We investigate the problem of last-mile delivery, where a large amount of crowd-workers have performed a great quantity of location-specific urban logistics parcels. Current existing approaches mainly focus on offline...
ISBN:
(数字)9781728170817
ISBN:
(纸本)9781728170824
We investigate the problem of last-mile delivery, where a large amount of crowd-workers have performed a great quantity of location-specific urban logistics parcels. Current existing approaches mainly focus on offline scenarios, where all the spatial-temporal information of parcels and workers are given. However, the offline scenarios can be impractical since parcels and workers appear dynamically in reality, and the information of workers is unknown in advance. In this paper, we study the problem of last-mile delivery on online scenarios to resolve the shortcomings of the offline setting. We first formalize the online parcel allocation in last-mile delivery problem, where all parcels were put in pop-stations in advance, and workers arrive dynamically. Then we propose a baseline algorithm with no competitive ratio, and an algorithm providing theoretical guarantee for the parcel allocation in last-mile delivery. Finally, we verify the effectiveness and efficiency of proposed algorithms through extensive experiments on real and synthetic datasets.
This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study ad...
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The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stro...
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The past few years have witnessed fast development in video quality enhancement via deep learning. Existing methods mainly focus on enhancing the objective quality of compressed video while ignoring its perceptual qua...
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The research of human facial age estimation(AE)has attracted increasing attention for its wide *** to date,a number of models have been constructed or employed to perform *** the goal of AE can be achieved by either c...
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The research of human facial age estimation(AE)has attracted increasing attention for its wide *** to date,a number of models have been constructed or employed to perform *** the goal of AE can be achieved by either classification or regression,the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age ***,the neighbor-similarity and ordinality of age labels are not taken into account *** overcome this issue,the cumulative attribute(CA)coding was *** such age label relationships can be parameterized via CA coding,the potential relationships behind age features are not incorporated to estimate *** this end,in this paper we propose to perform AE by encoding the potential age feature relationships with CA coding via an implicit modeling *** that,we further extend our model to gender-aware AE by taking into account gender variance in aging ***,we experimentally validate the superiority of the proposed methodology.
This paper probes into the all-to-all comparison of large dataset, and gives a formal mathematical description of the problem. Then, a multi-objective file distribution model was constructed based on the LP, aiming to...
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Time Series Classification has become one of the most challenging problems in many signal processing and machine learning applications, e.g., audio/video signal processing and EEG signal processing. We propose a novel...
Time Series Classification has become one of the most challenging problems in many signal processing and machine learning applications, e.g., audio/video signal processing and EEG signal processing. We propose a novel method of extracting features from data for classification and representing data on a Grassmann manifold by parameterizing it using the autoregressive moving model (ARMA). Then we perform classification on these extracted features by training support vector machines (SVM), with appropriate kernels on the Grassmann manifold. We performed tests on several publicly available datasets. We found that an SVM with a proper kernel on the Grassmann manifold consistently performs better than an SVM using a typical Gaussian kernel that acts on the data in Euclidean space. Furthermore, we found that the Grassmann SVM technique overperforms the literature in some high-dimensional datasets, without the need for any other preprocessing techniques. This work demonstrates the power of data-based manifold techniques in improving the performance of existing algorithms.
With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can shar...
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ISBN:
(数字)9781728175348
ISBN:
(纸本)9781728175355
With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can share their private data with efficient search function and high-strength security. However, the search method has yet to be perfected, compared with the plaintext search mechanism. In this paper, based LSSS matrix, we give a new searchable algorithm, which is suitable for many search method, such as exact search, Boolean search and range search. In order to improve the search efficiency, the 0, 1-coding theory is introduced in the process of ciphertext search. Meanwhile it is shown that multi-search mechanism can improve the efficiency of data sharing. Finally, the performance analysis is presented, which prove our scheme is secure, efficient, and human-friendly.
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