作者：
Rabiha, Suciana GhadatiWibowo, AntoniLukasHeryadi, YayaComputer Science Department
BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department
BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia
Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department
BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia
One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to det...
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In recent years, great progress has been made in the study of crowd counting. Although the crowd counting networks being proposed to solve different problems have achieved satisfactory counting results, the difference...
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The proliferation of large-scale applications has led to the generation of vast datasets across diverse scientific domains. The subsequent need to transfer such expansive data across geographical distances is essentia...
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ISBN:
(数字)9798350370997
ISBN:
(纸本)9798350371000
The proliferation of large-scale applications has led to the generation of vast datasets across diverse scientific domains. The subsequent need to transfer such expansive data across geographical distances is essential for collaborative data storage and analysis. While reserving bandwidth on dedicated links within high-performance networks (HPNs) has proved as an efficient means for such extensive data transfers, certain crucial challenges remain to be investigated. In this paper, we delve into the intricate tradeoff between cost and completion time of data transfers using bandwidth reservation on fixed paths with fixed bandwidth of the HPNs, the most common type of bandwidth reservation or data transfer paths. Our focus centers on the scheduling of two types of bandwidth reservation requests (BRRs) that encapsulate this tradeoff: (i) minimizing data transfer cost within prescribed deadlines, and (ii) achieving the earliest data transfer completion time while adhering to predefined cost constraints. We propose two algorithms to optimize the scheduling of individual BRRs of these two types. We then compare the proposed algorithms with existing ones from the perspective of different performance metrics, and efficacy of the proposed algorithms is verified through extensive simulations.
Makeup transfer aims to extract a specific makeup from a face and transfer it to another face, which can be widely used in portrait beauty, and cosmetics marketing. At present, existing methods can achieve the transfe...
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Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is sign...
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We report Ge23Sb7S70 chalcogenide ring resonators with up to 8 × 104 quality factors operating around 3.6 µm wavelength fabricated through e-beam lithography. Their rib waveguide geometry can be engineered t...
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We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework. This framework generates large amounts of predictive features for noisy multivariate t...
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Traffic prediction is of vital importance in intelligent transportation systems. It enables efficient route planning, congestion avoidance, and reduction of travel time, etc. However, accurate road traffic prediction ...
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Traffic prediction is of vital importance in intelligent transportation systems. It enables efficient route planning, congestion avoidance, and reduction of travel time, etc. However, accurate road traffic prediction is challenging due to the complex spatio-temporal dependencies within the traffic network. Establishing and learning spatial dependencies are pivotal for accurate traffic prediction. Unfortunately, many existing methods for capturing spatial dependencies only consider single relationships, disregarding potential temporal and spatial correlations within the traffic network. Moreover, the end-to-end training methods often lack control over the training direction during graph learning. Additionally, existing traffic forecasting methods often fail to integrate multiple traffic data sources effectively, which affects prediction accuracy adversely. In order to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. Firstly, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includes the distance relationship, transfer relationship and same-road segment relationship of traffic data. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, experimental results using two large real-world datasets demonstrate that the DTS-adapSTNet model outperforms other baseline models in terms of MAE, RMSE, and MAPE wh
作者：
Kim, Jon-LarkEor, EunjeeSogang University
Department of Mathematics Korea and Institute for Mathematical and Data Sciences Seoul Korea Republic of Sogang University
Department of Computer Science and Engineering Korea Republic of
In this paper, we introduce a Genetic Algorithm based Upper Confidence Bound (GA-UCB), an innovative hybrid genetic algorithm integrating Multi-Armed Bandit (MAB). It effectively addresses the challenges of solving la...
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With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magni...
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