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short-term memory</title> <meta name="description" content="Search results for: long short-term memory"> <meta name="keywords" content="long short-term memory"> <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 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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="long short-term memory"> <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> 6972</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: long short-term memory</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6972</span> Directed-Wald Test for Distinguishing Long Memory and Nonlinearity Time Series: Power and Size Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heri%20Kuswanto">Heri Kuswanto</a>, <a href="https://publications.waset.org/abstracts/search?q=Philipp%20Sibbertsen"> Philipp Sibbertsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Irhamah"> Irhamah </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Wald type test to distinguish between long memory and ESTAR nonlinearity has been developed. The test uses a directed-Wald statistic to overcome the problem of restricted parameters under the alternative. The test is derived from a model specification i.e. allows the transition parameter to appear as a nuisance parameter in the transition function. A simulation study has been conducted and it indicates that the approach leads a test with good size and power properties to distinguish between stationary long memory and ESTAR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=directed-Wald%20test" title="directed-Wald test">directed-Wald test</a>, <a href="https://publications.waset.org/abstracts/search?q=ESTAR" title=" ESTAR"> ESTAR</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20memory" title=" long memory"> long memory</a>, <a href="https://publications.waset.org/abstracts/search?q=distinguish" title=" distinguish"> distinguish</a> </p> <a href="https://publications.waset.org/abstracts/21296/directed-wald-test-for-distinguishing-long-memory-and-nonlinearity-time-series-power-and-size-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21296.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">480</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">6971</span> Long Memory and ARFIMA Modelling: The Case of CPI Inflation for Ghana and South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Boateng">A. Boateng</a>, <a href="https://publications.waset.org/abstracts/search?q=La%20Gil-Alana"> La Gil-Alana</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Lesaoana%3B%20Hj.%20Siweya"> M. Lesaoana; Hj. Siweya</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Belete"> A. Belete</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines long memory or long-range dependence in the CPI inflation rates of Ghana and South Africa using Whittle methods and autoregressive fractionally integrated moving average (ARFIMA) models. Standard I(0)/I(1) methods such as Augmented Dickey-Fuller (ADF), Philips-Perron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests were also employed. Our findings indicate that long memory exists in the CPI inflation rates of both countries. After processing fractional differencing and determining the short memory components, the models were specified as ARFIMA (4,0.35,2) and ARFIMA (3,0.49,3) respectively for Ghana and South Africa. Consequently, the CPI inflation rates of both countries are fractionally integrated and mean reverting. The implication of this result will assist in policy formulation and identification of inflationary pressures in an economy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Consumer%20Price%20Index%20%28CPI%29%20inflation%20rates" title="Consumer Price Index (CPI) inflation rates">Consumer Price Index (CPI) inflation rates</a>, <a href="https://publications.waset.org/abstracts/search?q=Whittle%20method" title=" Whittle method"> Whittle method</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20memory" title=" long memory"> long memory</a>, <a href="https://publications.waset.org/abstracts/search?q=ARFIMA%20model" title=" ARFIMA model"> ARFIMA model</a> </p> <a href="https://publications.waset.org/abstracts/46099/long-memory-and-arfima-modelling-the-case-of-cpi-inflation-for-ghana-and-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46099.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">368</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">6970</span> Memory Types in Hemodialysis Patients: A Study Based on Hemodialysis Duration, Zahedan, South East of Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sabayan">B. Sabayan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Alidadi"> A. Alidadi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ebrahimi"> S. Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20M.%20Bakhshani"> N. M. Bakhshani </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neuropsychological problems are more common in hemodialysis (HD) patients than in healthy individuals. The aim of this study was to investigate the effect of long term HD on memory types of HD patients. To assess the different type of memory, we used memory parts of the Persian Papers and Pencil Cognitive assessment package (PCAP) and Addenbrooke's Cognitive Examination (ACE-R). Our study included 80 HD patients of whom 39 had less than six months of HD and 41 patients and another group which had a history of HD more than six months. The population had a mean age of 51.60 years old and 27.5% of them were female. The scores of patients who have been hemodialyzed for a long time (median time of HD was up to 4 years) had lower score in anterograde, explicit, visual, recall and recognition memory (5.44±1.07, 9.49±3.472, 22.805±6.6913, 5.59±10.435, 11.02±3.190 score) than the HD patients who underwent HD for a shorter term, where the median time was 3 to 5 months (P<0.01). The regression result shows that, by increasing the HD duration, all memory types are reduced (R2=0.600, P<0.01). The present study demonstrated that HD patients who were under HD for a long time had significantly lower scores in the different types of memory. However, additional researches are needed in this area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hemodialysis%20patients" title="hemodialysis patients">hemodialysis patients</a>, <a href="https://publications.waset.org/abstracts/search?q=duration%20of%20hemodialysis" title=" duration of hemodialysis"> duration of hemodialysis</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20types" title=" memory types"> memory types</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahedan" title=" Zahedan"> Zahedan</a> </p> <a href="https://publications.waset.org/abstracts/83159/memory-types-in-hemodialysis-patients-a-study-based-on-hemodialysis-duration-zahedan-south-east-of-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83159.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">178</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">6969</span> Long Short-Term Memory Stream Cruise Control Method for Automated Drift Detection and Adaptation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Abu-Shaira">Mohammad Abu-Shaira</a>, <a href="https://publications.waset.org/abstracts/search?q=Weishi%20Shi"> Weishi Shi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20drift%20detection%20and%20adaptation" title="automated drift detection and adaptation">automated drift detection and adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20drift" title=" concept drift"> concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameters%20optimization" title=" hyperparameters optimization"> hyperparameters optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20and%20adaptive%20learning" title=" online and adaptive learning"> online and adaptive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/193474/long-short-term-memory-stream-cruise-control-method-for-automated-drift-detection-and-adaptation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193474.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">11</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">6968</span> Structural Breaks, Asymmetric Effects and Long Memory in the Volatility of Turkey Stock Market </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serpil%20T%C3%BCrky%C4%B1lmaz">Serpil Türkyılmaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesut%20Bal%C4%B1bey"> Mesut Balıbey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, long memory properties in volatility of Turkey Stock Market are being examined through the FIGARCH, FIEGARCH and FIAPARCH models under different distribution assumptions as normal and skewed student-t distributions. Furthermore, structural changes in volatility of Turkey Stock Market are investigated. The results display long memory property and the presence of asymmetric effects of shocks in volatility of Turkey Stock Market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FIAPARCH%20model" title="FIAPARCH model">FIAPARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=FIEGARCH%20model" title=" FIEGARCH model"> FIEGARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=FIGARCH%20model" title=" FIGARCH model"> FIGARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20break" title=" structural break"> structural break</a> </p> <a href="https://publications.waset.org/abstracts/14438/structural-breaks-asymmetric-effects-and-long-memory-in-the-volatility-of-turkey-stock-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14438.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">291</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">6967</span> Analysis of Multilayer Neural Network Modeling and Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson%20Vera"> Nelson Vera</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Pedraza"> Luis Pedraza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neuronal%20network" title=" recurrent neuronal network"> recurrent neuronal network</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20analysis" title=" mathematical analysis"> mathematical analysis</a> </p> <a href="https://publications.waset.org/abstracts/63507/analysis-of-multilayer-neural-network-modeling-and-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63507.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">420</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">6966</span> Hierarchical Tree Long Short-Term Memory for Sentence Representations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiuying%20Wang">Xiuying Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Changliang%20Li"> Changliang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Xu"> Bo Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly. <p class="card-text"><strong>Keywords:</strong> <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=hierarchical%20tree%20long%20short-term%20memory" title=" hierarchical tree long short-term memory"> hierarchical tree long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20representation" title=" sentence representation"> sentence representation</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/83787/hierarchical-tree-long-short-term-memory-for-sentence-representations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83787.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">349</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">6965</span> An Event Relationship Extraction Method Incorporating Deep Feedback Recurrent Neural Network and Bidirectional Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yin%20Yuanling">Yin Yuanling</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=event%20relations" title="event relations">event relations</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=DFRNN%20models" title=" DFRNN models"> DFRNN models</a>, <a href="https://publications.waset.org/abstracts/search?q=bi-directional%20long%20and%20short-term%20memory%20networks" title=" bi-directional long and short-term memory networks"> bi-directional long and short-term memory networks</a> </p> <a href="https://publications.waset.org/abstracts/156673/an-event-relationship-extraction-method-incorporating-deep-feedback-recurrent-neural-network-and-bidirectional-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156673.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">144</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">6964</span> Experimental Study of Hyperparameter Tuning a Deep Learning Convolutional Recurrent Network for Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bharatendra%20Rai">Bharatendra Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory%20networks" title="long short-term memory networks">long short-term memory networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20recurrent%20networks" title=" convolutional recurrent networks"> convolutional recurrent networks</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameter%20tuning" title=" hyperparameter tuning"> hyperparameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=Tukey%20honest%20significant%20differences" title=" Tukey honest significant differences"> Tukey honest significant differences</a> </p> <a href="https://publications.waset.org/abstracts/169795/experimental-study-of-hyperparameter-tuning-a-deep-learning-convolutional-recurrent-network-for-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169795.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">129</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">6963</span> The Role of Executive Attention and Literacy on Consumer Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fereshteh%20Nazeri%20Bahadori">Fereshteh Nazeri Bahadori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's competitive environment, any company that aims to operate in a market, whether industrial or consumer markets, must know that it cannot address all the tastes and demands of customers at once and serve them all. The study of consumer memory is considered an important subject in marketing research, and many companies have conducted studies on this subject and the factors affecting it due to its importance. Therefore, the current study tries to investigate the relationship between consumers' attention, literacy, and memory. Memory has a very close relationship with learning. Memory is the collection of all the information that we have understood and stored. One of the important subjects in consumer behavior is information processing by the consumer. One of the important factors in information processing is the mental involvement of the consumer, which has attracted a lot of attention in the past two decades. Since consumers are the turning point of all marketing activities, successful marketing begins with understanding why and how consumers behave. Therefore, in the current study, the role of executive attention and literacy on consumers' memory has been investigated. The results showed that executive attention and literacy would play a significant role in the long-term and short-term memory of consumers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=literacy" title="literacy">literacy</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20memory" title=" consumer memory"> consumer memory</a>, <a href="https://publications.waset.org/abstracts/search?q=executive%20attention" title=" executive attention"> executive attention</a>, <a href="https://publications.waset.org/abstracts/search?q=psychology%20of%20consumer%20behavior" title=" psychology of consumer behavior"> psychology of consumer behavior</a> </p> <a href="https://publications.waset.org/abstracts/167451/the-role-of-executive-attention-and-literacy-on-consumer-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167451.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">95</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">6962</span> Effects of the Visual and Auditory Stimuli with Emotional Content on Eyewitness Testimony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C4%B0rem%20Bulut">İrem Bulut</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Z.%20%20S%C3%B6y%C3%BCk"> Mustafa Z. Söyük</a>, <a href="https://publications.waset.org/abstracts/search?q=Ertu%C4%9Frul%20Yal%C3%A7%C4%B1n"> Ertuğrul Yalçın</a>, <a href="https://publications.waset.org/abstracts/search?q=Simge%20%C5%9Ei%C5%9Fman-Bal"> Simge Şişman-Bal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Eyewitness testimony is one of the most frequently used methods in criminal cases for the determination of crime and perpetrator. In the literature, the number of studies about the reliability of eyewitness testimony is increasing. The study aims to reveal the factors that affect the short-term and long-term visual memory performance of the participants in the event of an accident. In this context, the effect of the emotional content of the accident and the sounds during the accident on visual memory performance was investigated with eye-tracking. According to the results, the presence of visual and auditory stimuli with emotional content during the accident decreases the participants' both short-term and long-term recall performance. Moreover, the data obtained from the eye monitoring device showed that the participants had difficulty in answering even the questions they focused on at the time of the accident. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eye%20tracking" title="eye tracking">eye tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=eyewitness%20testimony" title=" eyewitness testimony"> eyewitness testimony</a>, <a href="https://publications.waset.org/abstracts/search?q=long-term%20recall" title=" long-term recall"> long-term recall</a>, <a href="https://publications.waset.org/abstracts/search?q=short-term%20recall" title=" short-term recall"> short-term recall</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20memory" title=" visual memory"> visual memory</a> </p> <a href="https://publications.waset.org/abstracts/115650/effects-of-the-visual-and-auditory-stimuli-with-emotional-content-on-eyewitness-testimony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115650.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">162</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">6961</span> Reading and Writing Memories in Artificial and Human Reasoning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ian%20O%27Loughlin">Ian O'Loughlin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20reasoning" title="artificial reasoning">artificial reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20memory" title=" human memory"> human memory</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=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/56833/reading-and-writing-memories-in-artificial-and-human-reasoning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56833.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">271</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">6960</span> Preparation on Sentimental Analysis on Social Media Comments with Bidirectional Long Short-Term Memory Gated Recurrent Unit and Model Glove in Portuguese</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leonardo%20Alfredo%20Mendoza">Leonardo Alfredo Mendoza</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristian%20Munoz"> Cristian Munoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Marco%20Aurelio%20Pacheco"> Marco Aurelio Pacheco</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoela%20Kohler"> Manoela Kohler</a>, <a href="https://publications.waset.org/abstracts/search?q=Evelyn%20%20Batista"> Evelyn Batista</a>, <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Moura"> Rodrigo Moura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Processing (NLP) techniques are increasingly more powerful to be able to interpret the feelings and reactions of a person to a product or service. Sentiment analysis has become a fundamental tool for this interpretation but has few applications in languages other than English. This paper presents a classification of sentiment analysis in Portuguese with a base of comments from social networks in Portuguese. A word embedding's representation was used with a 50-Dimension GloVe pre-trained model, generated through a corpus completely in Portuguese. To generate this classification, the bidirectional long short-term memory and bidirectional Gated Recurrent Unit (GRU) models are used, reaching results of 99.1%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20processing%20language" title="natural processing language">natural processing language</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20long%20short-term%20memory" title=" bidirectional long short-term memory"> bidirectional long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=BI-LSTM" title=" BI-LSTM"> BI-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=gated%20recurrent%20unit" title=" gated recurrent unit"> gated recurrent unit</a>, <a href="https://publications.waset.org/abstracts/search?q=GRU" title=" GRU"> GRU</a> </p> <a href="https://publications.waset.org/abstracts/131061/preparation-on-sentimental-analysis-on-social-media-comments-with-bidirectional-long-short-term-memory-gated-recurrent-unit-and-model-glove-in-portuguese" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131061.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6959</span> Memory Based Reinforcement Learning with Transformers for Long Horizon Timescales and Continuous Action Spaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shweta%20Singh">Shweta Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudaman%20Katti"> Sudaman Katti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning. <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=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention" title=" self-attention"> self-attention</a>, <a href="https://publications.waset.org/abstracts/search?q=transformers" title=" transformers"> transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=unity" title=" unity"> unity</a> </p> <a href="https://publications.waset.org/abstracts/163301/memory-based-reinforcement-learning-with-transformers-for-long-horizon-timescales-and-continuous-action-spaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163301.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">136</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">6958</span> Real-Time Episodic Memory Construction for Optimal Action Selection in Cognitive Robotics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deon%20de%20Jager">Deon de Jager</a>, <a href="https://publications.waset.org/abstracts/search?q=Yahya%20Zweiri"> Yahya Zweiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Makris"> Dimitrios Makris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20robotics" title="cognitive robotics">cognitive robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20memory" title=" semantic memory"> semantic memory</a>, <a href="https://publications.waset.org/abstracts/search?q=episodic%20memory" title=" episodic memory"> episodic memory</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20entropy%20principle" title=" maximum entropy principle"> maximum entropy principle</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/114710/real-time-episodic-memory-construction-for-optimal-action-selection-in-cognitive-robotics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114710.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">6957</span> Micro-Rest: Extremely Short Breaks in Post-Learning Interference Support Memory Retention over the Long Term</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Marhenke">R. Marhenke</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Martini"> M. Martini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The distraction of attentional resources after learning hinders long-term memory consolidation compared to several minutes of post-encoding inactivity in form of wakeful resting. We tested whether an 8-minute period of wakeful resting, compared to performing an adapted version of the d2 test of attention after learning, supports memory retention. Participants encoded and immediately recalled a word list followed by either an 8 minute period of wakeful resting (eyes closed, relaxed) or by performing an adapted version of the d2 test of attention (scanning and selecting specific characters while ignoring others). At the end of the experimental session (after 12-24 min) and again after 7 days, participants were required to complete a surprise free recall test of both word lists. Our results showed no significant difference in memory retention between the experimental conditions. However, we found that participants who completed the first lines of the d2 test in less than the given time limit of 20 seconds and thus had short unfilled intervals before switching to the next test line, remembered more words over the 12-24 minute and over the 7 days retention interval than participants who did not complete the first lines. This interaction occurred only for the first test lines, with the highest temporal proximity to the encoding task and not for later test lines. Differences in retention scores between groups (completed first line vs. did not complete) seem to be widely independent of the general performance in the d2 test. Implications and limitations of these exploratory findings are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=long-term%20memory" title="long-term memory">long-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=retroactive%20interference" title=" retroactive interference"> retroactive interference</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a>, <a href="https://publications.waset.org/abstracts/search?q=forgetting" title=" forgetting"> forgetting</a> </p> <a href="https://publications.waset.org/abstracts/113879/micro-rest-extremely-short-breaks-in-post-learning-interference-support-memory-retention-over-the-long-term" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113879.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">132</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">6956</span> Retrieval-Induced Forgetting Effects in Retrospective and Prospective Memory in Normal Aging: An Experimental Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Merve%20Akca">Merve Akca</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retrieval-induced forgetting (RIF) refers to the phenomenon that selective retrieval of some information impairs memory for related, but not previously retrieved information. Despite age differences in retrieval-induced forgetting regarding retrospective memory being documented, this research aimed to highlight age differences in RIF of the prospective memory tasks for the first time. By using retrieval-practice paradigm, this study comparatively examined RIF effects in retrospective memory and event-based prospective memory in young and old adults. In this experimental study, a mixed factorial design with age group (Young, Old) as a between-subject variable, and memory type (Prospective, Retrospective) and item type (Practiced, Non-practiced) as within-subject variables was employed. Retrieval-induced forgetting was observed in the retrospective but not in the prospective memory task. Therefore, the results indicated that selective retrieval of past events led to suppression of other related past events in both age groups but not the suppression of memory for future intentions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prospective%20memory" title="prospective memory">prospective memory</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval-induced%20forgetting" title=" retrieval-induced forgetting"> retrieval-induced forgetting</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval%20inhibition" title=" retrieval inhibition"> retrieval inhibition</a>, <a href="https://publications.waset.org/abstracts/search?q=retrospective%20memory" title=" retrospective memory"> retrospective memory</a> </p> <a href="https://publications.waset.org/abstracts/57915/retrieval-induced-forgetting-effects-in-retrospective-and-prospective-memory-in-normal-aging-an-experimental-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57915.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">316</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">6955</span> Groundwater Level Prediction Using hybrid Particle Swarm Optimization-Long-Short Term Memory Model and Performance Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sneha%20Thakur">Sneha Thakur</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Karmakar"> Sanjeev Karmakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title="long short-term memory">long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</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=groundwater%20level" title=" groundwater level"> groundwater level</a> </p> <a href="https://publications.waset.org/abstracts/171101/groundwater-level-prediction-using-hybrid-particle-swarm-optimization-long-short-term-memory-model-and-performance-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171101.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">78</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">6954</span> The Characterisation of TLC NAND Flash Memory, Leading to a Definable Endurance/Retention Trade-Off</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sorcha%20Bennett">Sorcha Bennett</a>, <a href="https://publications.waset.org/abstracts/search?q=Joe%20Sullivan"> Joe Sullivan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Triple-Level Cell (TLC) NAND Flash memory at, and below, 20nm (nanometer) is still largely unexplored by researchers, and with the ever more commonplace existence of Flash in consumer and enterprise applications there is a need for such gaps in knowledge to be filled. At the time of writing, there was little published data or literature on TLC, and more specifically reliability testing, with a further emphasis on both endurance and retention. This paper will give an introduction to NAND Flash memory, followed by an overview of the relevant current research on the reliability of Flash memory, along with the planned future work which will provide results to help characterise the reliability of TLC memory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=endurance" title="endurance">endurance</a>, <a href="https://publications.waset.org/abstracts/search?q=patterns" title=" patterns"> patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=raw%20flash" title=" raw flash"> raw flash</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=retention" title=" retention"> retention</a>, <a href="https://publications.waset.org/abstracts/search?q=TLC%20NAND%20flash%20memory" title=" TLC NAND flash memory"> TLC NAND flash memory</a>, <a href="https://publications.waset.org/abstracts/search?q=trade-off" title=" trade-off"> trade-off</a> </p> <a href="https://publications.waset.org/abstracts/45350/the-characterisation-of-tlc-nand-flash-memory-leading-to-a-definable-enduranceretention-trade-off" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45350.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6953</span> Uncovering the Role of Crystal Phase in Determining Nonvolatile Flash Memory Device Performance Based on 2D Van Der Waals Heterostructures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yunpeng%20Xia">Yunpeng Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiajia%20Zha"> Jiajia Zha</a>, <a href="https://publications.waset.org/abstracts/search?q=Haoxin%20Huang"> Haoxin Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hau%20Ping%20Chan"> Hau Ping Chan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chaoliang%20Tan"> Chaoliang Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although the crystal phase of two-dimensional (2D) transition metal dichalcogenides (TMDs) has been proven to play an essential role in fabricating high-performance electronic devices in the past decade, its effect on the performance of 2D material-based flash memory devices still remains unclear. Here, we report the exploration of the effect of MoTe₂ in different phases as the charge trapping layer on the performance of 2D van der Waals (vdW) heterostructure-based flash memory devices, where the metallic 1T′-MoTe₂ or semiconducting 2H-MoTe₂ nanoflake is used as the floating gate. By conducting comprehensive measurements on the two kinds of vdW heterostructure-based devices, the memory device based on MoS2/h-BN/1T′-MoTe₂ presents much better performance, including a larger memory window, faster switching speed (100 ns) and higher extinction ratio (107), than that of the device based on MoS₂/h-BN/2H-MoTe₂ heterostructure. Moreover, the device based on MoS₂/h-BN/1T′-MoTe₂ heterostructure also shows a long cycle (>1200 cycles) and retention (>3000 s) stability. Our study clearly demonstrates that the crystal phase of 2D TMDs has a significant impact on the performance of nonvolatile flash memory devices based on 2D vdW heterostructures, which paves the way for the fabrication of future high-performance memory devices based on 2D materials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crystal%20Phase" title="crystal Phase">crystal Phase</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20van%20der%20Waals%20heretostructure" title=" 2D van der Waals heretostructure"> 2D van der Waals heretostructure</a>, <a href="https://publications.waset.org/abstracts/search?q=flash%20memory%20device" title=" flash memory device"> flash memory device</a>, <a href="https://publications.waset.org/abstracts/search?q=floating%20gate" title=" floating gate"> floating gate</a> </p> <a href="https://publications.waset.org/abstracts/185722/uncovering-the-role-of-crystal-phase-in-determining-nonvolatile-flash-memory-device-performance-based-on-2d-van-der-waals-heterostructures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185722.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">51</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">6952</span> Mnemotopic Perspectives: Communication Design as Stabilizer for the Memory of Places </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Galasso">C. Galasso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ancestral relationship between humans and geographical environment has long been at the center of an interdisciplinary dialogue, which sees one of its main research nodes in the relationship between memory and places. Given its deep complexity, this symbiotic connection continues to look for a proper definition that appears increasingly negotiated by different disciplines. Numerous fields of knowledge are involved, from anthropology to semiotics of space, from photography to architecture, up to subjects traditionally far from these reasonings. This is the case of Design of Communication, a young discipline, now confident in itself and its objectives, aimed at finding and investigating original forms of visualization and representation, between sedimented knowledge and new technologies. In particular, Design of Communication for the Territory offers an alternative perspective to the debate, encouraging the reactivation and reconstruction of the memory of places. Recognizing <em>mnemotopes</em> as a cultural object of vertical interpretation of the memory-place relationship, design can become a real mediator of the territorial fixation of memories, making them increasingly accessible and perceptible, contributing to build a topography of memory. According to a mnemotopic vision, Communication Design can support the passage from a memory in which the observer participates only as an individual to a collective form of memory. A mnemotopic form of Communication Design can, through geolocation and content map-based systems, make chronology a topography rooted in the territory and practicable; it can be useful to understand how the perception of the memory of places changes over time, considering how to insert them in the contemporary world. <em>Mnemotopes</em> can be materialized in different format of translation, editing and narration and then involved in complex systems of communication. The memory of places, therefore, if stabilized by the tools offered by Communication Design, can make visible ruins and territorial stratifications, illuminating them with new communicative interests that can be shared and participated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=memory%20of%20places" title="memory of places">memory of places</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20of%20communication" title=" design of communication"> design of communication</a>, <a href="https://publications.waset.org/abstracts/search?q=territory" title=" territory"> territory</a>, <a href="https://publications.waset.org/abstracts/search?q=mnemotope" title=" mnemotope"> mnemotope</a>, <a href="https://publications.waset.org/abstracts/search?q=topography%20of%20memory" title=" topography of memory"> topography of memory</a> </p> <a href="https://publications.waset.org/abstracts/132198/mnemotopic-perspectives-communication-design-as-stabilizer-for-the-memory-of-places" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132198.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">132</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">6951</span> Design and Implementation of a Memory Safety Isolation Method Based on the Xen Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dengpan%20Wu">Dengpan Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Liu"> Dan Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In view of the present cloud security problem has increasingly become one of the major obstacles hindering the development of the cloud computing, put forward a kind of memory based on Xen cloud environment security isolation technology implementation. And based on Xen virtual machine monitor system, analysis of the model of memory virtualization is implemented, using Xen memory virtualization system mechanism of super calls and grant table, based on the virtual machine manager internal implementation of access control module (ACM) to design the security isolation system memory. Experiments show that, the system can effectively isolate different customer domain OS between illegal access to memory data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20security" title="cloud security">cloud security</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20isolation" title=" memory isolation"> memory isolation</a>, <a href="https://publications.waset.org/abstracts/search?q=xen" title=" xen"> xen</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20machine" title=" virtual machine"> virtual machine</a> </p> <a href="https://publications.waset.org/abstracts/22897/design-and-implementation-of-a-memory-safety-isolation-method-based-on-the-xen-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22897.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">409</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">6950</span> Learning with Music: The Effects of Musical Tension on Long-Term Declarative Memory Formation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nawras%20Kurzom">Nawras Kurzom</a>, <a href="https://publications.waset.org/abstracts/search?q=Avi%20Mendelsohn"> Avi Mendelsohn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=musical%20tension" title="musical tension">musical tension</a>, <a href="https://publications.waset.org/abstracts/search?q=declarative%20memory" title=" declarative memory"> declarative memory</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20and%20memory" title=" learning and memory"> learning and memory</a>, <a href="https://publications.waset.org/abstracts/search?q=musical%20perception" title=" musical perception"> musical perception</a> </p> <a href="https://publications.waset.org/abstracts/168139/learning-with-music-the-effects-of-musical-tension-on-long-term-declarative-memory-formation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168139.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">98</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">6949</span> Bidirectional Long Short-Term Memory-Based Signal Detection for Orthogonal Frequency Division Multiplexing With All Index Modulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmut%20Yildirim">Mahmut Yildirim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20long%20short-term%20memory" title="bidirectional long short-term memory">bidirectional long short-term memory</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=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM%20with%20all%20index%20modulation" title=" OFDM with all index modulation"> OFDM with all index modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20detection" title=" signal detection"> signal detection</a> </p> <a href="https://publications.waset.org/abstracts/183512/bidirectional-long-short-term-memory-based-signal-detection-for-orthogonal-frequency-division-multiplexing-with-all-index-modulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183512.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">72</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">6948</span> Trimma: Trimming Metadata Storage and Latency for Hybrid Memory Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiwei%20Li">Yiwei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Boyu%20Tian"> Boyu Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingyu%20Gao"> Mingyu Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hybrid main memory systems combine both performance and capacity advantages from heterogeneous memory technologies. With larger capacities, higher associativities, and finer granularities, hybrid memory systems currently exhibit significant metadata storage and lookup overheads for flexibly remapping data blocks between the two memory tiers. To alleviate the inefficiencies of existing designs, we propose Trimma, the combination of a multi-level metadata structure and an efficient metadata cache design. Trimma uses a multilevel metadata table to only track truly necessary address remap entries. The saved memory space is effectively utilized as extra DRAM cache capacity to improve performance. Trimma also uses separate formats to store the entries with non-identity and identity mappings. This improves the overall remap cache hit rate, further boosting the performance. Trimma is transparent to software and compatible with various types of hybrid memory systems. When evaluated on a representative DDR4 + NVM hybrid memory system, Trimma achieves up to 2.4× and on average 58.1% speedup benefits, compared with a state-of-the-art design that only leverages the unallocated fast memory space for caching. Trimma addresses metadata management overheads and targets future scalable large-scale hybrid memory architectures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=memory%20system" title="memory system">memory system</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20cache" title=" data cache"> data cache</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20memory" title=" hybrid memory"> hybrid memory</a>, <a href="https://publications.waset.org/abstracts/search?q=non-volatile%20memory" title=" non-volatile memory"> non-volatile memory</a> </p> <a href="https://publications.waset.org/abstracts/183183/trimma-trimming-metadata-storage-and-latency-for-hybrid-memory-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183183.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">78</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">6947</span> Short-Term and Working Memory Differences Across Age and Gender in Children</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzaneh%20Badinloo">Farzaneh Badinloo</a>, <a href="https://publications.waset.org/abstracts/search?q=Niloufar%20Jalali-Moghadam"> Niloufar Jalali-Moghadam</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Kormi-Nouri"> Reza Kormi-Nouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study was to explore the short-term and working memory performances across age and gender in school aged children. Most of the studies have been interested in looking into memory changes in adult subjects. This study was instead focused on exploring both short-term and working memories of children over time. Totally 410 school child participants belonging to four age groups (approximately 8, 10, 12 and 14 years old) among which were 201 girls and 208 boys were employed in the study. digits forward and backward tests of the Wechsler children intelligence scale-revised were conducted respectively as short-term and working memory measures. According to results, there was found a general increment in both short-term and working memory scores across age (p ˂ .05) by which whereas short-term memory performance was shown to increase up to 12 years old, working memory scores showed no significant increase after 10 years old of age. No difference was observed in terms of gender (p ˃ .05). In conclusion, this study suggested that both short-term and working memories improve across age in children where 12 and 10 years of old are likely the crucial age periods in terms of short-term and working memories development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=age" title="age">age</a>, <a href="https://publications.waset.org/abstracts/search?q=gender" title=" gender"> gender</a>, <a href="https://publications.waset.org/abstracts/search?q=short-term%20memory" title=" short-term memory"> short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20memory" title=" working memory"> working memory</a> </p> <a href="https://publications.waset.org/abstracts/30471/short-term-and-working-memory-differences-across-age-and-gender-in-children" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30471.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">478</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">6946</span> Digital Memory plus City Cultural Heritage: The Peking Memory Project Experience</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huiling%20Feng">Huiling Feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoshuang%20Jia"> Xiaoshuang Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Jihong%20Liang"> Jihong Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Niu"> Li Niu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Beijing, formerly romanized as Peking, is the capital of the People's Republic of China and the world's second most populous city proper and most populous capital city. Beijing is a noted historical and cultural whose city history dates back three millennia which is extremely rich in terms of cultural heritage. In 2012, a digital memory project led by Humanistic Beijing Studies Center in Renmin University of China started with the goal to build a total digital collection of knowledge assets about Beijing and represent Beijing memories in new fresh ways. The title of the entire project is ‘Peking Memory Project(PMP)’. The main goal is for safeguarding the documentary heritage and intellectual memory of Beijing, more specifically speaking, from the perspective of historical humanities and public participation, PMP will comprehensively applied digital technologies like digital capture, digital storage, digital process, digital presentation and digital communication to transform different kinds of cultural heritage of Beijing into digital formats that can be stored, re-organized and shared. These digital memories can be interpreted with a new perspective, be organized with a new theme, be presented in a new way and be utilized with a new need. Taking social memory as theoretical basis and digital technologies as tools, PMP is framed with ‘Two Sites and A Repository’. Two sites mean the special website(s) characterized by ‘professional’ and an interactive website characterized by ‘crowdsourcing’. A Repository means the storage pool used for safety long-time preservation of the digital memories. The work of PMP has ultimately helped to highlight the important role in safeguarding the documentary heritage and intellectual memory of Beijing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20memory" title="digital memory">digital memory</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20heritage" title=" cultural heritage"> cultural heritage</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20technologies" title=" digital technologies"> digital technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=peking%20memory%20project" title=" peking memory project"> peking memory project</a> </p> <a href="https://publications.waset.org/abstracts/87341/digital-memory-plus-city-cultural-heritage-the-peking-memory-project-experience" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87341.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">6945</span> The Involvement of Visual and Verbal Representations Within a Quantitative and Qualitative Visual Change Detection Paradigm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laura%20Jenkins">Laura Jenkins</a>, <a href="https://publications.waset.org/abstracts/search?q=Tim%20Eschle"> Tim Eschle</a>, <a href="https://publications.waset.org/abstracts/search?q=Joanne%20Ciafone"> Joanne Ciafone</a>, <a href="https://publications.waset.org/abstracts/search?q=Colin%20Hamilton"> Colin Hamilton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An original working memory model suggested the separation of visual and verbal systems in working memory architecture, in which only visual working memory components were used during visual working memory tasks. It was later suggested that the visuo spatial sketch pad was the only memory component at use during visual working memory tasks, and components such as the phonological loop were not considered. In more recent years, a contrasting approach has been developed with the use of an executive resource to incorporate both visual and verbal representations in visual working memory paradigms. This was supported using research demonstrating the use of verbal representations and an executive resource in a visual matrix patterns task. The aim of the current research is to investigate the working memory architecture during both a quantitative and a qualitative visual working memory task. A dual task method will be used. Three secondary tasks will be used which are designed to hit specific components within the working memory architecture – Dynamic Visual Noise (visual components), Visual Attention (spatial components) and Verbal Attention (verbal components). A comparison of the visual working memory tasks will be made to discover if verbal representations are at use, as the previous literature suggested. This direct comparison has not been made so far in the literature. Considerations will be made as to whether a domain specific approach should be employed when discussing visual working memory tasks, or whether a more domain general approach could be used instead. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20organisation" title="semantic organisation">semantic organisation</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20memory" title=" visual memory"> visual memory</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title=" change detection"> change detection</a> </p> <a href="https://publications.waset.org/abstracts/22696/the-involvement-of-visual-and-verbal-representations-within-a-quantitative-and-qualitative-visual-change-detection-paradigm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22696.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">595</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">6944</span> Hydrogen: Contention-Aware Hybrid Memory Management for Heterogeneous CPU-GPU Architectures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiwei%20Li">Yiwei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingyu%20Gao"> Mingyu Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Integrating hybrid memories with heterogeneous processors could leverage heterogeneity in both compute and memory domains for better system efficiency. To ensure performance isolation, we introduce Hydrogen, a hardware architecture to optimize the allocation of hybrid memory resources to heterogeneous CPU-GPU systems. Hydrogen supports efficient capacity and bandwidth partitioning between CPUs and GPUs in both memory tiers. We propose decoupled memory channel mapping and token-based data migration throttling to enable flexible partitioning. We also support epoch-based online search for optimized configurations and lightweight reconfiguration with reduced data movements. Hydrogen significantly outperforms existing designs by 1.21x on average and up to 1.31x. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20memory" title="hybrid memory">hybrid memory</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20systems" title=" heterogeneous systems"> heterogeneous systems</a>, <a href="https://publications.waset.org/abstracts/search?q=dram%20cache" title=" dram cache"> dram cache</a>, <a href="https://publications.waset.org/abstracts/search?q=graphics%20processing%20units" title=" graphics processing units"> graphics processing units</a> </p> <a href="https://publications.waset.org/abstracts/183187/hydrogen-contention-aware-hybrid-memory-management-for-heterogeneous-cpu-gpu-architectures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6943</span> Power Grid Line Ampacity Forecasting Based on a Long-Short-Term Memory Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang-Yao%20Zheng">Xiang-Yao Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Jen-Cheng%20Wang"> Jen-Cheng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Joe-Air%20Jiang"> Joe-Air Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20dispatch" title="electricity dispatch">electricity dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20ampacity%20prediction" title=" line ampacity prediction"> line ampacity prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20thermal%20rating" title=" dynamic thermal rating"> dynamic thermal rating</a>, <a href="https://publications.waset.org/abstracts/search?q=long-short-term%20memory%20neural%20network" title=" long-short-term memory neural network"> long-short-term memory neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title=" smart grid"> smart grid</a> </p> <a href="https://publications.waset.org/abstracts/63755/power-grid-line-ampacity-forecasting-based-on-a-long-short-term-memory-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63755.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">282</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=long%20short-term%20memory&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory&page=3">3</a></li> <li class="page-item"><a class="page-link" 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