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learning</title> <meta name="description" content="Search results for: dynamic learning"> <meta name="keywords" content="dynamic 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="dynamic 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> 10858</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: dynamic learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10858</span> An Online Mastery Learning Method Based on a Dynamic Formative Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeongim%20Kang">Jeongim Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Moon%20Hee%20Kim"> Moon Hee Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Seong%20Baeg%20Kim"> Seong Baeg Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a novel e-learning model that is based on a dynamic formative evaluation. On evaluating the existing format of e-learning, conditions regarding repetitive learning to achieve mastery, causes issues for learners to lose tension and become neglectful of learning. The dynamic formative evaluation proposed is able to supplement limitation of the existing approaches. Since a repetitive learning method does not provide a perfect feedback, this paper puts an emphasis on the dynamic formative evaluation that is able to maximize learning achievement. Through the dynamic formative evaluation, the instructor is able to refer to the evaluation result when making estimation about the learner. To show the flow chart of learning, based on the dynamic formative evaluation, the model proves its effectiveness and validity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title="online learning">online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20formative%20evaluation" title=" dynamic formative evaluation"> dynamic formative evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=mastery%20learning" title=" mastery learning"> mastery learning</a>, <a href="https://publications.waset.org/abstracts/search?q=repetitive%20learning%20method" title=" repetitive learning method"> repetitive learning method</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20achievement" title=" learning achievement"> learning achievement</a> </p> <a href="https://publications.waset.org/abstracts/2483/an-online-mastery-learning-method-based-on-a-dynamic-formative-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2483.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">511</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">10857</span> Learning for the Future: Flipping English Language Learning Classrooms for Future </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natarajan%20Hema">Natarajan Hema</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamilarasan%20Karunakaran"> Tamilarasan Karunakaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technology is remodeling the process of teaching and learning. An inflection point is faced where technological interventions are rewiring learning process in formal classrooms. Employment depends on dynamic learning capability. Transforming the functionalities of teaching-learning-assessment through innovation is needed to modify the roles of teacher to enabler and learner to the dynamic learner. This makeover is vital for English language teaching where English is acquired as a skill, exercised as ability and get stabilized as a competence. This reshaping could be achieved through providing autonomy to participants of learning. This paper explores parameters and components aiding such a transformation. The differentiated responsibilities and other critical learning support systems are projected as viable options. New age teaching practices are studied for feasibilities to aid transformation and being put forth an inter-operable teaching-learning system for a learner-centric ELT classrooms. LOTUS model developed by the authors is also studied for its inclusiveness to promote skill acquisition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ELT%20methodology" title="ELT methodology">ELT methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=communicative%20competence" title=" communicative competence"> communicative competence</a>, <a href="https://publications.waset.org/abstracts/search?q=skill%20acquisition" title=" skill acquisition "> skill acquisition </a>, <a href="https://publications.waset.org/abstracts/search?q=new%20age%20teaching" title=" new age teaching"> new age teaching</a> </p> <a href="https://publications.waset.org/abstracts/88120/learning-for-the-future-flipping-english-language-learning-classrooms-for-future" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88120.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">358</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">10856</span> Movies and Dynamic Mathematical Objects on Trigonometry for Mobile Phones</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuhisa%20Takagi">Kazuhisa Takagi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is about movies and dynamic objects for mobile phones. Dynamic objects are the software programmed by JavaScript. They consist of geometric figures and work on HTML5-compliant browsers. Mobile phones are very popular among teenagers. They like watching movies and playing games on them. So, mathematics movies and dynamic objects would enhance teaching and learning processes. In the movies, manga characters speak with artificially synchronized voices. They teach trigonometry together with dynamic mathematical objects. Many movies are created. They are Windows Media files or MP4 movies. These movies and dynamic objects are not only used in the classroom but also distributed to students. By watching movies, students can study trigonometry before or after class. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20mathematical%20object" title="dynamic mathematical object">dynamic mathematical object</a>, <a href="https://publications.waset.org/abstracts/search?q=javascript" title=" javascript"> javascript</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20drive" title=" google drive"> google drive</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20jet" title=" transfer jet"> transfer jet</a> </p> <a href="https://publications.waset.org/abstracts/67497/movies-and-dynamic-mathematical-objects-on-trigonometry-for-mobile-phones" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67497.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">260</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">10855</span> The Relationship between Organization Culture and Organization Learning in Three Different Types of Companies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Timar">Mahmoud Timar</a>, <a href="https://publications.waset.org/abstracts/search?q=Javad%20Joukar%20Borazjani"> Javad Joukar Borazjani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A dynamic organization helps the management to overcome both internal and external uncertainties and complexities of the organization with more confidence and efficiency. Regarding this issue, in this paper, the influence of organizational culture factors over organizational learning components, which both of them are considered as important characteristics of a dynamic organization, has been studied in three subsidiary companies (production, consultation and service) of National Iranian Oil Company, and moreover we also tried to identify the most dominant culture in these three subsidiaries. Analysis of 840 received questionnaires by SPSS shows that there is a significant relationship between the components of organizational culture and organizational learning; however the rate of relationship between these two factors was different among the examined companies. By the use of Regression, it has been clarified that in the servicing company the highest relationship is between mission and learning environment, while in production division, there is a significant relationship between adaptability and learning needs satisfaction and however in consulting company the highest relationship is between involvement and applying learning in workplace. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denison%20model" title="denison model">denison model</a>, <a href="https://publications.waset.org/abstracts/search?q=culture" title=" culture"> culture</a>, <a href="https://publications.waset.org/abstracts/search?q=leaning" title=" leaning"> leaning</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20culture" title=" organizational culture"> organizational culture</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20learning" title=" organizational learning"> organizational learning</a> </p> <a href="https://publications.waset.org/abstracts/34142/the-relationship-between-organization-culture-and-organization-learning-in-three-different-types-of-companies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34142.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">376</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">10854</span> The Relevance of Smart Technologies in Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rachael%20Olubukola%20Afolabi">Rachael Olubukola Afolabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Immersive technologies known as X Reality or Cross Reality that include virtual reality augmented reality, and mixed reality have pervaded into the education system at different levels from elementary school to adult learning. Instructors, instructional designers, and learning experience specialists continue to find new ways to engage students in the learning process using technology. While the progression of web technologies has enhanced digital learning experiences, analytics on learning outcomes continue to be explored to determine the relevance of these technologies in learning. Digital learning has evolved from web 1.0 (static) to 4.0 (dynamic and interactive), and this evolution of technologies has also advanced teaching methods and approaches. This paper explores how these technologies are being utilized in learning and the results that educators and learners have identified as effective learning opportunities and approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=immersive%20technologoes" title="immersive technologoes">immersive technologoes</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality" title=" virtual reality"> virtual reality</a>, <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality" title=" augmented reality"> augmented reality</a>, <a href="https://publications.waset.org/abstracts/search?q=technology%20in%20learning" title=" technology in learning"> technology in learning</a> </p> <a href="https://publications.waset.org/abstracts/146219/the-relevance-of-smart-technologies-in-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146219.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">145</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">10853</span> A Framework of Dynamic Rule Selection Method for Dynamic Flexible Job Shop Problem by Reinforcement Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Wu">Rui Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20scheduling%20problem" title="dynamic scheduling problem">dynamic scheduling problem</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop" title=" flexible job shop"> flexible job shop</a>, <a href="https://publications.waset.org/abstracts/search?q=dispatching%20rules" title=" dispatching rules"> dispatching rules</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a> </p> <a href="https://publications.waset.org/abstracts/159322/a-framework-of-dynamic-rule-selection-method-for-dynamic-flexible-job-shop-problem-by-reinforcement-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159322.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">108</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">10852</span> Dynamic Measurement System Modeling with Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Changqiao%20Wu">Changqiao Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Guoqing%20Ding"> Guoqing Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Xin%20Chen"> Xin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20system%20modeling" title="dynamic system modeling">dynamic system modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20equation" title=" normal equation"> normal equation</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20order%20gradient%20descent" title=" second order gradient descent"> second order gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/98265/dynamic-measurement-system-modeling-with-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98265.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">127</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">10851</span> Noise Reduction in Web Data: A Learning Approach Based on Dynamic User Interests</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Julius%20Onyancha">Julius Onyancha</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentina%20Plekhanova"> Valentina Plekhanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=web%20log%20data" title="web log data">web log data</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20user%20profile" title=" web user profile"> web user profile</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20interest" title=" user interest"> user interest</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20web%20data%20learning" title=" noise web data learning"> noise web data 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/77482/noise-reduction-in-web-data-a-learning-approach-based-on-dynamic-user-interests" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77482.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">265</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">10850</span> Collaborative and Context-Aware Learning Approach Using Mobile Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sameh%20Baccari">Sameh Baccari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Neji"> Mahmoud Neji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the rapid developments on mobile devices and wireless technologies enable new dimension capabilities for the learning domain. This dimension facilitates people daily activities and shortens the distances between individuals. When these technologies have been used in learning, a new paradigm has been emerged giving birth to mobile learning. Because of the mobility feature, m-learning courses have to be adapted dynamically to the learner’s context. The main challenge in context-aware mobile learning is to develop an approach building the best learning resources according to dynamic learning situations. In this paper, we propose a context-aware mobile learning system called Collaborative and Context-aware Mobile Learning System (CCMLS). It takes into account the requirements of Mobility, Collaboration and Context-Awareness. This system is based on the semantic modeling of the learning context and the learning content. The adaptation part of this approach is made up of adaptation rules to propose and select relevant resources, learning partners and learning activities based not only on the user’s needs, but also on its current context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20learning" title="mobile learning">mobile learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20technologies" title=" mobile technologies"> mobile technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=collaboration" title=" collaboration"> collaboration</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation%20engine" title=" adaptation engine"> adaptation engine</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation%20strategy" title=" adaptation strategy"> adaptation strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20object" title=" learning object"> learning object</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20context" title=" learning context"> learning context</a> </p> <a href="https://publications.waset.org/abstracts/86589/collaborative-and-context-aware-learning-approach-using-mobile-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86589.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">308</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">10849</span> SAP-Reduce: Staleness-Aware P-Reduce with Weight Generator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lizhi%20Ma">Lizhi Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Chengcheng%20Hu"> Chengcheng Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuxian%20Wong"> Fuxian Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collective%20communication" title="collective communication">collective communication</a>, <a href="https://publications.waset.org/abstracts/search?q=decentralized%20distributed%20training" title=" decentralized distributed training"> decentralized distributed training</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=P-Reduce" title=" P-Reduce"> P-Reduce</a> </p> <a href="https://publications.waset.org/abstracts/188657/sap-reduce-staleness-aware-p-reduce-with-weight-generator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188657.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">33</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">10848</span> Active Development of Tacit Knowledge Using Social Media and Learning Communities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Zanetich">John Zanetich</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper uses a pragmatic research approach to investigate the relationships between Active Development of Tacit Knowledge (ADTK), social media (Facebook) and classroom learning communities. This paper investigates the use of learning communities and social media as the context and means for changing tacit knowledge to explicit and presents a dynamic model of the development of a classroom learning community. The goal of this study is to identify the point that explicit knowledge is converted to tacit knowledge and to test a way to quantify the exchange using social media and learning communities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tacit%20knowledge" title="tacit knowledge">tacit knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=college%20programs" title=" college programs"> college programs</a>, <a href="https://publications.waset.org/abstracts/search?q=experiential%20learning" title=" experiential learning"> experiential learning</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20communities" title=" learning communities"> learning communities</a> </p> <a href="https://publications.waset.org/abstracts/47471/active-development-of-tacit-knowledge-using-social-media-and-learning-communities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47471.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">362</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">10847</span> Simulation of Obstacle Avoidance for Multiple Autonomous Vehicles in a Dynamic Environment Using Q-Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andreas%20D.%20Jansson">Andreas D. Jansson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title="autonomous vehicles">autonomous vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=industry%204.0" title=" industry 4.0"> industry 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title=" obstacle avoidance"> obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=Q-learning" title=" Q-learning"> Q-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/132508/simulation-of-obstacle-avoidance-for-multiple-autonomous-vehicles-in-a-dynamic-environment-using-q-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132508.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">138</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">10846</span> The Value of Dynamic Priorities in Motor Learning between Some Basic Skills in Beginner's Basketball, U14 Years</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guebli%20Abdelkader">Guebli Abdelkader</a>, <a href="https://publications.waset.org/abstracts/search?q=Regiueg%20Madani"> Regiueg Madani</a>, <a href="https://publications.waset.org/abstracts/search?q=Sbaa%20Bouabdellah"> Sbaa Bouabdellah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goals of this study are to find ways to determine the value of dynamic priorities in motor learning between some basic skills in beginner’s basketball (U14), based on skills of shooting and defense against the shooter. Our role is to expose the statistical results in compare & correlation between samples of study in tests skills for the shooting and defense against the shooter. In order to achieve this objective, we have chosen 40 boys in middle school represented in four groups, two controls group’s (CS1, CS2) ,and two experimental groups (ES1: training on skill of shooting, skill of defense against the shooter, ES2: experimental group training on skill of defense against the shooter, skill of shooting). For the statistical analysis, we have chosen (F & T) tests for the statistical differences, and test (R) for the correlation analysis. Based on the analyses statistics, we confirm the importance of classifying priorities of basketball basic skills during the motor learning process. Admit that the benefits of experimental group training are to economics in the time needed for acquiring new motor kinetic skills in basketball. In the priority of ES2 as successful dynamic motor learning method to enhance the basic skills among beginner’s basketball. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=basic%20skills" title="basic skills">basic skills</a>, <a href="https://publications.waset.org/abstracts/search?q=basketball" title=" basketball"> basketball</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20learning" title=" motor learning"> motor learning</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a> </p> <a href="https://publications.waset.org/abstracts/92495/the-value-of-dynamic-priorities-in-motor-learning-between-some-basic-skills-in-beginners-basketball-u14-years" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92495.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">170</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">10845</span> Personality Composition in Senior Management Teams: The Importance of Homogeneity in Dynamic Managerial Capabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shelley%20Harrington">Shelley Harrington</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a result of increasingly dynamic business environments, the creation and fostering of dynamic capabilities, [those capabilities that enable sustained competitive success despite of dynamism through the awareness and reconfiguration of internal and external competencies], supported by organisational learning [a dynamic capability] has gained increased and prevalent momentum in the research arena. Presenting findings funded by the Economic Social Research Council, this paper investigates the extent to which Senior Management Team (SMT) personality (at the trait and facet level) is associated with the creation of dynamic managerial capabilities at the team level, and effective organisational learning/knowledge sharing within the firm. In doing so, this research highlights the importance of micro-foundations in organisational psychology and specifically dynamic capabilities, a field which to date has largely ignored the importance of psychology in understanding these important and necessary capabilities. Using a direct measure of personality (NEO PI-3) at the trait and facet level across 32 high technology and finance firms in the UK, their CEOs (N=32) and their complete SMTs [N=212], a new measure of dynamic managerial capabilities at the team level was created and statistically validated for use within the work. A quantitative methodology was employed with regression and gap analysis being used to show the empirical foundations of personality being positioned as a micro-foundation of dynamic capabilities. The results of this study found that personality homogeneity within the SMT was required to strengthen the dynamic managerial capabilities of sensing, seizing and transforming, something which was required to reflect strong organisational learning at middle management level [N=533]. In particular, it was found that the greater the difference [t-score gaps] between the personality profiles of a Chief Executive Officer (CEO) and their complete, collective SMT, the lower the resulting self-reported nature of dynamic managerial capabilities. For example; the larger the difference between a CEOs level of dutifulness, a facet contributing to the definition of conscientiousness, and their SMT’s level of dutifulness, the lower the reported level of transforming, a capability fundamental to strategic change in a dynamic business environment. This in turn directly questions recent trends, particularly in upper echelons research highlighting the need for heterogeneity within teams. In doing so, it successfully positions personality as a micro-foundation of dynamic capabilities, thus contributing to recent discussions from within the strategic management field calling for the need to empirically explore dynamic capabilities at such a level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20managerial%20capabilities" title="dynamic managerial capabilities">dynamic managerial capabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=senior%20management%20teams" title=" senior management teams"> senior management teams</a>, <a href="https://publications.waset.org/abstracts/search?q=personality" title=" personality"> personality</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamism" title=" dynamism"> dynamism</a> </p> <a href="https://publications.waset.org/abstracts/68229/personality-composition-in-senior-management-teams-the-importance-of-homogeneity-in-dynamic-managerial-capabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68229.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">270</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">10844</span> Autonomous Kuka Youbot Navigation Based on Machine Learning and Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gordon">Carlos Gordon</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricio%20Encalada"> Patricio Encalada</a>, <a href="https://publications.waset.org/abstracts/search?q=Henry%20Lema"> Henry Lema</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Leon"> Diego Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=Dennis%20Chicaiza"> Dennis Chicaiza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20navigation" title="autonomous navigation">autonomous navigation</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=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20operative%20system" title=" robotic operative system"> robotic operative system</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source%20computer%20vision%20library" title=" open source computer vision library"> open source computer vision library</a> </p> <a href="https://publications.waset.org/abstracts/101726/autonomous-kuka-youbot-navigation-based-on-machine-learning-and-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101726.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">177</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">10843</span> Optimal Dynamic Regime for CO Oxidation Reaction Discovered by Policy-Gradient Reinforcement Learning Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lifar%20M.%20S.">Lifar M. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Tereshchenko%20A.%20A."> Tereshchenko A. A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Bulgakov%20A.%20N."> Bulgakov A. N.</a>, <a href="https://publications.waset.org/abstracts/search?q=Guda%20S.%20A."> Guda S. A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Guda%20A.%20A."> Guda A. A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Soldatov%20A.%20V."> Soldatov A. V.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes - adsorption, activation, reaction, and desorption. These processes, in turn, depend on the inlet feed concentrations, temperature, and pressure. At stationary conditions, the active surface sites may be poisoned by reaction byproducts or blocked by thermodynamically adsorbed gaseous reagents. Thus, the yield of reaction products can significantly drop. On the contrary, the dynamic control accounts for the changes in the surface properties and adjusts reaction parameters accordingly. Therefore dynamic control may be more efficient than stationary control. In this work, a reinforcement learning algorithm has been applied to control the simulation of CO oxidation on a catalyst. The policy gradient algorithm is learned to maximize the CO₂ production rate based on the CO and O₂ flows at a given time step. Nonstationary solutions were found for the regime with surface deactivation. The maximal product yield was achieved for periodic variations of the gas flows, ensuring a balance between available adsorption sites and the concentration of activated intermediates. This methodology opens a perspective for the optimization of catalytic reactions under nonstationary conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=catalyst" title=" catalyst"> catalyst</a>, <a href="https://publications.waset.org/abstracts/search?q=co%20oxidation" title=" co oxidation"> co oxidation</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=dynamic%20control" title=" dynamic control"> dynamic control</a> </p> <a href="https://publications.waset.org/abstracts/163434/optimal-dynamic-regime-for-co-oxidation-reaction-discovered-by-policy-gradient-reinforcement-learning-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10842</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">10841</span> Detecting Manipulated Media Using Deep Capsule Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Uzuazomaro%20Oju">Joseph Uzuazomaro Oju</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20capsule%20network" title="deep capsule network">deep capsule network</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20routing" title=" dynamic routing"> dynamic routing</a>, <a href="https://publications.waset.org/abstracts/search?q=fake%20media%20detection" title=" fake media detection"> fake media detection</a>, <a href="https://publications.waset.org/abstracts/search?q=manipulated%20media" title=" manipulated media"> manipulated media</a> </p> <a href="https://publications.waset.org/abstracts/123371/detecting-manipulated-media-using-deep-capsule-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123371.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">134</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">10840</span> An Integrated Architecture of E-Learning System to Digitize the Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Touhidul%20Islam%20Sarker">M. Touhidul Islam Sarker</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammod%20Abul%20Kashem"> Mohammod Abul Kashem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to improve the e-learning system and digitize the learning method in the educational sector. The learner will login into e-learning platform and easily access the digital content, the content can be downloaded and take an assessment for evaluation. Learner can get access to these digital resources by using tablet, computer, and smart phone also. E-learning system can be defined as teaching and learning with the help of multimedia technologies and the internet by access to digital content. E-learning replacing the traditional education system through information and communication technology-based learning. This paper has designed and implemented integrated e-learning system architecture with University Management System. Moodle (Modular Object-Oriented Dynamic Learning Environment) is the best e-learning system, but the problem of Moodle has no school or university management system. In this research, we have not considered the school’s student because they are out of internet facilities. That’s why we considered the university students because they have the internet access and used technologies. The University Management System has different types of activities such as student registration, account management, teacher information, semester registration, staff information, etc. If we integrated these types of activity or module with Moodle, then we can overcome the problem of Moodle, and it will enhance the e-learning system architecture which makes effective use of technology. This architecture will give the learner to easily access the resources of e-learning platform anytime or anywhere which digitizes the learning method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=database" title="database">database</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=LMS" title=" LMS"> LMS</a>, <a href="https://publications.waset.org/abstracts/search?q=Moodle" title=" Moodle"> Moodle</a> </p> <a href="https://publications.waset.org/abstracts/84210/an-integrated-architecture-of-e-learning-system-to-digitize-the-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84210.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">188</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">10839</span> Adaption Model for Building Agile Pronunciation Dictionaries Using Phonemic Distance Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akella%20Amarendra%20Babu">Akella Amarendra Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rama%20Devi%20Yellasiri"> Rama Devi Yellasiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Natukula%20Sainath"> Natukula Sainath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pronunciation%20variations" title="pronunciation variations">pronunciation variations</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming" title=" dynamic programming"> dynamic programming</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=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/93281/adaption-model-for-building-agile-pronunciation-dictionaries-using-phonemic-distance-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93281.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">176</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">10838</span> A Case Study on English Camp in UNISSA: An Approach towards Interactive Learning Outside the Classroom</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liza%20Mariah%20Hj.%20Azahari">Liza Mariah Hj. Azahari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper will look at a case study on English Camp which was an activity coordinated at the Sultan Sharif Ali Islamic University in 2011. English Camp is a fun and motivation filled activity which brings students and teachers together outside of the classroom setting into a more diverse environment. It also enables teacher and students to gain proximate time together for a mutual purpose which is to explore the language in a more dynamic and relaxed way. First of all, the study will look into the background of English Camp, and how it was introduced and implemented from different contexts. Thereafter, it will explain the objectives of the English Camp coordinated at our university, UNISSA, and what types of activities were conducted. It will then evaluate the effectiveness of the camp as to what extent it managed to meet its motto, which was to foster dynamic interactive learning of English Language. To conclude, the paper presents a potential for further research on the topic as well as a guideline for educators who wish to coordinate the activity. Proposal for collaboration in this activity is further highlighted and encouraged within the paper for future implementation and endeavor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=English%20camp" title="English camp">English camp</a>, <a href="https://publications.waset.org/abstracts/search?q=UNISSA" title=" UNISSA"> UNISSA</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20learning" title=" interactive learning"> interactive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=outside" title=" outside"> outside</a> </p> <a href="https://publications.waset.org/abstracts/19670/a-case-study-on-english-camp-in-unissa-an-approach-towards-interactive-learning-outside-the-classroom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19670.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">569</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">10837</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">10836</span> Dynamics of Piaget’s Cognitive Learning Approach and Vygotsky’s Sociocultural Theory in Different Stages of Medical and Allied Health Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ferissa%20B.%20Ablola">Ferissa B. Ablola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The two learning theories which were evidently used in medical education include cognitive and sociocultural frameworks. The interplay of different learning theories in education is vital since most of the existing theories have specific focus of development. In addition, a certain theory is best fit with a particular learning outcome and audience profile. The application of learning theories is education is said to be dynamic and becomes more complex with increasing educational level. This systematic review aims to describe the possible shift from integration of cognitive learning theory to employment of socio-cultural approach in medical and health-allied education over the years among students, educators and the learning institution through systematic review following the PRISMA guidelines. In addition, the changes in teaching modality and individual acceptance of the shift of learning framework among cognitive constructivist and social constructivist will also be documented. This present review may serve as baseline information on the connection of two widely used theories in medical education in different year levels. Further, this study emphasizes the significance of the alignment of different learning theories and combination of insights from several educational frameworks, would permit the creation of a teaching/learning design with real theoretical depth. A more inclusive systematic review is necessary to involve more related studies, and exploration of interaction among other learning theories in health and other fields of study is encouraged. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20theory" title="learning theory">learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive" title=" cognitive"> cognitive</a>, <a href="https://publications.waset.org/abstracts/search?q=sociocultural" title=" sociocultural"> sociocultural</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20education" title=" medical education"> medical education</a> </p> <a href="https://publications.waset.org/abstracts/189284/dynamics-of-piagets-cognitive-learning-approach-and-vygotskys-sociocultural-theory-in-different-stages-of-medical-and-allied-health-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189284.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">27</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">10835</span> A Novel Exploration/Exploitation Policy Accelerating Learning In Both Stationary And Non Stationary Environment Navigation Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wiem%20Zemzem">Wiem Zemzem</a>, <a href="https://publications.waset.org/abstracts/search?q=Moncef%20Tagina"> Moncef Tagina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we are addressing the problem of an autonomous mobile robot navigating in a large, unknown and dynamic environment using reinforcement learning abilities. This problem is principally related to the exploration/exploitation dilemma, especially the need to find a solution letting the robot detect the environmental change and also learn in order to adapt to the new environmental form without ignoring knowledge already acquired. Firstly, a new action selection strategy, called ε-greedy-MPA (the ε-greedy policy favoring the most promising actions) is proposed. Unlike existing exploration/exploitation policies (EEPs) such as ε-greedy and Boltzmann, the new EEP doesn’t only rely on the information of the actual state but also uses those of the eventual next states. Secondly, as the environment is large, an exploration favoring least recently visited states is added to the proposed EEP in order to accelerate learning. Finally, various simulations with ball-catching problem have been conducted to evaluate the ε-greedy-MPA policy. The results of simulated experiments show that combining this policy with the Qlearning method is more effective and efficient compared with the ε-greedy policy in stationary environments and the utility-based reinforcement learning approach in non stationary environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20mobile%20robot" title="autonomous mobile robot">autonomous mobile robot</a>, <a href="https://publications.waset.org/abstracts/search?q=exploration%2F%20exploitation%20policy" title=" exploration/ exploitation policy"> exploration/ exploitation policy</a>, <a href="https://publications.waset.org/abstracts/search?q=large" title=" large"> large</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20environment" title=" dynamic environment"> dynamic environment</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a> </p> <a href="https://publications.waset.org/abstracts/23926/a-novel-explorationexploitation-policy-accelerating-learning-in-both-stationary-and-non-stationary-environment-navigation-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23926.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">417</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">10834</span> Intrinsic Motivational Factor of Students in Learning Mathematics and Science Based on Electroencephalogram Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norzaliza%20Md.%20Nor">Norzaliza Md. Nor</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=Mahyar%20Hamedi"> Mahyar Hamedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadrina%20Hussain"> Hadrina Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Wahab%20Abdul%20Rahman"> Wahab Abdul Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motivational factor is mainly the students’ desire to involve in learning process. However, it also depends on the goal towards their involvement or non-involvement in academic activity. Even though, the students’ motivation might be in the same level, but the basis of their motivation may differ. In this study, it focuses on the intrinsic motivational factor which student enjoy learning or feeling of accomplishment the activity or study for its own sake. The intrinsic motivational factor of students in learning mathematics and science has found as difficult to be achieved because it depends on students’ interest. In the Program for International Student Assessment (PISA) for mathematics and science, Malaysia is ranked as third lowest. The main problem in Malaysian educational system, students tend to have extrinsic motivation which they have to score in exam in order to achieve a good result and enrolled as university students. The use of electroencephalogram (EEG) signals has found to be scarce especially to identify the students’ intrinsic motivational factor in learning science and mathematics. In this research study, we are identifying the correlation between precursor emotion and its dynamic emotion to verify the intrinsic motivational factor of students in learning mathematics and science. The 2-D Affective Space Model (ASM) was used in this research in order to identify the relationship of precursor emotion and its dynamic emotion based on the four basic emotions, happy, calm, fear and sad. These four basic emotions are required to be used as reference stimuli. Then, in order to capture the brain waves, EEG device was used, while Mel Frequency Cepstral Coefficient (MFCC) was adopted to be used for extracting the features before it will be feed to Multilayer Perceptron (MLP) to classify the valence and arousal axes for the ASM. The results show that the precursor emotion had an influence the dynamic emotions and it identifies that most students have no interest in mathematics and science according to the negative emotion (sad and fear) appear in the EEG signals. We hope that these results can help us further relate the behavior and intrinsic motivational factor of students towards learning of mathematics and science. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=intrinsic%20motivational%20factor" title=" intrinsic motivational factor"> intrinsic motivational factor</a> </p> <a href="https://publications.waset.org/abstracts/52426/intrinsic-motivational-factor-of-students-in-learning-mathematics-and-science-based-on-electroencephalogram-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52426.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">367</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">10833</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">10832</span> A System Dynamics Approach to Technological Learning Impact for Cost Estimation of Solar Photovoltaics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rong%20Wang">Rong Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Hasanefendic"> Sandra Hasanefendic</a>, <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20von%20Hauff"> Elizabeth von Hauff</a>, <a href="https://publications.waset.org/abstracts/search?q=Bart%20Bossink"> Bart Bossink</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technological learning and learning curve models have been continuously used to estimate the photovoltaics (PV) cost development over time for the climate mitigation targets. They can integrate a number of technological learning sources which influence the learning process. Yet the accuracy and realistic predictions for cost estimations of PV development are still difficult to achieve. This paper develops four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technology experience and the knowledge stock. This paper specifically focuses on the non-linear relationship between the costs and technological learning source and their dynamic interaction and uses the system dynamics approach to predict a more accurate PV cost estimation for future development. As the case study, the data from China is gathered and drawn to illustrate that the learning curve model that incorporates both the global and local experience is more accurate and realistic than the other three models for PV cost estimation. Further, absorbing and integrating the global experience into the local industry has a positive impact on PV cost reduction. Although the learning curve model incorporating knowledge stock is not realistic for current PV cost deployment in China, it still plays an effective positive role in future PV cost reduction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photovoltaic" title="photovoltaic">photovoltaic</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20dynamics" title=" system dynamics"> system dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=technological%20learning" title=" technological learning"> technological learning</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20curve" title=" learning curve"> learning curve</a> </p> <a href="https://publications.waset.org/abstracts/168940/a-system-dynamics-approach-to-technological-learning-impact-for-cost-estimation-of-solar-photovoltaics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168940.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10831</span> The Influence of Students’ Learning Factor and Parents’ Involvement in Their Learning and Suspension: The Application of Big Data Analysis of Internet of Things Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih%20Ming%20Kung">Chih Ming Kung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is an empirical study examining the enrollment rate and dropout rate of students from the perspectives of students’ learning, parents’ involvement and the learning process. Methods: Using the data collected from the entry website of Internet of Things (IoT), parents’ participation and the installation pattern of exit poll website, an investigation was conducted. Results: This study discovered that in the aspect of the degree of involvement, the attractiveness of courses, self-performance and departmental loyalty exerts significant influences on the four aspects: psychological benefits, physical benefits, social benefits and educational benefits of learning benefits. Parents’ participation also exerts a significant influence on the learning benefits. A suitable tool on the cloud was designed to collect the dynamic big data of students’ learning process. Conclusion: This research’s results can be valuable references for the government when making and promoting related policies, with more macro view and consideration. It is also expected to be contributory to schools for the practical study of promotion for enrollment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=students%E2%80%99%20learning%20factor" title="students’ learning factor">students’ learning factor</a>, <a href="https://publications.waset.org/abstracts/search?q=parents%E2%80%99%20involvement" title=" parents’ involvement"> parents’ involvement</a>, <a href="https://publications.waset.org/abstracts/search?q=involvement" title=" involvement"> involvement</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a> </p> <a href="https://publications.waset.org/abstracts/95772/the-influence-of-students-learning-factor-and-parents-involvement-in-their-learning-and-suspension-the-application-of-big-data-analysis-of-internet-of-things-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95772.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">146</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">10830</span> Dynamic Amplification Factors of Some City Bridges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Paeglite">I. Paeglite</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Paeglitis"> A. Paeglitis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents a study of dynamic effects obtained from the dynamic load testing of the city highway bridges in Latvia carried out from 2005 to 2012. 9 pre-stressed concrete bridges and 4 composite bridges were considered. 11 of 13 bridges were designed according to the Eurocodes but two according to the previous structural codes used in Latvia (SNIP 2.05.03-84). The dynamic properties of the bridges were obtained by heavy vehicles passing the bridge roadway with different driving speeds and with or without even pavement. The obtained values of the Dynamic amplification factor (DAF) and bridge natural frequency were analyzed and compared to the values of built-in traffic load models provided in Eurocode 1. The actual DAF values for even bridge deck in the most cases are smaller than the value adopted in Eurocode 1. Vehicle speed for uneven pavements significantly influence Dynamic amplification factor values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bridge" title="bridge">bridge</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20effects" title=" dynamic effects"> dynamic effects</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20testing" title=" load testing"> load testing</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20amplification%20factor" title=" dynamic amplification factor"> dynamic amplification factor</a> </p> <a href="https://publications.waset.org/abstracts/10727/dynamic-amplification-factors-of-some-city-bridges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10727.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">383</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">10829</span> Omni-Modeler: Dynamic Learning for Pedestrian Redetection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Karnes">Michael Karnes</a>, <a href="https://publications.waset.org/abstracts/search?q=Alper%20Yilmaz"> Alper Yilmaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20learning" title="dynamic learning">dynamic learning</a>, <a href="https://publications.waset.org/abstracts/search?q=few-shot%20learning" title=" few-shot learning"> few-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20redetection" title=" pedestrian redetection"> pedestrian redetection</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20recognition" title=" visual recognition"> visual recognition</a> </p> <a href="https://publications.waset.org/abstracts/172265/omni-modeler-dynamic-learning-for-pedestrian-redetection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172265.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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20learning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20learning&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20learning&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20learning&page=5">5</a></li> <li class="page-item"><a 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