Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the maj...
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Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.
Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth ***,in practice,it is not always feasible to obtain clean point *** this...
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Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth ***,in practice,it is not always feasible to obtain clean point *** this paper,we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during *** demonstrate that it is feasible for neural networks to only take noisy point clouds as input,and learn to approximate and restore their clean *** particular,we generate two noise levels for the original point clouds,requiring the second noise level to be twice the amount of the first noise *** this,we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise,and thus learn the displacement of each noisy point in order to recover the corresponding clean *** experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise,obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.
Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the infor...
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Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial express...
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This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schem...
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This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.
Cross-Site Scripting (XSS) is one of the most grievous vulnerabilities-a pitfall through which web applications are affected. These types of attacks are complex, and the available threat landscape is always changing, ...
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In the era of 6G, cellular networks will no longer be locked into a small set of equipment manufacturers;instead, cellular networks will be disaggregated and support open interfaces. Thus, there is an inherent need fo...
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Interoperability is a crucial aspect of the effective functioning of Internet of Things (IoT) devices, particularly in the healthcare industry. Although the use of IoT devices in healthcare has brought numerous benefi...
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In response to the escalating energy demands of mobile networks, exacerbated by the disproportionate usage between urban and suburban areas, this study introduces a transformative approach using a heterogeneous UAV-ba...
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In the era of digital information overload, the ability to summarize books efficiently emerges as an invaluable skill. Book summarization condenses extensive texts into digestible, concise summaries, enabling readers ...
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