The evaluation and diagnosis of diseases related to blood cancer can be intricate and time-consuming. This complexity is compounded by the reliance on manual analyses employing techniques that consume a significant am...
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In this paper, we propose a point-cloudbased algorithm for human-following robots to detect and follow the target person in a complex outdoor environment. Specifically, we exploit Ada Boost to train a binary classifie...
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In this paper, we propose a point-cloudbased algorithm for human-following robots to detect and follow the target person in a complex outdoor environment. Specifically, we exploit Ada Boost to train a binary classifier in a designed feature space based on sparse point-cloud to distinguish the target person from other objects. Then a particle filter is applied to continuously track the target's position. Motivated by the interference of obstacles in long-distance human-following scenarios, a motion plan algorithm based on vector field histogram is adopted. Experiments are carried out both on the dataset we collected and in real application scenarios. The results show that our algorithm has the ability of real-time target detection and tracking, and is robust to deal with complex situations in outdoor environments.
In this study, we investigate the efficacy of incorporating custom local binary patterns (LBP) as a preprocessing technique to enhance the performance of Siamese neural networks for face matching tasks. Our research f...
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The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban...
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The segmentation of medical images is crucial, particularly in brain tumor MR imaging, as it aids doctors in accurate diagnosis and treatment planning. However, conventional UNet models often face limitations due to t...
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Conventional feature selection methods select the same feature subset for all classes, which means that the selected features might work better for some classes than the others. Towards this end, this paper proposes a...
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Conventional feature selection methods select the same feature subset for all classes, which means that the selected features might work better for some classes than the others. Towards this end, this paper proposes a new semi-supervised local feature selection method(S2 LFS) allowing to select different feature subsets for different classes. According to this method, class-specific feature subsets are selected by learning the importance of features considering each class separately. In particular, the class labels of all available data are jointly learned under a consistent constraint over the labeled data, which enables the proposed method to select the most discriminative features. Experiments on six data sets demonstrate the effectiveness of the proposed method compared to some popular feature selection methods.
Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low...
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Predicting the future course of the stock market is challenging because of its volatility and multifaceted dependencies. This study introduces a novel approach that harnesses the power of Deep Learning Recurrent Neura...
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This paper examines the fine-tuning methods of two Code Language Models, namely Code-Reviewer and GraphCodeBERT, with the objective of enhancing code clone detection in C++ code. Two experiments are conducted: sentenc...
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Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provabl...
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Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate *** is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive *** this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data *** the open problem presented by Wang et *** whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its *** to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve *** prove that the MC problem and the MK problem can be Turing-reduced to each ***,we discuss the update of MK under insertion and deletion operations,***,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of *** efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic *** show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,*** speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic ***-SCMK runs up to three times faster than SCMK on synthetic datasets.
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