Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. Howeve...Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.展开更多
Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empow...Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.展开更多
Machine translation of low-resource languages(LRLs)has long been hindered by limited corpora and linguistic complexity.This review summarizes key developments,from traditional methods to recent progress with large lan...Machine translation of low-resource languages(LRLs)has long been hindered by limited corpora and linguistic complexity.This review summarizes key developments,from traditional methods to recent progress with large language models(LLMs),while highlighting ongoing challenges such as data bottlenecks,biases,fairness,and computational costs.Finally,it discusses future directions,including efficient parameter fine-tuning,multimodal translation,and community-driven corpus construction,providing insights for advancing LRL translation research.展开更多
Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartph...Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,an...Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,and patient education,persist.With the growing progress of artificial intelligence,particularly large language models(LLMs)like ChatGPT,new applications have emerged in the field of LT.Current studies demonstrating usage of ChatGPT in LT include various areas of application,from clinical settings to research and education.ChatGPT usage can benefit both healthcare professionals,by decreasing the time spent on non-clinical work,but also LT recipients by providing accurate information.Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork.Additionally,the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability.Although ChatGPT usage presents promising applications,there are certain ethical and practical limitations.Key concerns include patient data privacy,information accuracy,misinformation possibility and lack of legal framework.Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT.The aim of this minireview is to summarize current literature on ChatGPT in LT,highlighting both opportunities and limitations,while also providing future possible applications.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step...Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance （C41SR） systems are full of uncertain and vague information, which makes it difficult to mode...The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance （C41SR） systems are full of uncertain and vague information, which makes it difficult to model the C41SR architecture. The paper presents an approach to modeling the capability requirements with the fuzzy unified modeling language （UML） and building domain ontologies with fuzzy description logic （DL）. The UML modeling constructs are extended according to the meta model of Depart- ment of Defense Architecture Framework to improve their domain applicability, the fuzzy modeling mechanism is introduced to model the fuzzy efficiency features of capabilities, and the capability requirement models are converted into ontologies formalized in fuzzy DL so that the model consistency and reasonability can be checked with a DL reasoning system. Finally, a case study of C41SR capability requirements model checking is provided to demonstrate the availability and applicability of the method.展开更多
With direct expression of individual application domain patterns and ideas,domain-specific modeling language（DSML） is more and more frequently used to build models instead of using a combination of one or more gener...With direct expression of individual application domain patterns and ideas,domain-specific modeling language（DSML） is more and more frequently used to build models instead of using a combination of one or more general constructs.Based on the profile mechanism of unified modeling language（UML） 2.2,a kind of DSML is presented to model simulation testing systems of avionic software（STSAS）.To define the syntax,semantics and notions of the DSML,the domain model of the STSAS from which we generalize the domain concepts and relationships among these concepts is given,and then,the domain model is mapped into a UML meta-model,named UML-STSAS profile.Assuming a flight control system（FCS） as system under test（SUT）,we design the relevant STSAS.The results indicate that extending UML to the simulation testing domain can effectively and precisely model STSAS.展开更多
In order to provide a quantitative analysis and verification method for activity diagrams based business process modeling, a formal definition of activity diagrams is introduced. And the basic requirements for activit...In order to provide a quantitative analysis and verification method for activity diagrams based business process modeling, a formal definition of activity diagrams is introduced. And the basic requirements for activity diagrams based business process models are proposed. Furthermore, the standardized transformation technique between business process models and basic Petri nets is presented and the analysis method for the soundness and well-structured properties of business processes is introduced.展开更多
The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A n...The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A new UML profile that may customize UML for framework modeling was presented using the extension mechanisms of UML, providing a group of UML extensions to meet the needs of framework modeling. In this profile, the extended class diagrams and sequence diagrams were defined to straightforwardly identify the hot-spots and describe their instantiation restrictions. A transformation model based on design patterns was also put forward, such that the profile based framework design diagrams could be automatically mapped to the corresponding implementation diagrams. It was proved that the presented profile makes framework modeling more straightforwardly and therefore easier to understand and instantiate.展开更多
A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed.Firstly,the system framework and a rapid modeling method of CNC machine tool a...A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed.Firstly,the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software.Then,a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language(VRML)by attaining communication among VRML,JavaApplet,JavaScript and Html so as to realize the virtual operation for CNC machine tool.Moreover,the algorithm of material removed simulation based on VRML Z-map is presented.The advantages of this algorithm include less memory requirement and much higher computation.Lastly,the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.展开更多
Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ...Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecol- ogy and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language (VRML) finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.展开更多
This paper informally introduces colored object-oriented Petri Nets(COOPN) with the application of the AUV system.According to the characteristic of the AUV system's running environment,the object-oriented method ...This paper informally introduces colored object-oriented Petri Nets(COOPN) with the application of the AUV system.According to the characteristic of the AUV system's running environment,the object-oriented method is used in this paper not only to dispart system modules but also construct the refined running model of AUV system,then the colored Petri Net method is used to establish hierarchically detailed model in order to get the performance analyzing information of the system.After analyzing the model implementation,the errors of architecture designing and function realization can be found.If the errors can be modified on time,the experiment time in the pool can be reduced and the cost can be saved.展开更多
Nowadays an increasing number of workflow products and research prototypes begin to adopt XML for representing workflow models owing to its easy use and well understanding for people and machines. However, most of wor...Nowadays an increasing number of workflow products and research prototypes begin to adopt XML for representing workflow models owing to its easy use and well understanding for people and machines. However, most of workflow products and research prototypes provide the few supports for the verification of XML-based workflow model, such as free-deadlock properties, which is essential to successful application of workflow technology. In this paper, we tackle this problem by mapping the XML-based workflow model into Petri-net, a kind of well-known formalism for modeling, analyzing and verifying system. As a result, the XML-based workflow model can be automatically verified with the help of general Petri-net tools, such as DANAMICS. The presented approach not only enables end users to represent workflow model with XML-based modeling language, but also the correctness of model can be ensured, thus satisfying the needs of business processes.展开更多
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金supported by the National Key Research and Development Program of China(2023YFA1507601)the National Natural Science Foundation of China(22278127,22378038)+2 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Shanghai Pilot Program for Basic Research(22T01400100-18)the Natural Science Foundation of Liaoning Province,China(2024-MSBA-15).
文摘Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.
基金supported supported by the Fundamental Research Funds for the Central Universities(226-2024-00004)the National Natural Science Foundation of China(U23 A20326)Key Research and Development Program of Zhejiang Province(2025C01061).
文摘Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.
基金supported by China Undergraduate Innovation Training Program[Grant No.202410699184]Humanities and Social Sciences Research Project funded by the Ministry of Education of China[Grant No.23YJAZH139].
文摘Machine translation of low-resource languages(LRLs)has long been hindered by limited corpora and linguistic complexity.This review summarizes key developments,from traditional methods to recent progress with large language models(LLMs),while highlighting ongoing challenges such as data bottlenecks,biases,fairness,and computational costs.Finally,it discusses future directions,including efficient parameter fine-tuning,multimodal translation,and community-driven corpus construction,providing insights for advancing LRL translation research.
文摘Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
文摘Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,and patient education,persist.With the growing progress of artificial intelligence,particularly large language models(LLMs)like ChatGPT,new applications have emerged in the field of LT.Current studies demonstrating usage of ChatGPT in LT include various areas of application,from clinical settings to research and education.ChatGPT usage can benefit both healthcare professionals,by decreasing the time spent on non-clinical work,but also LT recipients by providing accurate information.Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork.Additionally,the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability.Although ChatGPT usage presents promising applications,there are certain ethical and practical limitations.Key concerns include patient data privacy,information accuracy,misinformation possibility and lack of legal framework.Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT.The aim of this minireview is to summarize current literature on ChatGPT in LT,highlighting both opportunities and limitations,while also providing future possible applications.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
文摘Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
文摘The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance （C41SR） systems are full of uncertain and vague information, which makes it difficult to model the C41SR architecture. The paper presents an approach to modeling the capability requirements with the fuzzy unified modeling language （UML） and building domain ontologies with fuzzy description logic （DL）. The UML modeling constructs are extended according to the meta model of Depart- ment of Defense Architecture Framework to improve their domain applicability, the fuzzy modeling mechanism is introduced to model the fuzzy efficiency features of capabilities, and the capability requirement models are converted into ontologies formalized in fuzzy DL so that the model consistency and reasonability can be checked with a DL reasoning system. Finally, a case study of C41SR capability requirements model checking is provided to demonstrate the availability and applicability of the method.
基金Aeronautical Science Foundation of China (20095551025)
文摘With direct expression of individual application domain patterns and ideas,domain-specific modeling language（DSML） is more and more frequently used to build models instead of using a combination of one or more general constructs.Based on the profile mechanism of unified modeling language（UML） 2.2,a kind of DSML is presented to model simulation testing systems of avionic software（STSAS）.To define the syntax,semantics and notions of the DSML,the domain model of the STSAS from which we generalize the domain concepts and relationships among these concepts is given,and then,the domain model is mapped into a UML meta-model,named UML-STSAS profile.Assuming a flight control system（FCS） as system under test（SUT）,we design the relevant STSAS.The results indicate that extending UML to the simulation testing domain can effectively and precisely model STSAS.
文摘In order to provide a quantitative analysis and verification method for activity diagrams based business process modeling, a formal definition of activity diagrams is introduced. And the basic requirements for activity diagrams based business process models are proposed. Furthermore, the standardized transformation technique between business process models and basic Petri nets is presented and the analysis method for the soundness and well-structured properties of business processes is introduced.
文摘The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A new UML profile that may customize UML for framework modeling was presented using the extension mechanisms of UML, providing a group of UML extensions to meet the needs of framework modeling. In this profile, the extended class diagrams and sequence diagrams were defined to straightforwardly identify the hot-spots and describe their instantiation restrictions. A transformation model based on design patterns was also put forward, such that the profile based framework design diagrams could be automatically mapped to the corresponding implementation diagrams. It was proved that the presented profile makes framework modeling more straightforwardly and therefore easier to understand and instantiate.
基金Selected from Proceedings of the 7th International Conference on Frontiers of Design and Manufacturing(ICFDM'2006)This project is supported by National Natural Science Foundation of China(No.50775047)Scientific and Technological Foundation of Guangdong Province,China(No.2004B10201032).
文摘A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed.Firstly,the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software.Then,a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language(VRML)by attaining communication among VRML,JavaApplet,JavaScript and Html so as to realize the virtual operation for CNC machine tool.Moreover,the algorithm of material removed simulation based on VRML Z-map is presented.The advantages of this algorithm include less memory requirement and much higher computation.Lastly,the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.
基金Under the auspices of National High-Tech Research and Development Program of China (863 Program) (No. 2006AA10Z227)the "Eleventh Five-year Plan" of Jilin Province Educational Office (No. 2007[456])
文摘Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecol- ogy and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language (VRML) finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.
基金Supported by the Foundation of Harbin Engineering University Foundation under Grant No.HEUFT05035
文摘This paper informally introduces colored object-oriented Petri Nets(COOPN) with the application of the AUV system.According to the characteristic of the AUV system's running environment,the object-oriented method is used in this paper not only to dispart system modules but also construct the refined running model of AUV system,then the colored Petri Net method is used to establish hierarchically detailed model in order to get the performance analyzing information of the system.After analyzing the model implementation,the errors of architecture designing and function realization can be found.If the errors can be modified on time,the experiment time in the pool can be reduced and the cost can be saved.
文摘Nowadays an increasing number of workflow products and research prototypes begin to adopt XML for representing workflow models owing to its easy use and well understanding for people and machines. However, most of workflow products and research prototypes provide the few supports for the verification of XML-based workflow model, such as free-deadlock properties, which is essential to successful application of workflow technology. In this paper, we tackle this problem by mapping the XML-based workflow model into Petri-net, a kind of well-known formalism for modeling, analyzing and verifying system. As a result, the XML-based workflow model can be automatically verified with the help of general Petri-net tools, such as DANAMICS. The presented approach not only enables end users to represent workflow model with XML-based modeling language, but also the correctness of model can be ensured, thus satisfying the needs of business processes.
