The cost and strict input format requirements of GraphRAG make it less efficient for processing large documents. This paper proposes an alternative approach for constructing a knowledge graph (KG) from a PDF document ...The cost and strict input format requirements of GraphRAG make it less efficient for processing large documents. This paper proposes an alternative approach for constructing a knowledge graph (KG) from a PDF document with a focus on simplicity and cost-effectiveness. The process involves splitting the document into chunks, extracting concepts within each chunk using a large language model (LLM), and building relationships based on the proximity of concepts in the same chunk. Unlike traditional named entity recognition (NER), which identifies entities like “Shanghai”, the proposed method identifies concepts, such as “Convenient transportation in Shanghai” which is found to be more meaningful for KG construction. Each edge in the KG represents a relationship between concepts occurring in the same text chunk. The process is computationally inexpensive, leveraging locally set up tools like Mistral 7B openorca instruct and Ollama for model inference, ensuring the entire graph generation process is cost-free. A method of assigning weights to relationships, grouping similar pairs, and summarizing multiple relationships into a single edge with associated weight and relation details is introduced. Additionally, node degrees and communities are calculated for node sizing and coloring. This approach offers a scalable, cost-effective solution for generating meaningful knowledge graphs from large documents, achieving results comparable to GraphRAG while maintaining accessibility for personal machines.展开更多
In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,prov...In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,providing favorable conditions for students’in-depth and efficient learning.Against this backdrop,how to scientifically apply emerging technologies to automatically collect,process,and integrate digital learning resources such as voices,videos,and courseware texts,and better innovate the organization and presentation forms of course knowledge has become an important development direction for“artificial intelligence+education.”This article elaborates on the elements and characteristics of knowledge graphs,analyzes the construction steps of knowledge graphs,and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection,course knowledge ontology identification,knowledge discovery,and association,providing references for the intelligent application of online open courses.展开更多
This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foun...This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foundations of knowledge graph technology,the“Same Course with Different Structures”teaching model,and curriculum resource construction,and integrating existing literature,the paper analyzes the methods for constructing curriculum resources using knowledge graphs.The research finds that knowledge graphs can effectively integrate multi-source data,support personalized teaching and precision education,and provide both a scientific foundation and technical support for the development of curriculum resources within the“Same Course with Different Structures”framework.展开更多
There is increase in the issues related to noise pollution due to their negative impacts on the individual.The ability of materials to absorb noise creates future problems for the building and for the residents;althou...There is increase in the issues related to noise pollution due to their negative impacts on the individual.The ability of materials to absorb noise creates future problems for the building and for the residents;although,temporary presence of construction noise holds minor importance for some projects.The study aims to assess the role of materials and labour allocation in cost-effective soundproof house construction projects.The efficiency of synthetic foam,polyurethane and cellular materials was explored in providing insulation in multiple construction projects.Various soundproofing solutions such as rubber,gypsum,homasote,plywood and natural fibres were discussed in the light of cost-effectivity.Soundproofing can be guaranteed by using environmentally-friendly,natural,degradable and recycled products in the construction industry.The study helped in highlighting the materials that can help in building soundproof construction projects.The results indicated the need for applying modern,synthetic,cost-effective and green sound-absorbing systems in construction projects.The safety along with minimal maintenance and longevity of the completed construction projects is maintained by advancements in technological efficiency of building materials.The adaptation of Western construction technologies will ensure success in building construction projects as modern building materials play a significant role in building of sound proof construction buildings.展开更多
Let G(V,E) be a connected graph and W{w 1,w 2,…,w k} an ordered set of V. Given v∈V, the representation of v with respect to W is the k-vector r(v|W)(d(v,w 1),d(v,w 2),…,d(v,w k)). The set W is a resolving set of G...Let G(V,E) be a connected graph and W{w 1,w 2,…,w k} an ordered set of V. Given v∈V, the representation of v with respect to W is the k-vector r(v|W)(d(v,w 1),d(v,w 2),…,d(v,w k)). The set W is a resolving set of G if r(u|W)r(v|W) implies that uv for all pairs {u,v} of vertices of G. The resolving set of G with the smallest cardinality is called a basis of G. The dimension of G, dim (G), is the cardinality of a basis for G. The bound of a Cartesian product of a connected graph H and a path P k was reached: dim(H)≤dim(H×P k)≤dim(H)+1. Then, the dimension value of some graphs was given. At last, the constructions of some graphs’ bases were showed.展开更多
Performance Management is the core course of human resource management major,but its knowledge points lack multi-dimensional correlations.There are problems such as scattered content and unclear system,and it is urgen...Performance Management is the core course of human resource management major,but its knowledge points lack multi-dimensional correlations.There are problems such as scattered content and unclear system,and it is urgent to reconstruct the content system of the course.Knowledge graph technology can integrate massive and scattered information into an organic structure through semantic correlation and reasoning.The application of knowledge graph to education and teaching can promote scientific and personalized teaching evaluation and better realize individualized teaching.This paper systematically combs the knowledge points of Performance Management course and forms a comprehensive knowledge graph.The knowledge point is associated with specific questions to form the problem map of the course,and then the knowledge point is further associated with the ability target to form the ability map of the course.Then,the knowledge point is associated with teaching materials,question bank and expansion resources to form a systematic teaching database,thereby giving the method of building the content system of Performance Management course based on the knowledge map.This research can be further extended to other core management courses to realize the deep integration of knowledge graph and teaching.展开更多
The theory of quantum error correcting codes is a primary tool for fighting decoherence and other quantum noise in quantum communication and quantum computation. Recently, the theory of quantum error correcting codes ...The theory of quantum error correcting codes is a primary tool for fighting decoherence and other quantum noise in quantum communication and quantum computation. Recently, the theory of quantum error correcting codes has developed rapidly and been extended to protect quantum information over asymmetric quantum channels, in which phase-shift and qubit-flip errors occur with different probabilities. In this paper, we generalize the construction of symmetric quantum codes via graphs (or matrices) to the asymmetric case, converting the construction of asymmetric quantum codes to finding matrices with some special properties. We also propose some asymmetric quantum Maximal Distance Separable (MDS) codes as examples constructed in this way.展开更多
Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique...Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique architecture and regional landscape.It gave a world-recognized achievement in China s modem development and manifested a major milestone in China's economic development.In the course of metro construction projects,there are substantial interwoven municipal structures influencing the success of the projects,which including,but the least,all underground cables and ducts,sewage system,the power consumption of construction works,traffic diversion,air pollution,expatriate business activities and social security.There are many US and UK project insurance companies moving into Asia Pacific.They are doing re-insurance business on major construction guarantee,such as machinery damage,project on-time,power consumption,claims from contractors and communities.Environmental information,such as water quality,indoor and outdoor air quality,people inflow and lift waiting time play deterministic roles in construction's fit-touse.Big Data is a contemporary buzzword since 2013,and the key competence is to provide real time response to heuristic syndrome in order to make short-term prediction.This paper attempts to develop a conceptual model in big data for construction展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
Following publication of the original article[1],Consent for publication is not applicable and it has been removed.The statement of Competing interests has been added.
In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relationa...In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.展开更多
Within any scientific disciplines, a large amount of data are buried within various literature depositories and archives, making it difficult to manually extract useful information from the datum swamps. The machine-l...Within any scientific disciplines, a large amount of data are buried within various literature depositories and archives, making it difficult to manually extract useful information from the datum swamps. The machine-learning extraction of data therefore is necessary for the big-data-based studies. Here, we develop a new text-mining technique to reconstruct the global database of the Precambrian to Recent stromatolites, providing better understanding of secular changes of stromatolites though geological time. The step-by-step data extraction process is described as below. First, the PDF documents of stromatolite-containing literatures were collected, and converted into text formation. Second, a glossary and tag-labeling system using NLP(Natural Language Processing) software was employed to search for all possible candidate pairs from each sentence within the papers collected here. Third, each candidate pair and features were represented as a factor graph model using a series of heuristic procedures to score the weights of each pair feature. Occurrence data of stromatolites versus stratigraphical units(abbreviated as Strata), facies types, locations, and age worldwide were extracted from literatures, respectively, and their extraction accuracies are 92%/464, 87%/778, 92%/846, and 93%/405 from 3 750 scientific abstracts, respectively, and are 90%/1 734, 86%/2 869, 90%/2 055 and 91%/857 from 11 932 papers, respectively. A total of 10 072 unique datum items were identified. The newly obtained stromatolite dataset demonstrates that their stratigraphical occurrences reached a pronounced peak during the Proterozoic(2 500 – 541 Ma), followed by a distinct fall during the Early Phanerozoic, and overall fluctuations through the Phanerozoic(541–0 Ma). Globally, seven stromatolite hotspots were identified from the new dataset, including western United States, eastern United States, western Europe, India, South Africa, northern China, and southern China. The proportional occurrences of inland aquatic stromatolites remain rather low(~20%) in comparison to marine stromatolites from the Precambrian to Jurassic, and then display a significant increase(30%–70%) from the Cretaceous to the present.展开更多
As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in th...As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures.In this paper,we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information.These two methods explore local information into the collaborative graph through competing constraints,thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm.By classifying four benchmark hyperspectral data,the proposed methods are proved to be superior to several advanced algorithms,even under small-sample-size conditions.展开更多
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t...In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.展开更多
文摘The cost and strict input format requirements of GraphRAG make it less efficient for processing large documents. This paper proposes an alternative approach for constructing a knowledge graph (KG) from a PDF document with a focus on simplicity and cost-effectiveness. The process involves splitting the document into chunks, extracting concepts within each chunk using a large language model (LLM), and building relationships based on the proximity of concepts in the same chunk. Unlike traditional named entity recognition (NER), which identifies entities like “Shanghai”, the proposed method identifies concepts, such as “Convenient transportation in Shanghai” which is found to be more meaningful for KG construction. Each edge in the KG represents a relationship between concepts occurring in the same text chunk. The process is computationally inexpensive, leveraging locally set up tools like Mistral 7B openorca instruct and Ollama for model inference, ensuring the entire graph generation process is cost-free. A method of assigning weights to relationships, grouping similar pairs, and summarizing multiple relationships into a single edge with associated weight and relation details is introduced. Additionally, node degrees and communities are calculated for node sizing and coloring. This approach offers a scalable, cost-effective solution for generating meaningful knowledge graphs from large documents, achieving results comparable to GraphRAG while maintaining accessibility for personal machines.
基金University-level Scientific Research Project in Natural Sciences“Research on the Retrieval Method of Multimodal First-Class Course Teaching Content Based on Knowledge Graph Collaboration”(GKY-2024KYYBK-31)。
文摘In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,providing favorable conditions for students’in-depth and efficient learning.Against this backdrop,how to scientifically apply emerging technologies to automatically collect,process,and integrate digital learning resources such as voices,videos,and courseware texts,and better innovate the organization and presentation forms of course knowledge has become an important development direction for“artificial intelligence+education.”This article elaborates on the elements and characteristics of knowledge graphs,analyzes the construction steps of knowledge graphs,and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection,course knowledge ontology identification,knowledge discovery,and association,providing references for the intelligent application of online open courses.
基金Educational and Teaching Reform Project of Beihua University:Research on the Construction of“Same Course with Different Structures”Course Resources Based on Knowledge Graphs。
文摘This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foundations of knowledge graph technology,the“Same Course with Different Structures”teaching model,and curriculum resource construction,and integrating existing literature,the paper analyzes the methods for constructing curriculum resources using knowledge graphs.The research finds that knowledge graphs can effectively integrate multi-source data,support personalized teaching and precision education,and provide both a scientific foundation and technical support for the development of curriculum resources within the“Same Course with Different Structures”framework.
文摘There is increase in the issues related to noise pollution due to their negative impacts on the individual.The ability of materials to absorb noise creates future problems for the building and for the residents;although,temporary presence of construction noise holds minor importance for some projects.The study aims to assess the role of materials and labour allocation in cost-effective soundproof house construction projects.The efficiency of synthetic foam,polyurethane and cellular materials was explored in providing insulation in multiple construction projects.Various soundproofing solutions such as rubber,gypsum,homasote,plywood and natural fibres were discussed in the light of cost-effectivity.Soundproofing can be guaranteed by using environmentally-friendly,natural,degradable and recycled products in the construction industry.The study helped in highlighting the materials that can help in building soundproof construction projects.The results indicated the need for applying modern,synthetic,cost-effective and green sound-absorbing systems in construction projects.The safety along with minimal maintenance and longevity of the completed construction projects is maintained by advancements in technological efficiency of building materials.The adaptation of Western construction technologies will ensure success in building construction projects as modern building materials play a significant role in building of sound proof construction buildings.
文摘Let G(V,E) be a connected graph and W{w 1,w 2,…,w k} an ordered set of V. Given v∈V, the representation of v with respect to W is the k-vector r(v|W)(d(v,w 1),d(v,w 2),…,d(v,w k)). The set W is a resolving set of G if r(u|W)r(v|W) implies that uv for all pairs {u,v} of vertices of G. The resolving set of G with the smallest cardinality is called a basis of G. The dimension of G, dim (G), is the cardinality of a basis for G. The bound of a Cartesian product of a connected graph H and a path P k was reached: dim(H)≤dim(H×P k)≤dim(H)+1. Then, the dimension value of some graphs was given. At last, the constructions of some graphs’ bases were showed.
基金Education and Teaching Reform Research Project of Chongqing Institute of Engineering(JY2023206)。
文摘Performance Management is the core course of human resource management major,but its knowledge points lack multi-dimensional correlations.There are problems such as scattered content and unclear system,and it is urgent to reconstruct the content system of the course.Knowledge graph technology can integrate massive and scattered information into an organic structure through semantic correlation and reasoning.The application of knowledge graph to education and teaching can promote scientific and personalized teaching evaluation and better realize individualized teaching.This paper systematically combs the knowledge points of Performance Management course and forms a comprehensive knowledge graph.The knowledge point is associated with specific questions to form the problem map of the course,and then the knowledge point is further associated with the ability target to form the ability map of the course.Then,the knowledge point is associated with teaching materials,question bank and expansion resources to form a systematic teaching database,thereby giving the method of building the content system of Performance Management course based on the knowledge map.This research can be further extended to other core management courses to realize the deep integration of knowledge graph and teaching.
基金supported by the National High Technology Research and Development Program of China under Grant No. 2011AA010803
文摘The theory of quantum error correcting codes is a primary tool for fighting decoherence and other quantum noise in quantum communication and quantum computation. Recently, the theory of quantum error correcting codes has developed rapidly and been extended to protect quantum information over asymmetric quantum channels, in which phase-shift and qubit-flip errors occur with different probabilities. In this paper, we generalize the construction of symmetric quantum codes via graphs (or matrices) to the asymmetric case, converting the construction of asymmetric quantum codes to finding matrices with some special properties. We also propose some asymmetric quantum Maximal Distance Separable (MDS) codes as examples constructed in this way.
文摘Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique architecture and regional landscape.It gave a world-recognized achievement in China s modem development and manifested a major milestone in China's economic development.In the course of metro construction projects,there are substantial interwoven municipal structures influencing the success of the projects,which including,but the least,all underground cables and ducts,sewage system,the power consumption of construction works,traffic diversion,air pollution,expatriate business activities and social security.There are many US and UK project insurance companies moving into Asia Pacific.They are doing re-insurance business on major construction guarantee,such as machinery damage,project on-time,power consumption,claims from contractors and communities.Environmental information,such as water quality,indoor and outdoor air quality,people inflow and lift waiting time play deterministic roles in construction's fit-touse.Big Data is a contemporary buzzword since 2013,and the key competence is to provide real time response to heuristic syndrome in order to make short-term prediction.This paper attempts to develop a conceptual model in big data for construction
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
基金supported by the Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
文摘Following publication of the original article[1],Consent for publication is not applicable and it has been removed.The statement of Competing interests has been added.
文摘In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.
基金supported by three grants from the National Natural Science Foundation of China (Nos.41821001,41902315,41930322)。
文摘Within any scientific disciplines, a large amount of data are buried within various literature depositories and archives, making it difficult to manually extract useful information from the datum swamps. The machine-learning extraction of data therefore is necessary for the big-data-based studies. Here, we develop a new text-mining technique to reconstruct the global database of the Precambrian to Recent stromatolites, providing better understanding of secular changes of stromatolites though geological time. The step-by-step data extraction process is described as below. First, the PDF documents of stromatolite-containing literatures were collected, and converted into text formation. Second, a glossary and tag-labeling system using NLP(Natural Language Processing) software was employed to search for all possible candidate pairs from each sentence within the papers collected here. Third, each candidate pair and features were represented as a factor graph model using a series of heuristic procedures to score the weights of each pair feature. Occurrence data of stromatolites versus stratigraphical units(abbreviated as Strata), facies types, locations, and age worldwide were extracted from literatures, respectively, and their extraction accuracies are 92%/464, 87%/778, 92%/846, and 93%/405 from 3 750 scientific abstracts, respectively, and are 90%/1 734, 86%/2 869, 90%/2 055 and 91%/857 from 11 932 papers, respectively. A total of 10 072 unique datum items were identified. The newly obtained stromatolite dataset demonstrates that their stratigraphical occurrences reached a pronounced peak during the Proterozoic(2 500 – 541 Ma), followed by a distinct fall during the Early Phanerozoic, and overall fluctuations through the Phanerozoic(541–0 Ma). Globally, seven stromatolite hotspots were identified from the new dataset, including western United States, eastern United States, western Europe, India, South Africa, northern China, and southern China. The proportional occurrences of inland aquatic stromatolites remain rather low(~20%) in comparison to marine stromatolites from the Precambrian to Jurassic, and then display a significant increase(30%–70%) from the Cretaceous to the present.
基金supported by the National Natural Science Foundation of China(No.41601344)the Fundamental Research Funds for the Central Universities(Nos.300102320107 and 201924)+2 种基金the National Key Research and Development Project(No.2020YFC1512000)in part by the General Projects of Key R&D Programs in Shaanxi Province(No.2020GY-060)Xi’an Science&Technology Project(Nos.2020KJRC0126 and 202018)。
文摘As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures.In this paper,we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information.These two methods explore local information into the collaborative graph through competing constraints,thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm.By classifying four benchmark hyperspectral data,the proposed methods are proved to be superior to several advanced algorithms,even under small-sample-size conditions.
基金Supported by the National Natural Science Foundation of China(No.62203390)the Science and Technology Project of China TobaccoZhejiang Industrial Co.,Ltd(No.ZJZY2022E004)。
文摘In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.
