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LinguTimeX a Framework for Multilingual CTC Detection Using Explainable AI and Natural Language Processing 认领 引用
1
作者 Omar Darwish Shorouq Al-Eidi +4 位作者 Abdallah Al-Shorman Majdi Maabreh Anas Alsobeh Plamen Zahariev Yahya Tashtoush 《Computers, Materials & Continua》 SCIE EI 2026年第1期2231-2251,共21页
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain... Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem. 展开更多
关键词 Arabic language Chinese language covert timing channel cybersecurity deep learning English language language processing machine learning
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Research on the Application of CLT in Hungarian Language Education for Chinese Universities 认领 引用
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作者 Miao Hu 《Journal of Contemporary Educational Research》 2026年第6期16-23,共8页
This study investigates the application of communicative language teaching(CLT)in Hungarian language education for Chinese universities,with a case study of Tianjin Foreign Studies University.Using a qualitative resea... This study investigates the application of communicative language teaching(CLT)in Hungarian language education for Chinese universities,with a case study of Tianjin Foreign Studies University.Using a qualitative research method,25 semi-structured interviews were conducted with Hungarian majors at different academic levels.The research framework is based on Nunan’s five features of CLT,focusing on interaction,authentic materials,learning awareness,personal expression,and language use outside the classroom.The findings reveal both the benefits and challenges of implementing CLT in a non-native environment.While interactive and authentic learning strategies show promise,issues such as limited classroom communication,outdated textbooks,and insufficient attention to learning processes hinder effectiveness. 展开更多
关键词 Communicative language teaching Hungarian language Foreign language education Classroom interaction Language transfer
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Agentic Robotic Boxes for Perovskite Solar Cell Fabrication with Recipe Language Model 认领 引用
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作者 Zijian Chen Wenjin Yu +22 位作者 Chuang Wu Feibei Chen Zixuan Wang Chao Zhou Yimeng You Shaojie Li Qiyuan Zhu Ning Ma Yao Sun Donghui Li Billy Fanady Shengchou Jiang Zhongliang Yan Shumin Zhou Liang Li Chang-Yu Hsieh Yang Bai Lixin Xiao Chi-yung Chung Ching-chuen Chan Zhanfeng Cui Michael Grätzel Haitao Zhao 《Engineering》 SCIE EI CSCD 2026年第6期365-374,共10页
Perovskite solar cells(PSCs)have been undergoing rapid development with the vast combinatorial explo-ration of recipes;however,the related research suffers from time-consuming trial-and-error synthesis and labor-inten... Perovskite solar cells(PSCs)have been undergoing rapid development with the vast combinatorial explo-ration of recipes;however,the related research suffers from time-consuming trial-and-error synthesis and labor-intensive fabrication.As a promising alternative,interconnected robotic boxes that integrate fabrication and characterization enable high-throughput experimentation and data collection;however,the resulting numerical datasets are often insufficiently analyzed and fail to provide effective feedback for semantic recipe optimization.Here,we conceived and realized an emerging scientific tool of robotic boxes enabled by a domain-specific recipe language model(RLM)and a coordinating language agent for PSCs research.The developed agent features two loops of seven artificial intelligence(AI)layers,in which both numerical and semantic recipes were continuously learned and optimized from the literature and robotic corpora for iterative fine-tuning of the RLM.Guided by the agent,11 robotic boxes executed the controllable synthesis,fabrication,and characterization of 50764 PSCs,increasing the power conver-sion efficiency(PCE)to 27.0%(26.5%certified).Simultaneously,more than 578 million tokens were gen-erated and augmented to improve the ability to recommend a recipe and mechanistic reasoning,achieving an overall score of about 80%based on the dedicated evaluation criteria.Thus,such agentic robotic boxes provide an advanced tool for the next-generation synthesis,fabrication,characterization,and even mechanistic reasoning of PSCs and beyond. 展开更多
关键词 Robotics Language agent Recipe language model Materials intelligence Perovskite solar cells Device fabrication
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When Federated Learning Meets Large Language Models:Taxonomy,Challenges,and Opportunities 认领 引用
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作者 Shan Jiang Wenxin You +2 位作者 Haoran Zhang Shichang Xuan Jiaxing Shen 《Computers, Materials & Continua》 SCIE EI 2026年第8期1-34,共34页
Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized... Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training.Federated Learning(FL)provides a promising alternative by enabling training LLMs collaboratively without sharing raw data.However,integrating FL and LLMs introduces new challenges,including model size,device heterogeneity,non-IID data,and alignment requirements.This survey offers a structured overview of the federated LLM ecosystem.We present a comprehensive taxonomy encompassing system architectures,advanced data strategies for addressing heterogeneity,and retrieval-augmented generation in federated contexts.Additionally,we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns.We conclude by summarizing emerging applications in healthcare,industry,software engineering,and finance,and by outlining open problems and research opportunities for scalable,secure,and responsible federated LLM deployment. 展开更多
关键词 Large language models federated learning foundation models federated large language models
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ComAlign:A Benchmark Aligning Natural Language with Operating System Commands 认领 引用
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作者 Shasha Li Bin Ji +2 位作者 Xiaodong Liu Jun Ma Jie Yu 《Computers, Materials & Continua》 SCIE EI 2026年第5期1255-1271,共17页
Aligning natural language with operating system(OS)commands allows users to performcomplex computer tasks through simple natural language descriptions.However,due to the complex nature of natural language,it still rem... Aligning natural language with operating system(OS)commands allows users to performcomplex computer tasks through simple natural language descriptions.However,due to the complex nature of natural language,it still remains challenging to achieve precise alignment.In this paper,we present ComAlign,a Chinese benchmark dataset that pairs Chinese natural language descriptions with corresponding OS commands.ComAlign covers a broad range of 82 distinct OS command types with a total of 1811 natural language descriptions.We elaborate on the construction of ComAlign and construct three baselines to evaluate the alignment accuracy on ComAlign.Experimental results show that even advanced large language models struggle with certain ambiguously phrased OS commands.Specifically,the best performing baseline achieves 46.9%alignment accuracy.We demonstrate that ComAlign is collected from realworld application scenarios,making it particularly suitable for developing and benchmarking intelligent OS and agent systems that support user-machine interactions through natural language. 展开更多
关键词 Large language model language-command alignment artificial intelligence operating system
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Large Language Models for Cybersecurity Intelligence: A Systematic Review of Emerging Threats, Defensive Capabilities, and Security Evaluation Frameworks 认领 引用
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作者 Hamed Alqahtani Gulshan Kumar 《Computers, Materials & Continua》 SCIE EI 2026年第6期297-367,共71页
Large Language Models(LLMs)are becoming integral components of modern cybersecurity ecosystems,simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence-Generated ... Large Language Models(LLMs)are becoming integral components of modern cybersecurity ecosystems,simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence-Generated Content(AIGC)-driven threats.This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat-defence-evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research.Guided by this taxonomy,the review first examines AIGC-enabled threats,including automated and highly personalised phishing,polymorphic malware and exploit generation,jailbreak and adversarial prompting,prompt-injection attack vectors,multimodal deception,persona-steering attacks,and large-scale disinformation campaigns.The surveyed evidence indicates a qualitative escalation in adversarial capabilities,with LLMs significantly enhancing scalability,adaptability,and realism while markedly reducing the technical barriers to conducting sophisticated attacks.Second,the review analyses LLM-enabled defensive applications spanning intrusion and anomaly detection,malware analysis and log-semantic modelling,multilingual threat intelligence extraction,vulnerability discovery and code repair,and Security Operations Center(SOC)automation through Retrieval-Augmented Generation(RAG)and multi-agent systems.Although these approaches demonstrate strong potential as semantic reasoning and decision-support components within hybrid security architectures,their real-world effectiveness remains constrained by hallucination risks,adversarial susceptibility,distributional shifts,and operational overhead.Third,the review synthesises current security evaluation and red-teaming practices,revealing a fragmented assessment landscape characterised by narrow benchmarks,inconsistent evaluation metrics,and limited longitudinal robustness analysis.Overall,the taxonomy-driven synthesis highlights a structurally imbalanced ecosystem in which offensive innovation outpaces defensive maturity and governance,and it informs a structured,research-question-aligned roadmap for developing trustworthy,resilient,and policy-aligned LLM-powered cybersecurity systems. 展开更多
关键词 Artificial intelligence-generated content threats cybersecurity intelligence large language model-based defensive systems large language models red-teaming and evaluation frameworks
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Words at Work Chinese language education in Timor-Leste is gradually taking shape as the country deepens its integration into ASEAN 认领 引用
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作者 Zheng Ji’e 《China Report ASEAN》 2026年第4期52-52,共1页
Since gaining independence in 2002,Timor-Leste’s education system has been undergoing a continuous process of reconstruction.Due to historical factors between 1975 and 2002,Chinese language education was interrupted ... Since gaining independence in 2002,Timor-Leste’s education system has been undergoing a continuous process of reconstruction.Due to historical factors between 1975 and 2002,Chinese language education was interrupted for an extended period.Today,Chinese is positioned as a foreign or second language within the country’s linguistic structure.Prior to 2023,Chinese language instruction in Timor-Leste was primarily conducted through short-term training programs,limited in scale and lacking stable institutional support. 展开更多
关键词 second language Chinese language education foreign language Timor Leste ASEAN integration historical factors education system reconstruction short term training programs
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Evaluating Spanish Medical Entity Recognition: Large Language Models with Prompting versus Fine-Tuning 认领 引用
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作者 Ronghao Pan Tomás Bernal-Beltrán +2 位作者 Alejandro Rodríguez-González Ernestina Menasalvas-Ruíz Rafael Valencia-García 《Computers, Materials & Continua》 SCIE EI 2026年第6期2583-2606,共24页
The digitization of healthcare has resulted in the production of large amounts of structured and unstructured clinical data,creating the need for accurate and efficient named entity recognition(NER)to support medical ... The digitization of healthcare has resulted in the production of large amounts of structured and unstructured clinical data,creating the need for accurate and efficient named entity recognition(NER)to support medical procedures.This study evaluates and compares three approaches to NER in the medical domain in Spanish:using Large Language Models(LLMs)with In-Context Learning techniques(Zero-Shot,Few-Shot,and Chain-of-Thought);fine-tuning of LLMs;and fine-tuning of encoder-only models.Experiments were conducted on the Meddocan,Meddoprof,Meddoplace and Symptemist benchmark datasets.Fine-tuned encoder-only models achieve the best performance across all datasets,reaching macro-F1 scores of up to 76.71 on Meddocan,71.51 on Meddoplace,66.07 on Meddoprof and 63.50 on Symptemist.While LLMs with prompting offer flexibility and require no task-specific training,their performance varies significantly depending on the entity type.In addition,we evaluated fine-tuning of LLMs using QLoRA,but the improvements were limited due to the small amount of training data available per entity type,which made model adaptation less effective. 展开更多
关键词 Named entity recognition medical entity detection large language models transformers prompt-tuning fine-tuning in-context learning natural language processing
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PROMPTx-PE:Adaptive Optimization of Prompt Engineering Strategies for Accuracy and Robustness in Large Language Models 认领 引用
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作者 Talha Farooq Khan Fahad Ali +2 位作者 Majid Hussain Lal Khan Hsien-Tsung Chang 《Computers, Materials & Continua》 SCIE EI 2026年第5期685-715,共31页
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml... The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs. 展开更多
关键词 Prompt engineering large language models adaptive optimization robustness multi-objective optimization reinforcement learning natural language processing
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Detection of Maliciously Disseminated Hate Speech in Spanish Using Fine-Tuning and In-Context Learning Techniques with Large Language Models 认领 引用
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作者 Tomás Bernal-Beltrán RonghaoPan +3 位作者 JoséAntonio García-Díaz María del Pilar Salas-Zárate Mario Andrés Paredes-Valverde Rafael Valencia-García 《Computers, Materials & Continua》 SCIE EI 2026年第4期353-390,共38页
The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in S... The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns. 展开更多
关键词 Hate speech detection malicious communication campaigns AI-driven cybersecurity social media analytics large language models prompt-tuning fine-tuning in-context learning natural language processing
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CIT-Rec:Enhancing Sequential Recommendation System with Large Language Models 认领 引用
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作者 Ziyu Li Zhen Chen +2 位作者 Xuejing Fu Tong Mo Weiping Li 《Computers, Materials & Continua》 SCIE EI 2026年第3期2328-2343,共16页
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact... Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations. 展开更多
关键词 Large language models vision language models sequential recommendation instruction tuning
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Automating the Initial Development of Intent-Based Task-Oriented Dialog Systems Using Large Language Models:Experiences and Challenges 认领 引用
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作者 Ksenia Kharitonova David Pérez-Fernández +1 位作者 Zoraida Callejas David Griol 《Computers, Materials & Continua》 SCIE EI 2026年第5期1021-1062,共42页
Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate unt... Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms. 展开更多
关键词 Task-oriented dialog systems large language models(LLMs) RASA dialog automation natural language understanding(NLU) slot filling conversational AI human-in-the-loop NLP
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Clinical decision and prescription generation for diarrhea in traditional Chinese medicine based on large language model 认领 引用
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作者 Jiaze Wu Hao Liang +2 位作者 Haoran Dai Hongliang Rui Baoli Liu 《Digital Chinese Medicine》 CAS CSCD 2026年第1期13-30,共18页
Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standa... Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation.Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge,medical records,and chain-ofthought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms→pathogenesis→therapeutic principles→prescriptions)into the model’s reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of established open-source TCM LLMs.Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differentiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the“symptoms→pathogenesis→therapeutic principles→prescriptions”logic chain.It provided complete prescriptions,whereas other models often omitted dosages or generated mismatched prescriptions.Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tuning strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM. 展开更多
关键词 Diarrhea Traditional Chinese medicine Large language model Clinical decision and prescription generation Natural language processing
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Secondary Realignment:An Embodied Intelligent Operational Framework Integrating Vision-Language and Action Two-Stage Models 认领 引用
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作者 Jinjiang Lin Yuan Lu +3 位作者 Han Li Xiaolong Cai Enyi Chen Jiansheng Guan 《Computers, Materials & Continua》 SCIE EI 2026年第7期2047-2066,共20页
Manipulating objects based on verbal commands in cluttered environments remains a critical challenge in robotic arm research.Verbal commands possess high semantic abstraction,while precise grasping and placement actio... Manipulating objects based on verbal commands in cluttered environments remains a critical challenge in robotic arm research.Verbal commands possess high semantic abstraction,while precise grasping and placement actions rely on fine-grained geometric perception.The disparity between these two domains is the primary cause of operational errors.Particularly in certain cluttered scenarios,visual-spatial noise and background redundancy further disrupt attention distribution,significantly degrading the generalization capabilities of existing methods in unseen environments.To address these issues,this paper proposes the Secondary Realignment(SR)framework.It decouples vision-language alignment and vision-action alignment into two stages,mitigating semantic-geometric discrepancies through a hierarchical approach to substantially reduce errors in cross-modal mapping.Simultaneously,to address noise and redundancy in visual-language features,we design a Deep Sparse Self-Attention(DSSA)module.This module dynamically fuses sparse and dense attention mechanisms through self-learning parameters,adaptively enhancing relevant features while suppressing irrelevant noise.Extensive simulation experimental results demonstrate that compared to the state-of-the-art method A2,our approach achieves 9.7%,9.9%,and 17.6%higher task success rates in grasping,placing,and pick-and-place tasks,respectively,further validating its effectiveness. 展开更多
关键词 Robot grasping visual language model language-conditional grasping attention mechanism
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Gloss-Internal Graph Construction and Encoding for Sign Language Translation 认领 引用
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作者 Sam Nguyen-Xuan Han Nguyen 《Computers, Materials & Continua》 SCIE EI 2026年第7期1408-1430,共23页
We propose a Gloss-Internal Graph Construction and Encoding framework that represents compound glosses as directed,labeled graphs and integrates them into a Transformer via a graph-aware encoder.We evaluate our approa... We propose a Gloss-Internal Graph Construction and Encoding framework that represents compound glosses as directed,labeled graphs and integrates them into a Transformer via a graph-aware encoder.We evaluate our approach against Rule-Based Gloss Decomposition(RBGD)and Linear Gloss Sequence Encoding(LGSE)baselines on ASLG-PC12 and PHOENIX-2014T.Results show consistent improvements over both baselines,achieving gains of up to+3.2 BLEU-4 over LGSE and+7.0 BLEU-4 over RBGD on ASLG-PC12.On PHOENIX-2014T,our method yields gains of up to 1.9 BLEU-4 on the development set and 2.4 BLEU-4 on the test set.Ablation studies further indicate that agreement and reference edges contribute most to translation quality,that attention pooling outperforms mean pooling for graph-level aggregation,and that a single message-passing step offers a reasonable accuracy-efficiency trade-off for the compact gloss-internal graphs encountered in practice.These results suggest that explicit modeling of gloss-internal structure is a promising direction for sign language translation. 展开更多
关键词 Sign language translation gloss-to-Text translation gloss-internal graph sign language gloss transformer-based models
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Integrative innovation of large language models in industries:technologies,applications,and challenges 认领 引用
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作者 Shikai Wang Yiwen Shao 《Data Science and Management》 EI CSCD 2026年第1期68-84,共17页
This paper examines the transformative potential of large language models(LLMs)across diverse industries,emphasizing their ability to enhance natural language processing tasks through pre-training on extensive dataset... This paper examines the transformative potential of large language models(LLMs)across diverse industries,emphasizing their ability to enhance natural language processing tasks through pre-training on extensive datasets.Although LLMs offer significant opportunities to automate customer interactions,improve decisionmaking,and optimize workflows,their rapid adoption also presents challenges such as information security,data quality,model interpretability,ethical implications,and regulatory compliance.To address these issues,this review proposes integrative strategies,including scenario-based applications,methodological innovation,and data-model integration,to boost LLMs’performance and adaptability.It also explores the evolution of LLMs toward multimodal and multitask general-purpose models,highlighting future trends in sustainable development and human-centric AI.Future research directions include achieving a balance between enhancing model capabilities and managing energy consumption,as well as improving transparency and explainability to strengthen user trust. 展开更多
关键词 Large language models(LLMs) Natural language processing(NLP) Human-centric AI Artificial general intelligence(AGI)
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Domain-Specific Large Language Model for Maintenance Decision-Making on Wind Farms by Labeled-Data-Supervised Fine-Tuning 认领 引用
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作者 Dongming Fan Meng Liu +5 位作者 Yi Shao Linchao Yang Yiliu Liu Yue Zhang Yi Ren Zili Wang 《Engineering》 SCIE EI CSCD 2026年第5期343-361,共19页
Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches a... Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios.This study addressed these challenges by using a large language model(LLM)to understand,generate,and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs.A labelled-data-supervised fine-tuning LLM for maintenance,named LLM4M,is proposed.The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms.Compared with other large parameter LLMs,the fine-tuned LLM4M model demonstrates remarkable accuracy,with an error of approximately 2%from the optimal strategy.In addition,the generalization of the proposed LLM4M model has achieved remarkable results.If the LLM4M model correctly generates the maintenance strategy,the maintenance cost deviates from the optimal solution by only approximately 5%.Furthermore,phase transition behavior is observed,which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain. 展开更多
关键词 Large language model Maintenance decision-making Wind farms Fine-tuning
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ZhongdaChat-ED:a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation 认领 引用
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作者 Yi Xia Yong-Kun Zhu +6 位作者 Chun-Hui Liu Xiang Hong Rui-Xin Zhang Nie-Ke Zhang Ming Chen Guang-Yuan Zhang Chao Sun 《Asian Journal of Andrology》 SCIE CAS CSCD 2026年第1期71-79,共9页
Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medica... Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medical LLM integrating retrieval-augmented generation(RAG)technology,was developed to enhance erectile dysfunction(ED)counseling and clinical decision-making.The model was built using the open-source Deepseek-r1:32b framework,augmented with two specialized databases:a patient health consultation database and a clinical decision support database updated with real-time medical advancements.Two versions of ZhongdaChat-ED were developed:a Consumer Version for patient-facing health consultations and a Professional Version for clinician support.Performance was evaluated against four commonly used LLMs(ChatGPT4,Copilot,Claude,and Gemini)through simulated clinical consultations and case analyses.Three urologists and three patients assessed responses across various dimensions,including accuracy,human caring,ease of understanding,clinical significance,and informational frontier.The Consumer Version outperformed commonly used LLMs in accuracy(4.77/5),human caring(4.86/5),and ease of understanding(4.88/5)with all P85.2%case score rate)and informational frontier scores(4.52/5)than those of other models(P<0.001).ZhongdaChat-ED effectively addresses limitations of conventional LLMs by leveraging RAG to integrate real-time,domain-specific data.ZhongdaChat-ED shows promise in enhancing patient health consultation and clinician decision-making for ED,underscoring the value of tailored AI systems in bridging gaps between generalized AI and specialized medical needs.Future work should expand multimodal capabilities and cross-disciplinary integration to broaden clinical utility. 展开更多
关键词 artificial intelligence clinical decision-making erectile dysfunction large language models medical consultation ZhongdaChat-ED
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Diversified Composite Prompting to Enhance Generalisation of Vision-Language Models 认领 引用
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作者 Xiaoyong Mei Chong Tang +3 位作者 Zhengqun Dai Fudan Zheng Kongwen Zhang Tianyu Lin 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期935-950,共16页
In recent years,prompt learning has shown promise in transferring pretrained vision-language models(VLMs)to downstream tasks.However,existing methods face two challenges in improving generalisation:(1)When leveraging ... In recent years,prompt learning has shown promise in transferring pretrained vision-language models(VLMs)to downstream tasks.However,existing methods face two challenges in improving generalisation:(1)When leveraging the collaborative effect of multimodal prompts,it is often assumed that text and visual modalities share the same prompt requirements,neglecting the distinct hierarchical processing of their encoders,leading to prompt imbalance;and(2)current methods exhibit limited adaptability when facing diverse distribution shift scenarios,including class distribution shifts and image content variations.To address these challenges,we propose a diversified composite prompt learning(DCPL)framework that integrates unified and specific prompts.Specifically,to alleviate multimodal prompt imbalance,we design a shared root multimodal prompting strategy,which employs a shared root prompt and an independent derivation mechanism to generate the derived multimodal prompt(DMP),enabling independent deep prompting while maintaining implicit synergy across modalities.Furthermore,we design a dual-branch dynamic adaptive prompting strategy that produces the derived class-specific prompt(DCP)and imagespecific prompt(ISP),driven by inter-class relations and image-patch context,respectively,to enhance adaptability across different distribution shifts.Extensive experiments on base-to-novel,cross-dataset,domain generalisation and few-shot learning demonstrate that the DCPL achieves superior performance,validating its robustness and generalisation. 展开更多
关键词 domain generalisation few‐shot learning prompt learning transfer learning vision‐language model
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Multitype Game Optimisation:A Two-Stage Fine-Tuning Framework for Multi-Game Optimisation With Large Language Models 认领 引用
20
作者 Xiali Li Jingshi Gu +3 位作者 Feifan He Yang Xiao Yuanli Jia Ping Lan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期739-753,共15页
Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such ... Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.This paper presents multitype game optimisation(MyGO),a two-stage fine-tuning framework tailored for two-player perfect information board games,exploring the applicability of LLMs to nonlinguistic decision-making domains.In the supervised fine-tuning stage,we propose a unified structural encoding method,action semantic unit(ASU),which efficiently converts heterogeneous game records into discrete token sequences compatible with LLMs.In the reinforcement learning stage,we design TA-PPO(token-level adaptive proximal policy optimisation),an enhanced PPO-based algorithm to address the issue of sparse feedback commonly encountered in game reinforcement learning.Experimental results demonstrate that the fine-tuned models achieve superior or comparable performance to traditional game-playing algorithms in terms of strategy quality,rule generalisation and inference efficiency.This work provides a scalable paradigm for fine-tuning LLMs in complex decision-making tasks and lays a foundation for future research in game AI and generalisable strategy optimisation. 展开更多
关键词 computer game fine‐tuning large language models reinforcement learning reward function
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