The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function v...
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The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function values than the K-means ***,CDKM is sensitive to initialization,which makes the K-means objective function values not small *** selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of *** proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value *** split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains *** algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition *** on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional *** proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.
Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item ***,existing recommendation methods have significant shor...
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Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item ***,existing recommendation methods have significant shortcomings in capturing the dynamic preference changes of users and discovering their true potential *** address these problems,a novel framework named Intent-Aware Graph-Level Embedding Learning(IaGEL)is proposed for *** this framework,the potential user interest is explored by capturing the co-occurrence of items in different periods,and then user interest is further improved based on an adaptive aggregation algorithm,forming generic intents and specific *** addition,for better representing the intents,graph-level embedding learning is designed based on the mutual information comparison among positive intents and negative ***,an intent-based recommendation strategy is designed to further mine the dynamic changes in user *** on three public and industrial datasets demonstrate the effectiveness of the proposed IaGEL in the task of recommendation.
In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,a...
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In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.
In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a ***,this kind ofmethod is dependent on a...
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In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a ***,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video *** address this issue,this paper proposes a video captioning method by semantic topic-guided ***,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the ***,the semantic topics of video data are extracted using the visual labels retrieved from similar video *** the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video *** this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted ***,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text *** experimental results demonstrate that the proposed method outperforms several state-of-art ***,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset
Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a se...
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Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.
Generating selfie images on the surface of a celestial body poses several challenges,including the position of the robotic arm,camera field of view,and limited shooting *** address these challenges,the PCMIS(3D Point ...
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Generating selfie images on the surface of a celestial body poses several challenges,including the position of the robotic arm,camera field of view,and limited shooting *** address these challenges,the PCMIS(3D Point Cloud Matching Based Image Stitching)algorithm is designed,along with a corresponding shooting *** algorithm estab-lishes a correspondence between depth and color information,enabling the generation of stitching views under any given view ***,the algorithm is accelerated using GPU processing,resulting in a significant reduction in stitching *** algorithm is successfully applied to generate selfie images for the Chang'e-5 mission.
Aerosol-transmitted viruses possess strong infectivity and can spread over long distances,earning the difficult-to-control *** cause various human diseases and pose serious threats to human *** can increase the transm...
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Aerosol-transmitted viruses possess strong infectivity and can spread over long distances,earning the difficult-to-control *** cause various human diseases and pose serious threats to human *** can increase the transmissibility and virulence of the strains,reducing the protection provided by vaccines and weakening the efficacy of antiviral *** this study,we established a manually curated database(termed AVM)to store information on aerosol-transmitted viral mutations(VMs).The current version of the AVM contains 42,041 VMs(including 2613 immune escape mutations),45 clinical information datasets,and 407 drugs/antibodies/***,we recorded 88 human diseases associated with viruses and found that the same virus can target multiple organs in the body,leading to diverse ***,the AVM database offers a straightforward user interface for browsing,retrieving,and downloading *** database is a comprehensive resource that can provide timely and valuable information on the transmission,treatment,and diseases caused by aerosol-transmitted viruses(http://***/AVM).
When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication ***,it is necessary t...
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In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transm...
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In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transmit live videos has not been studied *** paper first investigates the achievable quality of experience(QoE)of streaming live videos over TCP,QUIC,and their multipath extensions Multipath TCP(MPTCP)and Multipath QUIC(MPQUIC).We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission ***,network fluctuations may cause heterogeneous paths,high path loss,and band-width degradation,resulting in significant QoE *** by the above observations,we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP,a multipath adaptive packet scheduling scheme over ***,a linear upper confidence bound(LinUCB)-based online learning algorithm,along with four novel scheduling mechanisms,i.e.,Dispatch,Duplicate,Discard,and Decompensate,is proposed to conquer the above problems.4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission *** results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.
As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empi...
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As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.
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