Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,transl...Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.展开更多
With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the effor...In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.展开更多
Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame...Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.展开更多
Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,inclu...Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability cla...The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.展开更多
Medical visual question answering(MedVQA)aims to enhance diagnostic confidence and deepen patientsunderstanding of their health conditions.While the Transformer architecture is widely used in multimodal fields,its app...Medical visual question answering(MedVQA)aims to enhance diagnostic confidence and deepen patientsunderstanding of their health conditions.While the Transformer architecture is widely used in multimodal fields,its application in MedVQA requires further enhancement.A critical limitation of contemporary MedVQA systems lies in the inability to integrate lifelong knowledge with specific patient data to generate human-like responses.Existing Transformer-based MedVQA models require enhancing their capabitities for interpreting answers through the applications of medical image knowledge.The introduction of the medical knowledge graph visual language transformer(MKGViLT),designed for joint medical knowledge graphs(KGs),addresses this challenge.MKGViLT incorporates an enhanced Transformer structure to effectively extract features and combine modalities for MedVQA tasks.The MKGViLT model delivers answers based on richer background knowledge,thereby enhancing performance.The efficacy of MKGViLT is evaluated using the SLAKE and P-VQA datasets.Experimental results show that MKGViLT surpasses the most advanced methods on the SLAKE dataset.展开更多
Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes important.Many previous approaches are static,this posed a notable problem that all meani...Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes important.Many previous approaches are static,this posed a notable problem that all meanings of a polysemous entity share one embedding vector.This study aims to propose a polysemous embedding approach,named KG embedding under relational contexts(ContE for short),for missing link prediction.Design/methodology/approach:ContE models and infers different relationship patterns by considering the context of the relationship,which is implicit in the local neighborhood of the relationship.The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors,which represent the contextual information of the relationship.Then,according to the position of the entity,the entity’s polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship.Findings:ContE is a fully expressive,that is,given any ground truth over the triples,there are embedding assignments to entities and relations that can precisely separate the true triples from false ones.ContE is capable of modeling four connectivity patterns such as symmetry,antisymmetry,inversion and composition.Research limitations:ContE needs to do a grid search to find best parameters to get best performance in practice,which is a time-consuming task.Sometimes,it requires longer entity vectors to get better performance than some other models.Practical implications:ContE is a bilinear model,which is a quite simple model that could be applied to large-scale KGs.By considering contexts of relations,ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning,it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks.Originality/value:ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to.It decomposes a relation vector into two vectors,namely,forward impact vector and backward impact vector in order to capture the relational contexts.ContE has the same low computational complexity as TransE.Therefore,it provides a new approach for contextualized knowledge graph embedding.展开更多
Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling com...Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.展开更多
Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(...Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.展开更多
In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming...In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming at the problem of cold start and data sparseness in recommendation,a Hybrid Recommendation method based on Graph Embedding(HRGE)is proposed.First,three types of network are built,including user-user network based on user tag,househouse network based on house tag,and user-user network based on user behavior.Then,by using the method of graph embedding,three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes.Finally,these characterization vectors are used to make a hybrid recommendation.The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province.The experimental results show that,compared with traditional recommendation algorithms,the comprehensive evaluation index(F1)of the HRGE is improved by 20% and the Mean Average Precision(MAP)is increased by 11%.展开更多
Objective:To explore the therapeutic effect of acupoint catgut embedding combined with gingerseparated moxibustion for sequelae of peripheral facial paralysis and whether surface electromyography(sEMG)can be an outcom...Objective:To explore the therapeutic effect of acupoint catgut embedding combined with gingerseparated moxibustion for sequelae of peripheral facial paralysis and whether surface electromyography(sEMG)can be an outcome to evaluate the effect of this disease.Methods:Thirty-five patients with sequelae of peripheral facial paralysis were treated with catgut embedding at the acupoints selected according to their individual symptoms and traditional Chinese medicine(TCM)differentiated syndromes.At the same time,the ginger-separated moxibustion was applied at Yifeng(翳风TE17)and Qianzheng(牵正EX-HN16)of the affected side.The catgut embedding was applied once every 15 days,one time of treatment is of one course,and a total of three courses were given.The moxibustion would be taken after 5 days of catgut embedding each time,once every other day,5 times as one course,a total of 3 courses are given.After treatment,the recovery of facial nerve functions was evaluated by House-Brackmann(H-B)facial nerve function evaluation grading system.Before and after treatment,the surface electromyography(sEMG)was used to detect the root-mean-square(RMS)of frontal muscles,cheek muscles,and orbicularis oris muscles to compare the RMS ratio of these muscles of affected side and healthy side.Results:After 3 courses of treatment,9 cases were cured,22 cases were effective,and 4 cases were ineffective.The total effective rate was 88.6%(31/35).After treatment,the RMS of frontal muscles,cheek muscles,and orbicularis oris muscles of the affected side were 31.56±4.25,34.13±4.28,and 7.46±1.53 respectively,significantly increased in comparing with 11.69±2.45,12.98±3.34,and 2.62±1.41 respectively before treatment(all P<0.05).After treatment,the RMS ratios of frontal muscles,cheek muscles,and orbicularis oris muscles of the affected side and healthy side were 0.73±0.09,0.71±0.11,and 0.68±0.08 respectively,greatly increased in comparing with 0.28±0.10,0.27±0.08,and 0.22±0.09 respectively before treatment(all P<0.05).Conclusion:Acupoint catgut embedding and ginger-separated moxibustion in combination can significantly improve the facial nerve functions,and sEMG used for evaluating the therapeutic effect can objectively reflect the effect of treatment.展开更多
To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are cla...To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.展开更多
In this paper,an effective intra prediction mode-based video strganography is proposed.Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact.The influenc...In this paper,an effective intra prediction mode-based video strganography is proposed.Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact.The influence on the sum of absolute difference(SAD)in intra prediction modes(IPMs)reversion phenomenon is sharp when modifying IPMs.It inspires us to take the SAD prediction deviation(SPD)to define the distortion function.What is more,the mapping rule between IPMs and the codewords is introduced to further reduce the SPD values of each intra block.Syndrome-trellis code(STC)is used as the practical embedding implementation.Experimental results demonstrate that our proposed steganographic scheme presents high undetectability compared with existing IPMs-based steganographic approaches.It also outperforms these schemes on stego video quality.展开更多
Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algeb...Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.展开更多
In this paper, it is shown that for every maximal planar graph G=(V,E) , a strong embedding on some non orientable surface with genus at most |V(G)|-22 is admitted such that the surface dual of G is also a...In this paper, it is shown that for every maximal planar graph G=(V,E) , a strong embedding on some non orientable surface with genus at most |V(G)|-22 is admitted such that the surface dual of G is also a planar graph. As a corollary, an interpolation theorem for strong embeddings of G on non orientable surfaces is obtained.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.72293575.
文摘Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01176).
文摘In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R234)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.
文摘Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605,50%)in part by the Institute of Information&Communications Technology Planning&Evaluation(lITP)grant funded by the Korea government(MSIT)(RS-2025-02305436,Development of Digital Innovative Element Technologies for Rapid Prediction of Potential Complex Disasters and Continuous Disaster Prevention,30%)supported by the Chung-Ang University Graduate Research Scholar-ship in 2023(20%).
文摘The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.
基金Supported by the National Natural Science Foundation of China(No.62001313)the Liaoning Professional Talent Protect(No.XLYC2203046)the Shenyang Municipal Medical Engineering Cross Research Foundation of China(No.22-321-32-09).
文摘Medical visual question answering(MedVQA)aims to enhance diagnostic confidence and deepen patientsunderstanding of their health conditions.While the Transformer architecture is widely used in multimodal fields,its application in MedVQA requires further enhancement.A critical limitation of contemporary MedVQA systems lies in the inability to integrate lifelong knowledge with specific patient data to generate human-like responses.Existing Transformer-based MedVQA models require enhancing their capabitities for interpreting answers through the applications of medical image knowledge.The introduction of the medical knowledge graph visual language transformer(MKGViLT),designed for joint medical knowledge graphs(KGs),addresses this challenge.MKGViLT incorporates an enhanced Transformer structure to effectively extract features and combine modalities for MedVQA tasks.The MKGViLT model delivers answers based on richer background knowledge,thereby enhancing performance.The efficacy of MKGViLT is evaluated using the SLAKE and P-VQA datasets.Experimental results show that MKGViLT surpasses the most advanced methods on the SLAKE dataset.
基金supported by the Key R&D Program Project of Zhejiang Province under Grant no.2019 C01004 and 2021C02004.
文摘Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes important.Many previous approaches are static,this posed a notable problem that all meanings of a polysemous entity share one embedding vector.This study aims to propose a polysemous embedding approach,named KG embedding under relational contexts(ContE for short),for missing link prediction.Design/methodology/approach:ContE models and infers different relationship patterns by considering the context of the relationship,which is implicit in the local neighborhood of the relationship.The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors,which represent the contextual information of the relationship.Then,according to the position of the entity,the entity’s polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship.Findings:ContE is a fully expressive,that is,given any ground truth over the triples,there are embedding assignments to entities and relations that can precisely separate the true triples from false ones.ContE is capable of modeling four connectivity patterns such as symmetry,antisymmetry,inversion and composition.Research limitations:ContE needs to do a grid search to find best parameters to get best performance in practice,which is a time-consuming task.Sometimes,it requires longer entity vectors to get better performance than some other models.Practical implications:ContE is a bilinear model,which is a quite simple model that could be applied to large-scale KGs.By considering contexts of relations,ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning,it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks.Originality/value:ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to.It decomposes a relation vector into two vectors,namely,forward impact vector and backward impact vector in order to capture the relational contexts.ContE has the same low computational complexity as TransE.Therefore,it provides a new approach for contextualized knowledge graph embedding.
基金supported by the National Natural Science Foundation of China under grants U19B2044National Key Research and Development Program of China(2021YFC3300500).
文摘Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.
基金Major Key Project of Pengcheng Laboratory,Grant/Award Number:PCL2022A03。
文摘Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.
文摘In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming at the problem of cold start and data sparseness in recommendation,a Hybrid Recommendation method based on Graph Embedding(HRGE)is proposed.First,three types of network are built,including user-user network based on user tag,househouse network based on house tag,and user-user network based on user behavior.Then,by using the method of graph embedding,three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes.Finally,these characterization vectors are used to make a hybrid recommendation.The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province.The experimental results show that,compared with traditional recommendation algorithms,the comprehensive evaluation index(F1)of the HRGE is improved by 20% and the Mean Average Precision(MAP)is increased by 11%.
文摘Objective:To explore the therapeutic effect of acupoint catgut embedding combined with gingerseparated moxibustion for sequelae of peripheral facial paralysis and whether surface electromyography(sEMG)can be an outcome to evaluate the effect of this disease.Methods:Thirty-five patients with sequelae of peripheral facial paralysis were treated with catgut embedding at the acupoints selected according to their individual symptoms and traditional Chinese medicine(TCM)differentiated syndromes.At the same time,the ginger-separated moxibustion was applied at Yifeng(翳风TE17)and Qianzheng(牵正EX-HN16)of the affected side.The catgut embedding was applied once every 15 days,one time of treatment is of one course,and a total of three courses were given.The moxibustion would be taken after 5 days of catgut embedding each time,once every other day,5 times as one course,a total of 3 courses are given.After treatment,the recovery of facial nerve functions was evaluated by House-Brackmann(H-B)facial nerve function evaluation grading system.Before and after treatment,the surface electromyography(sEMG)was used to detect the root-mean-square(RMS)of frontal muscles,cheek muscles,and orbicularis oris muscles to compare the RMS ratio of these muscles of affected side and healthy side.Results:After 3 courses of treatment,9 cases were cured,22 cases were effective,and 4 cases were ineffective.The total effective rate was 88.6%(31/35).After treatment,the RMS of frontal muscles,cheek muscles,and orbicularis oris muscles of the affected side were 31.56±4.25,34.13±4.28,and 7.46±1.53 respectively,significantly increased in comparing with 11.69±2.45,12.98±3.34,and 2.62±1.41 respectively before treatment(all P<0.05).After treatment,the RMS ratios of frontal muscles,cheek muscles,and orbicularis oris muscles of the affected side and healthy side were 0.73±0.09,0.71±0.11,and 0.68±0.08 respectively,greatly increased in comparing with 0.28±0.10,0.27±0.08,and 0.22±0.09 respectively before treatment(all P<0.05).Conclusion:Acupoint catgut embedding and ginger-separated moxibustion in combination can significantly improve the facial nerve functions,and sEMG used for evaluating the therapeutic effect can objectively reflect the effect of treatment.
基金The State Grid Technology Project(No.5108202340042A-1-1-ZN).
文摘To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.
基金This work was supported by National Key R&D Plan of China(Grant No.2017YFB0802203)National Natural Science Foundation of China(Grant No.U173620045,61732021,61472165 and 61373158)+4 种基金Natural Science Foundation of Guangdong Province,China(Grant No.2017A030313390)Science and Technology Program of Guangzhou,China(Grant No.201804010428)Guangdong Provincial Engineering Technology Research Center on Network Security Detection and Defence(Grant No.2014B090904067)Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve(Grant No.2016B010124009)the Zhuhai Top Discipline-Information Security,Guangzhou Key Laboratory of Data Security and Privacy Preserving,Guangdong Key Laboratory of Data Security and Privacy Preserving,the Fundamental Research Funds for the Central Universities.
文摘In this paper,an effective intra prediction mode-based video strganography is proposed.Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact.The influence on the sum of absolute difference(SAD)in intra prediction modes(IPMs)reversion phenomenon is sharp when modifying IPMs.It inspires us to take the SAD prediction deviation(SPD)to define the distortion function.What is more,the mapping rule between IPMs and the codewords is introduced to further reduce the SPD values of each intra block.Syndrome-trellis code(STC)is used as the practical embedding implementation.Experimental results demonstrate that our proposed steganographic scheme presents high undetectability compared with existing IPMs-based steganographic approaches.It also outperforms these schemes on stego video quality.
基金Supported by the Grant-in-Aid for Scientific Research(C)by the Japan Society for the Promotion of Science(20K03615)。
文摘Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.
文摘In this paper, it is shown that for every maximal planar graph G=(V,E) , a strong embedding on some non orientable surface with genus at most |V(G)|-22 is admitted such that the surface dual of G is also a planar graph. As a corollary, an interpolation theorem for strong embeddings of G on non orientable surfaces is obtained.
