Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to bes...Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.展开更多
In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields loc...In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.展开更多
A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science.The research presented in t...A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science.The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout.Such structural properties are modeled as an aggregation of parcels,buildings,and road networks.We introduce several computational measures(Ratio Minimum Distance,Minimum Ratio Minimum Distance,and Metric Compactness)that respectively evaluate the capability for a given road to be connected with the whole road network.These measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the network.Based on these local and global properties derived from the topological and graph-based representation,and on building density metrics,this paper proposes an analysis of road and building layouts at different levels of granularity.The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment.Overall,and by integrating the parcels and buildings layouts,this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout.展开更多
Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale...Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.展开更多
The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based fea...The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.展开更多
Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involve...Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues;therefore investigators concentrate on using Meta-heuristic approaches.Dragonfly Optimization(DFO)is an effective meta-heuristic approach to resolve these problems by providing optimal solutions.Moreover,Meta-heuristic approaches(DFO)turn to be slower in convergence problems and need proper computational time while expanding network size.Thus,DFO is adaptively improved as Adaptive Dragonfly Optimization(ADFO)to fit this model and re-formulated using graph-based m-connection establishment(G-𝑚𝑚CE)to overcome computational time and DFO’s convergence based problems,considerably enhancing DFO performance.In(G-𝑚𝑚CE),Connectivity Zone(CZ)is chosen among source to destination in which optimality should be under those connected regions and ADFO is used for effective route establishment in CZ indeed of complete networking model.To measure complementary features of ADFO and(G-𝑚𝑚CE),hybridization of DFO-(G-𝑚𝑚CE)is anticipated over dense circumstances with reduced energy consumption and delay to enhance network lifetime.The simulation was performed in MATLAB environment.展开更多
Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrast...Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrastructures,the information technology platform is anticipating a completely new level of devel-opment.The following concepts are proposed in this research paper:1)A reliable authentication method Data replication that is optimised;graph-based data encryp-tion and packing colouring in Redundant Array of Independent Disks(RAID)sto-rage.At the data centre,data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph’s jump graph.In order to achieve space efficiency,the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security,enhanced authentication,and low cost.展开更多
Electric vehicles(EVs)continue to gain popularity over internal combustion vehicles,but driving and charging an EV is a fundamentally different experience.EV usage patterns include features such as charging speed,batt...Electric vehicles(EVs)continue to gain popularity over internal combustion vehicles,but driving and charging an EV is a fundamentally different experience.EV usage patterns include features such as charging speed,battery state of charge,and battery depth of discharge.Understanding EV usage patterns from data is essential to optimizing the performance and management of EVs.However,extracting useful conclusions from individual EV data is computationally intensive.Traditional clustering methods offer a solution by grouping vehicles by behavior pattern,but they still pose computational challenges for long time-series data profiles.To efficiently extract EV behavior insights,we propose a scalable two-level clustering approach and test it on a dataset of 3,082 real EVs over the course of a year.We first use level one clustering to weekly data segments,revealing distinct patterns in state of charge utilization.Next,we apply level two graph-based clustering to group individual vehicles that operate similarly on a longer timescale.Our scalable and adaptable clustering approach can aid in battery lifecycle management,charge demand forecasting,and fleet energy management,all critical tasks to facilitate the continued growth of the EV market.展开更多
Potato is the world’s most important nongrain crop.In this study,to assess genetic diversity within the Petota section,29 genomes from Petota and Etuberosum sections were newly de novo assembled and 248 accessions of...Potato is the world’s most important nongrain crop.In this study,to assess genetic diversity within the Petota section,29 genomes from Petota and Etuberosum sections were newly de novo assembled and 248 accessions of wild potatoes,landraces,and modern cultivars were re-sequenced at>253 depth.Subsequently,a graph-based pangenome was constructed using DM8.1 as the backbone,integrating194,330 nonredundant structural variants.To characterize the metabolome of tubers and illuminate the genomic basis of metabolic traits,LC-MS/MS was employed to obtain the metabolome of 157 accessions,and 9,321 structural variants(SVs)were detected to be significantly associated with 1,258 distinct metabolites via PAV(presence and absence variations)-based metabolomics-GWAS analysis,including metabolites of flavonoids,phenolic acids,and phospholipids.To facilitate the utilization of pangenome resources,a comprehensive platform,the Potato Pangenome Database(PPDB),was developed.Our study provides a comprehensive genomic resource for dissecting the genomic basis of agronomic and metabolic traits in potato,which will accelerate functional genomics studies and genetic improvements in potato.展开更多
Chickens are one of the most important domesticated animals,serving as an important protein source.Studying genetic variations in chickens to enhance their production performance is of great potential value.The emerge...Chickens are one of the most important domesticated animals,serving as an important protein source.Studying genetic variations in chickens to enhance their production performance is of great potential value.The emergence of next-generation sequencing has enabled precise analysis of single nucleotide polymorphisms and insertions/deletions in chicken,while third-generation sequencing achieves the accurate structural variant identification.However,the high cost of third-generation sequencing technology limits its application in population studies.The graph-based pan-genome strategy can overcome this challenge by enabling the detection of structural variations using cost-effective next-generation sequencing data.This study constructed a graph-based pangenome for chickens using 12 high-quality genomes.This pan-genome used linear genome GRCg6a as the reference genome,containing variant information from two commercial and nine native chicken breeds.Compared to the linear genome,the pan-genome provided significant improvements in the efficiency of structural variation identification.On the basis of the graph-based pan-genome,high-frequency structural variations related to high egg production in Leghorn chicken were predicted.Additionally,it was discovered that potential structural variations was associated with highland adaptation in Tibetan chickens according to next-generation sequencing and transcriptomics data.Using the pan-genome graph,a new strategy to identify structural variations related to traits of interest in chickens is presented.展开更多
Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,ter...Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,termed graph-based transform(GBT)and dual graph Laplacian regularization(DGLR)(DGLR-GBT).This model specifically aims to remove Gaussian white noise by capitalizing on the nonlocal self-similarity(NSS)and the piecewise smoothness properties intrinsic to depth maps.Within the group sparse coding(GSC)framework,a combination of GBT and DGLR is implemented.Firstly,within each group,the graph is constructed by using estimates of the true values of the averaged blocks instead of the observations.Secondly,the graph Laplacian regular terms are constructed based on rows and columns of similar block groups,respectively.Lastly,the solution is obtained effectively by combining the alternating direction multiplication method(ADMM)with the weighted thresholding method within the domain of GBT.展开更多
The domestication of Brassica oleracea has resulted in diverse morphological types with distinct patterns of organ development.Here we report a graph-based pan-genome of B.oleracea constructed from high-quality genome...The domestication of Brassica oleracea has resulted in diverse morphological types with distinct patterns of organ development.Here we report a graph-based pan-genome of B.oleracea constructed from high-quality genome assemblies of different morphotypes.The pan-genome harbors over 200 structural variant hotspot regions enriched in auxin-andflowering-related genes.Population genomic analyses revealed that early domestication of B.oleracea focused on leaf or stem development.Geneflows resulting from agricultural practices and variety improvement were detected among different morphotypes.Selective-sweep and pan-genome analyses identified an auxin-responsive small auxin up-regulated RNA gene and a CLAV-ATA3/ESR-RELATED family gene as crucial players in leaf–stem differentiation during the early stage of B.oleracea domestication and the BoKAN1 gene as instrumental in shaping the leafy heads of cabbage and Brussels sprouts.Our pan-genome and functional analyses further revealed that variations in the BoFLC2 gene play key roles in the divergence of vernalization andflowering characteristics among different morphotypes,and variations in thefirst intron of BoFLC3 are involved infine-tuning theflowering process in cauliflower.This study provides a comprehensive understanding of the pan-genome of B.oleracea and sheds light on the domestication and differential organ development of this globally important crop species.展开更多
Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affe...Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods.展开更多
Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resu...Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.展开更多
The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear ...The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear neighborhood propagation(LNP)which can automatically construct the optimal graph;2)a novel multilevel scheme to make our algorithm scalable for large data sets;3)a generalized point charge scheme for GBSSL;4)a multilabel GBSSL method by solving a Sylvester equation;5)an information fusion framework for GBSSL;and 6)an application of GBSSL on fMRI image segmentation.展开更多
Defect inspection,also known as defect detection,is significant in mobile screen quality control.There are some challenging issues brought by the characteristics of screen defects,including the following:(1)the proble...Defect inspection,also known as defect detection,is significant in mobile screen quality control.There are some challenging issues brought by the characteristics of screen defects,including the following:(1)the problem of interclass similarity and intraclass variation,(2)the difficulty in distinguishing low contrast,tiny-sized,or incomplete defects,and(3)the modeling of category dependencies for multi-label images.To solve these problems,a graph reasoning module,stacked on a classification module,is proposed to expand the feature dimension and improve low-quality image features by exploiting category-wise dependency,image-wise relations,and interactions between them.To further improve the classification performance,the classifier of the classification module is redesigned as a cosine similarity function.With the help of contrastive learning,the classification module can better initialize the category-wise graph of the reasoning module.Experiments on the mobile screen defect dataset show that our two-stage network achieves the following best performances:97.7%accuracy and 97.3%F-measure.This proves that the proposed approach is effective in industrial applications.展开更多
Visual Question and Answering(VQA)has garnered significant attention as a domain that requires the synthesis of visual and textual information to produce accurate responses.While existing methods often rely on Convolu...Visual Question and Answering(VQA)has garnered significant attention as a domain that requires the synthesis of visual and textual information to produce accurate responses.While existing methods often rely on Convolutional Neural Networks(CNNs)for feature extraction and attention mechanisms for embedding learning,they frequently fail to capture the nuanced interactions between entities within images,leading to potential ambiguities in answer generation.In this paper,we introduce a novel network architecture,Dual-modality Integration Attention with Graph-based Feature Extraction(DIAGFE),which addresses these limitations by incorporating two key innovations:a Graph-based Feature Extraction(GFE)module that enhances the precision of visual semantics extraction,and a Dual-modality Integration Attention(DIA)mechanism that efficiently fuses visual and question features to guide the model towards more accurate answer generation.Our model is trained with a composite loss function to refine its predictive accuracy.Rigorous experiments on the VQA2.0 dataset demonstrate that DIAGFE outperforms existing methods,underscoring the effectiveness of our approach in advancing VQA research and its potential for cross-modal understanding.展开更多
In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm ba...In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was proposed.In this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization.The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction environment.Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization.The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified.展开更多
基金supported by the DOD National Defense Science and Engineering Graduate(NDSEG)Research Fellowshipsupported by the NGA under Contract No.HM04762110003.
文摘Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.
基金supported by the National Science and Technology Major Project of China(No.2011ZX05029-003)CNPC Science Research and Technology Development Project,China(No.2013D-0904)
文摘In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.
文摘A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science.The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout.Such structural properties are modeled as an aggregation of parcels,buildings,and road networks.We introduce several computational measures(Ratio Minimum Distance,Minimum Ratio Minimum Distance,and Metric Compactness)that respectively evaluate the capability for a given road to be connected with the whole road network.These measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the network.Based on these local and global properties derived from the topological and graph-based representation,and on building density metrics,this paper proposes an analysis of road and building layouts at different levels of granularity.The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment.Overall,and by integrating the parcels and buildings layouts,this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout.
基金the Project Fund for Key Discipline of the Shanghai Municipal Education Commission(No.J50104)the Major State Basic Research Development Program of China(No.2017YFB0403500)。
文摘Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.
文摘The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.
文摘Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues;therefore investigators concentrate on using Meta-heuristic approaches.Dragonfly Optimization(DFO)is an effective meta-heuristic approach to resolve these problems by providing optimal solutions.Moreover,Meta-heuristic approaches(DFO)turn to be slower in convergence problems and need proper computational time while expanding network size.Thus,DFO is adaptively improved as Adaptive Dragonfly Optimization(ADFO)to fit this model and re-formulated using graph-based m-connection establishment(G-𝑚𝑚CE)to overcome computational time and DFO’s convergence based problems,considerably enhancing DFO performance.In(G-𝑚𝑚CE),Connectivity Zone(CZ)is chosen among source to destination in which optimality should be under those connected regions and ADFO is used for effective route establishment in CZ indeed of complete networking model.To measure complementary features of ADFO and(G-𝑚𝑚CE),hybridization of DFO-(G-𝑚𝑚CE)is anticipated over dense circumstances with reduced energy consumption and delay to enhance network lifetime.The simulation was performed in MATLAB environment.
文摘Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrastructures,the information technology platform is anticipating a completely new level of devel-opment.The following concepts are proposed in this research paper:1)A reliable authentication method Data replication that is optimised;graph-based data encryp-tion and packing colouring in Redundant Array of Independent Disks(RAID)sto-rage.At the data centre,data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph’s jump graph.In order to achieve space efficiency,the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security,enhanced authentication,and low cost.
文摘Electric vehicles(EVs)continue to gain popularity over internal combustion vehicles,but driving and charging an EV is a fundamentally different experience.EV usage patterns include features such as charging speed,battery state of charge,and battery depth of discharge.Understanding EV usage patterns from data is essential to optimizing the performance and management of EVs.However,extracting useful conclusions from individual EV data is computationally intensive.Traditional clustering methods offer a solution by grouping vehicles by behavior pattern,but they still pose computational challenges for long time-series data profiles.To efficiently extract EV behavior insights,we propose a scalable two-level clustering approach and test it on a dataset of 3,082 real EVs over the course of a year.We first use level one clustering to weekly data segments,revealing distinct patterns in state of charge utilization.Next,we apply level two graph-based clustering to group individual vehicles that operate similarly on a longer timescale.Our scalable and adaptable clustering approach can aid in battery lifecycle management,charge demand forecasting,and fleet energy management,all critical tasks to facilitate the continued growth of the EV market.
基金supported by the National Natural Science Foundation of China(31972969)the Science and Technology Department of Yunnan Province(2019FY003015)+4 种基金Research Startup Funding of Yunnan University in China(C176220100033)the Science and Technology Major Project of the Department of Science and Technology of Yunnan(K204204210017)Yunnan Fundamental Research Projects(202301BF070001-026)the Project of Central Guiding Local Technology Development(202407AB110005)the Yunnan Talent Support Plan(C619300A036)。
文摘Potato is the world’s most important nongrain crop.In this study,to assess genetic diversity within the Petota section,29 genomes from Petota and Etuberosum sections were newly de novo assembled and 248 accessions of wild potatoes,landraces,and modern cultivars were re-sequenced at>253 depth.Subsequently,a graph-based pangenome was constructed using DM8.1 as the backbone,integrating194,330 nonredundant structural variants.To characterize the metabolome of tubers and illuminate the genomic basis of metabolic traits,LC-MS/MS was employed to obtain the metabolome of 157 accessions,and 9,321 structural variants(SVs)were detected to be significantly associated with 1,258 distinct metabolites via PAV(presence and absence variations)-based metabolomics-GWAS analysis,including metabolites of flavonoids,phenolic acids,and phospholipids.To facilitate the utilization of pangenome resources,a comprehensive platform,the Potato Pangenome Database(PPDB),was developed.Our study provides a comprehensive genomic resource for dissecting the genomic basis of agronomic and metabolic traits in potato,which will accelerate functional genomics studies and genetic improvements in potato.
基金supported by the National Key Research and Development Program of China(2023YFF1001000)。
文摘Chickens are one of the most important domesticated animals,serving as an important protein source.Studying genetic variations in chickens to enhance their production performance is of great potential value.The emergence of next-generation sequencing has enabled precise analysis of single nucleotide polymorphisms and insertions/deletions in chicken,while third-generation sequencing achieves the accurate structural variant identification.However,the high cost of third-generation sequencing technology limits its application in population studies.The graph-based pan-genome strategy can overcome this challenge by enabling the detection of structural variations using cost-effective next-generation sequencing data.This study constructed a graph-based pangenome for chickens using 12 high-quality genomes.This pan-genome used linear genome GRCg6a as the reference genome,containing variant information from two commercial and nine native chicken breeds.Compared to the linear genome,the pan-genome provided significant improvements in the efficiency of structural variation identification.On the basis of the graph-based pan-genome,high-frequency structural variations related to high egg production in Leghorn chicken were predicted.Additionally,it was discovered that potential structural variations was associated with highland adaptation in Tibetan chickens according to next-generation sequencing and transcriptomics data.Using the pan-genome graph,a new strategy to identify structural variations related to traits of interest in chickens is presented.
基金National Natural Science Foundation of China(No.62372100)。
文摘Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,termed graph-based transform(GBT)and dual graph Laplacian regularization(DGLR)(DGLR-GBT).This model specifically aims to remove Gaussian white noise by capitalizing on the nonlocal self-similarity(NSS)and the piecewise smoothness properties intrinsic to depth maps.Within the group sparse coding(GSC)framework,a combination of GBT and DGLR is implemented.Firstly,within each group,the graph is constructed by using estimates of the true values of the averaged blocks instead of the observations.Secondly,the graph Laplacian regular terms are constructed based on rows and columns of similar block groups,respectively.Lastly,the solution is obtained effectively by combining the alternating direction multiplication method(ADMM)with the weighted thresholding method within the domain of GBT.
基金supported by grants from the National Key Research and Development Program of China (2022YFF1003001)the National Natural Science Foundation of China (32072576)+3 种基金the National Modern Agriculture Industry Technology System (CARS-23-G42)the Jiangsu Provincial Key Research and Development Program (BE2021376)the Innovation Program of the Beijing Academy of Agricultural and Forestry Sciences (KJCX20230121)the Collaborative Innovation Program for Leafy and Root Vegetables of the Beijing Vegetable Research Center,Beijing Academy of Agricultural and Forestry Sciences (XTCX202302).
文摘The domestication of Brassica oleracea has resulted in diverse morphological types with distinct patterns of organ development.Here we report a graph-based pan-genome of B.oleracea constructed from high-quality genome assemblies of different morphotypes.The pan-genome harbors over 200 structural variant hotspot regions enriched in auxin-andflowering-related genes.Population genomic analyses revealed that early domestication of B.oleracea focused on leaf or stem development.Geneflows resulting from agricultural practices and variety improvement were detected among different morphotypes.Selective-sweep and pan-genome analyses identified an auxin-responsive small auxin up-regulated RNA gene and a CLAV-ATA3/ESR-RELATED family gene as crucial players in leaf–stem differentiation during the early stage of B.oleracea domestication and the BoKAN1 gene as instrumental in shaping the leafy heads of cabbage and Brussels sprouts.Our pan-genome and functional analyses further revealed that variations in the BoFLC2 gene play key roles in the divergence of vernalization andflowering characteristics among different morphotypes,and variations in thefirst intron of BoFLC3 are involved infine-tuning theflowering process in cauliflower.This study provides a comprehensive understanding of the pan-genome of B.oleracea and sheds light on the domestication and differential organ development of this globally important crop species.
文摘Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods.
文摘Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.
基金supported by the National Natural Science Foundation of China(Grant Nos.60835002,61075004).
文摘The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear neighborhood propagation(LNP)which can automatically construct the optimal graph;2)a novel multilevel scheme to make our algorithm scalable for large data sets;3)a generalized point charge scheme for GBSSL;4)a multilabel GBSSL method by solving a Sylvester equation;5)an information fusion framework for GBSSL;and 6)an application of GBSSL on fMRI image segmentation.
基金Project supported by the National Key Research and Development Program of China(No.2020AAA0108302)the Fundamental Research Funds for the Central Universities,China(No.xtr072022001)。
文摘Defect inspection,also known as defect detection,is significant in mobile screen quality control.There are some challenging issues brought by the characteristics of screen defects,including the following:(1)the problem of interclass similarity and intraclass variation,(2)the difficulty in distinguishing low contrast,tiny-sized,or incomplete defects,and(3)the modeling of category dependencies for multi-label images.To solve these problems,a graph reasoning module,stacked on a classification module,is proposed to expand the feature dimension and improve low-quality image features by exploiting category-wise dependency,image-wise relations,and interactions between them.To further improve the classification performance,the classifier of the classification module is redesigned as a cosine similarity function.With the help of contrastive learning,the classification module can better initialize the category-wise graph of the reasoning module.Experiments on the mobile screen defect dataset show that our two-stage network achieves the following best performances:97.7%accuracy and 97.3%F-measure.This proves that the proposed approach is effective in industrial applications.
基金supported by the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(No.ZD2019-183-001)the Fundamental Research Funds for the Central Universities(Nos.22CX01001A-1 and 22CX01001A-3).
文摘Visual Question and Answering(VQA)has garnered significant attention as a domain that requires the synthesis of visual and textual information to produce accurate responses.While existing methods often rely on Convolutional Neural Networks(CNNs)for feature extraction and attention mechanisms for embedding learning,they frequently fail to capture the nuanced interactions between entities within images,leading to potential ambiguities in answer generation.In this paper,we introduce a novel network architecture,Dual-modality Integration Attention with Graph-based Feature Extraction(DIAGFE),which addresses these limitations by incorporating two key innovations:a Graph-based Feature Extraction(GFE)module that enhances the precision of visual semantics extraction,and a Dual-modality Integration Attention(DIA)mechanism that efficiently fuses visual and question features to guide the model towards more accurate answer generation.Our model is trained with a composite loss function to refine its predictive accuracy.Rigorous experiments on the VQA2.0 dataset demonstrate that DIAGFE outperforms existing methods,underscoring the effectiveness of our approach in advancing VQA research and its potential for cross-modal understanding.
基金Natural Science Foundation of Shaanxi Province(No.2019JQ-004)Scientific Research Plan Projects of Shaanxi Education Department(No.18JK0438)Youth Talent Promotion Project of Shaanxi Province(No.20180112)。
文摘In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was proposed.In this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization.The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction environment.Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization.The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified.
