arXiv subjects

Huansheng Ning

Publications and source records attributed to Huansheng Ning.

At least 19 recordsLinked to original sources

arXiv2026

Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions

The development of the Innovative Ecosystem (IE) presents a new paradigm for economic integration, collaborative advancement, and shared achievements. The rise of Artificial Intelligence (AI) has significantly accelerated the global processes of digitization, informatization, and intelligence. Exploring how AI can leverage inherent characteristics to influence the development trajectory of IE is a topic that warrants further investigation. Given AI's increasing prominence and role within IE, the paper analyzes this new form, examining both AI's unique contributions to IE and its potential challenges. Firstly, the paper synthesizes the conceptual frameworks surrounding IE, decomposing them into manifestations in physical, social, and thinking spaces. Furthermore, the concept of Artificial Intelligence IE (AIIE) is introduced from a spatial perspective, with an exploration of the characteristics AI contributes to IE. Subsequently, the paper employs an evolutionary perspective to analyze the roles provided by AI during different development periods of AIIE. The paper then verifies the feasibility, effectiveness, and rationality of the AIIE's definition and analyzes AIIE development from an evolutionary perspective using enterprise development examples. Finally, acknowledging AI's inherent limitations, the paper examines potential challenges facing AIIE in the future from four perspectives, aiming to identify new research avenues for the further development of AIIE.

cs.AI↗

arXiv2026

Towards Proactive Detection of User-Side Implicit Conflicts in Human-LLM Dialogue

In Human-LLM dialogue, follow-up user utterances may implicitly conflict with earlier intents, leading the LLM to misinterpret user needs and generate inappropriate responses. A reliable dialogue system should proactively detect user-side conflicts before generating a response and seek clarification when necessary. However, prior work has largely focused on LLM-side conflicts, leaving user-side conflicts underexplored. To fill this gap, we construct UC-Bench, a human-annotated benchmark for evaluating user-side conflict detection. Preliminary experiments show that existing LLMs struggle with this task, especially when conflicts arise from implicit incompatibilities grounded in dialogue history. To improve lightweight LLMs with limited training data, we investigate data synthesis for user-side conflict detection. Existing synthesis methods do not explicitly model the implicit incompatibilities between historical and current user utterances, making it difficult to capture the evolution of conflicts and to generate reliably labeled implicit conflict samples. We propose SynUC, a constraint-guided synthesis method that represents user-side conflicts in a constraint space and uses the SPEAKING framework to guide traceable constraint transformations. Applying SynUC to WildChat, we construct UC-Data, a user-side conflict training set containing 2,487 samples. On UC-Bench, Qwen3.5-4B trained on UC-Data outperforms larger general-purpose LLMs such as Claude Opus 4.8, as well as the same backbone trained on data synthesized by existing methods.

cs.CL↗

arXiv2026

Beyond Tools and Persons: Who Are They? Classifying Robots and AI Agents for Proportional Governance

The rapid commercialization of humanoid robots and generative AI agents is outpacing legal frameworks built on a binary distinction between ``tools'' and ``persons.'' Current regulations, including the EU AI Act, classify systems by risk level but lack a foundational ontology for determining \emph{what kind of entity} an autonomous system is -- and what governance follows from that determination. We propose a classification framework grounded in Cyber-Physical-Social-Thinking (CPST) space theory, which categorizes autonomous entities by their degree of integration across four interconnected dimensions: computational, embodied, relational, and cognitive. The resulting three-tier taxonomy -- Confined Actors, Socially-Aware Interactors, and CPST-Integrated Agents -- provides principled scaffolding for proportional governance: enhanced product liability for isolated systems, relational duties of care for interactive companions, and qualified legal personhood for deeply integrated agents. We operationalize this taxonomy by identifying standardized assessment metrics drawn from robotics, human--robot interaction research, social computing, and cognitive science, and we propose a composite assessment protocol for regulatory use. We further address temporal dynamics -- how entities transition between categories as they evolve -- and the institutional design necessary for credible classification. We call for international standardization of this taxonomy before the 2027 review of the EU AI Act, and outline three concrete policy steps toward implementation.

cs.ET↗

arXiv2026

Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms

Cyberbullying has been a significant challenge in the digital era world, given the huge number of people, especially adolescents, who use social media platforms to communicate and share information. Some individuals exploit these platforms to embarrass others through direct messages, electronic mail, speech, and public posts. This behavior has direct psychological and physical impacts on victims of bullying. While several studies have been conducted in this field and various solutions proposed to detect, prevent, and monitor cyberbullying instances on social media platforms, the problem continues. Therefore, it is necessary to conduct intensive studies and provide effective solutions to address the situation. These solutions should be based on detection, prevention, and prediction criteria methods. This paper presents a comprehensive systematic review of studies conducted on cyberbullying detection. It explores existing studies, proposed solutions, identified gaps, datasets, technologies, approaches, challenges, and recommendations, and then proposes effective solutions to address research gaps in future studies.

cs.CY↗

arXiv2026

Cyberlanguage: Native Communication for the Cyber-Physical-Social-Thinking Fusion Space

Human communication is undergoing a fundamental paradigm shift. Physical space, social relations, mental states, and digital information are converging into a unified cyber-physical-social-thinking (CPST) fusion space, rendering them no longer separable domains. However, all existing communication systems, including natural and programming languages, as well as interaction protocols, were designed for a world in which these four dimensions remained distinct. We introduce Cyberlanguage, a theoretically grounded communicative framework that is native to the CPST fusion space. Grounded in the philosophical orientation of cyberism and employing CPST theory as an analytical framework, Cyberlanguage possesses four core characteristics: native four-dimensional fusion, multi-agent universality, dynamic compilability, and contextual adaptability. We have constructed a semiotic model based on the Cybersign unit, a four-dimensional synchronous grammar, a five-layer architectural stack, and a context-driven pragmatic mechanism. We also present testable empirical predictions and a staged implementation roadmap. Cyberlanguage is not intended to replace natural or programming languages, but rather to serve as a meta-communication infrastructure capable of coordinating heterogeneous agents, humans, artificial intelligences, robots, and digital entities, within an increasingly fused cyber-physical-social-cognitive reality.

cs.ET↗

arXiv2026

An Onto-Relational-Sophic Framework for Governing Synthetic Minds

The rapid evolution of artificial intelligence, from task-specific systems to foundation models exhibiting broad, flexible competence across reasoning, creative synthesis, and social interaction, has outpaced the conceptual and governance frameworks designed to manage it. Current regulatory paradigms, anchored in a tool-centric worldview, address algorithmic bias and transparency but leave unanswered foundational questions about what increasingly capable synthetic minds are, how societies should relate to them, and the normative principles that should guide their development. Here we introduce the Onto-Relational-Sophic (ORS) framework, grounded in Cyberism philosophy, which offers integrated answers to these challenges through three pillars: (1) a Cyber-Physical-Social-Thinking (CPST) ontology that defines the mode of being for synthetic minds as irreducibly multi-dimensional rather than purely computational; (2) a graded spectrum of digital personhood providing a pragmatic relational taxonomy beyond binary person-or-tool classifications; and (3) Cybersophy, a wisdom-oriented axiology synthesizing virtue ethics, consequentialism, and relational approaches to guide governance. We apply the framework to emergent scenarios including autonomous research agents, AI-mediated healthcare, and agentic AI ecosystems, demonstrating its capacity to generate proportionate, adaptive governance recommendations. The ORS framework charts a path from narrow technical alignment toward comprehensive philosophical foundations for the synthetic minds already among us.

cs.AI↗

arXiv2026

IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning

Class imbalance, overlap, and noise degrade data quality, reduce model reliability, and limit generalization. Although widely studied in binary classification, these issues remain underexplored in multi-class settings, where complex inter-class relationships make minority-majority structures unclear and traditional clustering fails to capture distribution shape. Approaches that rely only on geometric distances risk removing informative samples and generating low-quality synthetic data, while binarization approaches treat imbalance locally and ignore global inter-class dependencies. At the algorithmic level, ensembles struggle to integrate weak classifiers, leading to limited robustness. This paper proposes IMOVNO+ (IMbalance-OVerlap-NOise+ Algorithm-Level Optimization), a two-level framework designed to jointly enhance data quality and algorithmic robustness for binary and multi-class tasks. At the data level, first, conditional probability is used to quantify the informativeness of each sample. Second, the dataset is partitioned into core, overlapping, and noisy regions. Third, an overlapping-cleaning algorithm is introduced that combines Z-score metrics with a big-jump gap distance. Fourth, a smart oversampling algorithm based on multi-regularization controls synthetic sample proximity, preventing new overlaps. At the algorithmic level, a meta-heuristic prunes ensemble classifiers to reduce weak-learner influence. IMOVNO+ was evaluated on 35 datasets (13 multi-class, 22 binary). Results show consistent superiority over state-of-the-art methods, approaching 100% in several cases. For multi-class data, IMOVNO+ achieves gains of 37-57% in G-mean, 25-44% in F1-score, 25-39% in precision, and 26-43% in recall. In binary tasks, it attains near-perfect performance with improvements of 14-39%. The framework handles data scarcity and imbalance from collection and privacy limits.

cs.LG↗

arXiv2025

LLM-Driven Preference Data Synthesis for Proactive Prediction of the Next User Utterance in Human-Machine Dialogue

Proactively predicting a users next utterance in human-machine dialogue can streamline interaction and improve user experience. Existing commercial API-based solutions are subject to privacy concerns while deploying general-purpose LLMs locally remains computationally expensive. As such, training a compact, task-specific LLM provides a practical alternative. Although user simulator methods can predict a user's next utterance, they mainly imitate their speaking style rather than advancing the dialogue. Preference data synthesis has been investigated to generate data for proactive next utterance prediction and help align LLMs with user preferences. Yet existing methods lack the ability to explicitly model the intent reasoning that leads to the user's next utterance and to define and synthesize preference and non-preference reasoning processes for predicting the user's next utterance.To address these challenges, we propose ProUtt, an LLM-driven preference data synthesis method for proactive next utterance prediction. ProUtt converts dialogue history into an intent tree and explicitly models intent reasoning trajectories by predicting the next plausible path from both exploitation and exploration perspectives. It then constructs preference and non-preference reasoning processes by perturbing or revising intent tree paths at different future turns. Extensive evaluations using LLM-as-a-judge and human judgments demonstrate that ProUtt consistently outperforms existing data synthesis methods, user simulators, and commercial LLM APIs across four benchmark datasets. We release both the code and the synthesized datasets to facilitate future research.

cs.CL↗

arXiv2025

Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation

Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based adversarial defenses in cybersecurity (2021--August 31, 2025), consolidating recent progress, identifying gaps, and outlining future directions. Using a PRISMA-compliant systematic literature review protocol, we searched five major digital libraries. From 829 initial records, 185 peer-reviewed studies were retained and synthesized through quantitative trend analysis and thematic taxonomy development. We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model. GANs improve detection accuracy, robustness, and data utility across network intrusion detection, malware analysis, and IoT security. Notable advances include WGAN-GP for stable training, CGANs for targeted synthesis, and hybrid GAN models for improved resilience. Yet, persistent challenges remain such as instability in training, lack of standardized benchmarks, high computational cost, and limited explainability. GAN-based defenses demonstrate strong potential but require advances in stable architectures, benchmarking, transparency, and deployment. We propose a roadmap emphasizing hybrid models, unified evaluation, real-world integration, and defenses against emerging threats such as LLM-driven cyberattacks. This survey establishes the foundation for scalable, trustworthy, and adaptive GAN-powered defenses.

cs.CR↗

arXiv2025

Vehicle-to-Grid Integration: Ensuring Grid Stability, Strengthening Cybersecurity, and Advancing Energy Market Dynamics

The increasing adoption of electric vehicles has spurred significant interest in Vehicle-to-Grid technology as a transformative approach to modern energy systems. This paper presents a systematic review of V2G systems, focusing on their integration challenges and potential solutions. First, the current state of V2G development is examined, highlighting its growing importance in mitigating peak demand, enhancing voltage and frequency regulation, and reinforcing grid resilience. The study underscores the pivotal role of artificial intelligence and machine learning in optimizing energy management, load forecasting, and real-time grid control. A critical analysis of cybersecurity risks reveals heightened vulnerabilities stemming from V2G's dependence on interconnected networks and real-time data exchange, prompting an exploration of advanced mitigation strategies, including federated learning, blockchain, and quantum-resistant cryptography. Furthermore, the paper reviews economic and market aspects, including business models (V2G as an aggregator or due to self-consumption), regulation (as flexibility service provider) and factors influencing user acceptance shaping V2G adoption. Data from global case studies and pilot programs offer a snapshot of how V2G has been implemented at different paces across regions. Finally, the study suggests a multi-layered framework that incorporates grid stability resilience, cybersecurity resiliency, and energy market dynamics and provides strategic recommendations to enable scalable, secure, and economically viable V2G deployment.

cs.OH↗

arXiv2025

CTourLLM: Enhancing LLMs with Chinese Tourism Knowledge

Recently, large language models (LLMs) have demonstrated their effectiveness in various natural language processing (NLP) tasks. However, the lack of tourism knowledge limits the performance of LLMs in tourist attraction presentations and travel planning. To address this challenge, we constructed a supervised fine-tuning dataset for the Chinese culture and tourism domain, named Cultour. This dataset consists of three parts: tourism knowledge base data, travelogues data, and tourism QA data. Additionally, we propose CTourLLM, a Qwen-based model supervised fine-tuned with Cultour, to improve the quality of information about attractions and travel planning. To evaluate the performance of CTourLLM, we proposed a human evaluation criterion named RRA (Relevance, Readability, Availability), and employed both automatic and human evaluation. The experimental results demonstrate that CTourLLM outperforms ChatGPT, achieving an improvement of 1.21 in BLEU-1 and 1.54 in Rouge-L, thereby validating the effectiveness of the response outcomes. Our proposed Cultour is accessible at https://github.com/mrweiqk/Cultour.

cs.CL↗

arXiv2025

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models

Retrieval Augmented Generation (RAG) has gradually emerged as a promising paradigm for enhancing the accuracy and factual consistency of content generated by large language models (LLMs). However, existing RAG studies primarily focus on retrieving isolated segments using similarity-based matching methods, while overlooking the intrinsic connections between them. This limitation hampers performance in RAG tasks. To address this, we propose QMKGF, a Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval Augmented Generation. First, we design prompt templates and employ general-purpose LLMs to extract entities and relations, thereby generating a knowledge graph (KG) efficiently. Based on the constructed KG, we introduce a multi-path subgraph construction strategy that incorporates one-hop relations, multi-hop relations, and importance-based relations, aiming to improve the semantic relevance between the retrieved documents and the user query. Subsequently, we designed a query-aware attention reward model that scores subgraph triples based on their semantic relevance to the query. Then, we select the highest score subgraph and enrich subgraph with additional triples from other subgraphs that are highly semantically relevant to the query. Finally, the entities, relations, and triples within the updated subgraph are utilised to expand the original query, thereby enhancing its semantic representation and improving the quality of LLMs' generation. We evaluate QMKGF on the SQuAD, IIRC, Culture, HotpotQA, and MuSiQue datasets. On the HotpotQA dataset, our method achieves a ROUGE-1 score of 64.98\%, surpassing the BGE-Rerank approach by 9.72 percentage points (from 55.26\% to 64.98\%). Experimental results demonstrate the effectiveness and superiority of the QMKGF approach.

cs.IR↗

arXiv2025

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space

The integration of the Internet of Everything (IoX) and Artificial General Intelligence (AGI) has given rise to a transformative paradigm aimed at addressing critical bottlenecks across sensing, network, and application layers in Cyber-Physical-Social Thinking (CPST) ecosystems. In this survey, we provide a systematic and comprehensive review of AGI-enhanced IoX research, focusing on three key components: sensing-layer data management, network-layer protocol optimization, and application-layer decision-making frameworks. Specifically, this survey explores how AGI can mitigate IoX bottlenecks challenges by leveraging adaptive sensor fusion, edge preprocessing, and selective attention mechanisms at the sensing layer, while resolving network-layer issues such as protocol heterogeneity and dynamic spectrum management, neuro-symbolic reasoning, active inference, and causal reasoning, Furthermore, the survey examines AGI-enabled frameworks for managing identity and relationship explosion. Key findings suggest that AGI-driven strategies, such as adaptive sensor fusion, edge preprocessing, and semantic modeling, offer novel solutions to sensing-layer data overload, network-layer protocol heterogeneity, and application-layer identity explosion. The survey underscores the importance of cross-layer integration, quantum-enabled communication, and ethical governance frameworks for future AGI-enabled IoX systems. Finally, the survey identifies unresolved challenges, such as computational requirements, scalability, and real-world validation, calling for further research to fully realize AGI's potential in addressing IoX bottlenecks. we believe AGI-enhanced IoX is emerging as a critical research field at the intersection of interconnected systems and advanced AI.

cs.NI↗

arXiv2025

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

Time series analysis is pivotal in domains like financial forecasting and biomedical monitoring, yet traditional methods are constrained by limited nonlinear feature representation and long-term dependency capture. The emergence of Large Language Models (LLMs) offers transformative potential by leveraging their cross-modal knowledge integration and inherent attention mechanisms for time series analysis. However, the development of general-purpose LLMs for time series from scratch is still hindered by data diversity, annotation scarcity, and computational requirements. This paper presents a systematic review of pre-trained LLM-driven time series analysis, focusing on enabling techniques, potential applications, and open challenges. First, it establishes an evolutionary roadmap of AI-driven time series analysis, from the early machine learning era, through the emerging LLM-driven paradigm, to the development of native temporal foundation models. Second, it organizes and systematizes the technical landscape of LLM-driven time series analysis from a workflow perspective, covering LLMs' input, optimization, and lightweight stages. Finally, it critically examines novel real-world applications and highlights key open challenges that can guide future research and innovation. The work not only provides valuable insights into current advances but also outlines promising directions for future development. It serves as a foundational reference for both academic and industrial researchers, paving the way for the development of more efficient, generalizable, and interpretable systems of LLM-driven time series analysis.

cs.LG↗

arXiv2025

A Data Synthesis Method Driven by Large Language Models for Proactive Mining of Implicit User Intentions in Tourism

In the tourism domain, Large Language Models (LLMs) often struggle to mine implicit user intentions from tourists' ambiguous inquiries and lack the capacity to proactively guide users toward clarifying their needs. A critical bottleneck is the scarcity of high-quality training datasets that facilitate proactive questioning and implicit intention mining. While recent advances leverage LLM-driven data synthesis to generate such datasets and transfer specialized knowledge to downstream models, existing approaches suffer from several shortcomings: (1) lack of adaptation to the tourism domain, (2) skewed distributions of detail levels in initial inquiries, (3) contextual redundancy in the implicit intention mining module, and (4) lack of explicit thinking about tourists' emotions and intention values. Therefore, we propose SynPT (A Data Synthesis Method Driven by LLMs for Proactive Mining of Implicit User Intentions in the Tourism), which constructs an LLM-driven user agent and assistant agent to simulate dialogues based on seed data collected from Chinese tourism websites. This approach addresses the aforementioned limitations and generates SynPT-Dialog, a training dataset containing explicit reasoning. The dataset is utilized to fine-tune a general LLM, enabling it to proactively mine implicit user intentions. Experimental evaluations, conducted from both human and LLM perspectives, demonstrate the superiority of SynPT compared to existing methods. Furthermore, we analyze key hyperparameters and present case studies to illustrate the practical applicability of our method, including discussions on its adaptability to English-language scenarios. All code and data are publicly available.

cs.CL↗

arXiv2025

A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis

Accurate illness diagnosis is vital for effective treatment and patient safety. Machine learning models are widely used for cancer diagnosis based on historical medical data. However, data imbalance remains a major challenge, leading to hindering classifier performance and reliability. The SMOTEBoost method addresses this issue by generating synthetic data to balance the dataset, but it may overlook crucial overlapping regions near the decision boundary and can produce noisy samples. This paper proposes RE-SMOTEBoost, an enhanced version of SMOTEBoost, designed to overcome these limitations. Firstly, RE-SMOTEBoost focuses on generating synthetic samples in overlapping regions to better capture the decision boundary using roulette wheel selection. Secondly, it incorporates a filtering mechanism based on information entropy to reduce noise, and borderline cases and improve the quality of generated data. Thirdly, we introduce a double regularization penalty to control the synthetic samples proximity to the decision boundary and avoid class overlap. These enhancements enable higher-quality oversampling of the minority class, resulting in a more balanced and effective training dataset. The proposed method outperforms existing state-of-the-art techniques when evaluated on imbalanced datasets. Compared to the top-performing sampling algorithms, RE-SMOTEBoost demonstrates a notable improvement of 3.22\% in accuracy and a variance reduction of 88.8\%. These results indicate that the proposed model offers a solid solution for medical settings, effectively overcoming data scarcity and severe imbalance caused by limited samples, data collection difficulties, and privacy constraints.

cs.LG↗

arXiv2025

PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning

Class-incremental learning (CIL) for time series data faces critical challenges in balancing stability against catastrophic forgetting and plasticity for new knowledge acquisition, particularly under real-world constraints where historical data access is restricted. While pre-trained models (PTMs) have shown promise in CIL for vision and NLP domains, their potential in time series class-incremental learning (TSCIL) remains underexplored due to the scarcity of large-scale time series pre-trained models. Prompted by the recent emergence of large-scale pre-trained models (PTMs) for time series data, we present the first exploration of PTM-based Time Series Class-Incremental Learning (TSCIL). Our approach leverages frozen PTM backbones coupled with incrementally tuning the shared adapter, preserving generalization capabilities while mitigating feature drift through knowledge distillation. Furthermore, we introduce a Feature Drift Compensation Network (DCN), designed with a novel two-stage training strategy to precisely model feature space transformations across incremental tasks. This allows for accurate projection of old class prototypes into the new feature space. By employing DCN-corrected prototypes, we effectively enhance the unified classifier retraining, mitigating model feature drift and alleviating catastrophic forgetting. Extensive experiments on five real-world datasets demonstrate state-of-the-art performance, with our method yielding final accuracy gains of 1.4%-6.1% across all datasets compared to existing PTM-based approaches. Our work establishes a new paradigm for TSCIL, providing insights into stability-plasticity optimization for continual learning systems.

cs.LG↗

arXiv2024

Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms

Cyberbullying significantly contributes to mental health issues in communities by negatively impacting the psychology of victims. It is a prevalent problem on social media platforms, necessitating effective, real-time detection and monitoring systems to identify harmful messages. However, current cyberbullying detection systems face challenges related to performance, dataset quality, time efficiency, and computational costs. This research aims to conduct a comparative study by adapting and evaluating existing text classification techniques within the cyberbullying detection domain. The study specifically evaluates the effectiveness and performance of these techniques in identifying cyberbullying instances on social media platforms. It focuses on leveraging and assessing large language models, including BERT, RoBERTa, XLNet, DistilBERT, and GPT-2.0, for their suitability in this domain. The results show that BERT strikes a balance between performance, time efficiency, and computational resources: Accuracy of 95%, Precision of 95%, Recall of 95%, F1 Score of 95%, Error Rate of 5%, Inference Time of 0.053 seconds, RAM Usage of 35.28 MB, CPU/GPU Usage of 0.4%, and Energy Consumption of 0.000263 kWh. The findings demonstrate that generative AI models, while powerful, do not consistently outperform fine-tuned models on the tested benchmarks. However, state-of-the-art performance can still be achieved through strategic adaptation and fine-tuning of existing models for specific datasets and tasks.

cs.CL↗