Feng Xia
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View article: Menta: A Small Language Model for On-Device Mental Health Prediction
Menta: A Small Language Model for On-Device Mental Health Prediction Open
Mental health conditions affect hundreds of millions globally, yet early detection remains limited. While large language models (LLMs) have shown promise in mental health applications, their size and computational demands hinder practical …
View article: Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment
Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment Open
View article: Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification
Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification Open
Metaphors play a crucial role in human communication, yet their comprehension remains a significant challenge for natural language processing (NLP) due to the cognitive complexity involved. According to Conceptual Metaphor Theory (CMT), me…
View article: Reflection on community-diversified influence maximization in social networks
Reflection on community-diversified influence maximization in social networks Open
View article: Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection Open
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring t…
View article: Revolutionizing scholarly impact: advanced evaluations, predictive models, and future directions
Revolutionizing scholarly impact: advanced evaluations, predictive models, and future directions Open
Artificial intelligence (AI) is revolutionising scholarly impact evaluation and prediction. By integrating AI and machine learning techniques, researchers can leverage diverse academic networks and multiple sources of academic big data. Th…
View article: Beyond Scale: Small Language Models are Comparable to GPT-4 in Mental Health Understanding
Beyond Scale: Small Language Models are Comparable to GPT-4 in Mental Health Understanding Open
The emergence of Small Language Models (SLMs) as privacy-preserving alternatives for sensitive applications raises a fundamental question about their inherent understanding capabilities compared to Large Language Models (LLMs). This paper …
View article: Graph Learning
Graph Learning Open
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networ…
View article: BrainMAP: Multimodal Graph Learning For Efficient Brain Disease Localization
BrainMAP: Multimodal Graph Learning For Efficient Brain Disease Localization Open
Recent years have seen a surge in research focused on leveraging graph learning techniques to detect neurodegenerative diseases. However, existing graph-based approaches typically lack the ability to localize and extract the specific brain…
View article: Experimental study on the impact of Speed-Agility-Quickness Training method on the agility performance of collegiate sanda specialty students
Experimental study on the impact of Speed-Agility-Quickness Training method on the agility performance of collegiate sanda specialty students Open
Research objective This study investigates the effects of Speed, Agility, and Quickness (SAQ) training on the agility of collegiate sanda athletes at Henan Normal University. Research methods The experimental group (EG) ( n = 12, Age: 19.5…
View article: EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors Open
Metaphors play a pivotal role in expressing emotions, making them crucial for emotional intelligence. The advent of multimodal data and widespread communication has led to a proliferation of multimodal metaphors, amplifying the complexity …
View article: HALO: Half Life-Based Outdated Fact Filtering in Temporal Knowledge Graphs
HALO: Half Life-Based Outdated Fact Filtering in Temporal Knowledge Graphs Open
Outdated facts in temporal knowledge graphs (TKGs) result from exceeding the expiration date of facts, which negatively impact reasoning performance on TKGs. However, existing reasoning methods primarily focus on positive importance of his…
View article: Towards Better Evaluation of Recommendation Algorithms with Bi-directional Item Response Theory
Towards Better Evaluation of Recommendation Algorithms with Bi-directional Item Response Theory Open
View article: ED-Filter: dynamic feature filtering for eating disorder classification
ED-Filter: dynamic feature filtering for eating disorder classification Open
Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. Howeve…
View article: Refined causal graph structure learning via curvature for brain disease classification
Refined causal graph structure learning via curvature for brain disease classification Open
View article: Enhancing Knowledge Tracing through Decoupling Cognitive Pattern from Error-Prone Data
Enhancing Knowledge Tracing through Decoupling Cognitive Pattern from Error-Prone Data Open
View article: SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks
SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks Open
View article: Revisiting Dynamic Graph Clustering via Matrix Factorization
Revisiting Dynamic Graph Clustering via Matrix Factorization Open
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix factorization-based methods are promising approaches for this ta…
View article: A method for combing multi-beam bathymetric data to correct the deep sea sub-bottom profile
A method for combing multi-beam bathymetric data to correct the deep sea sub-bottom profile Open
View article: Joint Structural-Functional Brain Graph Transformer
Joint Structural-Functional Brain Graph Transformer Open
Multimodal brain graph transformers have become one of the foundational architectures of graph foundation models for brain science, relying on multimodal brain network fusion. However, most current multimodal brain network fusion methods p…
View article: FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning
FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning Open
Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting thei…
View article: A study on the effects of different isokinetic testing modes of knee flexion-extension muscle strength ratios on lower extremity stiffness during jumping
A study on the effects of different isokinetic testing modes of knee flexion-extension muscle strength ratios on lower extremity stiffness during jumping Open
The relationship between the knee flexion-extension strength ratio and lower limb stiffness at different movement speeds involves the interaction of biomechanics, neuromuscular control, and muscle physiology. The knee flexion-extension str…
View article: Factor Graph-based Interpretable Neural Networks
Factor Graph-based Interpretable Neural Networks Open
Comprehensible neural network explanations are foundations for a better understanding of decisions, especially when the input data are infused with malicious perturbations. Existing solutions generally mitigate the impact of perturbations …
View article: Biologically Plausible Brain Graph Transformer
Biologically Plausible Brain Graph Transformer Open
State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent. This l…
View article: Foundation Models for Anomaly Detection: Vision and Challenges
Foundation Models for Anomaly Detection: Vision and Challenges Open
As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, founda…
View article: Vision Graph Non-Contrastive Learning for Audio Deepfake Detection with Limited Labels
Vision Graph Non-Contrastive Learning for Audio Deepfake Detection with Limited Labels Open
Recent advancements in audio deepfake detection have leveraged graph neural networks (GNNs) to model frequency and temporal interdependencies in audio data, effectively identifying deepfake artifacts. However, the reliance of GNN-based met…
View article: GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection Open
Cyber-physical-social systems (CPSSs) have emerged in many applications over recent decades, requiring increased attention to security concerns. The rise of sophisticated threats like Advanced Persistent Threats (APTs) makes ensuring secur…
View article: Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification
Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification Open
Metaphors play a crucial role in human communication, yet their comprehension remains a significant challenge for natural language processing (NLP) due to the cognitive complexity involved. According to Conceptual Metaphor Theory (CMT), me…
View article: ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification
ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification Open
Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. Howeve…
View article: Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning Open
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these methods encounter bottl…