation Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marina%20Fomina">Marina Fomina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper focuses on the category of semantic subject within the framework of a functional approach to linguistics. The semantic subject is related to similar notions such as the grammatical subject and the bearer of predicative feature. It is the multifaceted nature of the category of subject that 1) triggers a number of issues that, syntax-wise, remain to be dealt with (cf. semantic vs. syntactic functions / sentence parts vs. parts of speech issues, etc.); 2) results in a variety of approaches to the category of subject, such as formal grammatical, semantic/syntactic (functional), communicative approaches, etc. Many linguists consider the prototypical approach to the category of subject to be the most instrumental as it reveals the integrity of denotative and linguistic components of the conceptual category. This approach relates to subject as a source of non-passive predicative feature, an element of subject-predicate-object situation that can take on a variety of semantic roles, cf.: 1) an agent (He carefully surveyed the valley stretching before him), 2) an experiencer (I feel very bitter about this), 3) a recipient (I received this book as a gift), 4) a causee (The plane broke into three pieces), 5) a patient (This stove cleans easily), etc. It is believed that the variety of roles stems from the radial (prototypical) structure of the category with some members more central than others. Translation-wise, the most “treacherous” subject types are the peripheral ones. The paper 1) features a peripheral status of spatial impersonal sentence models such as U menia v ukhe zvenit (lit. I-Gen. in ear buzzes) within the category of semantic subject, 2) makes a structural and semantic analysis of the models, 3) focuses on their Russian-English translation patterns, 4) reveals non-prototypical features of subjects in the English equivalents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearer%20of%20predicative%20feature" title="bearer of predicative feature">bearer of predicative feature</a>, <a href="https://publications.waset.org/abstracts/search?q=grammatical%20subject" title=" grammatical subject"> grammatical subject</a>, <a href="https://publications.waset.org/abstracts/search?q=impersonal%20sentence%20model" title=" impersonal sentence model"> impersonal sentence model</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20subject" title=" semantic subject"> semantic subject</a> </p> <a href="https://publications.waset.org/abstracts/42894/russian-spatial-impersonal-sentence-models-in-translation-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42894.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">370</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">7863</span> Biomedical Countermeasures to Category a Biological Agents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laura%20Cochrane">Laura Cochrane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The United States Centers for Disease Control and Prevention has established three categories of biological agents based on their ease of spread and the severity of the disease they cause. Category A biological agents are the highest priority because of their high degree of morbidity and mortality, ease of dissemination, the potential to cause social disruption and panic, special requirements for public health preparedness, and past use as a biological weapon. Despite the threat of Category A biological agents, opportunities for medical intervention exist. This work summarizes public information, consolidated and reviewed across the situational usefulness and disease awareness to offer discussion to three specific Category A agents: anthrax (Bacillus anthracis), botulism (Clostridium botulinum toxin), and smallpox (variola major), and provides an overview on the management of medical countermeasures available to treat these three (3) different types of pathogens. The medical countermeasures are discussed in the setting of pre-exposure prophylaxis, post-exposure prophylaxis, and therapeutic treatments to provide a framework for requirements in public health preparedness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anthrax" title="anthrax">anthrax</a>, <a href="https://publications.waset.org/abstracts/search?q=botulism" title=" botulism"> botulism</a>, <a href="https://publications.waset.org/abstracts/search?q=smallpox" title=" smallpox"> smallpox</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20countermeasures" title=" medical countermeasures"> medical countermeasures</a> </p> <a href="https://publications.waset.org/abstracts/146987/biomedical-countermeasures-to-category-a-biological-agents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146987.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">76</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">7862</span> Leveraging Learning Analytics to Inform Learning Design in Higher Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mingming%20Jiang">Mingming Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This literature review aims to offer an overview of existing research on learning analytics and learning design, the alignment between the two, and how learning analytics has been leveraged to inform learning design in higher education. Current research suggests a need to create more alignment and integration between learning analytics and learning design in order to not only ground learning analytics on learning sciences but also enable data-driven decisions in learning design to improve learning outcomes. In addition, multiple conceptual frameworks have been proposed to enhance the synergy and alignment between learning analytics and learning design. Future research should explore this synergy further in the unique context of higher education, identifying learning analytics metrics in higher education that can offer insight into learning processes, evaluating the effect of learning analytics outcomes on learning design decision-making in higher education, and designing learning environments in higher education that make the capturing and deployment of learning analytics outcomes more efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title="learning analytics">learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20design" title=" learning design"> learning design</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20higher%20education" title=" big data in higher education"> big data in higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning%20environments" title=" online learning environments"> online learning environments</a> </p> <a href="https://publications.waset.org/abstracts/149822/leveraging-learning-analytics-to-inform-learning-design-in-higher-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149822.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">172</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">7861</span> Stackelberg Security Game for Optimizing Security of Federated Internet of Things Platform Instances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Violeta%20Damjanovic-Behrendt">Violeta Damjanovic-Behrendt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=security" title="security">security</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title=" internet of things"> internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=stackelberg%20game" title=" stackelberg game"> stackelberg game</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=naive%20q-learning" title=" naive q-learning"> naive q-learning</a> </p> <a href="https://publications.waset.org/abstracts/64390/stackelberg-security-game-for-optimizing-security-of-federated-internet-of-things-platform-instances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64390.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">354</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">7860</span> Identification of Spam Keywords Using Hierarchical Category in C2C E-Commerce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao%20Bo%20Cheng">Shao Bo Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Jin%20Han"> Yong-Jin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Young%20Park"> Se Young Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Seong-Bae%20Park"> Seong-Bae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spam%20keyword" title="spam keyword">spam keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20features" title=" keyword features"> keyword features</a>, <a href="https://publications.waset.org/abstracts/search?q=spam%20%EF%AC%81ltering" title=" spam ﬁltering"> spam ﬁltering</a> </p> <a href="https://publications.waset.org/abstracts/15501/identification-of-spam-keywords-using-hierarchical-category-in-c2c-e-commerce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15501.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">294</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">7859</span> Shifted Window Based Self-Attention via Swin Transformer for Zero-Shot Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasaswi%20Palagummi">Yasaswi Palagummi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sareh%20Rowlands"> Sareh Rowlands</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalised" title="generalised">generalised</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-shot%20learning" title=" zero-shot learning"> zero-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=inductive%20learning" title=" inductive learning"> inductive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=shifted-window%20attention" title=" shifted-window attention"> shifted-window attention</a>, <a href="https://publications.waset.org/abstracts/search?q=Swin%20transformer" title=" Swin transformer"> Swin transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20transformer" title=" vision transformer"> vision transformer</a> </p> <a href="https://publications.waset.org/abstracts/155517/shifted-window-based-self-attention-via-swin-transformer-for-zero-shot-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155517.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">71</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">7858</span> OSEME: A Smart Learning Environment for Music Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Sofianos">Konstantinos Sofianos</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Stefanidakis"> Michael Stefanidakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, advances in information and communication technologies offer a range of opportunities for new approaches, methods, and tools in the field of education and training. Teacher-centered learning has changed to student-centered learning. E-learning has now matured and enables the design and construction of intelligent learning systems. A smart learning system fully adapts to a student's needs and provides them with an education based on their preferences, learning styles, and learning backgrounds. It is a wise friend and available at any time, in any place, and with any digital device. In this paper, we propose an intelligent learning system, which includes an ontology with all elements of the learning process (learning objects, learning activities) and a massive open online course (MOOC) system. This intelligent learning system can be used in music education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20learning%20systems" title="intelligent learning systems">intelligent learning systems</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20education" title=" music education"> music education</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a> </p> <a href="https://publications.waset.org/abstracts/168933/oseme-a-smart-learning-environment-for-music-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168933.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">312</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">7857</span> An Embarrassingly Simple Semi-supervised Approach to Increase Recall in Online Shopping Domain to Match Structured Data with Unstructured Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sachin%20Nagargoje">Sachin Nagargoje</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Complete labeled data is often difficult to obtain in a practical scenario. Even if one manages to obtain the data, the quality of the data is always in question. In shopping vertical, offers are the input data, which is given by advertiser with or without a good quality of information. In this paper, an author investigated the possibility of using a very simple Semi-supervised learning approach to increase the recall of unhealthy offers (has badly written Offer Title or partial product details) in shopping vertical domain. The author found that the semisupervised learning method had improved the recall in the Smart Phone category by 30% on A=B testing on 10% traffic and increased the YoY (Year over Year) number of impressions per month by 33% at production. This also made a significant increase in Revenue, but that cannot be publicly disclosed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20learning" title="semi-supervised learning">semi-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=recall" title=" recall"> recall</a>, <a href="https://publications.waset.org/abstracts/search?q=coverage" title=" coverage"> coverage</a> </p> <a href="https://publications.waset.org/abstracts/125267/an-embarrassingly-simple-semi-supervised-approach-to-increase-recall-in-online-shopping-domain-to-match-structured-data-with-unstructured-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125267.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 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