Search Results for author:
Found 61 papers, 10 papers with code
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
Multiple Instance Learning for Cheating Detection and Localization in Online Examinations
However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection.
Learning on Multimodal Graphs: A Survey
This survey paper conducts a comparative analysis of existing works in multimodal graph learning, elucidating how multimodal learning is achieved across different graph types and exploring the characteristics of prevalent learning techniques.
Deep Outdated Fact Detection in Knowledge Graphs
Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains.
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach
With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system.
Balanced Graph Structure Information for Brain Disease Detection
In this work, we proposed Bargrain (balanced graph structure for brains), which models two graph structures: filtered correlation matrix and optimal sample graph using graph convolution networks (GCNs).
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Many multivariate time series anomaly detection frameworks have been proposed and widely applied.
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method.
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels.
Coupled Attention Networks for Multivariate Time Series Anomaly Detection
To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships.
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender
Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity.
Explainable Knowledge Distillation for On-device Chest X-Ray Classification
To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Knowledge Graphs: Opportunities and Challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately.
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance.
Quantum Graph Learning: Frontiers and Outlook
However, facilitating quantum theory to enhance graph learning is in its infancy.
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades.
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study
To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements.
Pruning Pre-trained Language Models Without Fine-Tuning
These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights.
Reweighting Clicks with Dwell Time in Recommendation
Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time.
CHIEF: Clustering with Higher-order Motifs in Big Networks
In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs.
Graph Augmentation Learning
To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks.
Web of Scholars: A Scholar Knowledge Graph
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science.
Deep Graph Learning for Anomalous Citation Detection
To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks.
Physics-Informed Graph Learning
The graph learning models suffer from the inability to efficiently learn graph information.
Graph Lifelong Learning: A Survey
Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure.
Surf or sleep? Understanding the influence of bedtime patterns on campus
In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects.
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework
To reduce this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN).
The Gene of Scientific Success
Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success.
MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networks
The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge.
Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation
As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data.
VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation
Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items.
CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs
In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices.
Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks
We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation.
Curriculum Disentangled Recommendation with Noisy Multi-feedback
However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e. g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance.
Deep Video Anomaly Detection: Opportunities and Challenges
Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.
Heterogeneous network-based drug repurposing for COVID-19
The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world.
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) has attracted consid- erable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes.
Long Short-Term Temporal Meta-learning in Online Recommendation
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks).
Graph Learning: A Survey
In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.
Real-time Relevant Recommendation Suggestion
When finishing reading an item, users may want to access more relevant items related to the last read one as extended reading.
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data.
Matching Algorithms: Fundamentals, Applications and Challenges
Based on the preference list access, matching problems are divided into two categories, i. e., explicit matching and implicit matching.
Information Retrieval
Recommendation Systems
Social and Information Networks
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
OFFER: A Motif Dimensional Framework for Network Representation Learning
In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined.
Understanding the Advisor-advisee Relationship via Scholarly Data Analysis
Advisor-advisee relationship is important in academic networks due to its universality and necessity.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
The Role of Positive and Negative Citations in Scientific Evaluation
Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities.
Scientific Paper Recommendation: A Survey
The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation.
Improving Smart Conference Participation through Socially-Aware Recommendation
This research addresses recommending presentation sessions at smart conferences to participants.
DINE: A Framework for Deep Incomplete Network Embedding
Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks.
Multivariate Relations Aggregation Learning in Social Networks
Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.
Socially-Aware Conference Participant Recommendation with Personality Traits
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
Big Networks: A Survey
This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network.
Random Walks: A Review of Algorithms and Applications
In this paper, we aim to provide a comprehensive review of classical random walks and quantum walks.
MODEL: Motif-based Deep Feature Learning for Link Prediction
Link prediction plays an important role in network analysis and applications.
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes
To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor.
Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data
Transductive graph-based semi-supervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices.
