A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web ...
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A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a *** emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of *** a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage ***,the question of how to determine the most suitable mashups from big data has become a challenging *** this paper,we propose a mashup recommendation framework from big data in mobile networks and the *** proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix *** employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the *** also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization *** then crawl through a real-world large mashup dataset and perform *** experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.
Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate *** inherent traits often lead to increased miss and false detection rat...
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Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate *** inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing ***,these complexities contribute to inaccuracies in target localization and hinder precise target *** paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery ***,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex *** resolve these issues,we introduce a novel ***,we propose the implementation of a lightweight multi-scale module called *** module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature *** effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing ***,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone *** allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed ***,a dynamic head attentionmechanism is *** allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different ***,the precision of object detection is significantly *** trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 a
Sharding technology achieves parallel processing of transactions by dividing the network into multiple independent parts, namely shards, significantly increasing the throughput of the blockchain system and reducing tr...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.
With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism withou...
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With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism without prior ***,endogenous security lacks a scientific and formal definition in industrial ***,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost ***,the endogenous security innovation mechanism is clearly ***,an improved clone selection algorithm based on federated learning is ***,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge *** conduct identity authentication experiments based on two types of blockchains and compare their experimental *** on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial *** of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge ***,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.
Network traffic identification is critical for maintaining network security and further meeting various demands of network ***,network traffic data typically possesses high dimensionality and complexity,leading to pra...
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Network traffic identification is critical for maintaining network security and further meeting various demands of network ***,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data *** the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic ***,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal ***,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal ***,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate *** the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)*** simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO *** experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,***,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identificati
Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired c...
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Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point ***,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual *** tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency *** proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak *** having accurate co-peaks,one can efficiently infer responses of the targeted *** on four benchmark datasets validate the superior performance of the proposed method.
Branched polyolefins with controllable topology structures were generated from the chain-walking(co)polymerizations of ethylene,1-pentene(1P)and 2-pentene(2P)using Brookhart-typeα-diimine Ni(II)-based catalysts posse...
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Branched polyolefins with controllable topology structures were generated from the chain-walking(co)polymerizations of ethylene,1-pentene(1P)and 2-pentene(2P)using Brookhart-typeα-diimine Ni(II)-based catalysts possessing different para-substituted groups,{[(4-R-2-Et-6-Me-C6H2N=C)2Nap]NiBr2,Nap:1,8-naphthdiyl;R=CHMePh,Ni1;R=Ph,Ni2;R=H,Ni3}.The X-ray diffraction analysis demonstrated that the crystalline structure of Ni1′is in centrosymmetric dimer structure mode with the bimetallic Ni center connected by two bromide *** para-sec-phenethyl moiety in the catalyst Ni1 obviously improved its catalytic performance and thermal stability in the ethylene *** Ni1/Et2AlCl system showed great catalytic activities(up to 7.73×106 g·mol-1·h-1)and achieved polyethylene(PE)with alkyl chains,including Me,Et,n-Pr,n-Bu,sec-Bu branches and longer chains(Lg).Compared with the 1-pentene polymerization,this catalyst system successfully mediated the polymerization of 2P to give highly branched polymers with approximately 195 branches/1000C possessing Me,Et,and n-Pr branches and a long methylene sequence due to the monomer *** Et branches derived from 2,3-insertion is slightly less than the sum of Me and n-Pr branches derived from 3,2-insertion,indicating that the 3,2-insertion mode is a slightly favorable pathway in the polymerization of 2P.
Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance b...
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Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.
Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with ...
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