Xiaokang Zhou
Datos Biográficos
| ID | 7575359 |
|---|---|
| NOMBRE | Xiaokang Zhou |
| NOMBRES | Xiaokang |
| APELLIDO | Zhou |
| FIRMA | ZHOU X |
| AFILIACIONES | Shiga University |
| ORCID | 0000-0003-3488-4679 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 15 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 15 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2021 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
A Diffusion Model and Few-Shot Learning Framework for EEG Abnormality Classification and Cerebral Infarction Assessment
Deep learning has shown great potential in assisting medical experts in diagnosing cerebral infarction through electroencephalogram (EEG) analysis. Classifying EEG abnormality levels is a critical component of this process. However, the inherent complexity of raw EEG signals makes reliable feature extraction challenging, while the scarcity of severe cases leads to substantial class imbalance. The limited size of available datasets further exacerb…
VAE-Integrated Multiscale Generative Diffusion Modeling for Incomplete Multimodal Emotion Recognition
Cross-Modal Anomaly-Bridged Instance and Feature Contrastive Learning for Social Bot Detection
The rapid proliferation of social bots threatens the reliability of online platforms. Existing detection methods relying on single-modality or weak multimodal fusion often fail to generalize across diverse behaviors. We propose CABINET—a cross-modal anomaly-bridged instance and feature contrastive learning framework for robust social bot detection. CABINET integrates textual and structural information via two key modules: anomaly-targeted text au…
CDC
Scene graph generation (SGG) plays an important role in the intelligence of social things (IoST) framework by extracting structured semantic representations from social device data, thereby supporting advanced scene understanding and behavioral-cultural modeling. However, the intrinsic long-tail nature of real-world social device data, coupled with the semantic entanglement between head and tail categories (e.g., “on” versus “standing on”), prese…
Explainable Dual-Branch Combination Network With Key Words Embedding and Position Attention for Sentimental Analytics of Social Media Short Comments
Social media platforms such as Weibo and TikTok have become more influential than traditional media. Sentiment in social media comments reflects users’ attitudes and impacts society, making sentiment analysis (SA) crucial. AI driven models, especially deep-learning models, have achieved excellent results in SA tasks. However, most existing models are not interpretable enough. First, deep learning models have numerous parameters, and their transpa…
Counteracting Popularity Bias in Multimedia Web API Recommendation
With the widespread adoption of multimedia web APIs (API) in web and mobile applications, a substantial proliferation of these APIs is observed. These APIs have streamlined development processes, reducing both time and costs. Nevertheless, identifying the required APIs from the vast array of options has emerged as a significant challenge. Collaborative filtering (CF)-based recommendation technologies have demonstrated their efficiency in presenti…
Exploring cultivated land trade-off/synergy of production and ecological supply–demand mismatch for coordinated development
The balance between the production and ecological supply–demand of cultivated land is crucial for regional food and ecological security. However, most studies focus on either the production or ecological aspect, neglecting the trade-offs/synergies of multifunctional cultivated land supply–demand. This study utilized the Pearl River Delta (PRD) mega urban agglomeration in China as a case study. It employed the Enhanced Two-step Floating Catchment …
SLoB
Large language models (LLMs) are becoming powerful engines for social productivity in the manufacturing lifecycle. Existing application-level LLMs inference services focus on large datacenter and small edge intelligence (EI) scenarios, adopting iteration-level batch schedulers to solve resource utilization and inference speed problems. However, these services are incompatible with the scene of medium-sized local heterogeneous graphics processing …
An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment
Fake news is a prevalent issue in modern society, leading to misinformation, and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awareness and understanding of news, while also effectively mitigating the dissemination of fake news. However, news credibility assessment meets challenges when processing large-scale and constantly gr…
A Knowledge-Driven Anomaly Detection Framework for Social Production System
In the social production system, image data are rapidly generated from almost all fields such as factories, hospitals, and transportation, promoting higher requirements for image anomaly detection technologies, including low consumption, higher adaptability, and accuracy. However, existing anomaly detection methods are fragile to heterogeneous image data generated by complex social production systems and tend to require strong computing power and…
Trusted Multimodal Socio-Cyber Sentiment Analysis Based on Disentangled Hierarchical Representation Learning
The rapid development of the digital age has led to a qualitative leap in social media. To meet the cognitive needs of users, social media platforms have been mining users’ private information and disseminating information through various means. However, these platforms lack effective management of information release and various forms of emotional expressions make public propaganda increasingly diverse and complex. Therefore, accurately identify…
Deep Tensor Evidence Fusion Network for Sentiment Classification
Recently, a multimodal sentiment analysis of social media has attracted increasing attention, and its core idea is to discovery heuristic fusion strategy to analyze the sentiment orientations over heterogeneous multimodal source from a learned compact multimodal representation. The existing multimodal fusion techniques not only struggle to achieve full heterogeneous data interaction, but also they are unable to dynamically assess the quality of v…
Popularity-Aware and Diverse Web APIs Recommendation Based on Correlation Graph
The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API fu…
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications
The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning p…
Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous Big Data Recommendations
The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterogeneous big data environments. A hierarchical hybrid network (HHN) model is constructed to describe m…
Sin obras prominentes en esta página.
Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous Big Data Recommendations
The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterogeneous big data environments. A hierarchical hybrid network (HHN) model is constructed to describe m…
Popularity-Aware and Diverse Web APIs Recommendation Based on Correlation Graph
The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API fu…
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications
The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning p…
SLoB
Large language models (LLMs) are becoming powerful engines for social productivity in the manufacturing lifecycle. Existing application-level LLMs inference services focus on large datacenter and small edge intelligence (EI) scenarios, adopting iteration-level batch schedulers to solve resource utilization and inference speed problems. However, these services are incompatible with the scene of medium-sized local heterogeneous graphics processing …
An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment
Fake news is a prevalent issue in modern society, leading to misinformation, and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awareness and understanding of news, while also effectively mitigating the dissemination of fake news. However, news credibility assessment meets challenges when processing large-scale and constantly gr…
A Knowledge-Driven Anomaly Detection Framework for Social Production System
In the social production system, image data are rapidly generated from almost all fields such as factories, hospitals, and transportation, promoting higher requirements for image anomaly detection technologies, including low consumption, higher adaptability, and accuracy. However, existing anomaly detection methods are fragile to heterogeneous image data generated by complex social production systems and tend to require strong computing power and…
Trusted Multimodal Socio-Cyber Sentiment Analysis Based on Disentangled Hierarchical Representation Learning
The rapid development of the digital age has led to a qualitative leap in social media. To meet the cognitive needs of users, social media platforms have been mining users’ private information and disseminating information through various means. However, these platforms lack effective management of information release and various forms of emotional expressions make public propaganda increasingly diverse and complex. Therefore, accurately identify…
Deep Tensor Evidence Fusion Network for Sentiment Classification
Recently, a multimodal sentiment analysis of social media has attracted increasing attention, and its core idea is to discovery heuristic fusion strategy to analyze the sentiment orientations over heterogeneous multimodal source from a learned compact multimodal representation. The existing multimodal fusion techniques not only struggle to achieve full heterogeneous data interaction, but also they are unable to dynamically assess the quality of v…
Explainable Dual-Branch Combination Network With Key Words Embedding and Position Attention for Sentimental Analytics of Social Media Short Comments
Social media platforms such as Weibo and TikTok have become more influential than traditional media. Sentiment in social media comments reflects users’ attitudes and impacts society, making sentiment analysis (SA) crucial. AI driven models, especially deep-learning models, have achieved excellent results in SA tasks. However, most existing models are not interpretable enough. First, deep learning models have numerous parameters, and their transpa…
Counteracting Popularity Bias in Multimedia Web API Recommendation
With the widespread adoption of multimedia web APIs (API) in web and mobile applications, a substantial proliferation of these APIs is observed. These APIs have streamlined development processes, reducing both time and costs. Nevertheless, identifying the required APIs from the vast array of options has emerged as a significant challenge. Collaborative filtering (CF)-based recommendation technologies have demonstrated their efficiency in presenti…
Exploring cultivated land trade-off/synergy of production and ecological supply–demand mismatch for coordinated development
The balance between the production and ecological supply–demand of cultivated land is crucial for regional food and ecological security. However, most studies focus on either the production or ecological aspect, neglecting the trade-offs/synergies of multifunctional cultivated land supply–demand. This study utilized the Pearl River Delta (PRD) mega urban agglomeration in China as a case study. It employed the Enhanced Two-step Floating Catchment …
A Diffusion Model and Few-Shot Learning Framework for EEG Abnormality Classification and Cerebral Infarction Assessment
Deep learning has shown great potential in assisting medical experts in diagnosing cerebral infarction through electroencephalogram (EEG) analysis. Classifying EEG abnormality levels is a critical component of this process. However, the inherent complexity of raw EEG signals makes reliable feature extraction challenging, while the scarcity of severe cases leads to substantial class imbalance. The limited size of available datasets further exacerb…
VAE-Integrated Multiscale Generative Diffusion Modeling for Incomplete Multimodal Emotion Recognition
Cross-Modal Anomaly-Bridged Instance and Feature Contrastive Learning for Social Bot Detection
The rapid proliferation of social bots threatens the reliability of online platforms. Existing detection methods relying on single-modality or weak multimodal fusion often fail to generalize across diverse behaviors. We propose CABINET—a cross-modal anomaly-bridged instance and feature contrastive learning framework for robust social bot detection. CABINET integrates textual and structural information via two key modules: anomaly-targeted text au…
CDC
Scene graph generation (SGG) plays an important role in the intelligence of social things (IoST) framework by extracting structured semantic representations from social device data, thereby supporting advanced scene understanding and behavioral-cultural modeling. However, the intrinsic long-tail nature of real-world social device data, coupled with the semantic entanglement between head and tail categories (e.g., “on” versus “standing on”), prese…
Computer Science (10 obras) · Artificial Intelligence (8 obras) · Machine learning (5 obras) · Data science (4 obras) · Sentiment Analysis and Opinion Mining (4 obras) · Spam and Phishing Detection (4 obras) · World Wide Web (4 obras) · Feature extraction (3 obras) · Graph (3 obras) · Psychology (3 obras)